RECONSIDERING MINIMALIST DOCUMENTATION: DEVELOPING …

RECONSIDERING MINIMALIST DOCUMENTATION: DEVELOPING AND TESTING A VISUAL FOR EXPERIENTIAL

LEARNING

by Laura A. Palmer, B.A., M.A.T.C., Ph.D.

A Dissertation

In

TECHNICAL COMMUNICATION AND RHETORIC Submitted to the Graduate Faculty

of Texas Tech University in Partial Fulfillment of the Requirements for

the Degree of

DOCTOR OF PHILOSOPHY

Approved

Dr. Thomas Barker, Chair

Dr. Dennis Fehr

Dr. Miles Kimball

Dr. Susan Lang

Fred Hartmeister Dean of the Graduate School

December 2007

Copyright 2007, Laura A. Palmer

Texas Tech University, Laura Palmer, December 2007

ACKNOWLEDGEMENTS

Completing a dissertation marks the end of a process that has taken many

years. Projects like this are never really done alone; many people and places become

significant contributors to this, the final submission.

My time at the University of British Columbia shaped my life in ways I never

imagined; thus, I would like to extend my thanks as follows:

To Anna-Lisa, Heather, Jan and Louise of the “Five Uncommon

Women”: we all know what a journey it has been since undergrad. Some 25

years later, I’m still glad we made it together.

To Andrea: the G & T’s plus the bed downstairs are only a small part of what

made this possible.

To Dawn, Deborah, Lisa, Crystal, Vikki, Elizabeth, Fran and Mary: the

emails kept me going. It never felt like I was too far from home.

The people I met at Texas Tech both changed my life and watched my life

change. During both my master’s and doctoral work at Tech, I met people I would

have never otherwise known. It is difficult now to think of life without them. Grad

school began with Carlos, Jamie and Donna; along the way more and more names

were added: Susan, Kathy, Scott, William, Russell, Crystal, Susan Y. and others who I

came to know during my time as a student.

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My shared office, Room 476, was a place of both myth and legend. The

center desk with its rotating roster of short-term occupants provided the kind of

legacy and vacant flat surface that every doctoral student needs. And, of course, 476

would never have been what it was if not for my fellow “angry badger in a hole”,

Ryan.

Jonathan and “The Herd” were also significant contributors to this work.

Never was there a more steady and loyal bunch than this crew!

And finally, I would like to thank Winifred and Lawrence Leslie Palmer;

sometimes plaques are where you least expect them.

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TABLE OF CONTENTS

ACKNOWLEDGEMENTS............................................................................................. I

ABSTRACT ................................................................................................................... VIII

LIST OF TABLES ............................................................................................................. X

LIST OF FIGURES........................................................................................................... XI

CHAPTER ONE—INTRODUCTION .................................................................................. 1

Overview of the Research...................................................................................................................1 Directions for the Research ................................................................................................4

Where Carroll Began with Minimalist Documentation ..................................................................5 Where and Why Did Minimalism Stop?—The View from 1998..................................................8

1. Is computer documentation…even necessary? ...........................................................9 2. If minimalism is so good, why hasn’t it been adopted more widely? .....................12 3. Does minimalism mesh with the power structure of the

software development world? ..............................................................................13 4. How effective is minimalism? ......................................................................................14

The Limits of Minimalism ................................................................................................................15 The Current State of Minimalist Documentation .........................................................................16 The Need for Research .....................................................................................................................17

Framing the Problem Statement...........................................................................21 Research Questions and Hypotheses ..............................................................................................22 Looking Ahead ...................................................................................................................................23

CHAPTER TWO—LITERATURE REVIEW ........................................................................ 27 The Significance of John Carroll’s Minimalist Documentation Model......................................27

Eight Years Later—1998’s Minimalism Beyond the Nurnberg Funnel.....................32Perceptions of Minimalism—Shortcomings and Concerns.........................................33Directions for Research—1998........................................................................................39

The Fundamentals of Experiential Learning..................................................................................41

Framing the Importance of Experiential Learning .......................................................44The Beginnings of Experiential Learning............................................................46Valuing Learning from Experience—Dewey’s Perspective .............................47

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Experience as a Unique Model of Learning—Lewin’s Ideas ...........................48Developing Individual Expertise—Piaget’s Model of the Experiential..........51Facilitating the Learning Experience—Rogers and Instructions.....................54

Later Work in Experiential Learning...............................................................................56David A. Kolb.....................................................................................................................58 Learning and Knowledge—Process and Structure in Experiential Learning............62

Kolb’s Structure ......................................................................................................63Kolb’s Processes .....................................................................................................63

Peter Jarvis...........................................................................................................................64 Jarvis and Primary/Secondary Learning .........................................................................70

Experiential Styles and Learning Styles...........................................................................................72

Summary..............................................................................................................................................76

CHAPTER THREE—THEORY AND ARTIFACTS .............................................................. 78

The Minimalist Visual Instruction ...................................................................................................78 Research Focused on Screen Captures ...........................................................................................79 Understanding Pictures—Function and Surface ...........................................................................85

Functions .............................................................................................................................87 Surface Features..................................................................................................................91

Using Lines ..............................................................................................................94Size of a Visual ........................................................................................................97

Visual Syntax .......................................................................................................................99The Question of Detail....................................................................................................101Colour ................................................................................................................................101

Implications for the Function and Surface Features of Visuals ................................................104

CHAPTER FOUR—DEVELOPING A VISUAL INSTRUCTION INFORMED BY THEORY..... 106

Requirements of the Visual instruction.........................................................................................106

Carroll’s Requirements ....................................................................................................120 Kolb and Jarvis—Experiential Learning Tenets..........................................................122Goals for the Design of the Visual instruction............................................................123

The Standard Screen Capture.........................................................................................................123

Advancing the Visual instruction...................................................................................................123

Creating the Visual instruction.......................................................................................................125

CHAPTER FIVE—METHODS ....................................................................................... 126

Participants ........................................................................................................................................126 Recruitment .......................................................................................................................127Participant Demographics...............................................................................................129

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Instruments .......................................................................................................................................129 Random Number Generator ..........................................................................................129 Kolb’s Learning Styles Inventory...................................................................................130 Felder and Silverman’s Learning Styles Index (LSI) ...................................................131Developing the Drawing to be Replicated—“Big Bank”...........................................132Developing the Text Instructions ..................................................................................137

Design of the Study..........................................................................................................................138

Procedure...........................................................................................................................................139 Booking Participants for the Study................................................................................140 Consent and Privacy ........................................................................................................140 Administering Inventories ..............................................................................................141Assignment to Conditions ..............................................................................................143 Pre-test Briefing................................................................................................................143 Taking Observations........................................................................................................146 Concluding the Drawing Task........................................................................................146Post-Test Questionnaire..................................................................................................147

Coding the Drawing Artifacts ........................................................................................................148

Assumptions and Limitations.........................................................................................................150

Visual Learners, Visual Instructions and a Visual Task..............................................150Hawthorne Effect.............................................................................................................151Diffusion Effects..............................................................................................................153Researcher Bias and Expectancy Effects ......................................................................154Non-Random Sampling and Gender Dominance.......................................................155Age .................................................................................................................................157Education ..........................................................................................................................157

Conclusion.........................................................................................................................................158

CHAPTER 6—RESULTS................................................................................................ 160 Introduction ......................................................................................................................................160 Methods of Obtaining Results .......................................................................................................161

Statistics .............................................................................................................................162Data Mining ......................................................................................................................163Qualitative Analysis..........................................................................................................165

Describing the Population Studied................................................................................................165

Skills Self-Assessment......................................................................................................166

Differences between the Visual and Verbal Groups ..................................................................168

Time on Task ....................................................................................................................168 Initial Data Analysis .........................................................................................................169

Considering the Research Questions and Hypotheses ...............................................................173

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Research Question #1 .....................................................................................................173Research Question # 2 ....................................................................................................174

Results of Kolb Learning Styles Inventory (LSI) ........................................................................175

Research Question #3 .....................................................................................................180

Results of Felder Inventory of Learning Styles (ILS) .................................................................182

Dimension Strength, Time and Group .........................................................................187VIS-VRB Dimension............................................................................................187ACT-REF Dimension ..........................................................................................189SEN-INT Dimension...........................................................................................190GLO-SEQ Dimension.........................................................................................191

Intersecting Kolb, Felder and Artifacts ........................................................................................194

Correlations between the Kolb and Felder measures .................................................195Accommodators ...............................................................................................................196Converger ..........................................................................................................................199Assimilator.........................................................................................................................201Divergers............................................................................................................................203ILS VIS/VRB Preference, Experiential Style and Group..........................................205

High VIS, Strong Experiential Style and Group ..............................................206High VIS, Weak Experiential Style and Group................................................207Moderate VRB, Weak Experiential Style and Group ......................................209

Research Question #4 .....................................................................................................211

CHAPTER SEVEN—DISCUSSION ................................................................................. 212

Goals of the Research......................................................................................................................212

Research Question #1 .....................................................................................................................214



Breakdowns on Time.......................................................................................................222Tool Recall.........................................................................................................................224The Success of a Minimalist Visual Instruction...........................................................228


Intake Preference for Instructions—Visual or Verbal................................................232The Success of a Visual ...................................................................................................234


Strong Experientialism and High Visual Learning Preferences .....................235Lower Strengths of Visual Preference and Strong Experiential Style ...........236Verbal Learners and Weaker Experiential Style ...............................................237Addressing the Issue of Time .............................................................................239 Perceptions of Time on Task ..............................................................................241

The Success of a Visual ...................................................................................................241

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Summary............................................................................................................................................242

CHAPTER 8—CONCLUSION ........................................................................................ 245

Reviewing the Research Questions................................................................................................246

Addressing the Concerns from 1998.............................................................................................252

Directions for Future Research......................................................................................................256

APPENDIX A: “BIG BANK” DRAWING.......................................................................... 259

APPENDIX B: MINIMALIST VISUAL INSTRUCTIONS..................................................... 260

APPENDIX C: INSTITUTIONAL REVIEW BOARD MATERIALS ...................................... 261

APPENDIX D: KOLB EXPERIENTIAL LEARNING STYLE INVENTORY (LSI) VERSION 3.1............................................................................... 267

APPENDIX E: FELDER INVENTORY OF LEARNING STYLES (ILS)............................... 268

APPENDIX F: SCRIPT READ PRIOR TO TASK ............................................................... 269

APPENDIX G: POST-TEST QUESTIONS ....................................................................... 270

WORKS CITED ............................................................................................................. 272

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ABSTRACT

This dissertation uses the work of John Carroll and his model of minimalist

documentation to establish if a visual can effectively perform as a minimalist

instruction and activate the experiential learning that Carroll identified as critical to

the success of his model. In the study, best practices from information design and

visual theory were used to revisit and redesign the screen capture. The redesigned

screen capture, as a minimalist model of instruction, was tested in a study.

In the study, twenty-five participants were randomly assigned to the visual

instruction or verbal instruction and given two psychometric inventories: one for

experiential learning style and the other for learning styles. Next, participants were

asked to replicate a simple picture using a drawing program available via the internet.

Participants were observed and timed as they completed the drawing task. Comments

from the talk aloud protocol were noted and the final drawing artifacts were collected

for further analysis.

The study revealed that in a college population, 80% of the participants were

visual learners and half were not strong experiential learners. The hypothesis a visual

instruction would result in the drawing task being completed in less time was refuted;

participants in the visual condition took longer to complete the task. Artifact analysis

revealed that participants used more tools and completed the sample drawing with

more accuracy when assigned to the visual group—they were more engaged in the

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task. Styles that were less experiential created a better artifact in the visual group and,

for the five verbal learners in the study, the visual demonstrated some promise at

acting as an instructional device.

In conclusion, this study asserts that a need exists to create materials that

address what may be an increasing population of visual learners. For the artifact

designed here, there is a link indicating that experiential learning is fostered by a

visual. This visual focuses its design on elements key to the task, positions them

centrally for the viewer and addresses major areas of functionality. Such a visual

serves to engage the user more.

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LIST OF TABLES Table 5.1: Tool Use Distribution for Big Bank............................................................... 136 Table 6.1 Distribution of Students per Degree Program ............................................. 166 Table 6.2 Self-Reported Skill Assessment Level with

Computer-based Drawing Programs ...................................................... 167 Table 6.3 Task Times between Verbal and Visual Groups........................................... 172 Table 6.4 Central Tendency for Times on Task.............................................................. 172 Table 6.5 Distribution of Kolb Profiles in Research Population................................. 178 Table 6.6 Average Time in Minutes on Experimental and

Verbal Groups across Experiential Styles............................................... 180 Table 6.7 Felder’s Dimensions and Descriptions .......................................................... 182 Table 6.8 Distribution of Preferred Felder LSI Scores ................................................. 186 Table 6.9 Times on Individual Felder Dimension: Experimental and

Verbal Groups (No Categorical Breakdown)......................................... 187

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LIST OF FIGURES Figure 4.1 Standard Screen Capture ...................................................................................111Figure 4.2 Imagination Cubed Basic Screen .................................................................... 116 Figure 4.3 Basic rendition of the Imagination Cubed screen........................................ 117 Figure 4.4 Next phase of screen design............................................................................ 119 Figure 4.5 Sample of tool bar with line drawing palette active..................................... 121 Figure 4.6 Use of colour to link palette concepts ........................................................... 123 Figure 5.1 Minimalist Verbal Instructions........................................................................ 138 Figure 6.1 Histogram of Visual Group Times................................................................. 170 Figure 6.2 Histogram of Verbal Group Times................................................................ 171 Figure 6.3 Accommodators ............................................................................................... 197 Figure 6.4 Accommodator/High VIS Artifact Sample.................................................. 197 Figure 6.5 Converging Style Composite ........................................................................... 198 Figure 6.6 Accommodator/High VIS Artifact Sample.................................................. 199 Figure 6.7 Assimilating Composite Profile ...................................................................... 201 Figure 6.8 Assimilator Artifact Sample............................................................................. 203 Figure 6.9 Diverging Composite Profile........................................................................... 204 Figure 6.10 Diverger Sample Artifacts.............................................................................. 205 Figure 6.11 High VIS, Strong Experiential Style and Contraindicated Group.......... 206 Figure 6.12 High VIS Preference with Moderate SEQ ................................................. 208 Figure 6.13 Low VRB Assimilator Artifact...................................................................... 210

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CHAPTER I

INTRODUCTION

Overview of the Research

The over-arching goal of this research is to revisit and extend the work of

John Carroll in the area of minimalist documentation and determine if a minimalist

visual—a screen object with reduced complexity—would be the mechanism needed

to address the shortcomings of his model. Towards that end, the research presented

here develops and tests a minimalist visual instruction for experiential learning that

may address many of the perceived gaps with Carroll’s model. In conjunction with

developing the minimalist visual instruction, this study also administers experiential

and learning style inventories with the aim of gaining additional insight into other

factors that may influence the success of a visual instruction. Ultimately, this study

explores the question of whether a minimalist visual instruction derived from theory

can assist people in working with a software program.

Minimalist documentation, as developed by John Carroll in the 1980’s,

presented a powerful tool to assist computer users in their work with the software of

the era. Carroll’s assertions that users need to start quickly, recover from error

efficiently, and activate their experiential abilities presented a very different model

than the text-centric practices of the time. Rather than weigh software users down

with printed materials that overwhelmed them or left them uninterested in reading,

Carroll found, from his work studying users and traditional written instructions that

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less verbiage could effectively function as more. Reduced amounts of text would

activate the experiential learning skills of participants—that is, participants would

engage in the exploratory activities that define a learning-by-doing approach. They

would, in turn, develop their own understanding of the protocols and procedures for

a software program and ultimately internalize this knowledge.

While Carroll’s ideas were sound, their primary failing was the lack of a visual

model that would work in conjunction with experiential learning. As a text-only

model, minimalist documentation did not appear to support the broader concept of

learning styles; additionally, as it had no visual component, the model was perceived

to be an incomplete methodology for delivering instructional material. Rather than

revolutionizing documentation practices at the time, Carroll’s model languished due

to its apparent shortcomings.

In 2007, some twenty years after Carroll originally proposed minimalist

documentation several important considerations support the need for new research.

First, computers and software are no longer in the realm of new and unfamiliar;

current users are familiar with operating systems, standard productivity software,

entertainment programs and the greater offerings of global connectivity. As a result,

they are far removed from the user profile of twenty years ago when the technology

was just penetrating the marketplace. Ultimately, today’s computer users have a core

competency of baseline knowledge that eliminates the need for details regarding basic

operations and processes.

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Developing and testing a minimalist visual instruction—one that supports and

guides the exploratory processes at the core of the minimalist model—is an area

requiring additional research. By framing such research within the tenets of

minimalist documentation practices as developed by John Carroll, the study

presented here asserts that a visual instruction will contribute to minimalism and

bolster it as a methodology for providing instruction. Additionally, this study will

provide important insights into our understanding of the dynamics and key elements

of experiential learning.

While direct instruction provides a formalized methodology for learning such

as delivering content by notes, lectures and demonstrations, it does not allow for

personal engagement and exploration on the part of the learner. Rote learning, as

another model of knowledge acquisition, again limits the engagement of the learner;

this time, however, learning is relegated to an act of memorization and exacting recall.

Experiential learning, as a third model, places the agency on the learner to define

her/his own path and develop a unique understanding of a concept through

exploration and experimentation. In order to gain more knowledge about what will

facilitate individual exploration, the research conducted here seeks to assert what, in

terms of an instructional medium, can enhance individual experiential abilities and, as

an outcome, provide a significantly more valuable learning experience.

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DIRECTIONS FOR THE RESEARCH This study examines and compares how two groups, one using a minimalist

verbal instruction and the other a minimalist visual instruction perform on a

computer-based task. The remainder of this first chapter will frame John Carroll’s

work both in 1990 and again, when it was reconsidered in 1998. This review of his

work will highlight the shortcomings seen in the model and set the stage for the

research conducted in this dissertation.

Subsequent chapters in this study include a literature review on cognitive

processing and experiential theories; a secondary review of the literature focuses

entirely on the visual theories that would best inform the creation of a minimalist

visual instruction. Chapter Four explains the development of the minimalist visual

instruction as a theoretically derived artifact—that is, it defines what best theoretical

practices can be used to create a visual instruction. Chapter Five documents the

methods used to test this visual instruction against a text-based counterpart

(minimalist verbal instruction) and confirm or refute the research hypotheses guiding

the study. Chapter Six presents, in detail, the findings from the study and, in order to

summarize the major findings and implications from the research, Chapter Seven is

comprised of the discussion. The concluding comments and directions for future

research are found in Chapter Eight, the final chapter of this study.

Additional details about each of the above chapters will be found at the

conclusion of this chapter. To start with, however, the discussion presented here will

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examine the genesis of John Carroll’s minimalist documentation model and how such

a promising model failed to take hold in the world of documentation practices.

Where Carroll Began with Minimalist Documentation

John Carroll’s model of minimalist documentation—developed in the late

1980s and first published in 1990—represented the culmination of over 10 years of

work in examining and understanding how people engaged with instructional

materials and computers. Early work done at IBM on instructional design resulted in

two distinct conclusions for Carroll: users needed a model that both reduced text to

its essential elements and simultaneously provided a means of starting quickly and

recovering from error efficiently.

Carroll’s lab-based observations of users demonstrated to him that they

represented a more complex group than previously imagined. The complexity of this

group manifested itself in the very “unpredictable” reading and problem-solving

strategies used to make sense of tasks, systems and instructions (Carroll, Nurnberg

Funnel, 41). In fact, most users, Carroll noted, were so completely absorbed in the

task that the instructional materials received little or no attention; this would become,

for Carroll, what he considered the “paradox of sense making”—the disconnect

evident between task completion and understanding the system via its documentation

(Carroll, Nurnberg Funnel 74). Aside from the users’ erratic approaches to problem

solving, one of Carroll’s other findings was that users were resistant to or

uninterested in the help offered by the documentation and that in many cases, their

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own initiatives caused them to completely ignore the instructions and proceed along a

self-determined course of action (Carroll, Nurnberg Funnel 26). In fact, according to

Carroll, his early research indicated that users became so absorbed in their own

explorations that, for the most part, they would lose track entirely of what they were

doing (Carroll, Nurnberg Funnel 25).

While users brought an unexpected reticence to their encounters with

documentation and computers (and dogmatic perseverance to tasks), Carroll also

found that users brought to the task-documentation-technology table a heavy reliance

on analogical comparisons (Carroll, Nurnberg Funnel 32) as well as a degree of

“bounded intelligence” that limited the nature of their explorations (Carroll, Nurnberg

Funnel 57). Attempts to design documentation that relied on metaphors proved not to

resolve users’ problems (64); rather, the metaphors added an unexpected layer of

difficultly to the task by making it less “meaningful” (32). A systematic approach

failed as it modeled a too passive approach to understanding the system—and, by all

accounts, passivity was not a hallmark of users. Carroll noted there was no lack of

ability to follow a sequential model but that users simply had no interest in this type

of instruction (74). There was, as Carroll put it, a “misfit between the nature of self-

instruction and the self-instruction needs of users” (Carroll, Nurnberg Funnel 5).

As a result, the minimalist documentation model began its genesis—the

culmination of cognitive learning paired with observations from lab studies. Carroll’s

understanding of human nature, as derived from studying people working with

instructions and technology, led him to reconsider the types of information being

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provided to users under the guise of ‘help’ for instructional purposes. Information, as

it had been designed and delivered, failed to meet the needs of its intended audience

and served, much of the time, only to exacerbate tensions between users and

technology. Consequently, Carroll saw the need to develop a new instructional

paradigm that would better meet the requirements of real users. He would call the

artifact the “minimal manual” (Carroll, Nurnberg Funnel 143) and document its

genesis—as designing minimalist instruction—in his first book, The Nurnberg Funnel

(1990).

At the outset, it appeared the design for instructional materials would provide

an alternative paradigm for informing users of how to complete tasks with a

computer. However, the model was not adopted with overwhelming enthusiasm for

several reasons including its perceived academicism, its failure to adequately address

novices and very un-documentation-like approach. Significant to this research,

however, are two other key failings of minimalism: it lacked a proper linkage with

learning styles and provided no visual support for users. Minimalism, according to

Carroll, was textual only. Thus, while he sought to maximize experiential learning, he

had not considered visual objects or the spectrum of learning styles and their impact

on performance. It is doubtful too whether Carroll considered that as the first true

MTV generation tottered towards the controller on the video game console, this was

the genesis of an entirely new type of learner—one who would later prefer visuals to

text.

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Where and Why Did Minimalism Stop?—The View from 1998

The year 1998 saw the publication of John Carroll’s second book, Minimalism

Beyond the Nurnberg Funnel; this follow-up publication—a collection of articles

containing comments and criticisms about minimalist documentation—explored how

the minimalist model was considered and (re-)considered since The Nurnberg Funnel.

Carroll’s comments indicate he was pleased with both the academic and practical

exchanges that resulted from his first book; in fact, he lauded many of the

contributors for helping shape the next direction(s) for minimalist documentation.

Of the many voices in this edited collection, one contributor stood out as the

best representative of the group. Greg Kearsley posed a thought-provoking set of

questions regarding what he envisioned as “some more fundamental considerations”

for minimalism (Kearsley 400). His chapter, “An Agenda for Research and Practice”

summarized many of the comments from other authors in the collection and best

positioned the potential minimalism has always had as a documentation practice. As

well, his writing not only articulated what he saw as the immediate difficulties with

the model but it provided an opportunity to guess the shape of an unknown

technological future and the role minimalism could have as a model for

documentation. From the point of view of 2007, Kearsley’s ideas make a

reconsideration of Carroll’s work a poignant avenue for research, especially in light of

new generations of documentation users.

Four of Kearsley’s questions illustrate why revisiting Carroll’s model more

than 15 years after its inception is a valid course of action. Many of the gaps found in

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the original work—cognitive modalities, learning styles, lack of a visual—remained

unexplored. In order to engage others who may revisit minimalist documentation and

address these gaps, Kearsley uses his four questions to, as he states, probe

possibilities for the scope and direction of future research (Kearsley 402):

1. Is computer documentation…even necessary?

2. If minimalism is so good, why hasn’t it been adopted more widely?

3. Does minimalism mesh with the power structure of the software

development world?

4. How effective is minimalism?

Kearsley’s questions, presented as follows, help to frame the viability of

conducting the research presented in this study.

1. IS COMPUTER DOCUMENTATION…EVEN NECESSARY?

In considering minimalism and its future, Kearsley’s 1998 question concerning

the necessity of documentation (print or electronic) is provocative. From a point of

view situated in the late 1990’s when he wrote the piece, it would seem a logical

conclusion that the standardization of computer programs/suites and the ubiquitous

presence of computing technology would be such, in 2007, that no one would require

any instructional material. Aptly, Kearsley points out that even in the mid-1990s, the

ATM proves “we can design computer programs and information systems that do

not require any user documentation or training” (400). However, our current

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technological reality has repeatedly shown that documentation needs still exist. As an

example, the July 2007 release of the Apple® iPhone© was prefaced by marketing

strategies that touted its ease of use and technological brilliance. The manual with its

124 pages seems to belie its simplicity (Apple Incorporated).

Kearsley postulates that minimalism was the “product of an awkward time in

the history of computing” when there was less common knowledge in the user

domain of the conceptual and procedural aspects of technology and a lack of

standardization in interface design (400). While some of the tension between users

and technology has been resolved due to the prevalence of computing technology,

the requirement for instructional materials remains. What has changed, however, is

the type of materials required—we have arrived at a time when Carroll’s “less is

more” approach has a new validity for both users and technology. Heavily

documented procedures or detailed step-by-step models are no longer required due

to the familiarity in our society with computers in general. What users need is what

Carroll originally promoted: materials that will allow them to start quickly work with

the product and solve problems efficiently. To meet this need, minimalist

documentation is ready to be reconsidered.

With technology changing and re-configuring itself so rapidly, there is certainly

no über-human born with an innate ability to deconstruct and understand the latest

advances. College classes in introductory technical communication prove that 19-year

olds have not received the genetic code to automatically master a word processing

program. They still require instruction, for the most part, on much of the

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functionality. While they may have more familiarity with the computer, in the general

sense of its overall operation, many specific programs and functions elude them—

these students are, in essence, the new version of the novice. Certainly, the basics of

computer operation and launching software can be managed via a limited conceptual

understanding of technology, but specific procedural and operational routines require

at least sufficient knowledge to begin using an application.

We also cannot ignore the fact that technologies are both evolving and

emerging; thus, again, the demand for instructional materials remains necessary. As

familiar software suites add more features with each iteration, users require

documentation to help them explore new aspects of a program’s functionality via

capitalizing on their existing understanding. And, while Kearsley’s question regarding

the necessity of documentation does seem relevant, it negates what users require

when encountering the unfamiliar, especially in the home (as compared to the more

collaborative nature of the workplace).

For example, TiVO, the system for recording television programs onto a hard

drive requires instructions for both setup and operation. While, as Kearsley noted,

many technologies such as the ATM can operate without instructions, it is our

familiarity with the conceptual overlay that allows us to function procedurally.

However, emergent or even reconfigured technologies require time to become

acclimated in the cognitive consciousness of society and, in most cases, there is no

previous conceptual knowledge to apply to a new product. Therefore, users must rely

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on documentation as a means of starting and managing errors—this would be, in

Carroll’s terms minimalist documentation.

2. IF MINIMALISM IS SO GOOD, WHY HASN’T IT BEEN ADOPTED MORE WIDELY?

In its text-based form from the late 1980s, minimalist documentation

practices—while both highly innovative and user focused—did not inspire

confidence among those who would take the ideas from the theoretical to the

practical. Practitioners, specifically, doubted the model and how what they saw as its

limited completeness would be received by the public; still others thought it might

mean the end of their jobs. Many felt it would not address variances in cognitive

processing or even learning styles; thus, it would abandon a segment of the user

population before they even began.

Additional research, especially if it can demonstrate the validity of minimalist

documentation as being visual, is paramount for the model and its penetration into

current professional practices. In its present state, minimalist documentation will

never be adopted more widely as the issues from the 1990s have not been addressed.

Since the model languished for almost a decade, any further academic

consideration—such as the study presented in this dissertation—will need to

encompass an innovative look at the model and a re-conceptualization of what would

make it effective in a current context. Additionally, indications of how a new version

of minimalist documentation would meet a variety of learning styles will be crucial if

the model it to gain acceptance.

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3. DOES MINIMALISM MESH WITH THE POWER STRUCTURE OF THE SOFTWARE

DEVELOPMENT WORLD?

In 2007, some ten years after Kearsley wrote these questions about the scope

and future directions of minimalist documents, the term “power structure” does not

accurately represent the zeitgeist of software development. Rather, it would be more

appropriate now to question if minimalism could mesh with corporate goals that are

aligned with market penetration, streamlined costs and enhanced models of customer

satisfaction. If revisited and re-conceived for a new group of users—those with a

preference for the visual—minimalist documentation could easily meet these

demands.

Documentation (and in the case of bad documentation, customer support) is

costly to produce, deliver and maintain; corporate reputation also becomes a

stakeholder in this paradigm making the issue event more pertinent. Consequently,

software developers seek better overall design in the product and better (read: less)

documentation regardless of whether the instructional artifact be print or electronic.

In terms of market perception of a product, a minimalist model of documentation

may have a tangible relevance for consumers and a direct impact on corporate profits.

While 15 years ago, a substantial manual was seen, as per Kearsley’s comments, to be

a sign of completeness, that is not the case today. A large user manual—regardless of

print or electronic delivery—would not necessarily instill confidence in the user.

Many would regard the product as too difficult to use, too time consuming to learn

or possibly not well designed and thus, abandon it. The software user of today is

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bolstered by 15 years more experience with technology and less likely to see the need

for bulky instructions or be motivated to engage with them.

4. HOW EFFECTIVE IS MINIMALISM? While this question may seem to be already answered by Kearsley’s second

question (If minimalism is so good…), the effectiveness of minimalism can best be

described as a paradox: it is perceived in theory to be effective yet, as it was never

widely adopted and tracked as a longitudinal study for any solid assertions to be made

about its effectiveness. The ubiquitous presence of computers in 2007 has, without a

doubt, made computer users more savvy about day-to-day use of the technology;

however, like the generation of users Carroll studied in the 1980’s, traits like

resistance to documentation still prevail.

Without a doubt, 2007 is the time to revisit the model and determine its

effectiveness in new contexts of user knowledge and with more refined models of

experiential and learning styles. Minimalism is not an artifact of a bygone era; rather,

Carroll’s ideas still circulate in some forms, especially in the consulting fields. Tech-

Ed, Inc., a usability firm, specializes in providing clients with documentation, help

systems and usability evaluation; they include minimalism as part of their

development portfolio. Citing Carroll’s seminal work directly, Tech-Ed explains the

many advantages of minimalist documentation as meeting the needs of diverse and

dynamic users’ requirements. The shortcomings of “traditional documentation”

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(Tech-Ed., 2004) as highlighted on their website, make a strong case for minimalist

practices as a viable approach for current documentation needs.

Another documentation consulting firm, Australia-based Wordware, also

promotes the use of a “minimalist documentation method” (Wordware, 2005) in their

products. Though they do not pay homage to Carroll, they characterize minimalism

in documentation in the same nomenclature and, in a turn toward marketing

jingoism, define the components of their minimalist approach as the “four R’s in

documentation: retrievable, relevant, readable and reusable” (Wordware, 2005).

Regardless, they still make Carroll’s paradigm as being viable and effective today.

As stated earlier, Carroll was pleased with the comments from contributors to

his second book on minimalist documentation; in particular, he felt these writings, in

many ways, shaped the future directions of the model. Regardless, though, of his

support, the next generation of the model never materialized. In asking the questions

he did in 1998, Kearsley sought to establish the obvious objections to Carroll’s as a

set of considerations for anyone wanting to take on this project. Certainly, the

capabilities of the model are worthy of deliberation however, determining what it

cannot or should not do is equally relevant.

The Limits of Minimalism

It is necessary to clarify what minimalism may and may not do so as to

properly frame how this study will seek to re-invigorate the model. Carroll did not

design this documentation practice to encompass every possible situation; rather,

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minimalism was developed to meet a specific kind of computer user engaged in a

certain kind of task. It may not be the approach for advanced users and complex

tasks as Barbara Mirel noted nor will it bypass each of the common problems

articulated by Stephen Draper (Mirel 179-218; Draper 349-74). Like Mirel and Draper

in their Minimalism Beyond the Nurnberg Funnel contributions, Mary Ann Eiler and

David Farkas express their hesitations with the model too. They elaborate on the

need for minimalism to be less risky, especially where safety and the potential for

litigation can occur. Unquestionably, concerns about this model arise due to gaps,

shortcomings and other perceived flaws; it is prudent to admit that minimalism will

never adequately resolve every concern.

However, with the development of a minimalist visual instruction and

consideration of variations in learning styles, a re-envisioned minimalism could

reduce critics’ concerns. Regardless, it is time, in light of new user competencies to

revisit the model and answer the questions about meeting broader learning styles with

a minimalist visual instruction. What Carroll and others may find is a model that is

highly viable now in light of changes to both the population of computer users and

how their work with documentation.

The Current State of Minimalist Documentation

Minimalism Beyond the Nurnberg Funnel examined—from a 1998 perspective—

where minimalism had been since 1990 in terms of its adoption into the mainstream

arena of technical communication’s professional practices. John Brockman’s apt

16


summation that technical communication circa 1998 assimilated minimalism

“selectively, partially and with resistance” explained its position within the practicing

core of technical communicators (Brockman 387). In the intervening years up to

2007, little has been done with respect to the model. The ideas that came out of

1998s Minimalism Beyond the Nurnberg Funnel have been explored only sporadically in

current research. Thus, the goals of the research proposed for the dissertation are to

revisit Carroll’s basic model and examine how learning styles and minimalist visual

instructions may facilitate experiential learning.

The Need for Research

What we currently know about minimalism and the role of a visual element is

found primarily in the work of Mark Gellevij and Hans van der Meij. For

approximately the last 10 years, Gellevij and van der Meij have explored the function

of screen captures in conjunction with text-based instructions. Their work has

examined how participants process dual sources of information (pictorial and textual)

and studied the placement of limited amounts of text in conjunction with the

presentation of screen captures in instructional documents (Gellevij and van der Meij,

2004, 2002). What Gellevij and van der Meij have not considered yet, however, is the

role of a stand alone visual instruction and if it would be sufficient to activate

experiential learning in participants.

Experiential learning seeks to establish knowledge via the process of doing;

that is, active experimentation assists the user to build their own internalized

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knowledge. Such experimentation with a task is, in most cases, accompanied by

instructional materials that serve as a guide if not a detailed rendering of operations;

however, what remains unclear is the role of a minimalist visual instruction as a guide

and instructor. In particular, the type of design useful for supporting learning styles

and experiential learning is an area lacking a methodological exploration and

evaluation.

Further substantiating the need for the research is the requirement to better

understand users of instructional material; little is known if a minimalist visual

instruction would adequately address a new generation of learners. At the time Carroll

conducted his initial studies regarding minimalist documentation, the average college

student of 2007 may not have been born. This 2007 cohort may be more inclined to

learn from a visual instruction rather than a textual instruction; they may, in fact, not

learn well from text-based scenarios at all. However, without further research this

assertion is only tentative.

Therefore, to determine if college students are more inclined to learn from a

visual instruction, this study will include Richard Felder’s Inventory of Learning

Styles (ILS). This inventory will help to clarify how a population of college students

process visual versus verbal information and how, via their own cognitive processes,

they construct that information to make knowledge. Comparing the visual and verbal

as input modalities for instructions and measuring their effectiveness in a study will

produce findings that assist in explaining the function of a minimalist visual for

instructional purposes.

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Critical for the research will be defining the exact nature of experiential

learning and determining if different cognitive modalities work more or less

successfully on computer tasks. David A. Kolb, the preeminently cited author on

experiential learning, describes it as "the process whereby knowledge is created

through the transformation of experience.” In an expanded definition, Kolb views

this transformation as derived from:

…a holistic model of the learning process and a multilinear model of adult development, both of which are consistent with what we know about how people learn, grow and develop. The theory is called “Experiential Learning” to emphasize the central role that experience plays in the learning process, an emphasis that distinguishes experiential learning theory (ELT) from other learning theories. The term “experiential” is used therefore to differentiate ELT both from cognitive learning theories, which tend to emphasize cognition over affect, and behavioral learning theories that deny any role for subjective experience in the learning process. (Kolb, Experiential Learning 41)

Kolb clearly delineates the experiential as a very separate type of information

processing—one reliant on neither standard learning or behaviourial models. Rather,

Kolb draws some of his model from early cognitive theorists such as Jean Piaget and

builds a paradigm that explains individual growth and development as a cyclical

model. What he arrives at through his theories is a psychometric inventory that

defines experiential learning style on four dimensions and provides an indication of

individual aptitude for active experimentation.

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It is through this model that Kolb sees the acquisition and internalization of

knowledge and thus, the process of learning. Carroll too, envisioned learning via

these same mechanisms, and his minimalist model strove to capitalize on building

knowledge, reinforcing understanding and applying it. Particularly, Carroll believed

that such a model would facilitate error recognition and recovery, an important

function in learning about new software. However, as discussed later, the limitations

of how Carroll perceived the experiential may have contributed to the model’s lack of

acceptance.

Most learning though, such as Carroll’s minimalism, is predicated on the

presentation of textual materials; experiential learning, as Carroll envisioned it, never

addressing a visual element, and only once in The Nurnberg Funnel did Carroll

comment that “manuals could periodically present a figure demonstrating what the

display should look like if all is well” (Carroll, Nurnberg Funnel 86). Consequently, an

understanding of visuals as a means to support and facilitate experiential learning will

become a necessary focal point of this study.

With Carroll’s constructs having persisted for academics and researchers,

minimalism—rather than being dismissed as outmoded—requires a re-consideration

based on a broader evaluation of the model. The most obvious problem with the

model is the lack of a visual to facilitate experiential learning; more problematic is the

question of designing a visual to pair effectively with experiential learning. The

computer users of 2007 will, undoubtedly, manifest the same unpredictability in task

completion as Carroll noted in 1990; their approaches to learning and their needs in

20


acquiring knowledge will, however, have changed as the cohort tested here may prove

to be users who prefer learning from a visual instruction.

The goal of this dissertation is to develop a minimalist visual instruction—a

screen object with reduced complexity—and consider it as a means to re-invigorate

the experiential aspect of the minimalist model by addressing what may be more

prevalent visual learning styles in a college population. The visual instruction in

particular—the one area Carroll neglected in his model—will be shown as a means to

support a new version of experiential learning.

FRAMING THE PROBLEM STATEMENT This study will use John Carroll’s model of minimalist documentation as a

framework to study experiential learning, learning styles and a minimalist visual

instruction; the ultimate goal of this study is to develop and test a visual. Measuring

its success will be based on multiple factors including task performance and the

participant creation of a test artifact. The results of the contributing inventories of

experiential styles and learning styles will illuminate specific factors about the

population tested—as a whole or as individual participants—regarding the success or

failure of the visual element.

If this study shows promise, the model that failed to gain significant status in

the documentation world 15 years ago may find its place today. Revisiting

minimalism, as a case study for experiential learning, and incorporating a minimalist

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visual instruction will, it is asserted, meet or surpass the requirements necessary to

make the model complete.

Research Questions and Hypotheses

In terms of the work conducted in this dissertation, four research questions

guide the over-arching directions of the study.

1. If we consider the term, “minimalist visual” as based on a static graphic like

the screen capture, what are its physical requirements—appearance, function,

colour and other—to engage users?

2. What will an study comparing minimalist text and visual instructions yield in

terms of speed and engagement with the task?

Hypothesis #1: a visual will reduce the amount of time on task.

Hypothesis #2: the visual will engage participants as measured by

detail, accuracy, tool use and overall completeness of the drawing

artifact.

3. Is there any significance as shown by the results of Kolb’s Learning Styles

Inventory and Felder’s Inventory of Learning Styles and the success (or

failure) of a visual element? What do these inventories reveal about the

population observed in this study and how will this information intersect with

experiential learning?

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4. Can a visual, such as the one developed and tested in this study, assist

individuals in completing the experimental task through the activation

experiential learning skills? In particular, does it stimulate more engagement

with the task and does it result in a better final artifact?

Looking Ahead

Towards fulfilling the goals of the above research questions, this dissertation

spans eight chapters in total. This chapter, the introduction, has framed the goals of

the research and positioned the work of John Carroll’s minimalist documentation as a

concept that will be revisited with a visual instruction. Chapters two and three both

serve as literature reviews; they define the important theories in the areas of cognitive

processing, experiential learning and visual design. Chapter Two begins by examining,

in more detail, the fundamentals of John Carroll’s original ideas as discussed in his

first book, The Nurnberg Funnel. It then reviews what the contributors to Carroll’s

second publication on the subject of minimalist documentation, Minimalism Beyond the

Nurnberg Funnel had to say with respect to their perceptions of the model. The

perceptions are fundamental for the research conducted here as they have guided

many of the goals of this study.

Chapter Two continues its review by examining the evolution of experiential

learning as seen from the point of view of early cognitive theorists. This early work in

understanding and validating the experiential as a significant form of learning is

fundamental to understanding how later theorists began to conceptualize the model.

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David Kolb, who created the Kolb Learning Styles Inventory administered in this

study, based his initial ideas on work by Piaget. A later researcher, Peter Jarvis also

owes his foundational understanding of the experiential to its progenitors in

cognitivism. Both Kolb and Jarvis are included here as their models of the

experiential show the dynamism and complexity of learning that Carroll may not have

adequately considered in his development of minimalist documentation practices.

The next chapter, Chapter Three, reviews the theory and practice informing

the development of visuals. Understanding the function and surface of pictures

serves to inform the study greatly on how visuals are understood and processed. In

particular, examining the visual syntax, or organization, of a picture is vital to its

comprehensibility. Without a proper consideration of the visual syntax, note several

authors, a picture will be unable to communicate its intended information. Therefore,

for the visual to be designed for testing, as per Chapter Four, these best practices are

paramount.

In Chapter Four the development of the visual is detailed. Integrating the

theories from Chapter Three, this chapter frames the basic requirements of the visual

as derived from the work of Carroll in the textual domain; additionally, it considers

what is necessary in terms of the experiential as defined by Kolb and Jarvis. The

thrust of this chapter, however, is on the construction of the visual and the choices

that were made in order to build what has been referred to here as the ‘theoretically

derived artifact’.

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The methods used to conduct the study comprise the content of Chapter Five.

Starting with the recruitment of the participants, and detailing the instruments

developed specifically for testing as well as those commercial psychometrics

administered in conjunction with the study—Kolb’s Learning Styles Inventory (LSI)

and Felder’s Inventory of Learning Styles (ILS)—this chapter explains the protocols

used to gather data. Chapter Five goes on to outline the experimental design of the

verbal and visual groups as well as the procedures conducted during the study.

Chapter Six presents the results of the study broken down by the original

research questions put forward here, in Chapter One. First, however, this chapter

describes the demographics of the participants and explains some of the methods

used to obtain both inferential and descriptive statistics. The next major section

examines the basic differences in time between the visual and verbal groups in the

study. Following that, the results review the outcomes from the LSI and the ILS with

respect to time on task. The final section of the results looks at the composite results

of time, condition, learning/experiential style and the artifact produced.

While some of the results are discussed in Chapter Six, they are mentioned

only in enough detail to give the findings a degree of context; the majority of the

discussion occurs in the penultimate chapter, Chapter Seven. Again, structured

around the research questions posed in this chapter, Chapter Seven considers, in

detail, the meaning and substance of the findings. Most telling from the study is that

time on task—where reduced speed is presumed to measure improved

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performance—was counterintuitive. More time on task ultimately produced a better

artifact and, of critical importance in this study, was the impact of learning styles on

the success of the participants. Chapter Seven describes these findings and their

implications for the design of minimalist visual documentation.

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CHAPTER II

LITERATURE REVIEW

As stated in Chapter One, Carroll’s ideas on minimalist documentation were

well informed by his academic work encapsulated in 1990’s The Nurnberg Funnel;

however, the model’s lack of widespread acceptance prevented it from gaining any

serious penetration into the canon of instructional documentation methodologies. In

1998s Minimalism Beyond the Nurnberg Funnel, constructive criticism of the model

acknowledged minimalism’s limitations and detailed where both academics and

practitioners believed Carroll’s model was insufficiently developed to fully address the

needs of documentation users. While contributors to Minimalism Beyond the Nurnberg

Funnel identified a spectrum of concerns about the model including its completeness

and ability to address both novices and experts, two thematic concerns of the

critiques focused on the following: (1) how the model would address variations in

learning styles, and (2) what would be the function of a instructional visual in the

minimalist model. It is these two themes that refine the direction of this research and

lay the groundwork for developing and testing a minimalist visual instruction that

could activate experiential learning.

In order to properly frame the relevance of this research, it is necessary to

consider in greater depth the development of John Carroll’s minimalist model and

understand how its origins, as well as its reception by both the academic and

practitioner communities, have positioned minimalist documentation. In addition to

27


considering the breadth of Carroll’s work and the academic commentary that

followed it, this literature review will describe in detail the evolution of experiential

learning. In particular, this review will demonstrate that learning theory—

experientialism, as per the research conducted here—has not considered what products

(artifacts) would engage the process of learning from experience. Thus, developing and

testing a visual for instructional purposes and determining its performance against a

textual instruction seeks to fill an unconsidered gap in experiential learning: can a

visual support experiential learning.

While early cognitive theorists such as John Dewey, Kurt Lewin, and Carl

Rogers, developed preliminary ideas of experiential learning and laid the groundwork

for important valuations of learning from experience, it was the theorists working in

the latter part of the 20th century who developed complex models of the experiential.

Though this later work advanced and refined knowledge about the experiential, it still

did not consider how learning from experience can be facilitated via the impetus of

an instructional device. Carroll may have been able to develop a minimalist model

that better met the needs of documentation users if he had know more about the

complexities of the experiential and how products (artifacts) could facilitate the

process; unfortunately, neither were available at the time of his original work. Thus,

the research conducted here will seek to remedy this disconnect between artifacts and

experientialism and determine the efficacy of a visual for experiential learning.

Towards the goal of developing a minimalist visual instruction to engage the

process of experientialism, this literature review will examine what learning style

28


measurements can add to the research. As one of the concerns about Carroll’s work

was its failure to address learning styles, this research asserts that including learning

style inventories as part of the experimental design will provide data to substantiate

the results. Two inventories suitable for the work presented here are the Felder-

Silverman Inventory of Learning Styles (ILS) and David Kolb’s Learning Styles

Inventory (LSI).

The ILS measures learning style as being composed of a preferred intake style

(visual or verbal) and provides metrics on the style pairs of sequential/global,

active/reflective and sensing/intuiting. Kolb’s LSI frames experiential learning as

having four unique styles with two of those styles being far more disposed towards

active experimentation. This inventory will demonstrate that the experiential is not a

monolithic construct and, in individual results, it will show how specific variations in

style function with different artifacts.

The Significance of John Carroll’s Minimalist Documentation Model

On his current website at Pennsylvania State University, John Carroll

describes one of his research areas as being concerned with ”minimalist techniques

for making information efficient” (Carroll n.p.). Derived from his studies as an HCI

(Human Computer Interaction) researcher for the last 30 years, Carroll’s

minimalism—as a philosophy for the design of instructional materials—grew from

his early work at IBM studying how people approached tasks and instructions with

early computing technology. Particularly, Carroll was interested in what was required

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in terms of documentation to support users of the new technology in effectively

completing their tasks. He determined, after several years of laboratory work in

studying computer users, that people were much more dynamic (and conversely, less

passive) in their interactions with technology than most documentation developers

gave them credit for. His findings demonstrated that the typical approach to

documentation—the weighty, system-centered volumes of instruction—did not meet

the needs of action-focused computer users. Thus, Carroll began to examine the very

nature of users and formulate his ideas on what would meet their needs.

Carroll’s understanding of human nature, as derived from observing people

working with instructions and technology, led him to reconsider the types of

information being provided to users under the guise of ‘help’ for instructional

purposes. Information, as it had been designed and delivered, failed to meet the

needs of its intended audience and served, much of the time, only to exacerbate

tensions between users and technology. Consequently, Carroll saw the need to

develop a new instructional paradigm that would better meet the requirements of real

users. He would call the artifact the “minimal manual” (Carroll, Nurnberg Funnel 143)

and document its genesis—as designing minimalist instruction—in his first book, The

Nurnberg Funnel (1990).

In The Nurnberg Funnel, Carroll traced the evolution of his model via his

empirical experiences working with participants and instructions. His documentation

construct would enable user comprehension of instructional processes by building an

experiential learning paradigm. His model encouraged an action-oriented approach to

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learning by providing information designed to anchor the tool within the task,

support error recovery/recognition and enable reading related to doing, studying and

locating. Key to Carroll’s model was the provision of only the most elemental amount

of guidance via the text—his goal being to empower the user to construct knowledge

via a heuristic approach. Carroll postulated that given a textual starting point, most

users would develop their own “fill in the blanks” method of understanding a

process. Carroll articulated successful instructions as having the following qualities:

1. Contains meaningful goals and tasks 2. Enables the user to start work quickly

3. Allows for individual reasoning and interpretation

4. Permits a non-linear reading

5. Coordinates the training with the system (software)

6. Allows for error recognition and recovery

7. Exploits prior knowledge of the users

8. Uses error situations to build knowledge

He came to call his “less is more” approach to documentation, minimalist and

waited to see how theory would morph into practice (Carroll, Nurnberg Funnel 1).

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EIGHT YEARS LATER—1998’S MINIMALISM BEYOND THE NURNBERG FUNNEL

Echoing what would be the overall outcome of minimalist documentation

practices, Stephanie Rosenbaum, in her contribution to Carroll’s 1998 edited text

Minimalism Beyond the Nurnberg Funnel, noted that minimalism had “not gained the

penetration I expected in commercial documentation practice” ( Rosenbaum 144).

While at first, it appeared that Carroll’s design for instructional materials would

provide an alternative paradigm for informing users on how to complete tasks with a

computer, the overall reception of the model in practitioners’ circles was lukewarm at

best. The enthusiasm for the model was limited for several reasons including its

perceived academicism, its failure to adequately address different levels of learners

and very un-documentation-like approach. Contributions to Minimalism Beyond the

Nurnberg Funnel provided, however, the opportunity to explore key voices in the

academy and the profession with respect to what the failings may have been and what

could have made minimalism a more viable approach. It is these voices that shape the

direction for revisiting and validating minimalism in 2007.

Minimalism Beyond the Nurnberg Funnel examined where minimalism had been

since 1990 in terms of its adoption into the mainstream arena of technical

communication’s professional practices. John Brockman’s apt summation that

technical communication circa 1998 assimilated minimalism “selectively, partially and

with resistance” explained its position within the practicing core of technical

communicators; however, Minimalism Beyond the Nurnberg Funnel also articulated in its

contributed writings a greater initiative for minimalism: where was it going from here

32


(Brockman 387)? Greg Kearsley’s submission to Minimalism Beyond the Nurnberg Funnel

provided a prescient message regarding the future of minimalist practices:

Where are [sic] the next generation of minimalists coming from? If minimalism is to grow and be more widely adopted (not to mention the conduct of research studies), it will need many more disciples and practitioners of the art (Kearsley 403).

PERCEPTIONS OF MINIMALISM—SHORTCOMINGS AND CONCERNS

Those who contributed to Minimalism Beyond the Nurnberg Funnel saw the lack

of minimalism’s acceptance as stemming from a variety of issues within the model.

Carroll had developed minimalism as a means to address the problems that

frequented his studies of users and instructional materials. More often that not,

according to Mary Ann Eiler’s summary of the issues, users were prone to the

following actions:

starting without thinking and planning

resisting the step-by-step protocols

incorrectly applying skills from similar tasks to the new task

improperly identifying and recovering from error (Eiler Downsizing)

Based on the above, Carroll’s later ideas became structured around the

documentation philosophy of “better-supporting self-initiated sense making”

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(experientialism) rather than on the system centered models that had prevailed for so

long (Carroll, Beyond Nurnberg 7). Carroll wanted to put learning in the hands of those

who needed it most: the learners. By increasing the opportunities for experiential

learning as coupled with the application of logic, context, prior knowledge and error

recovery he had hoped to empower the individual user to create their own experience

of a system. As this philosophy made its way into distributed practice however, it

transmogrified into scenarios that degraded the original intent of Carroll’s work and

left minimalist documentation practices as having a very uncertain future. This was

due, according to Patricia Anson in “Exploring Minimalism Today”, that Carroll had

only defined what minimalism is and what it should do, “not necessarily how to

design it” (Anson 95).

Minimalism Beyond the Nurnberg Funnel became the vehicle to examine where the

model was problematic. The contributors sought to understand the issues and

provide possible solutions or alternatives that would help minimalism find its footing

in the documentation world. Certainly, the volume contained many outright critics

who chose to upbraid aspects of the model however, the research here will

concentrate on two distinct schools of thought that emerged from Minimalism Beyond

the Nurnberg Funnel: those who framed what they perceived as the problems and posed

possible remedies, and others who mapped out future directions for the model.

Contributors David Farkas and Greg Kearsley articulate, in their individual

articles, most of the commonly perceived difficulties with Carroll’s model for

minimalist documentation. David Farkas in “Layering as a Safety Net for

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Minimalism” commented that the risks of minimalism outweighed the benefits. For

Farkas’ risks were associated with “how radically information is cut and just what is

cut and how” and the errors that could result from the omission of possibly critical

information (249). Farkas, while not denying that errors are useful as part of a

learning process, stated that “the efficacy of errors is highly situational” and defined

by the context or “pressure” of task completion and the user’s overall “interest” in

remembering specifics about the program (250).

A second concern of Farkas, as documented in “Layering as a Safety Net for

Minimalist Documentation” focused on how users choose to solve a problem—

usually, “on their own” (248). Unfortunately, this may lead to, in Farkas’ opinion, a

tendency to “abandon the documentation” if they do not find the solution in short

order (248). Abandonment, as indicative of a lack of engagement, meant for Farkas

that users were now on an undirected guided tour that could result in important

problems with respect to completing a task:

Users may be unable to complete the task successfully

Users may expend more time and energy than they wished

Users may develop an incorrect mental model of the system that will

negatively impact their future interactions with the system (Farkas 249)

By no means did Farkas advocate the outright dismissal of minimalism; rather,

he sought to provide a means by which Carroll, or others, could make the model

35


more applicable. His ideas of layering—adding secondary visual support to

minimalism—was one possible means to fill the gaps in what he saw as an otherwise

good model. Like Hans van der Meij who comments in “Joint Handling of Manual

and Screen” that “the role and design of screen captures, alone or in combination

with text, are unknown by and large”, Farkas puts forward a cogent argument that

proper research is necessary to better understand how minimalist practices, especially

in the area of visuals, can reach their audience (van der Meij 281). Farkas, in fact,

identifies a critical element of the research conducted here: that our understanding of

minimalism, as predicated on experiential learning, has not yet considered the role of

the visual as a device for facilitation or engagement.

Greg Kearsley, another contributor to Carroll’s Minimalism Beyond the Nurnberg

Funnel, summarized in his review of Carroll’s work that “minimalism faired well as a

theoretical framework” (194); however, he also noted that “there were some

significant theoretical lacunae in minimalism” and that these “mapped onto practical

problems in applying” the model (394). In other words, theory and reality seemingly

did not mesh in a way that made the model practical. To better understand this dis-

coordination between theory and practice, Kearsley elected to examine minimalism

from a “gaps in the theory” perspective and, in turn, articulate the lack of commercial

penetration realized by minimalism (394).

Kearsley noted several areas that minimalism failed to address, key of which

are:

36


Its “failure to link it with other major cognitive and instructional

frameworks” (395).

A neglect of “the coordination of text and graphics, an area in which

minimalism provides little guidance” (Kearsley 396).

Its lack of an immediate applicability to the skills of “decision making,

judgment and problem solving” (396).

Why the current model of minimalism gives little or no guidance on

“how to balance conceptual and procedural information” (398).

Kearsley’s points are well taken in that their intention is not to overlay

minimalism as a panacea for all that ailed traditional documentation practices. As will

be discussed later, Kearsley aptly queries the place of graphics and the linkage to

cognitive theories in his assessment of the minimalist model. He, like other

contributors to the collection, was acutely aware of a problem with the model;

however, without a detailed knowledge of the experiential learning process and its

shortcoming with respect to the role of a product (visual artifact) as an impetus for

engagement, Kearsley does not seize the problem at the core of the model.

Mary Ann Eiler noted in her 1997 article, “Minimalism and Documentation

Downsizing: The Issues and the Debate” that one of the major drawbacks to

minimalism is what she and David Farkas called “risk” (Eiler 2; Farkas 247). In Eiler’s

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terms, risk was a multi-faceted problem that included elements of liability, time

constraints and learning style. Eiler found from interviewing documentation

specialists that concerns arose from the completeness of a minimalist document as it

related to liability issues. By not including every step of a procedure in a “high stakes

environment, (e.g., aviation, medicine, etc.)”, managerial personnel worried that the

litigious climate of society would use the gaps in minimalism to launch legal action

(Eiler 2). As well, many large documentation projects—usually modular and

dependent on multiple functional units—have no leeway for the time and cross-unit

coordination required of minimalism (Eiler 2).

Finally, in terms of learning style, Eiler focuses what she perceives as an

unexplored area of minimalist documentation practices. She notes that “learning

theory experts” could have concerns with the model in that while “holistic learners”

may well be able to embrace the concept of minimalism, their counterparts, the

detailed-oriented serial learners, would feel inadequately supported (Eiler 2).

Eiler also concurs with John Brockman and David Farkas in respect to aspects

of minimalism that could have contributed to its lack of popularity. Brockman’s

concerns with minimalism stem from the “learning-by-discovery”, or active

experimentation that forms a core component of minimalism (Eiler 2). The model

may, according to Brockman, create gaps in learning that never become adequately

filled and result in the uninformed cutting of text. This random cutting was also a

concern expressed later by Carroll who stated that “brevity taken as the central thrust

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of minimalism” (Carroll 57) was not, without the identification of “the core structures

and content” a goal of the model (Carroll 58).

DIRECTIONS FOR RESEARCH—1998

While the research proposed in this dissertation will not find answers to all of

the concerns expressed in Minimalism Beyond the Nurnberg Funnel, re-visiting

minimalism provides the opportunity to review how it was received and what others

in the field thought with respect to the place of visuals and learning styles. The

preceding critiques of Carroll’s work were very close to identifying the core issues

explored here: that experiential learning had never been measured with respect to

what type of artifact (product) would facilitate the process. At several points in the

critiques, it appears as if some might articulate the central problem; however, no

matter how close they came, no one contributor reduced the problems of minimalism

to its core.

Other contributors to Minimalism Beyond the Nurnberg Funnel chose to look at

minimalism not from how it could be augmented or what its theoretical gaps might

be but what they could see, in general terms, for the future directions of Carroll’s

work. The contributions of Patricia Anson, JoAnn Hackos, Janice Redish and

Stephanie Rosenbaum provide two distinctive directions for minimalism—

understanding learning styles and considering visuals—that inform the research to be

carried out in the dissertation.

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Anson, Hackos, and Redish all state that in order for Carroll’s model to be

more successful, one of its future considerations must focus on how it could be

adapted for specific learning styles. Patricia Anson’s chapter, “Exploring Minimalism

Today”, concludes with a section regarding the future of minimalism. She calls for

technical communicators to “refocus minimalism from a writing style to a way of

communicating information” that emphasizes “the analysis of users needs”,

particularly in the area of “learning styles” (113-14). In a similar vein, JoAnn Hackos’

chapter, “Choosing a Minimalist Approach for Expert Users” repeats the call for a

better assessment of users. Particularly, she asserts that any successful documentation

model must focus on “the learning needs of users” and that there is a need to

“extend the minimalist text” for a “variety of learners” (175). Janice Redish in

“Minimalism in Technical Communication” considers individual “learning styles” (as

well; particularly; she focuses on the difference between “users with a propensity to

explore and those with a desire for more direct instruction” (223-27).

Continuing an important thematic concern, the role of a visual for minimalist

documentation is questioned again by many of the contributors to Minimalism Beyond

the Nurnberg Funnel. Greg Kearsley pointed out, as noted earlier, that one of the

fundamental gaps he perceived in Carroll’s theory is connected to “the coordination

of text and graphics, an area in which minimalism provides little guidance” (Kearsley

396). Hackos, in her work understanding more advanced users of minimalist

documentation and user interfaces, noted that “we might want to consider that

graphical user interfaces are an exercise in minimalist information design. By using a

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few words, a few graphics, and an effective use of two-dimensional space, a well-

designed interface should function as a minimalist text” (176). For minimalism as a

whole, Hackos recommend that “graphics and screen layout” become part of the

model (Hackos 175).

Stephanie Rosenbaum saw just such an event play out in her observations of

users working with minimalist documentation. She noted that several “participants

took advantage of graphics to assist in their implementation of minimalism”

(Rosenbaum 143). Even informally, there seemed to be an innate call to make use of

the visual as an instructional device. Thus, the research conducted here will capitalize

on this earlier call. By developing and testing a minimalist visual instruction against

minimalist verbal instruction, more will be learned about what best facilitates

experiential learning.

The Fundamentals of Experiential Learning

The overall problem explored in this research lies with questions predicated

on what kinds of materials support experiential learning. At its most basic,

experiential learning seeks to establish knowledge via the process of doing; that is, in

other words, task performance. However, as a theory of learning, experientialism

does not consider what product (artifact) best starts the process and engages the learner

with the task. This engagement with the task is, in many cases, accompanied by

instructional materials; however, what remains unknown at this point in time is the

role of a visual artifact in this paradigm. In particular, what modifications to a

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visual—such as the screen capture— would serve experiential learning is an area

lacking a methodological exploration and evaluation.

David A. Kolb, the preeminently cited author on experiential learning

describes it as "the process whereby knowledge is created through the transformation

of experience.” In an expanded definition, Kolb views this transformation as derived

from:

…a holistic model of the learning process and a multilinear model of adult development, both of which are consistent with what we know about how people learn, grow and develop. The theory is called “Experiential Learning” to emphasize the central role that experience plays in the learning process, an emphasis that distinguishes ELT from other learning theories. The term “experiential” is used therefore to differentiate ELT both from cognitive learning theories, which tend to emphasize cognition over affect, and behavioral learning theories that deny any role for subjective experience in the learning process. (Kolb 41)

Kolb’s contribution to experiential learning, and in particular, his value to the

research conducted here is derived directly from his work to establishing the

experiential as unique. Distinguishing the experiential as a discrete model of learning

and placing it uniquely apart from the much more monolithic constructs of cognitive

and behavioural theory has defined Kolb’s career and positioned him as the authority

in this area of learning theory. In promoting the experiential, Kolb validates the

acquisition and internalization of knowledge and thus, the process of learning as

importantly linked to experience. However, again, Kolb’s focus is on the process of

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learning, not on the product that may engage an individual with the learning

experience.

Carroll too, envisioned learning via these same mechanisms and his minimalist

model strove to capitalize on building knowledge, reinforcing understanding and

applying it through experience. Particularly, Carroll believed that such a model would

facilitate error recognition and recovery, an important function in learning about new

software. Nevertheless, as we know now, Carroll’s use of textual materials with the

goal of activating an learning process was not successful. His starting point is

commendable but as there was no prior research on products that facilitate

experiential learning, he was in very uncharted territory.

However, Carroll’s work—viewed as a case study for this research—was, at

the very least a good beginning, albeit not necessarily a completely successful one. In

theory, minimalism and the work that informs its design was a solid model.

Moreover, it was backed by Carroll’s years of work at IBM studying just how it is

people come to understand new tasks and technologies. In the field however, we

know that minimalism lacked acceptance due to its perceived shortcomings—

postulated in this research as the omission of an experiential visual and a lack of

understanding regarding learning styles. Therefore, to build a better model,

particularly a visual model, and determine how it functions in terms of an

instructional device, it is a requirement to examine the development of experiential

learning from past and current perspectives. This review will show that while learning

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has always been viewed as a process, the products that may facilitate it—such as a

visual—have been overlooked until this research.

FRAMING THE IMPORTANCE OF EXPERIENTIAL LEARNING

To better situate experiential learning as it formed the infrastructure for

minimalist documentation, it is helpful to trace the model’s evolution over more than

100 years of thought and through multiple disciplinary approaches. In terms of

formalizing the experiential and establishing it as a model of learning and knowledge

acquisition separate from cognitivism and behaviourism, early prototypes owe their

origins to the theoretical and practical applications of Carl Rogers, Jean Piaget, Kurt

Lewin and John Dewey (Schneider, Bugental and Pierson 2001). As philosophers,

psychologists and educators interested in learning and human behaviour, their work

in the latter part of the 19th up to the mid-twentieth century was foundational in

developing representations of how experience functioned in terms of human

information processing and adaptation—the process of learning.

As noted earlier, it is important to keep in mind that work on learning theory

has concentrated on the process, not the input stimulus. That is to say, experiential

theorists have not been concerned so much with what starts the process but what

happens when individuals are in the midst (process) of learning. Early work on

experiential theory does not address what kind of product—auditory, visual, and

textual—may be best for experiential learning. In addition, while 20th century

theorists like David Kolb and Peter Jarvis have substantially refined the experiential

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model neither touch on what kind of instructional device would be best for engaging

an experiential approach to learning. As a result, Carroll was at a disadvantage in

developing his model of minimalist documentation; in light of other options, text was

undoubtedly the best way to start but now, in hindsight, he might have considered

expanding minimalist practices in the direction of a visual.

Thus, in looking at Carroll’s work as a case study for experiential learning, it is

useful to first consider what ideas underpinned his development of minimalist

documentation. Carroll readily acknowledged that for those learning to use

technology, most “problems are embedded in scenarios of behavior and interaction”

(Carroll, Nurnberg Funnel 46); thus, the difficulties arose not from the actual learning

itself but from the need to provide a structured alternative to the fallibility of human

behaviour and cognitive processes. Therefore, the development of instructions to

support experiential learning was vital to Carroll’s work. His product in the form of

minimalist documentation was, of course, intended to bridge this gap; however, the

model’s limited acceptance was indicative that the gap still existed.

John Carroll’s early ideas asserted “prior knowledge”—previous experience in

a similar learning context—helped in guiding individuals through a computer-based

task (Carroll, Nurnberg Funnel 37). These users were, for the most part, working with

earlier and, in turn, more simple versions of hardware and software and could

laterally transfer concepts more easily; as well, this demographic could easily be called

‘generation typewriter’—a cohort familiar with key-stroke and ink-ribbon processing.

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Together, this knowledge set allowed for what Carroll called “analogical comparison”

and a consequent lateral transfer of skills to some extent (Carroll, Nurnberg Funnel 32).

However, in 2007, those who use instructions to engage with technology face

a double bind of sorts: as a group, they possess a greater general familiarity with

computers but no lateral transfer of concepts from earlier, more typewriter-like

computers interfaces (or even typewriters). As well, rather than starting with the

initial inception of a program—version 1.0—users must learn programs that are now

in an eighth, ninth or even tenth iteration of the original. Consequently, today’s users

of instructions require a different form of assistance than individuals of 20 years

previous. In fact, they require materials that will facilitate their exploration of new

concepts and engage them more substantively in the task rather than show the

location of the on/off switch.

The Beginnings of Experiential Learning

The 19th century heralded the reformation of the educational system to better

address the learner as an active participant in the educational experience. The early

progenitors of learning theory—Rogers, Piaget, Lewin and Dewey—were all

instrumental in shaping the directions of experiential learning as we know it today.

Beginning with Dewey, who was born in 1859 and concluding with Rogers’ 1902

birth, the latter part of the 19th century was a time for the rise of studies in human

behaviour and resulted in the formalization of the discipline known as psychology.

This time period also reflected an era when education was experiencing both rapid

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growth and philosophical reform. Thus, the convergence between psychology and

education began and understanding how learning occurs and in what environments

was a focus of study among academics. In particular, the process of learning—as it

occurs via experience—was a focus of study.

Valuing Learning from Experience—Dewey’s Perspective

John Dewey, an integral member of the American Pragmatists movement, is

considered the first to examine the ideas of “interaction, reflection and experience” as

they inform the learning experience (Dewey, Theories 206). As a philosopher and

educational reformist, Dewey’s assertion that significance in learning occurs only

when education can “engage and enlarge experience”, became the foundation for the

later work of Rogers and eventually Kolb and Jarvis in terms of an experiential

paradigm (Dewey, Theories 206). Dewey, in discussing the value of experience in an

educational structure, noted however that experience was not always deemed an

important part of learning—its vocational origins sullied the idea considerably in the

minds of many. To better explicate the evolutionary valuation of experience and

learning, Dewey considered it within the context of classical Greek life.

Synoptically, the Greeks "disparaged experience" as compared to the exercise

of pure intellect; what we would today call a ‘hands-on model”, the Greeks

considered an act of labour in the lowest form (Dewey, Experience 233). Knowledge

was for this early society a refined “contemplation” and not a mere “productive art”

(Dewey, Experience 236). As a result, earlier models of education stressed passivity on

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the part of learners rather than an active engagement. Dewey, however, would

challenge these notions and assert that students learn better when experience and

active engagement were factored into education.

As education was changing during the late 19th century, Dewey and his

contemporaries saw the value of experience—what would become experiential

learning—in the way people came to create understanding. From his work with

empirical models, Dewey found that "we learn from our failures when our endeavors

are seriously thoughtful" (Dewey, Theories 211). Thus, if we engage in an experience,

albeit it even an unsuccessful one, our vested involvement and considered reflection

will make that experience just as valuable, if not more so, than its successful corollary.

In addition, Dewey saw the active agency of the individual and felt that education,

and all learning in general, should "emphasize the individual factor in knowing"

(Dewey, Theories 217). Synoptically, Dewey was framing the essentialist features of

experiential learning as defined by Carroll—the importance of active engagement,

task relevance, and the significance of errors as a means of learning.

Experience as a Unique Model of Learning—Lewin’s Ideas

Mark K. Smith, in the article, “Kurt Lewin: Groups, experiential learning and

action research” describes Lewin as an individual who, among other things, deepened

our understanding of experiential learning. Kurt Lewin, born in 1890, began his

academic career in medicine and later moved to the biological sciences. His Ph.D.,

awarded in 1916, focused on empirical methodologies used to study learning. While

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his career was diverse, Lewin is known for his work in personality theory and group

dynamics (social processes) as well as his contributions to the war effort during the

1940s; interestingly, many of his ideas begin a refinement of the experiential learning

paradigm of the twentieth century and include the concept of a facilitator for

learning. (Smith, Lewin n.p.).

Of relevance to the experiential are Lewin’s findings from T-Groups and

Action Research; both of these studies served as starting points for his ideas

regarding stylistic differences and the cyclical nature of learning. In post-WWII

America, Kurt Lewin began studies that encouraged “group discussion and decision-

making”; his observations from these studies led to the formation of “basic skill

training groups”, a term that was later truncated to “T-Groups”. His studies in this

area led to the concept of styles—as categorical groupings of individual performance.

With sponsorship from a major training center, Lewin was able to spend the next 10

years refining his ideas and understanding key psychological elements of performance

in collective situations. Ultimately, his work led to the creation of what we know

today as the “encounter group” and an understanding of the dynamics that constitute

it (Smith, Lewin n.p.).

Four factors that influenced experience-based learning emerged from the T-

Groups study; these were, in Lewin’s terms: feedback, unfreezing, participant

observation and cognitive aids. Feedback was, for Lewin, “the adjustment of a

process informed by data about its results or effects” and unfreezing was the process

of changing a previously held belief. Observation called for reconciling the emotional

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and analytical parts of an experience and, finally, cognitive aids in Lewin’s work

involved “models or organizing ideas” on which the groups could base their

discussions and actions (Yalom in Smith, Lewin n.p.).

Lewin’s other important model of experiential learning is that of “Action

Research”. Though this model declined in popularity due to its “association with

radical political activism”, Action Research (AR) can be viewed as linking closely with

Jurgen Habermas’ communicative action and the more current field of “participatory

action research”. Fundamentally, this second concept of Lewin’s “parallels Dewey’s

conception of learning from experience” as it structures learning in an adaptive

model. While the model is criticized as being linear and thus not fitting well with later

process diagrams of the experiential, it is still a concentric model beginning with a

starting point, followed by an action and assessment. As the cycle is one of evaluation

and adaptation, it is experiential and empowers the individual to create their own

experience. This was also a pivotal feature of Carroll’s model of minimalism—that

the user should be the guide and the materials only a facilitator (Smith, Lewin n.p.).

However, what Carroll did not know, and what early work on the experiential

learning process did not consider, was the kind of instructional product (visual, text)

that would best activate experiential learning.

Developing Individual Expertise—Piaget’s Model of the Experiential

Jean Piaget, born in 1896, began his academic career as a biologist; however,

his work evolved to follow his greater fascination—studying and documenting his

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findings from research in the area of intellectual development and growth. As one of

the most prominent names in modern psychology, Piaget is noted for his work with

children and, from it, for establishing a stage-system of cognitive development still in

use decades later. Without a doubt, Piaget and his work present the most complex

breakdown of the constituents of experiential learning.

In the specific area of experiential learning, Piaget’s work postulates how

knowledge as we currently describe it—in the tangible sense, not as part of the

philosophical indeterminacy of knowledge explored by philosophers—is acquired, in

both epistemological and practical terms. In his landmark 1976 book, Genetic

Epistemology, Piaget explicates that the “parallelism between the progress made in the

logical and rational organization of knowledge and the corresponding formative

psychological processes” represented a shift away philosophical questions of what is

knowing and moved towards a construct that validated the individual’s possession of

a unique and internalized system of expertise (Piaget 13).

While Dewey was an early proponent of a cyclical model, Piaget saw

knowledge as acquired through a “system of transformation” that placed learning into

a constantly morphing sphere of actions and abstractions (Piaget 15). Specifically,

Piaget asserted that reflective abstraction based on “coordinated actions” was vital to

the transformative nature of learning via experience. These coordinated actions were,

for Piaget, broken into four components: additive coordination, sequential

coordination, before and after, and intersection (Piaget 18).

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The experiential then, for Piaget, became not just a linear construct; rather, it

contains a multi-dimensional mental operation as indicated by the variety of

coordinations that inform it. In practice, learners can, in the case of “additive

coordination” build an extension to an existing knowledge structure and thus reshape

the whole to a new form construct. In the case of sequential coordination, procedural

steps make an ever-increasing body of knowledge for the learner—a movement from

novice to expert. Before and after coordination represents the recognition of state

changes in an experiential procedure, and finally, intersection coordination describes

how two seemingly disparate tangents can connect in an experiential model.

As the above operations (or coordinations) become “internalized”, the

experiential paradigm then becomes “multidirectional” according to Piaget;

subsequently, coordinations that are “repeatable and generalizable in an action

[become] a scheme” and when many schemes become combined, the experiential

learner possesses a logic of “actions [and] order” to apply in future situations. In

other words, learners develop more than just knowledge; they also develop an

understanding that this knowledge is laterally transferable to other similar situations.

It is this ‘transfer’ that makes the experiential such a valuable and rich mode of

learning and one John Carroll wished to capitalize on with his minimalist

documentation practices (Piaget 42).

Piaget had also discussed his theory of coordinated actions and experiential

learning in, The Equilibration of Cognitive Structure (1975; reprinted and posthumously

revised 1985). In this work, he considered knowledge—as from the point of view of

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an experiential model—acquired from a three-stage process of

“equilibrium…disequilbria and re-equilibration”. He believed that “knowledge

cycles” were necessitated by an ongoing perturbation with “accommodation and

assimilation” as they occur via a set of systems and subsystems (Piaget 3-6). Learning,

then, became a “compensation” as the learner constantly evaluated the “success or

failure” (Piaget 22-4) of their active experimentations in the equil-dis-re-equilibration

cycle.

Thus, what Piaget provides for experiential learning is an advanced model of

the process by which individuals create knowledge for themselves. An important

consideration to note, however, is the closed nature of the learning cycle. At this

point in time, only Lewin, as discussed earlier, begins to make mention of the concept

of a facilitator in the learning model; that is, an individual who starts the process of

learning by experience. Piaget’s model (and later, Kolb’s) are entirely focused on the

process, not on a particular stimulus or artifact (product) that may engage the process

of learning. Carroll, of course, was developing a ‘facilitator’ in the form of minimalist

documentation; however, as no work in the field had determined if text or a visual

would be an ideal ‘facilitator’, it was unknown if his model would succeed. Thus, this

research is seeking to fill the gap in Carroll’s model and determine if a visual will

work for experiential learning.

Facilitating the Learning Experience—Rogers and Instructions

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Carl Rogers, another noted psychologist whose work was developing

concurrently with that of Lewin and Piaget in the early 20th century, can also be seen

as advancing a theory of learning predicated on the experiential. Though his work is

not normally conjoined to that of Dewey, Lewin and Piaget in terms of experiential

learning, many of his ideas bear consideration within the canon; as well, they play out

significantly in the later work of David Kolb and Peter Jarvis.

Rogers, born in 1902, studied theology in his early academic career before

switching to psychology and earning a Ph.D. in clinical psychology in the early 1930s.

Like his contemporaries, Rogers was concerned with how people learn and when is

knowledge most rapidly acquired (Rogers, 1969; Kirschenbaum and Land, 1989).

While his ideas of learning were focused within the context of the educational system

rather than workplace knowledge acquisition, they still bear significantly on


In his 1969 book, Freedom to Learn, Rogers defines the criteria necessary for

learning to occur. Three fundamental requirements for the learning are, according to

Rogers, that the “subject matter is perceived by the student as having relevance for

his own purposes” (158), that the student “chooses his own directions”, and that

“through doing” significant gains in understanding and internalizing knowledge will

be made (162). Again, it is very clear how Carroll’s ideas are soundly formed within

the work of these early theorists, as these same goals appear in his criteria for

minimalist documentation.

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As per Carroll’s conception of experiential learning, Rogers asserted that

concrete goals or objectives “clarify purpose” (164) and increase the “rapidity” (158)

with which people learn; simplistically, such goals are a “motivational force” and

drive the learning process (164). What Rogers’ views as equally important in his

experiential model of learning are that of agency—what he refers to as the

“facilitator” in the learning paradigm (Kirschenbaum and Land 306). For Rogers,

who was writing about education during a time of radical reform, the concept of

facilitator was enacted by the classroom instructor. Rogers’s goal of turning education

towards “significant independent learning” and transferring “power” to the learner

reflected what he viewed as the role of the instructor (Kirschenbaum and Land 300).

Rogers is vital to experientialism as he opens the closed model of the learning process

to include the idea that learning is started, or facilitated, by a stimulus. For Rogers,

this stimulus is an instructor; however, as instruction can also be delivered by a visual

or a text, learning which one may be more effective for experiential learning is an

important research question.

The facilitator for Carroll’s work on minimalism is not manifested by the

physical presence of an instructor; rather, in “learning things that matter”, the role of

the instructor becomes filled by the instructions themselves (Kirschenbaum and Land

302). What Carroll chose to redefine was the character of the instructions towards

minimalism versus a complete tome of materials. As per the genre of self-help

manuals, Rogers most precisely identifies the agency of the individual in an

instructional situation and Carroll follows suit with a manual designed to encourage

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exploration versus a rote adherence to prescribed protocols. Rogers’ work here is

helpful in that he begins to frame the idea that learning as a process must have a

starting point based on facilitation; he also builds an important bridge to current

studies. For the research conducted here, determining if facilitation and, in turn

experiential engagement can be derived from a visual will break new ground.

LATER WORK IN EXPERIENTIAL LEARNING

From reviewing the early theorists, the over-arching validity of the experiential

is made clear; the active engagement of the individual in the learning process stands

separately from behavioural and cognitive modalities. Experiential learning captures

the multifaceted nature of human experience and its concomitant interaction with

learning as a means to gain greater knowledge. Consequently, much of what Carroll

proposed and hoped for with his minimalist model does lie on a solid foundation.

However, what remains to be considered is where Carroll’s model could have erred

and how experiential learning—as supported by a visual—could work more

effectively.

As discussed previously, experiential learning as a process and the types of

products (artifacts) that could support it is an area not previously considered in the

academic literature. Early theorists only touch on the idea of a facilitator but linking

learning theory to what kind of instructional material may support it is breaking new

ground. As well, where earlier theorists may have faltered, and what Carroll may not

have considered, lies within the relationship between learning and knowledge; there is

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no one agreed upon understanding of their interconnectedness. In fact, earlier ideas

see only some degree of unity in these constructs. Dewey imbues the individual with

a predisposition that automatically facilitates learning, Lewin considers learning to be

the making of experience, Rogers view knowledge as acquired and Piaget sees

learning as the result of transformations. Consequently, any of these earlier views of

the experiential may not fully address the dynamics of the model; none of these

considers an artifact or product that may support the learner.

Certainly, each of Dewey, Lewin, Piaget and Rogers brings together aspects

that form a broad and useful model of experiential learning. As well, they all

demonstrate a distinct status for the experiential amidst the dominant cognitive and

behavioural theories. Using the experiential, as Carroll did, to provide an architecture

for how users functionally approach minimalist instructions was well considered at

the time, yet the gap between the model and its lack of practical acceptance was never

closed via further work. Fortunately, current theorists such as David Kolb and Peter

Jarvis do provide ways and means to address difficulties with Carroll’s use of an

experiential paradigm by proposing refined ideas about the dynamics of experiential

learning. While they diverge on several points, this later body of work in experiential

learning provides a hypothesis for Carroll’s lack of success with minimalism as well as

a framework for the research conducted in this study.

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DAVID A. KOLB

Unlike the founding fathers of Dewey, Lewin, Rogers and Piaget, David A.

Kolb’s work is considerably more current (1970 and onwards) and is based not only

on cognitive psychology but on his work as an organizational behaviourist. Kolb,

currently a professor of organizational behaviour at the Weatherhead School of

Management in Ohio has had a career in experiential learning that spans over 25

years. His work in the field has been influential in both training and educational

design for post-secondary teaching. Although his most influential publication

Experiential Learning: Experience as The Source of Learning and Development (1984) was

published over 20 years ago, he still continues to publish actively with his most recent

work focusing in the area of experiential learning and team dynamics. Kolb’s ideas are

most certainly a pastiche of the previously discussed experiential theorists; however,

his unique contribution is a detailed deconstruction of experiential learning and the

delineation and measurement of four specific experiential styles—as developed

through the administration and analysis of his own Learning Styles Inventory (LSI),

which will comprise a measurement instrument used in the experimental portion of

this dissertation.

Experiential learning, as considered by Dewey, Lewin, Piaget and Rogers

reflected both a significant change and acknowledgement in the field of educational

psychology. Education as a passive activity was in the early 20th century replaced with

active engagement on the part of the learner; however, this would not always be the

status quo. In Kolb’s view, our 20th century’s “overeager embrace of the rational,

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scientific and technological” led again to the decline of an experiential model in

general (Kolb 2). In the field of education, the value of the experiential was evident

however, it became closely aligned with “vocationalism” and relegated to only

specific types of education (Kolb 6).

Kolb, however, found that education could not deny the “central role that experience

plays” (Kolb 20); yet, experience required proper codification in order to be a useful

construct. Earlier rationalist and cognitive models of learning relied solely on the

“acquisition, manipulation and recall” of material as a means to validate their efficacy.

Behavioural models painted all human actions with the same broad strokes and, in

turn, denied the “subjective experience” of the individual (Kolb 20). It was the

experiential, Kolb asserted, that presented a “holistic” model that could include

relevant aspects of cognitivism and behaviourism as well as viewing “learning as a

process, not [as] outcomes” (Kolb 26). Learning was for Kolb, as it was for Jerome

Bruner, an intersection where “ideas formed and reformed”, not where rote

memorization occurred. Thus, learning for Kolb became “a continuous process

grounded in experience” (Kolb 27).

To best reflect the idea of continuous learning, knowledge and experience,

Kolb embraced Piaget’s circular model of cognitive development and learning, but

added multiple dimensions via concentric circles. Knowledge, for Piaget, was gained

through the process of reconciling inconsistent information (disequilibria) and

returning to a balanced state (equilibria and re-equilibria). Kolb re-framed this

construct in more complex terms where it would ultimately become his model of

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basic adaptive processes, or what he more readily calls learning. The outer ring of the

circular model captured what Kolb that saw as the most primary elements of

adaptation: inquiry and research; in other words, the greater whole of all learning and

knowledge occupies this outermost position. In this circle, the focus—one which

permeates all levels of the model—is finding a problem, asking questions, seeking

answers and conveying knowledge. He placed creativity on the next concentric circle

and noted its major phases as incorporation, incubation, insight and verification.

Decision making for Kolb only involved only the three key dimensions of

intelligence, design and choice.

The most important sectors of Kolb’s adaptive processes model are the two

inner rings of problem solving and learning. Problem solving as defined by Kolb

moves through eight phases in order to find resolution in the adaptive processes.

Problem solving begins with choosing a model or goal and then evaluating it in light

of reality. Problems are identified and one is selected for further exploration,

alternative solutions are considered and evaluated with one selected for final

implementation. In technical communication, the model follows with reasonable

adherence to several of the common report genres such as feasibility, decision-

making and recommendation.

At the very core of the basic adaptive processes are the foundations of the

experiential learning cycle. Learning then, defined as a break from memorization and

established as a “process”, positioned Kolb to move forward with his ideas of the

experiential as a holistic approach. Like Piaget, Kolb saw experiential learning as

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having four distinct dimensions and chose to emulate Piaget somewhat in the naming

of those dimensions. He does however, break from the Piaget’s cognitive

development paradigm and instead frames his model as an “experiential learning

cycle” with the four major dimensions labeled as: concrete experience (CE), reflective

observation (RO), abstract conceptualization (AC) and active experimentation (AE)

from a clockwise position.

In essence, Kolb elects to hybridize his model with that of Piaget’s and, in

turn, collapse the stages of learning Piaget felt were so important into four distinct

categories as noted above. These four dimensions form the core of a user-centered

model of learning and reflect the construct of process considered so paramount by

Kolb.

Kolb, although not using these terms explicitly in his description, sees learning

as a socially constructed phenomena. Learning, for Kolb, is both a “subjective and

personal and objective and environmental” form of basic human adaptation (Kolb

35); in the Dewey-ian world view, learning would be an “interaction” between the

two. However, Kolb sees the term interaction as too rigid—it does not accurately

represent the fluidity of exchange between the person and the environment.

Consequently, the Kolb-ian descriptor of “transaction”, captures the nuances of

reciprocal interchange between the two states (Kolb 36).

Kolb’s diagram, “The Process of Experiential Learning” places his principle

constructs of concrete experience (CE), reflective observation (RO), abstract

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conceptualization (AC) and active experimentation (AE) at the center of the model

and defines them as the primary aspects of learning and human adaptation..

It is important to note, however, that Kolb does not consider anywhere in this

closed process diagram, the place of an input stimulus that would facilitate or start

the learning process. How learning begins and by what mechanism is, for Kolb and

the early experiential theorists, not a consideration. Therefore, the research conducted

here seeks to fill this gap in our understanding by developing and testing a visual to

support experiential processes.

LEARNING AND KNOWLEDGE—PROCESS AND STRUCTURE IN EXPERIENTIAL LEARNING

Ultimately for Kolb, “learning is the process of creating knowledge” (Kolb 36)

and he expresses his surprise that “few learning and cognitive researchers other than

Piaget” recognize the distinct yet intertwined relationship between learning and

knowledge (Kolb 37). He differentiates between the two by stating that knowledge

“results from the transaction between [the] objective and subjective experiences in a

process called learning (Kolb 37). In his most simplified definitional form, Kolb

states that “learning is the process whereby knowledge is created through the

transformation of experience”; learning is not, however, memorization, which is an

important definitional characteristic of this model (Kolb 38). Interestingly, while

discussing learning and knowledge Kolb does not include what artifacts or products

may act as input devices that kindle the start of learning.

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The processes by which learning is facilitated, transformed and expressed as

knowledge through the adaptive cyclical modes of concrete experience (CE),

reflective observation (RO), abstract conceptualization (AC) and active

experimentation (AE) require, for Kolb, further deconstruction. The X and Y axis of

Kolb’s model place CE/AC and AE/RO as polemics, or what Kolb refers to as “two

diametrically opposed adaptive orientations” (Kolb 41). It is from this configuration

of opposites that Kolb begins to formulate his more detailed structure of experiential

learning and knowledge.

Kolb’s Structure

While Kolb has determined that the transactions occurring between the four

modes are key to experiential learning, he defines the ‘dialectics’ between the

opposing states as providing momentum for the learning process and the resultant

knowledge (Kolb 41). Kolb’s definition of dialectic with respect to the experiential

model, however, does require clarification. In a footnote on page 29, Kolb defines his

use of the term as coming “closest to Hegel’s use of the term” in that he wants to

capture the Hegelian essence of oppositional forms that, when merged, result in a

“higher order process that transcends and encompasses” (Kolb 29). While he is

careful to note that this definition does by no means encompass all of Hegel’s

ideology, he asserts that the Hegelian model is superior to that of the less dynamic

Kantian dialectic.

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Kolb places the abstract/concrete dialectic on the X axis. He labels this axis as

that of “prehension”, a term he uses to define “opposed processes of

grasping…experience”. This grasp, or what can be called internalization, is then

broken down into two further mechanisms: comprehension and apprehension.

Comprehension occurs via “conceptual interpretation and symbolic representation”

while apprehension is the result of “a tangible…immediate experience” (Kolb 41). In

other words, a process can be understood through indirect forms such as

instructions—as later explained Jarvian model of secondary experience—or by actual

engagement in a physical act.

The active/reflective dialectic located on the Y axis are dimensions of

transformation for the grasp of prehension (also called internalization) discussed

above (Kolb 41). Along this axis, transformation can occur based on two functions:

intention and extension. “Intention” allows for the prehension grasp of comprehension

to be based on “internal reflections—that is, a strictly thought-based process. On the

other hand, “extension” corresponds to apprehension and places the grasp within the

“active external manipulation of the external world” (Kolb 41). In other words, this is

the experiential component of the learning model.

Kolb’s Processes

With Kolb’s basic structural model defined, it is now possible to explicate how

he envisions learning; as well, this will preface an understanding of how he came to .

As stated earlier, knowledge “results from the combination of grasping experience

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and transforming it” (Kolb 41). Experience begins in the prehensile states of either

comprehension or apprehension and undergoes transformation based on either intension or

extension. As a result, there are four “elementary forms of knowledge” gained by

experience as shown below in Table 1.1:

Table 1.1 Kolb’s Transformative States and Resulting Knowledge Typology

Grasped

through

Transformed

by

Results in this Knowledge

Type

Apprehension Intention Divergent

Brainstorming, imaginative,

observation not action

Comprehension Intention Assimilative

Theoretical, integrating the

disparate

Comprehension Extension Convergent

Problem solving, decision

making, practical application

Exp

erie

nce

Apprehension Extension Accommodative

Action-oriented, doing

tasks/carrying out plans,

situationally adaptable

While all four forms are predicated on the common denominator of

experience, the styles that manifest themselves via an experiential situation vary with

the Converging and Accommodating profiles being most likely to engage in the

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exploratory tasks that underlie the success of minimalist documentation. As with any

assertion, sweeping the same broad brush across any kind of learning is dangerous;

however, Kolb has defined experiential learning into a much more detailed set of

criteria that previously considered. Early scholars made note of the experiential as an

important element in learning and Piaget took the model farther, pairing it with

cognitive development. It is, however, Kolb who began to build a more concrete and

holistic understanding of both learning and knowledge as derived from the

experiential. This would, however not be the final consideration of the model and in

the mid-1990’s, many of Kolb’s ideas of experiential learning would be challenged.

PETER JARVIS

Peter Jarvis, in 1995’s Adult & Continuing Education, examines learning from

the perspective of an adult education researcher and facilitator of lifelong learning.

He focuses on the underlying assumptions of the ways in which adults learn and

acquire new knowledge, considers the theories that have informed the field so far and

poses new directions for curriculum and pedagogy in continuing education. Although

Kolb’s model seems to present a sufficiently detailed model of experiential learning,

Jarvis is a theorist who considers how learning is begun by the use of instructional

materials.

Like Kolb and Piaget before him, Jarvis sees the experiential as central to how

learning and knowledge become conjoined. He does claim some indebtedness to

Kolb’s early work especially in that Kolb helped to steer ideas of learning away from

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a singular and simplistically reductive connection to standardized measures such as

intelligence and memorization. However, even though “experiential learning theory

has become quite central in recent years to a great deal of thinking about learning”,

Jarvis notes that discussions of experience are not had among those who theorize

about experiential learning (Jarvis 64-5).

Kolb, however, and his postulate that “the learning cycle may begin at any

stage and that it should be a continuous spiral” posed serious problems for Jarvis

whose model weighs both instinct and intuition as integral parts of the process. First,

Kolb’s model did not address the aspects of individual differences that may come

into effect in terms of a starting point. Jarvis, by all means, admits the popularity of

this model is so strong as much of the content is unequivocally accepted “since no

one theory” is adequately able to explain the process of learning (Jarvis 68). However,

this comment prefaces his later discussion —and second contentious point with

Kolb’s work—in which he finds the model “rather simplistic for such a complex

process” (Jarvis 69). Interestingly, Kolb’s work acts as a counterpoint for Jarvis’s own

research into the learning/knowledge experiential paradigm.

What Jarvis found was the very complex nature of learning via experience as

described by his research participants. The outcome of several interlinked research

projects articulated what Jarvis had thought earlier: Kolb’s model, while adequate in

some respects, was far too straightforward to capture the nuances of learning by

experience (Jarvis 71). Resembling a hobbyist’s miniature railroad layout, Jarvis’

structure for learning with its loops and crossovers, provides several paths for

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individual learning to take rather than the strictly circular model posed by Kolb. The

most simplistic path, running across the top of the diagram, shows that via situation

or experience, an individual may incur virtually no change/growth whatsoever.

However, as a learner wends their way through all or some of practice, evaluation,

memorization and/or reasoning, they may exit the model as “changed and more

experienced” (Jarvis 70).

For Jarvis, his model defined the much less linear path that individuals can

take towards learning. From this model, he was then able to codify learning (and non-

learning) to show the points and mechanisms from which learners do or do not

acquire knowledge. He categorized learning as having three major categories:

Non-Learning: this category encapsulates where learning does not

occur; from either previous knowledge or lack of interest, an individual

elects not to engage in the learning experience. This was a category

Kolb never addressed either by assumption—that there will be some

situations in which the net learning outcome is zero—or by omission.

Non-Reflective Learning: Jarvis considers this the most basic and

least invested model of learning. Through modeling, memorization or

pre-conscious states, a superficial engagement with learning occurs.

Such learning will not necessarily be strongly internalized.

Reflective Learning: As with Kolb’s model, reflection is the key to

acquiring understanding. The sub-categories of Contemplation,

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Reflective Skills and Experientialism provide the strongest model of

learning, according to Jarvis.

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Table 1.2. Jarvis' Typology of Learning

Non-Learning Non-Reflective

Learning

Reflective Learning

Presumption

Based on previous

learned experiences no

further knowledge is

required

Pre-conscious Learning

Learning below a level of

conscious

acknowledgement

Contemplation

Thoughts not connected

to wider social reality

Non-consideration

No response to learning

opportunity

Skills Learning

Acquired through

imitation or modeling

Reflective Skills Learning

Ability to “think on one’s

feet” and respond

Rejection

Declining the experience

Memorization

Rote reproduction

Experiential Learning

Knowledge tried out in

practice

Turning towards experiential learning, Jarvis points out the strength of

learning via this methodology as embodying a natural inquisitiveness in humans—or

experimentation—that results “in a form of knowledge that relates fully to social

reality” (Jarvis 74). However, he problematizes existing theories of experiential

learning as follows:

Most of the literature on experiential learning is actually about learning from

primary experience, which is learning through sense experiences, and,

unfortunately, it has tended to exclude the idea of secondary experience

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entirely. At the same time, there has been no agreement about the idea of

experiential learning among those who have embraced the term. (Jarvis 75)

Thus, the work of early theorists, while valuable at a foundational level for

understanding the concept of experiential learning, has not been adequately refined

by later scholars such as Kolb, in the opinion of Jarvis. Most distinctly, however, is

Jarvis’s validation of the concept of secondary experience—that we begin to learn

from notes, lectures and other instructional materials.

JARVIS AND PRIMARY/SECONDARY LEARNING

In order to rectify this oversight, Jarvis presents what he calls a “theory of

action” to better differentiate the specifics of learning into two modalities: primary

experience and secondary experience. Primary experience for Jarvis is what an

individual gains directly from experiential interactions in a situation. Secondary

experience, on the other hand, is derived from communicative acts such as lectures,

notes and instructions delivered prior to (or in conjunction with) encountering a

situation. Using action and non-action as the operational element, Jarvis defines in

greater detail—more than any of his predecessors in the field—where experiential

learning can begin (Jarvis 76).

He contrasts action with what he calls “non-action situations”, an occasion

where an individual does not “know how to behave in a specific situation” (Jarvis 76).

Action can occur when an individual uses their previous experience (primary or

secondary) to perform a task; however, non-action may occur when the same

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individual, who has no previous experience, uses trial and error experimentation,

observes experts or actively questions instructors (Jarvis 76). Because they have no

previous experience but have been able to extrapolate a protocol for problem solving,

there is what Jarvis calls a “potential learning experience”—an individual may learn

(action) or, not learn (non-action).

Unlike Kolb, Jarvis readily acknowledges states of non-learning—situations

where no engagement with learning nor enrichment of the learner occurs—but, more

importantly, he provides five categories of “action” that are significant as they

“constitute a process of habitualization” and thus learning from primary experience:

creative/experimental, repetitive, presumptive, ritualistic, and alienating. For Jarvis,

the importance of the first two categories in primary experience is their focus on

actual learning; as a result, he states, “learning occurs when people act

creatively/experimentally or repetitively, that is in the first two stages of the

process…they learn from trial-and-error” (Jarvis 76).

Secondary experience is, according to Jarvis, “mediated and usually linguistic”

but he extends this categorization to include “books and other forms of linguistic and

pictorial communication” (Jarvis 77).Thus, Jarvis frames the place of an instructional

device as valid within the experiential model. Jarvis is the only scholar to identify that

a model of learning—as a process of knowledge building—requires a starting point as

facilitated by a form of input stimulus. Thus, while previously no connections have

been made between experientialism and any form of instruction, Jarvis indicates that

some kind of instructional device also belongs with the model.

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Experience—even as a starting point for an experiential learning process—can

be communicated by a product such as a visual artifact according to Jarvis. Therefore,

where Jarvis differs from previous theorists is clear; yet, Jarvis does not distinguish

whether text or a visual would be better. As with other work on experiential theory,

consideration of what would function best has never been an issue of interest for

scholars. Carroll’s work proved that text may not have been the best choice for

experiential learning. Consequently, the research conducted here will—through

testing of both visual and textual artifacts—will confirm if the visual would be the

artifact necessary to activate experiential learning.

Learners must be motivated to engage with the materials that will help them to

learn and acquire knowledge. Text—whether voluminous or minimalist—is not

always the best way to facilitate and create the necessary engagement, so certainly

another avenue, such as the visual in this research, may fill the gap nicely. What is of

primary importance is the linkage of minimalism and the experiential with the

concepts discussed in Chapter 3—picture functions and picture forms (surface).

Experiential Styles and Learning Styles

One of the more commonly expressed concerns in Minimalism Beyond the

Nurnberg Funnel was the lack of coordination between what Carroll had developed as a

minimalist model and the relationship his ideas might have to individual learning

styles. In particular, several of the contributors to this second book articulated their

desire to see future work incorporate a better understanding of learning styles. This

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call to address learning styles connects well with the work of experiential theorists

Kolb and Jarvis who each explain that individual differences among learners are a

factor that must be acknowledged in any consideration of the experiential.

The call to address and understand how people learn is an ongoing study and

certainly, the inventories administered here will provide some view as to how this

occurs. Saul Carliner notes in his article, “Physical, Cognitive, and Affective: A Three-

Part Framework for Information Design” that understanding cognitive functions is

critically important as they inform the “process” for assisting users in understanding

information (Carliner 46). Carliner views document design practices such as

minimalism ideal as they give “users with the most appropriate information, at the

exact time and place they need it” (Carliner 51). However, without assessing and

developing what Carliner calls “affective design”—the aspects that motivate users to

perform—most documentation will be woefully inadequate. Experiential styles will,

however, show what styles are more strongly inclined to engage in active

experimentation. As well, cross-referencing Kolb’s experiential styles to Felder’s

learning styles will demonstrate which styles may be more or less successful with a

visual or textual condition.

Learning styles then can be defined as the individual differences that account

for variation in the perception and processing of information (Felder and Spurlin

103). While tests such as the Myers-Briggs Type Inventory (MBTI) inform many of

the ideas on learning styles, refinements in various inventories have created better

measures for mapping individual traits to the design of information. In 1988, Richard

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Felder and Linda Silverman began to develop the Felder-Silverman Inventory of

Learning Styles (ILS) as a means to further understand how to refine pedagogical

methods to better reach students. Their inventory positioned learning styles as having

the following four dimensions:

Sensing (concrete thinker, practical, oriented toward fact and

procedures) or intuitive (abstract thinker, innovative, oriented toward

theories and underlying meanings):

Visual (prefer visual representations of presented material, such as

pictures, diagrams and flow charts) or verbal (prefer written and

spoken explanations)

Active (learn by trying things out, enjoy working in groups) or

reflective (learn by thinking things through, prefer working alone or

with a single familiar partner)

Sequential (linear thinking process, learn in small incremental steps or

global (holistic thinking process, learn in leaps)

While Felder and Silverman’s model deals intake and processing preferences,

Kolb’s model provides insight into different modalities of the individual experiential

experience. In summary, Kolb validates all experience as having a transformative

effect on individuals; thus, he is able to categorize the types of experiential learners

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through their approaches to learning. Kolb’s breakdown of experiential learning

dimensions is, according to the material published by Hay Company Inc., the

commercial licenser of his materials as follows:

Diverging: combines preferences for experiencing and reflecting

Assimilating: combines preferences for reflecting and thinking

Converging: combines preferences for thinking and doing

Accommodating: combines preferences for doing and experiencing

(Hay Company n.p.)

In terms of the research conducted in this dissertation, examining learning

styles through the lens of learning style assessment inventories can provide additional

information as to how the visual developed here—the theoretical artifact derived

from best practices for visuals—serves to meet the needs of users. By tabulating the

results of these inventories and considering them in light of other testing outcomes, a

further understanding of experiential learning will be gained and better assertions

about the place of a visual can be ascertained.

Summary

This literature review, the first of two in this dissertation, has examined John

Carroll’s original ideas with respect to minimalist documentation and framed his

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over-arching goals for the model. In summary, John Carroll had, from his work with

the users of instructional manuals, determined that direct, rote or descriptive

materials produced did not meet the needs of people or their tasks. His work led him

to formulate an experientially-based reduced model of documentation—called

minimalist—that provided essential information for working with computer-based

tasks. One of Carroll’s chief goals was to activate the experiential skills of individuals

and ultimately have each user construct their own mental model of the system.

A review of this model—published eight years later—was the subject of

Carroll’s second book. Presented as an edited collection, the essays contemplated

what had been the goals and intents of the model, examined where it had gone over

the last eight years and considered both its theoretical strengths and its practical

shortcomings. As the model had not permeated the canon of documentation

practices, the shortcomings of the model are used to frame the research conducted in

this study. Revisiting minimalism, this study examines the gaps in the model and

seeks to determine if a minimalist visual could reinvigorate the paradigm.

As activating the experiential via a visual is a vital component of this study,

experiential learning has been covered in detail from both historical and current

perspectives. One failing with Carroll’s work may have been derived from his under-

consideration of the dynamic nature of how people learn from experience. To gain

more insight into this, both and experiential inventory and a learning styles inventory

have been incorporated into the research design.

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Chapter Three, the second literature review, examines the literature on both

screen captures and the form and function of visuals. Ultimately, this information will

be used in Chapter Four to construct the visual prototype—the theoretically derived

artifact—for testing.

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CHAPTER III

THEORY AND ARTIFACTS

The Minimalist Visual Instruction

For the purposes of this dissertation, the research conducted here develops a

minimalist instruction that relies on visual rather than verbal elements. This visual

instruction is derived, in part, from the static screen capture—sometimes referred to

as the ‘screen shot’. The screen capture is a visual that has received very little

attention in the academic literature. Hans van der Meij and Mark Gellevij have spent

considerable amounts of time understanding the function of this visual within the

context of text-based instructional tasks; however, even they admit there is “no real

guidance for creating effective screen captures” (van der Meij and Gellevij, Screen

Captures 529). Consequently, by using a static image such as the screen capture as a

starting point, this research will use theory to develop and test the minimalist visual

instruction. This minimalist visual instruction will be compared to a minimalist text-

based instruction in the study and via replicating a simple picture in an online drawing

program, this study will attempt to determine if the visual instruction does motivate


This chapter will first discuss the research on the screen capture in order to

provide a baseline of what is known about a static visual for instructional purposes.

The remainder of the chapter will concentrate on the functions of pictures as they

convey information to the reader and, it will conclude with discussion about the

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features that make a successful visual. Ultimately, what is presented here will be used

to create the minimalist visual instruction.

Research Focused on Screen Captures

Searching the term screen captures yields a small amount of academic literature;

the return to the query is a scant five articles. Other synonymous terms including

screen snapshot, screen dump, and screen shot result in a null return on the database query.

Of the five articles displayed, one discusses copyright issues associated with using

screen captures. The remaining four are all articles written by van der Meij and

Gellevij on the processing of screen images and text for instructional purposes.

Clearly, screen captures are an area receiving little attention in the literature if only

two researchers focus their energies on the topic.

Before working with Mark Gellevij, Hans van der Meij had collaborated with

John Carroll in 1995 in their article “Principles and Heuristics for Designing

Minimalist Instruction”, so he is no stranger to the constructs underlying effective

minimalist documentation. Authoring his next article alone, van der Meij began his

first steps toward considering the visual in 1996’s “A Closer Look at Visual Manuals”.

This article reflects the beginnings of examining manuals with screen captures, and

although the use of theory is not sophisticated, the article does provide information

on the efficacy of the visual manual for learning purposes. Interestingly, while the

article purports to provide design considerations for manuals, it does so at only a

superficial level. Most of the guidance is completely rudimentary and focuses on size

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or position of the graphic, not its essential elements or its theoretical suitability for

experiential learning; regardless, it is the starting point for a decade’s worth of theory

on the role and function of the visual in conjunction with the textual.

Visuals provide, in van der Meij’s research a signal for the “correct starting

position” (van der Meij, Closer Look 379) in a procedure, this connects well to

Carroll’s assertion that “getting started fast” is key (Carroll, Nurnberg Funnel 81).

Additionally, van der Meij sees the visual as establishing “a relationship” (379) for the

user in terms of what they are currently doing and what they future action will be. In

minimalism, this is an excellent corollary to the coordination of “system and training”

in Carroll’s text (Carroll, Nurnberg Funnel 85). That the visual can aid “recovery” as

well as provide a means of “problem detection” (van der Meij, Closer Look 379)

equates well with Carroll’s assertion that “error recognition and recovery” (86) are

important. Finally, according to van der Meij, visuals “motivate” (378) users to

explore and try a program; for Carroll that users “prefer to learn by trying” would be

its equivalent (Carroll, Nurnberg Funnel 153).

It should be noted, however, that the above research is not engaged in a

specific focus on experiential learning; it is instead directed towards providing a

comprehensive view of the visual manual (screen captures) as they function with a

minimalist model of documentation. It is, therefore, the intent of the research

proposed in this document to fill the gaps in knowledge regarding the visual by

providing the appropriate theoretical justification for a minimalist visual instruction

that these studies lack.

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Gellevij and van der Meij’s first collaborative foray into understanding screen

captures in documentation began in 1998 with the article “Screen Captures in

Software Documentation”. They noted in reviewing the literature from the 1990s on

screen captures that this type of image received almost no “attention” in the popular

handbooks (van der Meij and Gellevij, Screen Captures 529). This is a trend that has

persisted some eight years later—screen captures remain an unstudied type of

instructional artifact. This is enigmatic considering that van der Meij and Gellevij

found, from examining 100 manuals, that “seventy-six percent” of the pages

contained at least one screen capture (van der Meij and Gellevij, Screen Captures 529).

One of the only non-academic articles providing guidance on the effective

creation of screen captures appeared in 1993. It was William Horton who wrote a

short how-to article on the efficacy of screen snapshots (sometimes referred to as

screen dumps) for Technical Communication. In “Dump the Dumb Screen Dumps,”

Horton extols readers to consider that “about half the screen snapshots in computer

manuals and books do more harm than good.” According to Horton, screen

snapshots break up procedural steps, overwhelm textual information and may not be

the most helpful visual to include. Ultimately, they do not give users of instructional

information the kind of support required. Instead, Horton suggests alternatives for

“graphics and text that answer their [users’] questions as they occur” (Horton 146).

This can include cropping the shot for more effective focus and scaling it to 50% of

its original size (Horton 147).

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Horton (1993), as discussed previously, espoused a better design for screen

captures as they can convey computer operations more effectively. Visuals of all

kinds can, as Lester Gabis-Levine pointed out, make “the invisible visible” and

provide “structure for complex content” (Gabis-Levine in Willows 31). However, van

der Meij and Gellevij lament that regardless of the support for the visual—the screen

capture in this case—there is “no real guidance for creating effective screen

captures”. As a result of this lack of information, van der Meij and Gellevij sought to

“provide a high-level organization or taxonomy of the roles and designs of screen

captures” (529).

Towards the goal of synthesizing what he had learned about the form and

function of screen captures, an article appearing in the Journal of Computer Assisted

Learning (2000) was written solely by van der Meij and focused on the “design issues”

as they relate to screen captures. His interest in the design of screen captures is based

on earlier work with Carroll on minimalism (van der Meij and Carroll, Principles 1995).

Stemming from this interest, the article “The Role and Design of Screen Images in

Software Documentation” tests the effects of coverage, as defined in the 1998 article

with Gellevij, in full and partial screen captures as combined with a minimalist-style

manual. It is an attempt to provide more solid conclusions to the 1998 research.

Three experimental conditions for manual design were tested. Two of the

designs used full screen captures (coverage), scaled to 35% of their original size (size)

and including hairlines to key objects, in all relevant cases (cueing). The variation

between these two manuals was the reading order—did the captures occur first, on

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the left side, or was the text followed by captures on the right side of the page

(positioning). The third iteration for testing used a mixture of full and partial screen

images (mixed coverage), variable rates of image reduction (size), occasional hairlines

(cueing) with images on the right side (positioning).

van der Meij found no significant differences between the three conditions,

though he states his findings “hint at the predicted superiority” of the model where

text precedes the image. As well, van der Meij felt the outcome of the research

demonstrated that “screen images had a positive and significant impact on object

identification and location, and on mental model development” (van der Meij, Role

302). However, most interesting in this article is van der Meij’s discussion; he states

that “research has yet to go a long way before one can draw firm conclusions about

the optimal design solutions” for screen captures (van der Meij, Role 301). Problems

arise in the experimental modalities of learning versus doing and van der Meij wonders

how the “reference” versus “tutorials” needs of users might play out against the

documentation especially that of screen captures (van der Meij, Role 304).

It would not be until 2004, after work with cognitive processing models that

determined text/picture combinations contributed significantly to enhanced

performance effects on the cognitive functions discussed earlier (Gellevij, van der

Meij, de Jong, et al., 2002) that van der Meij would re-team with Gellevij and conduct

more definitive research. “Empirical Proof for Presenting Screen Captures” would

test each of the four cognitive functions (switching, mental model development,

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verifying and identifying) in specific conditions designed to provide a statistical

performance measurement.

Gellevij and van der Meij prove a strong connection between task

performance, knowledge acquisition and the learning process exists from the

combination of text and screen captures (Gellevij and van der Meij, Empirical 236).

Many of Carroll’s principles with respect to the goals of minimalism are also validated

in this 2004 research as well as studies done by van der Meij. However, their use of

screen captures—either full or partial—is rudimentary at best. Gellevij and van der

Meij explore the screen capture in either its entirety or by cropping small elements

from the main screen for accentuation. They do not attempt to explore what a better

theoretical visual design could be from areas beyond minimalism for the screen capture.

Nor do they consider that making the visual the primary modality of communication

may also be a viable course of action to consider. What Gellevij and van der Meij do,

however, is provide avenues for future research such as that which comprise the

dissertation’s third and fourth chapters.

Unequivocally, it is clear that a visual object is indeed a powerful mechanism

for conveying many of minimalism’s foundational elements; however, the missing

feature of the model is the continued development and refinement of a minimalist

visual. In terms of software documentation, an experiential learning visual—as a

theoretically derived artifact—would have several functions. It would reduce

guesswork and provide direction for the design of instructional visuals. It would be

based on theory and research; thus, unrefined visuals such as the full or partial screen

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capture would be replaced by a minimalist visual instruction that conveys more

meaning to the user. Finally, the minimalist visual instruction will more adequately

address users whose baseline understandings of technology have changed due to the

ubiquitous presence of computers.

As discussed earlier, experiential learning is a much more dynamic model than

what was posed by early theorists. Later work in experiential theory (Kolb 1989;

Jarvis 1998, 1995) demonstrated a broader and less cleanly linear model as being far

more representative of the actual processes of human understanding. Additionally,

these later theorists began to differentiate within the experiential the constructs of

learning and knowledge. While it was clear most people could learn via experience, it

was the aspect of gaining knowledge—and thus, a lateral transferability to other

tasks—that made the experiential so valuable.

Understanding Pictures—Function and Surface

In considering how images—such as the screen shot—work to facilitate

learning and knowledge acquisition, it is first necessary to define their two distinct

features of function and surface. In the text “Graphics for Learning”, authors Ruth

Colvin Clark and Chopeta Lyons detail their comprehensive methodology for

developing myriad visuals for a variety of educational and instructional tasks.

Important in the assertions of the authors is the correct codification and use of

visuals based on their function and their surface features.

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Function is, the most critical component of “learning effectiveness” (Colvin

Clark and Lyons 19) for a visual. While, as will be discussed shortly, visuals can be

superfluous due to their purely decorative nature, their job in instructional tasks is to

provide the learner with a means of comprehending and completing a task. This is

accomplished through a careful analysis of theory and subsequent application of form

(design) best suited to the learners’ goals.

For Colvin Clark and Lyons, one of the key choices to make in terms of

learners’ goals and therefore function of the visual is, whether the knowledge gained

should be “near-transfer” or “far transfer” (Colvin Clark and Lyons 32). A near-

transfer paradigm builds in the learner a set of procedural skills. As an example,

learning to use software is a skill-building task and therefore falls into the domain of

near-transfer. In the case of far-transfer, the visual is vested with creating a broader-

based problem-solving modality that allows the learner to easily perform the current

task and subsequently make lateral applications of their knowledge base to other

scenarios (a tenet of experiential learning). Colvin Cark and Lyons later state that far-

transfer facilitates “inductive learning” (Colvin Clark and Lyons 168) in which

individuals can then generate their own procedures from an example.

“Surface features” are the physical elements of design that increase or inhibit a

visual’s ability to convey meaning to an audience (Colvin Clark and Lyons 19). Using

Donis Dondis’s basic graphical elements from his 1973 publication, primary surface

features can be considered as, but not limited to: dot, line, shape, direction, tone,

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color, texture, scale, dimension and motion (Dondis 108). Not all of these apply to an

image such as the screen shot; however, line, shape, tone, colour, and scale would be

good points for consideration. As well, adding to Dondis’s criteria is Rune Pettersson

who asserts that graphical “layout”—the position and arrangement of surface features

on the page or screen—is key to how an image is understood. Layout may also be

understood, in an images-only scenario, as that of visual syntax, a concept to be

discussed shortly Based on the work of the above scholars, it is evident that any

visual occupying the position of a minimalist visual instruction must meet the

requirements of having an appropriate function and contain well-designed surface

features.

FUNCTIONS

The function of pictures is well documented across many disciplines, but

especially in the areas education and psychology. Levin’s work with images is some of

the most well known and his earlier work (1981) was responsible for the development

of a taxonomy of picture functions. In Levin et al.’s taxonomy, pictures served the

five functions: decoration, representation, organization, interpretation and

transformation. The first two functions are the most common and simplistic of the

picture functions. As the title implies, decoration serves only an ornamental function

and bears no relation to the written narrative. Representation on the part of pictures

portrays the actors, objects and activities in a text; illustrated children’s stories,

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according to Levin et al., are a good example of this function and can provide a

degree of concreteness (Levin et al. 55-56).

The more significant picture functions are found in Levin’s categories of

organization, interpretation and transformation. The function of pictures within the

category of organization is usually most notable in the “How-to” genre of

instructional or “procedural” materials (Levin et al. 56). According to Levin,

“passages that basically list the distinctive features of several characters or objects also

usually lend themselves to a more coherent organization via pictorial representation”

(Levin et al. 56). It is Levin’s concept of coherence—the sense-making of tying

concepts that becomes one of the constructs needed for the theoretically derived

artifact. The next most complex picture function is that of interpretation in which

pictures “clarify difficult-to-understand passages and abstract concepts within

passages” (Levin et al. 58). While the illustrations that accompany, for example,

“abstract or complex science and social studies concepts” use coherence to “make an

abstract concept more concrete” (Levin et al. 60) and thus increase what Levin refers

to as comprehensibility.

The transformational function of pictures is, by far, the most unique of the

five functions discussed by Levin. According to Levin, transformational pictures

“impact on…memory directly…by targeting the critical information to be learned”.

These images recode the abstract into a more memorable and concrete form, they

interconnect disparate pieces into a whole and, finally, they provide a means—a visual

mnemonic in Levin’s example—to retrieve the critical information (Levin et al. 61).

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In order for pictures to facilitate this transformative process, they must have

sufficient “substance, form and detail” (Levin et al. 65) and, as paired with prose

“must be developed with both specific picture functions and picture recipients in

mind” (Levin 77). Good images, then, according to Levin, must have the following:

concreteness, coherence, comprehensibility, substance, form, and detail. In addition

function and relevance are also important.

The work of Levin et al. is critical for understanding how pictures should

function and what they must do in order to work effectively with the audience. The

latter part of the 1980s brought, for cognitive psychology, an interest in determining

how pictures and text functioned. No doubt this interest was fueled by postmodern

thinking in which text, as the previously deemed dominant mode of communication,

was now being supplanted by the visual. Images were becoming an equal modality to

the written word and their function with text was of interest to those who studied

communicative modes. Joan Peeck in 1987’s “The Role of Illustrations in Processing

and Remembering Illustrated Text” proceeded on the well-founded assertion that

pictures do work well with text provided they “show something” (Peeck 116) from

the text and provide “additional information” (Peeck 116).

Peeck notes that early work (Smith and Smith, 1966) on pictures and text

indicated that the function of an illustration was to “regulate orientation and to

maintain a high level of concentration” (Smith and Smith in Peeck 224). Peeck went

on to surmise that pictures then increased the amount of time people dedicated time

resources to the page (Peek 117-118); however, she also noted a significant flaw in

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much of the research: the dimension measured—retention—as a means of determining

the overall effect of including images was not the truest indicator of an illustration’s

effectiveness. In examining the work of others, Peeck found that most studies

measured “retention” rather than “comprehension” which did not accurately convey

what the purpose of the extended observation might have been (119). Measuring

retention does not provide a gauge of how knowledge is operationalized across

situations; rather, retention is merely a snapshot of understanding while

comprehension is internalized knowledge. One of the only studies to measure

comprehension with adults operationally defined the construct based on how well

undergraduate students assembled a loading cart (120); in this case, the presence of

illustrations was shown to greatly facilitate the completion of the cart.

Work from researchers such as Levin and Peeck provides some basis for the

creation of the minimalist visual instruction developed this dissertation. Like Levin et

al., Peeck discusses the research concerning text/picture combinations; however, in

discussing this body of literature and research, Peeck also notes that studies of

information “presented by pictures only” are surprisingly scarce due to the primacy

of the written word in standard schooling and the modernist assertion of “pictorial

information as of only secondary importance” (Peeck 125).

Peeck laments this oversight as, from the limited studies to date, “it is useful

to establish the amount of pictorial information subjects remember [as]…pictures

presented without text have shown picture memory to be remarkable in both

duration and capacity” (Peeck 125). However, much of the research on the qualities

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of illustrations used in conjunction with text is problematic as “there is little

consistency in the pictures used in studies…the pictures vary in type, number, the

presence or absence of color, size density of information presented, and so on” (137).

SURFACE FEATURES

The work of Levin and Peeck provides some basic considerations required in

the development of the minimalist visual instruction for this research. In particular,

the quality of the object developed for this study must be sufficiently high for the

audience to make the correct inferences; as well, issues of density, complexity, and

the use of colour need to be carefully considered in the creation of the minimalist

visual. Peeck summarizes the requirements of basic picture variables as follows

(Peeck 137-138):

Aesthetic, artistic and technical quality including contrast, perspective,

color and composition are integral for good pictures.

Colour is more frequently used to define interrelationships and

highlight key features; however, it may also obfuscate meaning and

should be used judiciously.

Authenticity and validity must be present so that the image leads to

correct inferences.

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Density of information is critical as the number of picture elements

and the depiction of the degree of detail can influence comprehension.

Complexity and concreteness as effective devices are dependent on the

time and effort the audience is willing to invest in the picture; thus,

“self-paced” learners may work better with complex information.

While Peeck’s points define excellent guidance for basic design elements,

surface details used to extend meaning in a visual object can either assist or hinder the

correct interpretation. Consequently, in order to convey meaning properly, a well-

designed minimalist visual instruction must be informed by the function of details.

Elizabeth Boling et al. in their 2004 article “Instructional Illustrations: Intended

Meanings and Learner Interpretations” conducted their research based on the idea of

disparity between the illustrator’s intended message and the audiences’ interpretation

of that message. In summary, they found a striking disconnect between the two in

many instances.

With respect to illustrations, many of the graphical devices used by artists—

circular lines to show motion, for example—“to extend and clarify the meaning of

pictorial content”, were ineffective. Research conducted by Boling in 2002 (Boling,

Frick, Sheu and Huang) indicates that “learners may not always interpret, or even

attend to, these devices as the designer had intended” (Boling et al. 187). Carl

Szlichcinski in his work from the early 1980s found that most participants in his

studies indicated an “overwhelming preference for depicting actions by means of

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arrows” but he conducted no performance measures to determine if task completion

and accuracy were significantly improved by this device (Szlichcinski 451).

Boling et al. found in reviewing the research (Kennedy, 1994; Sless, 1981) that

it is standard for people to recognize pictures of things: animate or inanimate objects

(Boling et al. 189). However, problematic in any type of illustration, but especially an

instructional one, is that the audience may not always “recognize the intended

meaning” as noted above (Boling et al. 189). Thus, as Boling summarizes: “the visual

content of an illustration is frequently a vehicle used to communicate [yet]…this

intended meaning may often be misunderstood or unrecognized by the viewer”

(Boling et al. 189). One reason for this differential expectation in intention and

interpretation is due to learning: “our interpretations are often learned in a way

similar to, though perhaps not as explicitly as, language learning” (Boling et al. 190).

From their research in this area, Boling et al. found that while graphical

devices do increase interpretation as compared to images with no graphical devices, it

is the correctness of the interpretation that can vary widely. Basing their threshold on

the International Standards Organization, which demands an 85% rate of correct

interpretation for graphical symbols, Boling et al. found that their rate of accuracy

(depending on the illustration) ranged between 47%-70% (Boling et al. 201).

Therefore, graphical devices should be used judiciously as they can affect the

correctness of interpretation for an illustration

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Using Lines

Pettersson (1989, 2002) cautions anyone using illustrations to consider the

medium of presentation for the material, especially if lines are part of the visual and

used to direct attention or focus the user to a very specific place in the visual.

“General recommendations on how drawing styles should be used” are difficult to

make and decisions should be governed by the resolution of the monitor (television

or computer) or the quality of the printed artifact (Pettersson, Information Design 115).

As screen shots can be presented in online tutorials or in print, it would undoubtedly

pay to heed Pettersson’s advice.

As we read from left to right, it is assumed that most people will also read

lines in that direction (Pettersson, Information Design 116). Line weight is an element of

power, with thicker lines being more dominant; thus, stroke can be used to

emphasize or de-emphasize parts of an illustration. A “three-step gradation” is a good

guideline for differentiation in a technical illustration—that is, line width or stroke

should be, for example, light (1 pt), medium (2 pt.) or heavy (3 pt). Placement of

lines, though, is more complex in Pettersson’s view, especially when multiple lines are

incorporated in a visual. The optical illusion of three lines can occur when “two black

lines” with a white space between them are equally spaced and parallel (Pettersson

118).

Horizontal lines imbue a sense of rest and relaxation while vertical lines, via

their divisive nature, serve as “symbols of power”. Most problematic are diagonal

lines, according to Pettersson, as they “give the impression of movement, creating

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visual stress” (Pettersson, Information Design 117). As a result, lines need to be

considered as having a functional effect as well as acting as a surface feature. The best

practices, therefore, for lines are to:

Consider medium of presentation in conjunction with lines (or any

visual).

Remember that lines have a language of their own via weight (stroke),

horizontal, vertical or diagonal placement.

Use no more than three levels of differentiation in lines—more add

confusion to the image.

Avoid clustering of similar weight lines and equidistant spacing as they

can create erroneous optical illusions (like Escher drawings).

Size of a Visual

One of the infrequently discussed aspects of visuals is that of size. Many

earlier instructional texts—especially writing-intensive texts—viewed images as an

afterthought and placed them not as a helpful aid to learning but as a gratuitous

mechanism to occupy space. The size of the image was usually not well conceived

and varied usually on the factor of available page space. Later materials began to

integrate visuals more effectively onto the pages and strived, at the very least, for

some degree of consistency in design and placement. While the size of many visuals is

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constrained by physical space (page or monitor), Pettersson articulates that available

space or prettiness are poor parameters on which to base the size of an illustration

(Pettersson, Information Design 122).

Visuals can easily dominate any page they appear on, especially via their

physical size. In general terms, the “bigger a picture is on a page the more important

it is considered to be” for the reader (122); however, size should not be a means to

determine importance. Rather, “size must be large enough for the image to be

legible” and for the details to be apparent to the audience ((Pettersson, Information

Design 123). In particular, Pettersson indicates that these details should be sufficient to

include scale and contrast as these are both important for comprehension (123).

With respect to screen shots, William Horton is one of the very few

professionals to assert a prescriptive size range for this type of visual. In a very short

guest editorial article in Technical Communication (1993) entitled “Dump the Dumb

Screen Dumps”, Horton discusses the follies of screen shot size and the tendency at

the time to place these images on the page at 1:1 or, in other words at the exactly the

same size they appeared on the screen. Horton’s recommended size ranged between

50-75% of the original. Below, are other points taken from Horton:

Consider output medium (page or monitor) for clarity.

Don’t use size to determine importance, but make visual large enough

for reading labels/details.

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Don’t use visuals gratuitously—not for prettiness or to fill space; they

need to belong to the concept.

50-75% of original size is sufficient for most screen shots.

VISUAL SYNTAX

In order for images to be read—especially multiple screen shots for an

instructional task—they must be placed in an order that permits users to clearly

understand both the meaning and the sequence of the material. Without such an

articulated structure, the ability to complete a task via a series of visuals is seriously

compromised. Carl Szlichcinski in “Factors Affecting the Comprehension of

Pictographic Instructions” writes that multiple linked images for the “performance of

a single action” organized as “pictorial elements” form the syntax of visual

instructions (Szlichcinski 451). In other words, just as writing has a grammatical and

syntactical structure that leads the reader through content, meaning and action, so do

images. They have, as Szlichcinski notes an “iconic grammar” that allows readers to

form rules for assembling meaning (Szlichcinski 449).

Szlichcinski conducted research to determine how “pictorial instructions affect

comprehension and ease of use” via testing 12 pictorial variations on a participant

population (449). In his results, Szlichcinski, found that the syntax of linking

frames—vertical, horizontal, numbered—helped those who were unsure how to

proceed through a series of illustrations and, in particular, the “use of a comic strip

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frame” helped to “reduce errors” in task completion even though it had no

statistically significant effect on performance (Szlichcinski 463).

Additionally, Szlichcinski’s research demonstrated another positive effect of

visual instruction. While 90% of the cohort in the visual with the best condition

succeeded in operating unfamiliar equipment, Szlichcinski found in his research study

a range of 12%—30% across all other visual conditions in terms of mistakes made by

participants in using illustrations to operate unfamiliar equipment (Szlichcinski 463).

Rune Pettersson (2002) also discusses the importance of syntax—or

“organization” as he refers to it—in the placement and logical organization of visuals

(135). In Pettersson’s view of syntax, proper structure is critical as “complexity

without order produces confusion” and results in poor performance with

instructional materials (135). In difficult-to-understand, detailed or multi-step tasks,

Pettersson recommends using “several different pictures than to allow one schematic

picture to be overloaded” (135) and cautions designers to allow “at least a few

millimetres” between design elements for better reading (135). As well, Pettersson

considers overlapping two or three processes via superimposition as a syntactically

good mechanism for detailing multiple steps. Coupled with visual connectors (joining

lines), this technique can create a way to denote “several” stages or steps versus

singular instance (135). Therefore, best practices would require a designer to consider

that:

Pictorial elements read like text; therefore, they require a structure, not

random placement.

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Overloading complex visual is ineffective; use overlapping or

superimposition techniques to convey multiphase process and

complexity.

Using a syntactical linking device to infer sequence will work best,

especially with complex visuals.

THE QUESTION OF DETAIL

As discussed above, Pettersson cautions a designer against overloading an

instructional visual with too much information as it interferes with the syntax. In a

similar vein but different genre, Scott McCloud in his landmark book Understanding

Comics (1994) favours an approach of “amplification through simplification” as a

means of increasing the clarity of a visual message (McCloud 30). That is to say,

McCloud advocates the reduction of detail to include only what is necessary to move

the story forward. In the space allocated to comic book frames, making meaning

quickly and effectively with is paramount in order to communicate what the reader

requires to continue the narrative.

However, the question of too much information, or detail, as compared to too

little, gives rise to tension in terms of what a visual—such as a screen shot—should

convey. Szlichcinski notes this tension in the too little-too much dichotomy of detail

via the problems that occur in user comprehension. While “pictorial simplicity” can

facilitate understanding, a marked lack of detail in a visual results in participants being

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unable to “recognize the object” or its context of usage (450-1). Context, according

to Dr. Stephen R. Acker at Ohio State University, may very well be the most

definitive element in building comprehension (personal communication).

In particular, if insufficient cues (detail) are available for the user to effectively

derive a context, then a visual easily fails in its primary purpose of communicating

information. Little is written in the academic literature about what constitutes

necessary and sufficient amounts of detail for instructional visuals. Colvin Clark and

Lyons in Graphics for Learning, comment that additional detail via contextual cues

needs to be framed from the “performer’s perspective” (234). Consequently, their

example uses an “over-the-shoulder” view for the task of replacing an automotive

headlight bulb (234). While the authors make a good point with this visual, its

whiteness and angle are deceptive. Anyone who has ever changed this type of bulb is

much more familiar with the details of hanging upside down and navigating the

grimy, visually indeterminate blackness of a vehicle.

In terms of screen shots, however, Colvin Clark and Lyons are critical of the

failure of most designers to consider the correct function of the visual in conjunction

with appropriate amounts of detail. Screen shots without context, as shown in their

first example (235), are too minimalist and provide no cues for the user. Their second

sample (236), a multi-layered screen shot violates all of what McCloud, Pettersson

and Szlichcinski would say regarding simplicity—its multiple layers and menus are

detailed and therefore visually complex.

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While Colvin Clark and Lyons give no direction on how to mitigate the

complexity problem, they do note that the “representational rather than

transformational” (236) screen shots without labeling or indications of flow (progress

or steps) hinder the visual (237). Carl Szlichcinski, while not dealing explicitly with

screen shots but procedural instructions, found in his research that an “overall view

and an inset”—a large, general image and a close-up of the specific area in question

worked well in the case of procedural instructions (Szlichcinski 456). The following

then, may be viewed as good considerations in design:

There is a tension between too much and too little detail

Readers need enough to identify object and more importantly,

contextual cues.

Use perspective of user where possible and frame via context

Screen shots are problematic, especially in drill-down tasks

Overviews and insets can help

COLOUR

Without a doubt, colour is the most powerful element under the rubric of

surface features. Colour, also referred to as hue, is a complex combination of

physiological, psychological and cultural cues that, in turn, influence how it is

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interpreted. As an example, red, orange and yellow fall into the continuum of alert or

danger colours with red being the colour most commonly associated with serious

hazard or danger. Orange and yellow now also denote an elevated awareness due to

the frequency of terror alerts posted in the US in the last six years.

Research studying colour and task performance is myriad and like countless

others, Szlichcinski found the “use of color” increased participants’ performance time

with an experimental task (Szlichcinski 463). The value of using colour to aid in any

kind of instructional visual is obvious and colour “has been shown to be a very

powerful, though indiscriminate, device for directing attention within pictorial

materials” (Szlichcinski 451). This indiscriminate or perhaps arbitrary use of colour

problematizes how—if at all—it is used in visuals such as the screen shot. While

colour is a feasible surface feature for screen shots included in online materials (how-

to’s, tutorials), the cost of colour printing usually results in grey-scale renditions for

paper-based versions and helpful colour-visual cues for attention/focus are negated.

More importantly, however, is the fact that colour is not necessarily used well

in most pictorial materials; rather, it is applied, in many cases, on basis of what a

designer assumes will best capture the attention of the audience instead of through a

theoretically informed approach. Citing work by Fleming and Levie (1978; 1993),

Pettersson notes that colour is preferred in the following order: blue, red, green,

violet, orange and yellow. Unfortunately, it is this preferential ordering of colour that

makes its application as a surface feature difficult to theorize.

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One of the problems in making solid assertions about colour—as to how it

could inform an image such as the theoretically derived screen shot—relates to the

work done in studying this feature. Dominic McIver Lopes, a psychological

philosopher, writes in the 1999 article “Pictorial Color: aesthetics and cognitive

science” that while colour is studied extensively in terms perceptual and preferential

studies, the study of color still lacks a solid integration of ideas between the aesthetic

and bio-scientific camps of thought (McIver Lopes 424). That is to say, while colour

is understood as a cognitive aid, a personal preference and a process within the visual

cortex, how it functions in pictures as a form of depiction or representation requires

further work.

For the purposes of this research and the assertions about colour as a surface

feature, it is necessary to acknowledge that any inclusion of colour is based entirely

on the preferential aspect. As Pettersson notes from the work of others, colour

rankings have a proven persistence; therefore, there is a long-term logic in following

this pattern in terms of a surface feature. Blue as the number one preference could

easily serve the function of primary identifier; however, it might be wise to promote

green and violet, regardless of preference and demote red as its standard meaning is

usually that of a warning, especially in instructional or procedural materials.

As with any other surface feature, colour should be used judiciously—

overwhelming an audience with colour will detract rather than enhance performance.

Rune Pettersson in Information Design states that designers should avoid more than five

colours or “tones” in an illustration (Pettersson 131). More than five and the

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complexity of decoding and remembering their meaning becomes overwhelming for

the audience. Therefore, it can be summarized that:

Colour is difficult as it has meaning at multiple levels; needs to be

chosen carefully.

Arbitrary use in visuals is more common than not.

Preferential ordering from empirical research can be a guide.

Overuse will affect a visual negatively; about five colours (or tones) is

sufficient.

Implications for the Function and Surface Features of Visuals

As discussed in this chapter, the role and design of a visual is exceedingly

more complicated than most would imagine. A critical understanding and application

of both the function and the features of a visual are vital to the creation of a

significantly improved artifact that can indeed support and enhance experiential

learning. While Hans van der Meij and Mark Gellevij explored full versus partial

screen captures and captures with text placed in right/left juxtapositions, their work

did not look at ways in which the screen capture could—if redesigned—provide a

transformative function for the user via modifications to substance, form and various

aspects of detail.

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From the review of work in Chapter 2, van der Meij was, as far back as 1995,

examining minimalist documentation. His later work began to include visuals and tied

strongly to Carroll’s ideas of how the experiential would be activated via a minimalist

approach. However, work conducted independently by van der Meij and later in

conjunction with Gellevij, never considers theoretically informed innovations for the

screen capture; thus, Chapter 4—the design of the artifact for testing—opens a new

avenue of exploration for understanding how a visual could activate experiential

learning.

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CHAPTER IV

DEVELOPING A VISUAL INSTRUCTION INFORMED BY THEORY

In the review of literature found in Chapter Two, several contributors to

Minimalism Beyond the Nurnberg Funnel expressed the desire to see—in minimalist

documentation—a visual that would support the experiential learning paradigm put

forth by Carroll. Greg Kearsley, David Farkas, JoAnn Hackos and Stephanie

Rosenbaum in each of their articles noted how they saw the inclusion of a visual as a

distinct possibility for a future version of John Carroll’s model. Farkas’ succinctly

positioned the screen capture as the visual best suited for consideration stating that

the role of this particular visual is not well-defined or understood. It is, ironically the

most commonly used visual with, in a survey of 100 manuals, 76% of pages using one

or more screen captures in materials related to software instruction (van der Meij,

Screen Captures 529). As a result of these calls for further research, the goal of this

dissertation is to develop a visual instruction for experiential learning. This chapter

will develop and define this object and propose a model suitable for testing.

Requirements of the Visual instruction

For a visual instruction to assist with experiential learning, it must embrace

not only Carroll’s requirements for learning-by-doing, it must address experiential

learning as developed and defined by both David Kolb and Peter Jarvis. While many

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of the fundamental aspects of the experiential overlap in the work of these two

scholars, their unique differences add richness to the visual and dimensionality that

will, via a refined understanding of experiential learners, better address individual

learning styles. Finally, the visual developed in this research must be informed by the

qualities discussed in Chapter Three. It requires appropriate form and function in

order to take on this more active role in the learning process.

CARROLL’S REQUIREMENTS

In developing his minimalist documentation model, Carroll’s primary goal was

to empower the user to develop her/his own problem-solving methodology for

instructional tasks. Rather than overwhelm people with documentation, Carroll

posited that by anchoring the tool in the task, developing a means to recognize and

recover from error, and enhancing learning via doing, studying and locating, his

model would greatly address the active participants he saw during his years of

laboratory work. Consequently, the goal of the visual instruction developed in this

research is to meet and extend Carroll’s work and provide a means of addressing the

gaps cited in Minimalism Beyond the Nurnberg Funnel.

The visual proposed here must meet other criteria as established by Carroll in

order to function experientially. He had determined—in addition to the three

requirements noted above—that a minimalist documentation model is required to

support its users in other areas of task completion in order to be successful. One

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requirement of the model was to bring meaningful goals and tasks to the user’s

interaction with the program. While tutorials had the ability to demonstrate a portion

of the features available in a program, most bore no relation to the type of work the

user performed and, thus, were abandoned in most cases in favour of self-

exploration. The digression to self-exploration on the part of users led to Carroll’s

second requirement that minimalism must allow work to begin immediately. Fast

starts were, for Carroll, key to engagement with the tool and the task and any visual

developed as a theoretically derived artifact must be able to fulfill this requirement in

addition to that of a meaningful engagement.

Starting quickly was predicated for Carroll on two other factors vital to the

model: the ability of the instructions to be non-linear and to address individual

reasoning and differences. Prescriptive, sequential instructions were not what Carroll

found his lab participants wanting; rather, their inquisitive natures demanded a more

fluid type of instruction that allowed them to approach the task from a variety of

perspectives. This variety of perspectives—individual reasoning and differences—

also brought forward another important dimension for Carroll, that of prior

knowledge. In other words, experience with similar tasks or situations influenced

problem-solving strategies. As a result of these additional requirements, the visual

developed here must reach the intended user at a variety of levels in order to be

successful.

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KOLB AND JARVIS—EXPERIENTIAL LEARNING TENETS

The individual work of David Kolb and Peter Jarvis re-frame the experiential

in ways that, undoubtedly, John Carroll may never have considered. Both Kolb and

Jarvis add significantly to our understanding of this type of learning; they bring to the

model an enriched understanding of individual processes as well as the intersections

of success and failure. From Kolb’s work, the visual developed here must create

knowledge, not just facilitate learning. It must also predicate its function as a place

where ideas constantly form and re-form at any point in the experiential cycle;

therefore, it is vested with the requirement to facilitate process and not just

outcomes.

In Jarvis’s terms, any experiential model must engage individuals sufficiently

so they emerge from the process changed and more experienced than when they

began. Like Kolb, Jarvis sees memorization as not indicative of an experiential model;

it is through contemplation and reflection that experience will create knowledge—the

ultimate goal of the experiential and the visual designed in this research. However,

more critical for Jarvis than Kolb is the less-than-cyclical nature of the experiential.

His assertion that knowledge acquisition is neither sequential nor cyclical mandates

for the visual developed here a means to more substantively engage the individual

with the concepts and, in turn, provide motivation to explore and understand.

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GOALS FOR THE DESIGN OF THE VISUAL INSTRUCTION

The visual instruction developed in this chapter must meet with Carroll’s ideas

for minimalist documentation; as well, it must address the greater diversity found in

experiential learning styles. Finally, though, the visual instruction created here must

use visual functions and characteristics in order to communicate concepts in a way

that encourages exploration, contemplation and experimentation. Picture functions,

as discussed in Chapter Three, require that the visual object organize and interpret

core structures and content; also, it should provide coherence and comprehensibility

for the user and allow them to interconnect critical information. Finally, the visual

instruction should promote far-transfer skills or, in other words, create not just

learning but knowledge.

The Standard Screen Capture

In this chapter, the visual instruction is developed; it is informed, in part, by

the work done with screen captures by Hans van der Meij and Mark Gellevij. Their

later work on screen captures tested various cognitive processing functions across

variations of text/screen capture combinations (van der Meij and Gellevij 2003). The

standard presentation of the screen captures includes a single pane with brief

sequential instructions and hairline arrows pointing to key features or a pane with

multi-level (drill down) menus from the program again presented with the text/arrow

combinations. Figure 1, below, shows a standard screen capture for the multi-level

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function of inserting Field Codes in MS-Word. This presentation is typical in studies

by van der Meij and Gellevij.

Figure 4.1 Standard Screen Capture

The type of screen capture shown in Figure 1 displays screens and could, with

cueing, provide guidance for task completion; its function is limited to only the

information necessary to process the one task. As by van der Meij and Gellevij,

colour is not used in the above sample and nothing has been done to deliver the

visual in a way that could increase user engagement and understanding of the task.

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Advancing the Visual instruction

In order to develop a visual instruction that can be tested for its ability to

addresses an experiential learning style, the minimalist visual must be able to create an

inductive learning situation. Screen captures that perform merely as organizational

pictures, as defined by Levin in Chapter Three, lend themselves only to the most

elemental understanding—or learning (near-transfer)—rather than the acquisition of

knowledge (far-transfer). The utility of such images in assembling Swedish furniture

or connecting a DVD player is clear; the task is a once-only occurrence and not

connected to greater functionality or a broader, more integrated comprehension of a

system.

However, to be truly useful in experiential learning any visual must aid the

user in broader-based problem solving: therefore, an effective screen capture needs to

be, as Levin noted, transformational in its function. As well, it must have, as its

constituent parts, surface features that present the visual as allowing for different

styles or “readings” that can truly exploit the experiential.

Creating the Visual instruction

In developing a visual instruction for testing in this research, several important

considerations were made regarding the test technology selected. First, the

experiment needed to control for subjects’ previous exposure to the technology being

tested as the reliability and validity of any empirical method are based on reducing

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multiple forms of bias. Second, the technology must function in a way that is

unfamiliar to the participants but still easy-to-use within the limitations of the

experimental research. Finally, the product needed to be one in which no written

instructions exist.

With respect to the first consideration—previous exposure to a computer

technology—it was necessary to select a software program that would be uniquely

unfamiliar to participants. Software from the ubiquitous Microsoft® Office suite is

used almost exclusively in the workplace and in education with, as Microsoft reports

“millions of people” using the product group daily

(www.microsoft.com/presspass/press/2005/jun05/). Its seemingly universal

presence means even the operation of more advanced or obscure functions could be

extrapolated by a participant who was comfortable with the overall functionality of

the suite. Additionally, acculturation to the general tools and functions of this

program would negate the need and usefulness of instructional materials and, without

a doubt, many participants may proceed with the task without fully examining the

visual instruction.

In considering the task and the test for this research, other computer-based

programs were considered; however, the problem of previous exposure remained an

issue. Informal surveys of college students’ knowledge of computer software

indicated that while most knew the Microsoft line, others were familiar with other

brands of commercial software. While products from the Adobe® suite are less well-

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known among any general population of participants, there is no guarantee that a

complete unfamiliarity with them exists. Some informal exposure to Photoshop®,

either in the smaller LE version or the full program would not be considered

uncommon and could again introduce bias to the study’s results. At least four or five

students in any classroom indicated they had at least “played” with one or more

programs in the Adobe suite.

The second factor in selecting software suitable for testing was the

development of a testable task that would be manageable for participants in the time

allocated for the experiment. As stated earlier, familiarity with Microsoft Office

and/or the Adobe line of products could bring to the study participants with a

previous exposure bias (beyond the basics of computer/mouse operation); however,

more problematic with these programs would be the task—including its relevance

and complexity within the experiment.

Tasks that would be generally unfamiliar to a participant pool are difficult to

frame within the limits of a dissertation-size study. Less commonly used functions

such as inserting and editing various Field Codes or creating data merges via SQL

connections to a Microsoft Access database table require a strong necessity for the

participant to be familiar with the information. Bringing participants up to speed on

this functionality would be difficult. It would also take, no doubt, a considerable

amount of time to frame the task and establish any relevancy for the participants.

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In terms of Adobe products specifically, programs such as Photoshop or

Illustrator are designed for individuals with backgrounds in the visual arts. Many of

the tools and functions are predicated on knowledge of photography, graphic design

or illustration and use the vocabulary of their respective trades. This makes the

Adobe programs an unsuitable choice for the experiment.

Based on these considerations, the program selected for testing is an online

drawing package produced by General Electric Company’s (GE) imagination

division. As part of an enhanced model of corporate citizenship, GE has developed

the slogan “Imagination at Work” and used this as a springboard to their

“EcoimaginationSM” initiative. This area of the company fulfills a “commitment to

address global environmental challenges such as the need for cleaner, more efficient

sources of energy, reduced emissions and abundant sources of clean water.”

(http://www.ge.com/en/product/ecomagination/news.htm).

In order to demonstrate their creative approaches to the environmental issues

via the corporate brand position of “imagine, solve, build and lead”

(http://www.ge.com/en/company/companyinfo/at_a_glance/ge_values.htm), GE

has developed several innovative online programs that demonstrate the potential of

their ideas. For testing the theoretically derived visual instruction in this work, the site

http://www.imaginationcubed.com as shown in Figure , below, was deemed an

excellent program. It is not a well-known commercial product produced by

companies such as Microsoft or Adobe; thus, the confound of previous experience is

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eliminated. As well, it lends itself to the development of a testing-task that works

within the time allocated per participant in this study, it requires little to no

backgrounding in order to understand the task. It has one pre-defined ‘anchor’ or

starting point in the crayon, it has tools and operations that require an explanation

well-facilitated by a screen object, and finally, it is fun and interesting tool to learn

and undoubtedly, participants will find it engaging.

Figure 4.2 Imagination Cubed Basic Screen

In developing a testable visual instruction for the Imagination Cubed product,

it is necessary to isolate the core functionality of the program and determine how a

more-informed visual design would facilitate experiential learning. Imagination Cubed

(IC) presents any user with a unique variety of tools and techniques whether for

drawing independently or collaboratively; as well, it allows users to print, save or send

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files and either delete portions of the image step-by-step or erase the image entirely.

For the purposes of the test in this study, the visual instruction will focus on

providing a visual to explicate the drawing tools, defining the limits of the electronic

“canvas” and indicating the amount of digital “ink” available for drawing.

The basic canvas of Imagination Cubed (IC) as shown in Figure above,

presents the user with the drawing crayon, horizontally aligned menu selections,

functions for collaborative drawing and a measure of ink consumption. While the

crayon is an object immediately interpreted as being related to drawing, the fact that it

moves does not always provide a user with a fixed reference point. The most static

and predominant visual anchor is, however, the GE logo in the upper left-hand

corner. It provides a readily-identifiable graphical element that would be easy to

locate between the computer screen and printed or online documentation.

Consequently, retaining this icon for the visual instruction is the first step in

developing this artifact and determining its base form.

Figure 4.3 Basic rendition of the Imagination Cubed screen

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In essentializing the screen to its base form, it is then possible to build a

cogent theoretically derived artifact from ‘the ground up’. From Levin’s work, a vital

consideration is determining what is critical to be learned in the procedure. In

Carroll’s view, connecting the tools and the task were paramount to the beginnings of

an effective experiential approach to instructions; thus is it necessary that the

available tools in IC occupy a prominent position on the screen. Additionally, as per

Levin, there needs to be a conceptual connectivity between functions in order to

properly understand the operations(s) of the tools and to develop the ability to

effectively retrieve an understanding of the program during subsequent uses.

In presenting the operations of the program as a visual instruction, the goal

must be to convey functions easily and clearly. The tensions, however, between

showing too much visual detail are not easily solved. Thus, one area requiring

immediate consideration is that of visual complexity or, in other words, density.

Density was, for Peeck, an issue for any type of pictorial representation and thus, the

visual instruction developed for testing must also consider this dimension in the

development process. Peeck had noted that density can effectively vary on the

amount of time an individual is willing to invest in studying an image; in other words,

an image can be more detailed if the viewer has previously identified heightened

needs or interest levels. In light of a fundamental tenet of the experiential—starting

quickly—lowering the density of the visual instruction is indicated as a best practice

for the design.

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Consequently, in order to reduce the functional elements of Imagination

Cubed and focus on the tools, the visual instruction should make the functions of the

toolbar a prominent feature of the design. By not displaying the horizontal menu bar

and instead stressing the tools as a focal point, drawing becomes an immediate action

and the experiential is activated.

Figure 4.4 Next phase of screen design

In order to create a proper grammatical and functional reading of the

program, the position of the individual tool palettes is the next phase of the design

requiring consideration. Once a palette is selected via the crayon, it automatically

appears to the right of the tool bar thereby establishing a “reading”—left to right—

that emulates written language (Szlichcinski, McCloud). The palettes, unlike the tool

bar menu, float and can be moved to any place on the screen; as well, once drawing

begins, the palettes automatically re-align from the live drawing area to the upper

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right hand portion of the canvas. Consequently, matching their exact location on the

screen is not necessary for understanding the program nor would it accurately

replicate the program’s workings.

From Chapter 3, the ideas put forward by Rune Pettersson with respect to the

spacing between palette elements is the next consideration in building a visual

instruction Pettersson had asserted that position and arrangement of surface features

were critical for user comprehension of any visual. Figure .5 shows where palettes

open in relation to the toolbar—approximately one centimeter of space occurs

between the two features. In exploding palette functions, it is necessary to maintain

an equidistant amount of space between the tools. This visual connectedness, as

developed via spacing, serves to encapsulate the ideas within a core area and provide

a means to, in Szlinchinski’s terms, link frames and instantiate a stronger visual

grammar.

In order to provide clarification for the three most common functions of the

palettes—size, colour and style (or pattern)—and do so in way that makes these

functions visible, isolating the functions is the next necessity of the theoretically

derived artifact. As shown in Figure 4., the palettes only open to display one primary

function at a time while collapsing to close the other options available. While

providing a focal point for the palette under consideration, this can limit the ability of

an uninformed visual instruction to provide an overview of the operations; however,

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if the key functions are ‘exploded’ from their palette, accessing additional functions

within the palette becomes an obvious choice for the user.

Figure 4.5 Sample of tool bar with line drawing palette active

Important in exploding other palettes from their position is the requirement to

maintain the concept of a unified and functional connection as well as provide an

impetus to the user to explore each tool’s individual operations. Unlike many of the

more familiar commercial word processing and photo manipulation programs, there

are no overlapping of palettes in IC (shown in Figure 4.) as these are not layered or

‘drill down’ menus; as noted earlier, the operations of IC’s tool palettes utilize an

expanding/contracting system for accessing different options. Therefore, layering or

other stratified placement arrangements would not successfully imbue the visual with

the correct characteristics.

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From the work of van der Meij and Gellevij and countless instructional

manuals, the use of colour proves to be the most unexplored feature of the visual

instruction. While the costs associated with printing select pages of any manual in

colour have always prohibitively expensive and logistically impractical for print,

colour is one of the most effective mechanisms to direct users and instantiate the

cognitive processes for not only learning but also knowledge building. It is, however,

not frequently used or understood as a feature of images like a static screen capture

or screen object.

In drawing from the work of Szlichcinski and Pettersson, colour is shown to

be a powerful means to communicate concepts and provide cognitive linkages. As a

surface feature, colour is imbued with meaning e.g., warnings or hazards being

conveyed by the colour red. Its other strength, however, lies in its ability to provide a

mechanism to connect ideas, especially in an artifact such as the visual instruction.

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Figure 4.6 Use of colour to link palette concepts

Once the palettes are broken out into their functional areas, colour is then

used connect the elements of style, size and colour from the main options palette to

the sub-menus. The choice of colours, as shown above in Figure 4., is selected with

two considerations in mind: the meaning of colour and colour preferences. Red,

orange and yellow, while highly noticeable, are usually associated with hazards,

warnings or cautions and may, in acting as a graphical device, not be interpreted

correctly (Boling 1987). In terms of colour preferences, as discussed by Rune

Pettersson, blue, green and violet are well-rated preferentially and make good choices

for the theoretically derived visual instruction; therefore, these colours become the

logical choice for developing another element of the visual instruction.

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As shown in Figure 4., blue, green and violet are used to connect the palette

options to their detailed sub-menus. As blue is, according to Pettersson, the most

predominantly preferred colour, it is placed centrally in the visual with the goal of

establishing a focal point for the palette. By creating a blue focal point in the center

of the palette, this serves to disrupt a standard top down reading of the image and

assists the user in considering more of the program’s operations. The violet and green

used above and below the blue articulate the functions in those positions. In terms of

colour saturation, tints of blue, violet and green were selected rather than full

saturations of the colours.. One reason for choosing a tint versus the full saturations

relates to spectral colour continuum on the colour palette. With a display of full

saturation colours, any other full value colour used to depict or connect ideas would

undoubtedly be conveying an unclear meaning. The reduced intensity of the tint,

however, differentiates itself sufficiently that there should be no inappropriate visual

cueing.

Theories of pictorial representation and surface features do not address many

of the techniques now readily available via current software programs. As a result, the

use of overlays—a technique whereby opacity or transparency is incrementally

reduced—is not discussed as a visual function. Regardless, the effect as shown in

Figure 4. does provide yet another way to read a visual and develop a connection

between elements in the visual instruction. By being able to see the palettes under the

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colour, the function of the colour as a guide for comprehension is clear; additionally,

the palettes still remain in view and are part of a cohesive construct for the user.

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CHAPTER V

METHODS

This chapter outlines the methods used to gather information about the

efficacy of a minimalist visual instruction as compared to a minimalist verbal

instruction. The recruitment and demographics of the participants, the instruments

used in the investigation, the design of the study and, the procedure via which the

study was administered are included in this chapter. To assess the performance

differences between the visual and verbal instructions, participants were randomly

assigned to receive one of the two instruction sets. Regardless of the instructions

received, all participants were given the same drawing to replicate; they were

measured on the time taken to complete a computer-based drawing task.

Additionally, their spoken commentary was noted and their final drawing from the

task was analyzed. This chapter also includes a discussion of the assumptions and

limitations of the study.

Participants

To gauge whether the visual condition resulted in performative differences as

compared to the textual condition, participants were required for a small-scale

usability study in which they would complete two learning style inventories, perform

a small drawing task using a simple online program and complete a post-test

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interview. The following describes how participants were recruited; in addition, a

summary of the demographic data is presented.

RECRUITMENT

From the initial 33 participants who showed interest in the study, 25 ultimately

volunteered to be part of the research; the pool was comprised of seventeen females

and eight males. Recruitment of the participants was conducted by asking several

instructors of undergraduate classes if I could have a 10-minute block of time to

discuss the human subjects requirement of my study. English 2311 (Introduction to

Technical Communication), English 3365 (Professional Reports) and English 3367

(Usability Testing) were visited as part of the recruitment initiative—in total, seven

classes or approximately 140 individuals received information about the study and

how they could be involved. English 2311 and 3365 are service-learning courses and

therefore the students represented individuals from a variety of colleges on the Texas

Tech campus including Architecture, Agriculture, Business Administration and

Human & Family Sciences. These two courses provided access to a population more

diverse than just English majors provide and allowed for a representation of

backgrounds and skills that would better capture the nuances of a larger population.

In the classroom visits to English 2311 and 3365, a brief presentation (10

minutes) was made to the class about the research being conducted and what their

participation would involve in terms of tasks and time commitment. Additionally,

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these classes were informed that a small research stipend of $20 was available to

anyone who elected to participate in the study. For additional clarification, students

were referred to a website (see Appendix C) for written information about the study,

the approval given by the Institutional Research Board, the contact information for

the Principal Investigator and the name and email of this researcher. If they wished to

participate, students were asked to email me in order to set up a mutually convenient

time during the two-week period preceding the Thanksgiving break (mid-to-late

November 2006).

A second group approached regarding participation was the students of a

junior-level technical communication class. English 3367 is the undergraduate

usability course and is most often taken by students pursuing a degree in English with

a concentration in Technical Communication. This course is also taken by other

majors who have declared a minor in Technical Communication. Participants were

recruited in the same way as those in English 2311 and 3365 and were offered

identical remuneration for their time. If the student needed a research participant for

the undergraduate-level project in 3367, they had they option of forgoing the

remuneration and entering into a quid pro quo arrangement between their study and

this dissertation research. This recruitment resulted in three students requesting to be

scheduled for the study providing I participated in their research.

The remaining four students of the 25 participants responded to a recruitment

flyer posted in the third floor hallway of the English Building. These students—all of

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whom were in English or Technical Communication graduate programs—

approached the researcher individually and asked about the study. As per the

classroom visits, each potential participant from this cohort received the same

recruitment information as delivered in the undergraduate classrooms. Each person

was instructed to email the researcher regarding availability and scheduling.

PARTICIPANT DEMOGRAPHICS

All participants in this study were students at Texas Tech University with four

of the 25 being graduate students and the remaining 21 undergraduates.

Consequently, the population studied either has completed a college degree (16%)

and is pursing an advanced degree or would be classified as having some college due

to their current status as undergraduate students (84%).

Instruments

RANDOM NUMBER GENERATOR

In order to generate random numbers—five- or six-digit identifiers for all

participant materials, data sheets and artifacts—I used an online random number

generator. The “Research Randomizer”, found at

http://www.randomizer.org/form.htm provided an easy way to generate multi-digit

random numbers. The online form asked for the quantity of “sets” of numbers (1),

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http://www.randomizer.org/form.htm


the quantity of numbers per set (15), and a number range (150,000-200,000); on

selecting the submit button, the program then returned 15 six-digit numbers which I

printed in onto adhesive labels and affixed to participant materials including: the data

collection envelope, Kolb inventory (LSI), consent form, Felder-Silverman inventory

(ILS), printout of the drawing artifact and my observational notes. When I required

more numbers, I repeated the process but switched to five-digit numbers as a mental

reminder I was on my second set of materials.

KOLB’S LEARNING STYLES INVENTORY

David Kolb’s Learning Styles Inventory (LSI) was developed as a way to

measure the strength of an individual’s experiential learning style. The outcome of

administering this instrument in my research and calculating individual participant

scores is to provide a measure of which styles do better experientially in a visual

versus textual model and at minimalism as a whole. Correlated with results from the

Felder and Silverman Learning Styles Inventory, and considered in conjunction with

condition-based participant performance on the drawing task, Kolb’s inventory could

help to shed light on the question of the interaction between experiential learning

styles, minimalism, learning styles, and instructional modality.

In order to make a more substantial case for using the Kolb inventory, I

considered the longevity and usage of this instrument as well as research that

discusses its reliability and validity. Kolb’s LSI is used widely across a number of

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disciplines including pharmacy, nursing and education to name but a few. His first

iteration of the LSI—version 1.0—appeared in 1976 and, as an instrument, was well

received for the soundness of its psychometrics (Kolb and Boyatzis 16). The LSI

gained popularity throughout the 1980’s and, as with any attempt to measure and

codify individual styles, it also gained its fair share of detractors. In 1985, the second

release of the LSI was launched and over the next several years, according to Kolb

and Boyatzis, critiques focused on test-retest reliability and the internal consistency of

the measurement scales (17). These issues were resolved for version 3.0 and Hickcox

in a 1991 article concluded that the reliability and validity of version 3.0 were well-

established. In her review of research on this instrument from 1971-1991, Hickcox

analyzed 81 studies in the helping professions, medical professions, education, higher

education, accounting and business—50 of the studies supported ELT versus 31

studies showing partial or no support. She concluded that the Learning Style

Inventory (LSI), (Kolb, 1971, 1976, 1981), in comparison with 17 other North

American and Australian learning style instruments, had strong reliability and fair

validity. She agreed in this conclusion with Curry's (1987) study of 21 learning style

instruments. As I result, I felt warranted to include Kolb’s LSI as it is a long-

established inventory—as Myers-Briggs is considered—and use it to add an

additional dimension to the research conducted here.

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FELDER AND SILVERMAN’S LEARNING STYLES INDEX (LSI)

The other instrument selected for my study was the Index of Learning Styles

(ILS) created by Richard M. Felder and Linda K. Silverman in 1991 (Felder and

Spurlin 103). Prior to the release of the 1991 online version and its subsequent

refinement in 1996, Felder and Silverman studied the learning habits of engineers and

how the standard pedagogical practices found in colleges failed to address many of

the students in engineering programs. They asserted that the passive learning so

prevalent in engineering education did not meet the needs of the students and that in

order to effect change, a means to measure the learning styles of this population was

required (Felder and Silverman 681). Their ground-breaking article, “Learning And

Teaching Styles In Engineering Education” published in 1988 addressed the problem,

provided a schema to address learning styles and ultimately led to the creation three

years later of the 44-question web-based inventory.

With respect to the reliability and validity of the instrument, Felder and

Spurlin in their 2005 article, “Applications, Reliability and Validity of the Index of

Learning Styles” assess the research on, in particular, test-re-test and construct

validity of the ILS. Through an examination of studies done by others on the ILS,

Felder and Spurlin confidently accept that after 15 years of use, the ILS is, as

described by one group of researchers, “a suitable instrument for assessing learning

styles” (110).

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DEVELOPING THE DRAWING TO BE REPLICATED—“BIG BANK”

As discussed in Chapter Four, the creation of a theoretically derived artifact—

to act as a screen capture or stage-setting image—was required to determine if

participants could understand how the Imagination Cubed program functioned at a

rudimentary level with only a visual instruction.

In developing the sample drawing to be replicated by both the visual and

verbal groups in the study, the following considerations applied:

The sample drawing would not intimidate the participants by its

excessive complexity or advanced artistic execution.

The use of easily identifiable colours including the primaries of red,

yellow and blue plus the secondary colours of green, purple and

orange.

The inclusion of a variety of forms (shapes) for encouraging

participants to the freeform drawing pencil, geometric shapes,

autoshape stamps, line tool and type in their execution of the drawing.

The inclusion of shapes in a pictorial scene that would lend itself well

to a direct interpretation by the participants.

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With respect to the sample drawing presented for replication, my intent was to

create a picture with the tools available in Imagination Cubed. As the goal of the

study was not to test artistic skills but to determine if a visual instruction presented

provided a sufficient amount of information to use the tools and replicate the sample

drawing, detailed renderings and complicated visual elements would not serve my

study or the participants well. Requiring participants to replicate a precision image

with multiple nuances would be intimidating and, undoubtedly frustrating especially

for those who believe their creative talents are limited. Additionally, performing a

complex task in this study would measure only the perseverance and fortitude of the

participants rather than provide an accurate gauge between the groups. Therefore, the

drawing both groups would replicate—referred to as “Big Bank” here—is done in a

style reminiscent of a grade-schoolers drawing ability: it is un-intimidating and does

not rely on an individual’s ability to work with complete accuracy.

The complexity of colours was another important consideration in the

development of the “Big Bank” image. Anchoring the major images (sky, building,

sun) within the three primary colours of red, yellow and blue provided an easily

identifiable colour reading for the participants. Other colours in the image (green,

orange, pink and brown) were again not difficult to identify and, as developing

complex tints and tones was not part of the study, these colours provided a good

opportunity to explore the colour features of Imagination Cubed without belabouring

the task with minutiae that would make it frustrating for the participants.

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As the goal of the study is to encourage exploration on the part of the

participants, the shapes included in the “Big Bank” image needed to use each of the

different tools as possible. These tools included:

Pen—weight, pattern and colour.

Shapes—basic geometrics and colour

Stamps—patterns and colour

Line—weight and colour

Type—weight and colour

Background—solid colour or pre-defined pattern

The one tool not used during the course of this study was the background

tool. This tool painted the entire background with either a solid colour or one of the

program’s pre-defined patterns. By choosing a solid colour, the participants may not

have been able to create the sky (painting white on blue) or they may experienced

negative effects from the optical phenomena known as simultaneous contrast—an

effect whereby one colour influences how we perceive colours differently based on

what surrounds them (Experience of Color 6). Simultaneous contrast, especially as it

could occur from a solid block of colour, may have made it difficult for participants

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to select colours with any degree of accuracy. None of the patterns available in this

tool would encourage exploration; consequently, this provided a second reason to

omit the tool in the study.

In terms of tool use, the study was designed to encourage participants to

explore the more varied features found, in particular, under the shapes, stamps and

pen tools. The type and lines tools have only colour (spectrum plus 5 tint/tone

variations) and size (small to huge in six weights) as selectable elements; while both

are important features, their commonality and ease-of-understanding would not

require a significant amount of exploration. For the “Big Bank” drawing, however, I

chose to include elements that would require participants to investigate the menus

and make choices based on the menu items and the shapes/forms/ presented to

them in the “Big Bank” drawing. In particular, the pen, stamps and shapes require

scrolling through menus and making multiple choices bases on size, colour and

pattern. This multi-choice paradigm for tools

Table 5.1 summarizes each tool and the distribution of choices in each tool

category for the “Big Bank” drawing.

Table 5.1: Tool Use Distribution for Big Bank

Tools Colours Weight Patterns

Pen 2 2 n/a

Shapes 4 8 2

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Stamps 10 5 6

Line 1 1 n/a

Text 1 1 n/a

Background n/a n/a n/a

DEVELOPING THE TEXT INSTRUCTIONS

For the group using verbal instructions in this study, I created a set of written

minimalist instructions that corresponded to the visual instructions developed in

Chapter 4. In these text instructions, the goal was to direct the participants to the

tools menu where the available functions in the program are located. Next, following

the left-to-right reading of the visual instructions, the text then explained that each

tool can have combinations of style, size and colour to create unique visual effects on

the screen.

The written instructions articulated the expanded-for-scrolling palettes on the

visual instructions by indicating that participants should scroll to see what is available

to them in terms of features and effects. As well, as the visual version had an

emphasis on colour via the spectrum shown on the page; as a corollary to this, the

written instructions told participants that they would be able to select colours for any

of the tools.. Below, in Figure 5.0. are the text instructions provided to the verbal

group in the study.

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Figure 5.1 Minimalist Verbal Instructions

Design of the Study

The investigation conducted as part of this dissertation combines both

qualitative and quantitative elements to produce a rich and engaging study. Much of

the study is conducted as a usability test with participants situated in a lab-based

setting and completing pre-test questionnaires, a task and a post-test questionnaire.

As they worked on the assigned task of replicating the “Big Bank” sample drawing,

participants were observed and asked to provide a talk-aloud protocol so my notes

could be supplemented with the comments made during their time on the drawing

task. At the conclusion of the task, a post-test questionnaire was given and the

participants’ answers were recorded. The questions were designed to gain qualitative

feedback on the test and determine how well the participants in both the visual and

text instruction groups explored the features of the program during the drawing

exercise.

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In terms of gathering quantitative data for analysis, several measures are used

to provide descriptive statistics that articulate their own unique findings and add

additional depth to the qualitative measures. Both the Kolb Learning Styles Inventory

(LSI) and the Felder Inventory of Learning Styles (ILS) make their results available as

a numerical score. The use of these inventories will provide a means to measure

which learning styles perform better with text or visuals conditions.

As the participants were timed in both the overall task and the points at which

they completed major features of the drawing, time formed another statistical

measure. In particular, time on task as correlated with the inventories will provide

data to indicate if there were any significant differences between treatments and

preferred learning styles. Time was also used to show how engaged participants were

in the task by comparing their actual time on task with their perceived estimate.

Finally, the analysis of the final drawing artifact—the participants’ completed

“Big Bank” drawing—provided quantitative measures. From the drawing, factors

such as ink coverage, shape choice and colour choice will be calculated to see what

differences between conditions and learning style may exist.

Procedure

The study conducted in this research was completed in the English

Department’s usability lab; the observational portion of this dissertation would not

have been possible without the support of the lab’s directors and the use of the

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facility. The usability lab is a professional-calibre facility designed for both student

and commercial research studies. The lab consists of two rooms: a control room

containing equipment for audio-video captures and a test room with desks,

microphones, cameras and networked computers. For the purposes of my research,

the usability lab was an ideal location in that the room is quiet and it provides a

comfortable but not overly relaxed atmosphere for the participants. It reinforced that

I was conducting an observational protocol and thus participants needed to approach

the study seriously. Finally, the usability lab provided a control for the effects of

environmental influences. As the setting for my study was identical each time and for

each participant, this adds an additional degree of reliability and validity to the study.

As an undergraduate course in usability testing was running concurrently with

my research, it was necessary to book blocks of time around the class schedule or,

with permission, run participants during the scheduled undergraduate class time. Dr.

Brian Still, who was teaching the Fall 2006 section of ENGL 3367, was kind enough

to allow my study to overlap into some of the time booked for his students.

Otherwise, the lab was free for fall 2006 and I could schedule participants around

times that best met their needs. For security reasons, no sessions were scheduled

outside of regular business hours during the week and no sessions were scheduled

when the building was locked (and practically deserted) over the weekends.

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BOOKING PARTICIPANTS FOR THE STUDY

After the participants had received the initial solicitation for participation in

their classes, they were asked to email me and indicate their available times in one-

hour blocks for coming to the English Building and completing the study. Scheduling

for the self-selected participants was done via the MS-Outlook Calendar function and

auto-reminders were sent one day prior to the appointment as a reminder. Thirty-

three individuals were scheduled with a final tally of 25 presenting themselves at the

lab for testing.

CONSENT AND PRIVACY

Participants were scheduled to meet me in the Usability Lab—Room 355 in

the English Building—for their appointment; on arrival each individual was greeted,

and asked to make themselves comfortable at a desk while the nature of the study

was reviewed and the participation form was reviewed and signed. As this study—

approved by the Institutional Research Board at Texas Tech—posed no danger to

the participants, the form consisted of standard wording and information consistent

with an expedited review (See Appendix C). In discussing the form with each

participant, I reinforced the privacy protocol of the study by indicating that an

assigned ID number would be the only identifier used to catalogue the research

materials (forms, data collection tools, etc.) and discuss individual results in the

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research narrative. The consent form was the sole document to contain their

signature and would be kept in a dossier and locked in my office.

ADMINISTERING INVENTORIES

Once the consent form was signed, participants were given a paper-based

version of the Kolb Learning-Style Inventory to complete (See Appendix D). This

12-question inventory required participants to rate their responses to each question

on the following scale:

4 = most like you

3 = second most like you

2 = third most like you

1 = least like you

Once the Kolb inventory was completed, it was placed in the numbered

dossier for manual scoring at a later time.

At this point, participants were moved from the desk area to a computer

workstation so they could complete Felder and Silverman’s Learn Styles Inventory

(see Appendix E). This 44-question inventory is delivered as an online test that

returns an immediate analysis of the individual’s responses. In the field requesting the

individual’s name, participants were asked to use their assigned ID number and then

proceed with answering the questions. In the LSI, participants read a simple

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statement about a task and then selected either answer (a) or (b) depending on their

preference. On completion of the inventory, participants were told to click on the

submit button so the test could be automatically scored; the results returned,

however, were not made available in order to eliminate participant issues that could

arise from the Hawthorne effect.

ASSIGNMENT TO CONDITIONS

Once both inventories were complete, the next portion of study was initiated.

Random assignment of participants to the visual or verbal groups was done by a blind

selection. In a large envelope I placed 26 pieces of paper—13 marked “C” for the

verbal group, and another 13 marked “X”, for the visual group For each participant, I

reached into the envelope and selected a piece of paper; depending on the letter, this

dictated the participant’s assignment to a condition. Participants were not made aware

of the conditions, their significance to the study or to which condition they were

assigned. Once, however, participants were randomly assigned into either the verbal

group or the visual group, they were briefed on what was required of them during the

drawing task.

PRE-TEST BRIEFING

Participants were told that they would be working with a simple computer-

based drawing program and that they would be given a set of instructions to explain

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some basic operations of the program. Additionally, rather than create their own

drawing, they would be given an image (“Big Bank”) to replicate once we were ready

to begin the task. I also explained that during the drawing task, the participant should

talk out loud about what s/he was thinking and reveal to me verbally the internal

processes and logic underlying their actions. Finally, I told participants that I would

perform two tasks while I watched them work: I would glance at a watch on the desk

and I would be taking notes on what they were doing and what they were saying.

Time—in terms of how long the participants took to draw the image—was

not a vital measurement and I made this clear to each individual prior to the start of

my observations. Participants were told that there was no time limit with respect to

completing the drawing and that I was only keeping time to see how long, overall,

they spent on the drawing task and at what points throughout the study that they

completed different features of the drawings. The times would be noted on my data

collection sheet along with any notes I was making about their talk-aloud protocols,

their problem-solving approaches and other observational comments.

None expressed any concern about my timing and note-taking protocols; most

participants, however, expressed some degree of anxiety about their ability to draw,

especially using a computer. They were quite sure they would produce an artifact that

was, at the very least, a personal embarrassment or would somehow not fulfill the

requirements of the study. I had anticipated there would be some measure of

apprehension regarding the drawing task as a significant portion of adults are very

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unsure of themselves with respect to their artistic skills. In order to dispel any fears

the participants may have had and to properly frame the task, each participant was

briefed with the script found in Appendix F.

Based on their random assignment to the visual group or verbal group,

participants were then given either the visual or the written instructions and asked to

study it and indicate when they felt they had spent a sufficient amount of time with

the material. Each participant was told that they may refer back to the instructions

(visual or written) at any time during the drawing portion of the study and that they

should place the instructions in a convenient location. Before proceeding with the

drawing task, participants were reminded that they should use a talk aloud protocol

and explain verbally what they were thinking throughout the process of completing

the drawing.

When the participants verbally indicated they were finished reviewing the

instructions, I asked them if they had any further questions about the task they were

about to start. I also used this point in the set-up to again relax the participants about

the upcoming protocol by inquiring about their major at college, standing (freshman

to senior; graduate student), age and their familiarity with computer-based drawing

programs. In terms of this last dimension, I asked what programs—generally or

specifically—they had used and for a self-evaluation of their skill level based on the

categories of novice, intermediate or advanced. Notes on these factors were written

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in a corner of the observation sheet for later compilation into descriptive features

about the participant pool.

Once the above details for each participant were noted, the screen with

Imagination Cubed was maximized and I placed the task drawing “Big Bank” on the

desk. When participants reached for the mouse and started to engage with the

program, I wrote down the start time for the observation and prepared to take notes

on the data collection sheet.

TAKING OBSERVATIONS

Sitting opposite to the mouse-dominant hand of the participants, I made

written observations on how each individual approached the task in terms of a

starting point and the order in which they proceeded with the drawing. The times at

which the participant drew major features of the image were noted, their verbal

comments were taken down and their problem-solving strategies were also

documented. I particularly watched their exploration strategies and choice of tools,

their application of colour and their techniques for learning the features of the

program.

During their work on the drawing task, I remained as quiet as possible with

my most common utterance being a reminder to use the talk-aloud protocol.

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CONCLUDING THE DRAWING TASK

The drawing portion of the test concluded via two mechanisms: the

participants’ self-declared statement that they were finished the drawing or by the

program’s ink function. When a participant indicated they had finished the drawing—

by completing what they perceived as all the elements in the “Big Bank” picture—I

stopped the timer and noted “Finished” and the time on the observation sheet.

A second and less common way the drawing portion of the test could

conclude was via the digital inkwell running dry. For participants who overdrew—

that is, they chose to correct their work by drawing on top of existing portions of the

image—they could run out of ink before completing “Big Bank”. The dry inkwell, a

function of the digital memory of the online program, would cause the program to

stop and produce an alert message stating that in order to continue, previously drawn

elements on the screen would disappear. At this point, I would conclude the drawing

portion of the test and note the end time on the observation sheet. In all cases,

participants were almost complete re-creating “Big Bank” according to their

exclamation. In order to save the final artifact for later analysis, I would do a screen

capture of the completed drawing and email it to myself.

POST-TEST QUESTIONNAIRE

The intent of the post-test questionnaire (see Appendix G) was to determine

what participants thought about the experience, how useful they found the

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instructions and, in reviewing the available tools, their perceived time on task, what

functions they were familiar with from the test and what functions they had not used.

The post-test questionnaire consisted of nine questions and was designed to gain

feedback on either the visual or verbal group.

Once the participants were ready, I went ahead, began to ask them the

questions, and noted their answers. The first eight questions asked participants to

provide input on the instructions with respect to their usefulness in completing the

task; as well, they are designed to collect data on the participants’ perceived time on

task and their willingness to explore the program beyond just the instruction set.

Question 9 asked if they knew what some of the other functions beyond the

Tools palette; again, this question was trying to determine how exploratory each

participant was with respect to the program. Finally, I presented participants with a

picture of the tool palette, asked them to name, and identify the function for each

tool. In this instance, I wanted to determine not only their exploratory nature in the

drawing task but gain and elemental understanding of how well they had understood

the operations of the various tools.

At the conclusion of the 45-minute study, participants were thanked for their

involvement and given an envelope containing a $20 research stipend. I also asked

that they not discuss the details of the research conducted in this study as I did not

want future participants to be contaminated with a pre-existing knowledge of what

this study entailed.

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Coding the Drawing Artifacts

In order to assess the differences between the visual group and verbal group

with respect to the production of the drawing, two procedures were conducted. First,

the drawings on Imagination Cubed persist as active links for approximately six

weeks; consequently, once all 25 participants had completed the study, I was able to

review each drawing by replaying it. This allowed me to verify my original qualitative

comments on the participants’ approaches. This was done approximately three weeks

after the end of data collection to ensure my memory was clear.

As I had taken a screen capture of each completed drawing, I also had a digital

artifact that could be used for analysis. In order to obtain a consistent analysis of the

drawing and gather information that would provide comparative measures, it was

necessary to develop a coding process and rubric that could be applied to each

participants work. The source drawing—the “Big Bank” image—was used as the

basis for measuring how well the participants did at recreating the image. By placing

this drawing into the program PhotoShop®, and in a unique layer, it was then

possible to compare the source drawing with the test artifact by overlaying the

images.

On the base “Big Bank” layer, the screen was divided into a grid system of

one-centimeter increments. This enabled me to measure the approximate position of

major elements in the drawing and determine if the participant placed things

reasonably well. As PhotoShop provides horizontal and vertical rulers, it was easy to

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obtain numerical measures for comparative purposes. Additionally, the magnification

function of PhotoShop provided a much easier way to confirm if participants had

used the correct tool(s) in the creation of specific detail features in their picture.

Finally, PhotoShop provided a means to measure the accuracy of colour

choices in the artifact. While absolute replication was not the goal of the study,

determining participants’ engagement in the task via their attention to a degree of

detail is important in determining the differences between the text and visual

conditions. By measuring the colour values of objects in the original drawing and

comparing those values to what participants’ developed provides another measure of

their engagement with the task based on assigned condition.

Assumptions and Limitations

The value of any study is dependent on acknowledging the limitations of the

findings, the threats to validity, and the controls used to mitigate them. By the very

nature of being a lab-based study involving human participants, this dissertation

research must consider what could impact the findings and how then those findings

may or may not be generalizable to populations beyond the sample tested. The

following considers the limitation of the study in addition to the threats to both

internal and external validity. The following sections discuss explicate several

shortcomings in the research; as well, these sections show, where possible, how

problems with the study have been minimized..

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VISUAL LEARNERS, VISUAL INSTRUCTIONS AND A VISUAL TASK

Due to the small scope of this study, participants were not sorted and studied

based on their profiles on the Kolb LSI or the Felder ILS. Both inventories were

administered at the time participants visited the usability lab for the study and the

results were tallied at a later time. As discussed in Chapter Six, the participants in this

study were reasonably well-distributed across each of Kolb’s experiential learning

dimensions and thus not a threat to the validity of the results. However, the Felder

ILS demonstrated a prevalent trend that was unexpected in a population of college

students: 80% of the population or 20 of the 25 participants favoured a visual

learning style ranging from a mild to strong preference. Thus, that the study

conducted in this research involved half of the participants using a visual to complete

a drawing task does present a confound with respect to the results. In any

interpretation of the findings, it should be noted that there will be bias effects related

to the conflation of these three factors.

HAWTHORNE EFFECT

In the case of direct observation, the Hawthorne Effect can be problematic

(Frey, Botan and Kreps 121). Participants can behave in an atypical manner when

they are aware of a researcher’s intent; that is they could behave in ways they think

will improve the researcher’s findings or, contrarily, they may explicitly try to

sabotage the researcher’s efforts. Either way, this may contaminate their responses to

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the test situation and the research as a whole. To control for this confound, several

measures were taken to reduce or eliminate the problems associated with the

Hawthorne Effect.

First, I believed the best course of action was to minimize to the participants

what would be perceived as my personal investment in the study. Being acutely aware

that the study and its design were my unique creation and that the results would be an

integral part of my success in the doctoral program could easily lead participants to

perform in a way that would help (or hinder) my study. Rather than emphasizing the

“my” factor in the study, verbal and written communications were phrased in “we”

or “our” with the plural reference including the Principal Investigator, Dr. Thomas

Barker. Including mention of Dr. Barker as having a formalized, lead role in the study

removed the immediate focus from me and, I believe, permitted participants to view

me more as a test administrator versus test author.

Next, with the administration of the two learning styles inventories, I did not

explain exactly what dimensions these instruments would measure and how. The

inventories were explained to participants as questionnaires designed to assess

personal preferences for learning. As discussed earlier in this dissertation, neither the

participants nor I saw the scores once the inventories were completed; thus, no biases

that would affect performance were present for either party at this point.

Finally, for the drawing portion of the test, participants were not made aware

of either the groups or the purpose of this part of the study. While individuals were

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randomly assigned to either the visual group or verbal group, they were not privy to

this dichotomy or fact that there were two sets of instructional materials (verbal or

visual). In the pre-test narrative, participants were only told that they would be given

instructions and asked to complete a computer-based drawing task. Additionally,

when I informed participants that I would take notes, I did not make transparent the

exact details of what I would record. These three measures, I believe, contributed

significantly to the reduction of the Hawthorne Effect in this study.

DIFFUSION EFFECTS

A central concern among any researcher working with a population drawn

from a collective is the threat of participant contamination due to discussion—this is

most commonly referred to as a diffusion effect. Most typically, such a confound

occurs in studies where an entire classroom is put through a protocol but not

simultaneously. Venues such as lunch or recess, in the case of school-age children,

then provide the opportunity to discuss the research is about, the task(s) performed

and other key information about the study. As a result, later participants have an

understanding of the investigation and this may influence their choices or

performance.

While in a study such as this, it would be impossible to completely control for

the effects of discussion, I did wish to minimize the effect. Students in the service-

learning courses of ENGL 2311 and 3365 may not be sufficiently well acquainted to

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talk about the experience of participating in my research; however, I did not want to

rule out the fact that in-class acquaintanceships may have formed. I asked my

participants at the conclusion of the study to limit their discussion of the study as

prior knowledge would contribute to the contamination of the pool. If they wished to

support my research by verifying the remuneration of $20, I said that would be fine

as, I believed, it would reinforce participation in my study for those who had signed

up but not yet completed the study.

With respect to the three students from the undergraduate usability class,

ENGL 3367, I did acknowledge that they would be more inclined to discuss my

protocols and processes due to the work they were doing and the highly familiar and

interactive nature of their class with Dr. Still. In order to control discussion among

the three participants from that class, I tested all three within two days (Thursday

after their scheduled class and Friday morning during open lab time). While this

method does not guarantee their ultimate confidentiality, I asked that, as per the

regular participants, they do not discuss my study until all three had completed it.

Interestingly, when I reciprocated by doing one of their studies, I noticed their

emulation of several of my confidentiality protocols on their test materials.

RESEARCHER BIAS AND EXPECTANCY EFFECTS

In small studies—such as this dissertation—the question of researcher bias

and expectancy requires addressing. As the primary researcher, observer and note-

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taker for all 25 of the research participants, my biases could come into play in my

observation and interpretation of the participants. One mechanism used to control

for this phenomena relates to my delaying scoring of both the LSI and the ILS until

after the completion of the study. At the time the drawing portion of the test was

administered, those tests were already placed in the envelope and neither the

participant nor I had seen the raw results. Consequently, I had no idea of their scores

and how those scores might determine outcomes on either group.

In the case of controlling for expectancy effects, it was not possible for me to

be removed from the task of conducting the actual administration and observation;

however, when recording my observations, my physical location was such that I was

not within the participants’ peripheral range of view. Sitting on non-dominant side of

the participant, I had moved the chair back sufficiently so I could observe the

participants’ drawing techniques on the monitor and watch their references back to

the instruction sheet. There was no direct opportunity for direct visual contact with

me while a participant performed the task. Consequently, if I was providing any

unexpected facial cues or mannerisms, the participants would not have been able to

see them. Additionally, my speaking role during the drawing portion of the test was

very limited—only reminders to talk aloud or continue to the best of their ability

were made. Thus, I believe any expectancy effects were significantly minimized if not

entirely mitigated.

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NON-RANDOM SAMPLING AND GENDER DOMINANCE

As discussed earlier, participants for this study self-selected to be involved

based on the information their received during the recruitment presentations; thus,

the sample for the study in considered non-random. A volunteer population—as

derived from technical communication classes at Texas Tech—will not, more than

likely, be generalizable to the population as a whole. Rather, this non-random sample

might serve to show some inferences for the direction of the research indicating the

need for further work with a larger, random sample.

Additionally, research indicates that individuals who do volunteer are not

necessarily the same type of people as found within a general population. Rosenthal

(1965) found that volunteers exhibited different characteristics than non-volunteers

including higher intelligence and increased motivation. While Rosenthal’s research is

more than 40 years old, it can be presumed to apply to volunteers and populations in

2007.

This study did not control for gender but the outcome that 65% of the self-

selected participants are women is an interesting finding in itself and may, in general

reflect gender-based tendencies towards volunteering. As reported on the website,

World Volunteer Web, the likeliest volunteer, “is a white female who gives 50 hours per

year volunteering”. In terms of a general adult population in the US, World Volunteer

Web found that women volunteer at a higher rate than men—32.4 percent versus 25

percent for men. “Mothers with children at home”, the article notes, “volunteer at

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the highest rate of any group—nearly 40 percent—reflecting strong demands from

schools, preschools and religious groups”

(http://www.worldvolunteerweb.org/news-views/news/doc/us-volunteering-rate-

remains.html).

As a persistent phenomena, the higher volunteerism rates of female college

students is also noted by other researchers. Among college populations, in particular,

females were much more likely to volunteer (Sax et al., 2002; Abowitz and Knox,

2002). Sax et al. found that in a survey of male freshmen at a four-year college only

38.5 percent volunteered on a regular basis. For women of the same standing,

however, their rate of volunteerism was 54 percent. In their 2002 study of gender

differences in college students, Abowitz and Knox found that of their 154

participants, approximately 60% of the research volunteers were women. Though the

$20 stipend in this study was the same for both men and women, the differential

findings with respect to volunteering may explain why more women participated in

the study. This preponderance of women in the study may, however, limit the

generalizability of the study to both sexes.

AGE

As with most studies involving college populations, the question of age—as

manifested in findings that can apply to a broader population—comes into question.

Twenty-three of the participants were between 18 and 24 years old; one participant

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was in her late 20’s and the oldest participant was 35. As a result, it would be

erroneous to conclude that the results of this study could apply to precisely any age

group but it is possible to consider that even the two older participants in the study

are still young enough to have gown up with a significant amount of digital

technology in their lives. Therefore, it is fair to assert that this study explores a

population considered to be almost entirely part of a digital generation.

EDUCATION

In drawing on a sample derived from a college setting, it is important to make

note of the education levels of the participants as compared to the general

population. In terms of applicability to a general population, the US Census Bureau

report in its 2004 findings that the national average for individuals with college

(undergraduate) degrees was 25% (US Census Bureau n.p.)

Consequently, it can be determined that results from this study may only apply

to the 25% of Americans who possess a college degree. The results derived from this

study may not be typical for the remaining 75% of the population or the 15% of that

cohort not in possession of a high school diploma. (US Census Bureau n.p.).

However, as in any study of this size, such assertions with respect to the population,

as compared to the sample studied, are not uncommon.

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Conclusion

The methods developed here—as a combination of participants, instruments,

and procedures—were designed to provide a rich and rigorous means to assess if a

visual instruction was equal to, or superior to that of, a text. The inclusion of two

learning styles inventories should add to the results, additional information regarding

the performance of different learning styles as they occur between the visual and

verbal groups. Ultimately, as shown in Chapter 6, Results and Discussion, the

implications for using theoretically designed visuals as instructional materials will be

articulated.

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CHAPTER VI

RESULTS

Introduction

In this chapter, the findings from the study—as detailed in the preceding

chapter—are presented. The results documented here are based on the instruments

and processes used to determine if a minimalist visual can act as a suitable

instructional device and if that visual instruction can stimulate experiential learning in

its users. The results in this chapter seek to substantiate the theory that a minimalist

visual—a re-envisioned screen capture based on best practices in design—may be a

superior form of instruction for computer-based tasks. The following sections

present the results of the study as framed around the original research questions:

1. If we consider the term, “minimalist visual” as based on a static graphic like



2. What will a study comparing minimalist text and visual instructions yield in





artifact.

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4. Can a visual instruction, such as the one developed and tested in this study,

assist individuals in completing the drawing task through the activation



A full analysis of this material and an interpretation of the findings can be

found in Chapter 7—Discussion; the results are presented as follows.

Methods of Obtaining Results

This research uses a combination of methods to tabulate and interpret both

the numerical data and artifacts collected. Multiple techniques add to the richness of

the material and, in turn, produce more multi-dimensional interpretations that

contribute to the results presented here. The following procedures frame the

analytical methods used and describe why these methods are appropriate.

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STATISTICS

Where applicable inferential statistics are used to assist in making assertions

about the population studied and the results obtained. In studies such as this one

where the pool of participants is not large, the use of inferential statistics can be

limited by their ability to be both a powerful and robust means of measuring

phenomena. However, applied properly and to specific instances of the data,

indications of significance—at levels appropriate to the best practices in data

analysis—can be achieved.

The question of the level of significance is important for studies done in

disciplines outside of areas typically known for experimental research. Experimental

work done in biology, chemistry or psychology, for example, is defined by its

adherence to the probability norms of significance where p ≤ 0.05 or p ≤ 0.001. In

the humanities, however, such levels can be difficult to achieve based the population,

the data collected and myriad other factors that contribute to work that is

undoubtedly rich but essentially more difficult to circumscribe within standard

empirical parameters. In work such as this study, it is important to keep in mind that

a statistically insignificant finding, as defined by the above-noted probability

standards, is only an indication that the result did not meet these two benchmarks.

The result itself may have meaning and add important information to the canon and

may indeed be significant in ways that cannot be codified strictly within exacting

probabilities. Where possible, the results presented here will be considered significant

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at p ≤ 0.05; however, results approximating that measurement will be included to

add dimensionality to the study and to support the assertion for additional research in

the subject area.

Descriptive statistics—as measures separate from empirical rigour—provide

another means to frame the results of the research done for this dissertation.

Variances, totals and percentages—to name but a few—serve to describe the data

and provide a structure to interpret its meaning, especially for sub-categorical

breakdowns that return a sample inappropriate in size for inferential measures.

Descriptive statistics, in particular, are useful for examining trends in data and

magnitude of difference between groups and categories. While lacking the

scientifically predictive power of inferential statistics, descriptive statistics create

another paradigm for parsing the results obtained in the research.

DATA MINING

Data mining, sometimes called Exploratory Data Analysis (EDA), separates

itself from standard statistical measures by its more flexible approach to configuring

and making sense of data. At its most advanced—using enterprise-level data

warehouses—data mining applies sets of complex algorithmic formulas to structure

and seek information that shows patterns, trends or associations among data.

Ultimately, the types of knowledge found in data mining are the basis for significant

predictive analyses that guide decision-making. Data mining occurs most commonly

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in areas such as the retail sector (marketing), corporate strategy/planning, risk

management and even national intelligence. As a methodology, data mining is a

technique available in any area where massive volumes of information are stored in a

retrievable, electronic format.

In terms of the relevance of data mining to this study, several of the guiding

theoretical principles provide a methodology to reconfigure information for

additional meaning and richness. The philosophy that data itself is open to

interpretations other than those strictly dictated by conventional analysis underpins

this methodology. Data mining engages in knowledge discovery through the

manipulation of information as it reveals trends, patterns, clusters and associations in

the data. As much of the data collected in this research is numerical, the tabular

formats afforded by spreadsheets and relational databases make data mining possible.

With the application of SQL queries, advanced sorts and Boolean search operators to

data tables, data mining reveals relationships that may have been hidden from

substantively traditional analytical procedures. In particular, dynamic data mining can

parse out other possible relationships from observed phenomena and present—via

data visualization methods—previously unconsidered trends and associations. Based

on the versatility of these methods, data mining is used to examine the results

between the learning style inventories, time and the drawing artifacts.

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QUALITATIVE ANALYSIS

In this study, data was also collected by artifact, observational notes and

through participants’ responses to a post-test questionnaire. Using individual

participant numbers, artifacts and observational notes, the results presented here will

examine some of the approaches used by participants to complete the drawings.

Engagement with the task will define the success of either group for activating

experiential learning. It will be measured by the approximate placement of major

shapes on the page, colour choice and tool usage. Ultimately, increased engagement

will result in an artifact that more closely resembles the original and demonstrates an

enhanced use of the features of the program.

Describing the Population Studied

As discussed in Chapter Five, 25 participants volunteered to come to the

Usability Lab and complete the study outlined to them during the recruitment

process. The participant pool was composed of 17 females and 8 males; the average

age of a participant was 21. In terms of their distribution across available degree

programs at Texas Tech, Table 1 shows that most participants were English majors

(8) or Human Development and Family Studies majors (5).

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Table 6.1 Distribution of Students per Degree Program

Degree Program

# of

Participants

per Program

Agriculture 1

Animal Science 1

Architecture 2

Arts (General B.A.) 2

English 8

Human Development and

Family Studies 5

Mathematics 1

Mass Communication 2

Political Science 1

Psychology 1

Public Relations 1

Total 25

SKILLS SELF-ASSESSMENT

During the observational session, each participant was asked to provide a self-

assessment of skills with using computer-based drawing programs. Using a four-

category scale to rank their ability, I asked each participant if they considered

themselves to have no experience or experience at either novice, intermediate or

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advanced levels. Table 2 indicates that more than half of the population studied had

no experience in this area while only two of the 25 indicated they believed their skills

to be advanced. Of the self-reported advanced participants, one was in the

Architecture program and had worked with software such as AutoCAD, Illustrator

and Photoshop; the other was a graduate student in English with a technology-based

background.

Table 6.2 Self-Reported Skill Assessment Level with Computer-based Drawing

Programs

Self-Report Level

# of

ParticipantsMale % Female %

No

Experience 13 6 75% 7 41%

Novice 5 1 12.5% 4 24%

Intermediate 5 0 0% 5 29%

Advanced 2 1 12.5% 1 6%

Total 25 8 100% 17 100%

In reporting the self-assessment of their skills, more males than females

indicated they had no experience with computer based drawing programs. In total,

75% of the males classified themselves as completely inexperienced while only 41%

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of the females did the same. This difference may be explained by research conducted

by Witmer and Katzman in 1997 in which they found women tended to use more

graphical artifacts in their messages and therefore may have a higher degree of

familiarity with drawing programs.

Differences between the Visual and Verbal Groups

TIME ON TASK

In order to determine the significance of completion times between the verbal

and visual groups, it was first necessary to establish if the data was parametric or non-

parametric. As many of the common statistical tests are based on specific distribution

models of data along the normal curve (sometimes called the Gaussian curve), the

results from the study and verbal groups were input into GraphPad’s InStat, a

statistical analysis program in order to assess some fundamental assumptions about

the data and determine appropriate tests.

With research involving smaller sample sizes—especially in the area of sub-

categorical groupings of data—the importance of establishing the normalcy of the

data and choosing the correct test is critical for assessing the reliability of the results.

It is important to keep in mind that a large sample size (a large “n”) does not always

guarantee the data follows an approximation of the normal curve. Skewed (non-

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symmetrical) data or data that exhibits kurtosis (extreme peaks or flatness) can occur

in large or small samples depending on the nature of the population and the study.

Ultimately, any statistical measure chosen must be appropriate for a given

distribution (robust) and able to measure a difference when a difference is present

(powerful). Where the data is normal, most standard parametric tests are sufficiently

robust and powerful when the sample of compared groups is greater than 10. In cases

where the data exhibits kurtosis or skewedness, or the sample size is under 10, the

statistical tools must be non-parametric—that is to say, the test cannot be based on a

regularized distributional assumption. In light of these necessary considerations, the

results obtained in this research were analyzed for their adherence to parametric or

non-parametric qualities for inferential statistical analysis. Where sub-categorical

groupings are insufficient in terms of the size of the sample for true statistical testing,

descriptive statistics form the basis for analysis.

INITIAL DATA ANALYSIS

In order to begin assessing the results of this study, two basic data sets were

created from the random assignment of participants to groups: one for the visual

group and the other for the verbal. Before providing an analysis, it is useful to

remember that multiple tests exist for analyzing data and, within the field of statistics,

there are countless measures to assess the nuances of a unique data set in a way that

best matches the information and the researcher’s goals. However, there are also

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more standardized tests used in general analysis where the data is considered to be

regular. Such tests are commonly incorporated into commercially available statistical

analysis programs such as the one used here. Graph Pad’s InStat, like SPSS and other

similar programs, uses formulas that are well accepted for standard statistical work.

According to the results of the Kolmogorov-Smirnov test for normalcy, the

data from the timed drawing task (for both visual and verbal groups) approximated

the normal distribution with a P value of > 0.10; this numerical finding asserts that

there are no significant variations in the data to indicate the need for non-parametric

testing. 6.1 and Figure .2 below confirm the shape of both the visual and verbal data

via histograms.

Visual Group Histogram

0

1

2

3

4

5

6

5 10 15 20 25 30 38 40

5-Minute Intervals

Freq

uenc

y of

Tim

es

Figure 6.1 Histogram of Visual Group Times

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Verbal Group Histogram

01234567

5 10 15 20 25 30

5-Minute IntervalsFr

eque

ncy

of T

imes

Figure 6.2 Histogram of Verbal Group Times

Participants were timed from the start of the drawing task until completion. In

Table 6.3, times for each participant in the visual and verbal groups are noted.

Participant #13806 had a time of 38 minutes on the task, which Kolmogorov-

Smirnov detected as an outlier and, in the case of this data, recommended removing

the score from the data set. However, Kolmogorov-Smirnov’s sensitivity at

measuring the significance of outliers is weaker when the data set is around 12 or

fewer entries. As a result, Grubbs’ model for outlier detection was used to verify or

refute the Kolmogorov-Smirnov recommendation. According to the Grubbs model,

which is better for smaller data sets, the score of 38 minutes is a significant outlier at

p <.05 and was dropped for this calculation to determine significance on task time.

The 38-minute score was retained for later computations.

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Table 6.3 Task Times between Verbal and Visual Groups

Verbal Participant #

Time on Task in Minutes

Visual Participant #

Time on Task in Minutes

10016 0:08 9092 0:09

13006 0:09 12352 0:14

11271 0:10 14525 0:15

11345 0:11 12763 0:16

11221 0:11 9507 0:17

9890 0:12 5009 0:17

6180 0:13 10761 0:19

12071 0:13 13774 0:19

14082 0:14 10340 0:21

13914 0:17 10608 0:21

14021 0:19 11278 0:25

13500 0:21 12774 0:27

13806 0:38

From the above data (less #13806), the following measures of central

tendency were found:

Table 6.4 Central Tendency for Times on Task

Verbal Visual

N 12.000 12.000

Mean 13.167 18.333

Std Deviation 3.996 4.868

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Std Error 1.154 1.405

Minimum 8.000 9.000

Maximum 21.000 27.000

Median 12.500 18.000

To determine if significant differences between the two groups exist, InStat

was used to calculate a t-test on the above data. From that test, t (23) = 2.841, p

<0.0093, where 23 is the number of degrees of freedom calculated from (n-2). This

finding is statistically significant and indicates a difference in performance measures

(time) between the visual and verbal groups. Differences between the two standard

deviations where F = 1.484 and P = .5236 are not significant.

Considering the Research Questions and Hypotheses

RESEARCH QUESTION #1

If we consider the term, “minimalist visual instruction” as based on a static

graphic like the screen capture, what are its physical requirements—appearance,

function, colour and other—to engage users?

This first research question asked what the physical requirements of the

minimalist visual instruction would be in order to engage participants with the

drawing task. At this point in the results, the ideas put forward from Chapter Four

show promise for experiential learning via a minimalist visual instruction. The

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characteristics such as proximity, colour and visual syntax appear to have provided a

reasonable structure for encouraging exploration and active experimentation on the

part of the participants in the visual group.

RESEARCH QUESTION # 2

What will a study comparing minimalist text and visual instructions yield in





artifact.

With respect to completion speed and engagement, the outcomes of this study

demonstrated two specific findings. A visual instruction did not reduce the amount of

time taken to complete the drawing task—there was a significant difference in terms

of time between the verbal and visual groups. Those who performed the drawing task

with text were faster and thus, this first hypothesis is not supported. Why this

difference exists and its significance for the efficacy of visual instructions will be an

important concern for the analyses and results that follow in this section. At a glance,

it would appear that people perform a computer-based drawing task more quickly

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when they are given written instructions; however, this is a simplistic and insufficient

assertion.

The second hypothesis asserted that a visual instruction would indicate the

participants increased engagement with the task through the use of tools, attention to

detail and completeness of the final drawing. Overall, the drawings produced by the

visual group did substantiate this hypothesis. Although the time on task increased

significantly with the visual instructions, the final product was a significantly better

imitation of the “Big Bank” drawing.

To understand more about these differences, the next phase of this analysis

considers results from both the Kolb Learning Styles Inventory (LSI) and the Felder

Inventory of Learning Styles (ILS). Examining key aspects of these psychometrics,

and later, the artifact created, will undoubtedly provide additional insight into this

phenomenon.

Results of Kolb Learning Styles Inventory (LSI)

David Kolb developed the Kolb Learning Styles Inventory (LSI) in 1984. As a

researcher interested in—among other areas—how people learn, Kolb developed a

self-administered inventory that allowed individuals to receive a profile of their

experiential learning style. Synoptically, Kolb’s assertion was that learning style

preferences are structured along a think/feel and do/watch axis. Experiential

learning, he believed, was not a monolithic construct—as had possibly been assumed

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by John Carroll—rather, it was a cyclical model of development on which individuals

expressed preferential modalities and were more significantly predisposed in some

modalities towards being experiential. The think/feel/do individuals were, according

to Kolb more exploratory and, in essence, better experiential learners that their

think/feel/watch counterparts.

In order to determine the experiential strengths and ultimately, learning styles

of the participants in this study, individuals took the Kolb Experiential Learning

Styles Inventory (LSI). Designed to provide a unique measure of experiential style,

the Kolb inventory was selected as it may highlight important gaps in Carroll’s

original ideas of minimalist documentation. As well, it may provide a way to examine

where hypotheses are supported or, more importantly, where they diverge from the

expected. As the LSI is a copyrighted instrument and normally only available on a fee

per use basis, it is not, at the explicit request of the Hay Group, Inc., reprinted in this

study. A free paper and pencil version of the test and its scoring key was graciously

provided for this research by the Hay Group, Inc.

The LSI was administered to determine if specific experiential styles

performed well or poorly depending on the randomly assigned group, how the LSI

styles intersected with the Felder Inventory of Learning Styles (ILS) and ultimately

how the ratings on these inventories compared—in juxtaposition to either the visual

or verbal group—to the visual artifact created during the drawing task.

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Scoring the Kolb inventory requires tabulating the raw data on each of the

four (CE, RO, AC, AE) dimensions. The twelve preference statements on the LSI

necessitate a participant to provide a ranking of one to four on the four options

provided. For example, a typical LSI statement is:

When I learn:

____ I am open to new experiences.

____ I look at all sides of issues.

____ I like to analyze things.

____ I like to try things.

Once all four dimensions are scored, the AE tally minus that of RO equals a

plot point; AC less CE also provides a plot point. These two integers are then plotted

on a set of axes and the intersection—as it falls in a specific quadrant of the axes—

indicates the Kolb learning style of the participant. The plot points then result in the

determination of an individual’s learning style (Accommodator, Diverger, Assimilator

or Converger).

Table , below, highlights the distribution of LSI preferences in the population

studied in this research and provides a brief summary of each style. The score from

participant #10340 was dropped due to the incorrect completion of the inventory;

thus, only 24 scores were obtained for Kolb’s measure.

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Table 6.5 Distribution of Kolb Profiles in Research Population

Kolb Learning Style

# of Participants

% of Sampled

Population

Style Description

Accommodating CE/AE

4 16% Concrete Experience and Active Experimentation (feel and do). Uses intuition, enjoys new challenges and is very experiential.

Assimilating AC/RO

7 29% Abstract Conceptualization and Reflective Observation (think and watch). Enjoys theory more than practice; least experiential of the four styles

Converging AC/AE

7 29% Abstract Conceptualization and Concrete Experience (think and do) Highly experimental and good with technology.

Diverging CE/RO

6 25% Concrete Experience and Reflective Observation (feel and watch) Creative, open-minded thinkers; enjoy exchanging ideas with others.

Total 24

Across the four Kolb categories, the number of participants per category was

reasonably well distributed. According to Kolb’s profiles for this psychometric

instrument, the four Accommodators and the seven Convergers should engage most

quickly with the task based on their more experiential nature. Measures of time on

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task between the verbal and visual groups are noted in Table ; results are shown via

categorical breakdowns.

In an overall analysis of Kolb’s four styles, Table indicates large differences in

average task time across the verbal group with the range of scores between 11.0 to

20.0 minutes (9.0 total variation) for completion of the drawing; therefore, a marked

difference between LSI types occurs between several of the styles with respect to text.

However, in the visual group, scores ranging between 14.3 minutes and 22.33 (8.03

total variation) are noted and demonstrate minimal to considerable differences in

how quickly participants completed the drawing. In particular, assimilating and

diverging styles spent more time on the drawing task.

The Converging style—in both the verbal and visual groups—did complete

the task rapidly and with little difference between the two groups. Based on the

propensity of this style towards exploration and working with technology, these

outcomes align as expected. The Accommodators were slow in the verbal category as

compared to the Assimilators but were much quicker in the visual group. This style is

rated as being very experiential; consequently, another variable—possibly revealed by

the analysis of the Felder ILS scores or the drawing artifact—may explain why this

cohort performed more slowly in one group over another.

In contrast, the Assimilators—a ‘think and watch’ profile—defied expectation

and were quickest to complete the task in the verbal group but were ranked third for

task completion in the visual group. Divergers were, as expected, slow in both groups

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as this group aligns more with a ‘feel and watch’ dynamic than one of active

engagement.

Table 6.6 Average Time in Minutes on Experimental and Verbal Groups across

Experiential Styles

Accommodating Assimilating Converging Diverging

Verbal 15.5 11.0 13.0 20.00 Experimental 16.5 19.2 14.3 22.33 Time Difference +1.0 +6.2 +1.3 +2.33


Is there any significance as shown by the results of Kolb’s Learning Styles

Inventory and Felder’s Inventory of Learning Styles and the success (or failure) of a

visual element?

If time alone is the measure of success then certainly the more experiential

styles of Accommodating and Converging exhibit a tendency to perform somewhat

more quickly when in the visual group. Time on task, then, is not only influenced by

random assignment to one of the groups in the study but by an individual’s particular

experiential aptitude.

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Another performance inconsistency occurred with the Assimilating style—a

less experiential modality. In the verbal group, Assimilators were faster than any other

style and two minutes faster than average. The visual group, however, increased this

cohort’s completion time significantly; therefore, the question of what a visual does

to performance becomes salient. Specifically, is the visual engaging this learning style

or is it providing a degree of frustration?

The Convergers and Divergers performed, more or less, as expected for their

respective styles; however, the question of what decreased speed so much for

Convergers and increased it for Divergers also becomes a significant consideration.

In particular, what elements of these two styles—one very experiential, the other less

so—contribute to their performance differences seen on the drawing task.

At this point, no particular experiential style offers solid answers for

determining the underlying effectiveness of a visual for instructional purposes. The

two more exploratory styles of Accommodating and Converging seem to fare better,

in terms of time, than others but the Kolb LSI does not provide any conclusive

answers to the question of why differences exist between the verbal and visual

groups. Consequently, the results from the Felder Inventory of Learning Styles (ILS)

will be examined to determine if this psychometric provides further explanation.

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Results of Felder Inventory of Learning Styles (ILS)

The online Felder ILS, developed by Richard M. Felder and Linda K.

Silverman, is a 44-question inventory that provides an assessment of an individual’s

preferences with respect to the “characteristic ways of taking in and processing

information” (Felder and Brent 57). Rather than assessing their experiential learning

style as per Kolb, this inventory considers how an individual receives (takes in)

information—through a visual or verbal modality—and examines what preferences

that person has with respect to facts, abstractions and processes. Finally, the ILS

looks at how individuals assemble knowledge: do they like to connect it in a logical

sequence of steps or do they prefer to have an ‘Ah ha!’ moment after joining several

seemingly disparate ideas into one?

The ILS determines a preferential ranking on the following four dimensions as

shown in Table below:

Table 6.7 Felder’s Dimensions and Descriptions

Dimension #1—Active or Reflective

Preferential way to work with knowledge

ACT likes applying knowledge or

explaining concepts to

others; enjoys group work

REF prefers to sit quietly and

think before starting a

task; enjoys working

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alone.

Dimension #2—Sensing or Intuiting

Preferential style for working with knowledge

SEN has a methodological and

precise learning style; patient

and detail oriented.

INT enjoys using innovation to

solve problems; open to

unexpected approaches

that require quick

thinking.

Dimension #3—Visual or Verbal

Preferential intake modality

VIS learns best when presented

with visual information.

VRB prefers written or spoken

information for learning.

Dimension #4—Sequential or Global

Preferential method to build and internalize knowledge

SEQ step-by-step knowledge

builders; prefer to follow a

logical path in developing

understanding

GLO approaches knowledge

building in what seems

like disconnected steps;

builds big picture

understanding from these

steps.

Scoring on the ILS ranks an individual as having a degree of preference on

each of the four dimensions of the scale. A score will be in the one to three point

range if a person is reasonably balanced to mildly preferential on that dimension, five

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to seven for a moderate preference and nine to eleven if the preference is dominant.

Individuals can have significant preferences on multiples dimensions of the ILS or

they may be well-balanced across any or all of the four.

The next phase of analysis conducted as part of this research was to tabulate

the scores and significant findings from the results of the Felder ILS. How the

participant population, as a whole, ranked on this scale and the variations in

dimensions and strength on dimensions helps describe what learning style

preferences exist in a college population and how those preferences may be helped or

hindered in learning from a visual. Additionally, the findings on the various rankings

of the Felder scale, as compared to time on task between the visual and verbal groups

will be used to examine experiential nature regarding the use of instructional materials

and determine if a visual can motivate a more exploratory approach to learning.

These results will also serve as the basis for later correlations between the ILS, the

LSI and other measures of participant performance such as the analysis of the

drawing artifact.

The results that follow examine each of Felder’s major dimensions and, where

applicable, calculate the statistical significance of the findings. Findings such as the

differences between preferential strengths on the Felder scale and how the verbal and

visual groups performed on the drawing task will also be discussed in instances where

variations are notable. Descriptive statistics will be used to highlight these variations

and explicate trends in the data.

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All 25 of the participants successfully completed Felder’s psychometric and

Table shows the distribution of scores on ACT-REF, SEN-INT, VIS-VRB and

SEQ-GLO dimensions. The online version of the LSI automatically scores results

and returns a preference on the dimensions described in Table . As discussed earlier,

strength on any dimension is measured on a scale between one and eleven. The most

commonly ranked dimensions in the participant population studied here were ACT,

SEN, VIS and GLO. Consequently, this group can be described generally as a cohort

that prefers to learn with visuals in order to build their own big-picture ideas; in

addition, they are more detail oriented and precise, and like to demonstrate what they

have learned by using the information or teaching it to others. Conversely, this group

is somewhat less disposed towards reflection and innovation in their learning styles;

they are particularly not well suited to learning from written or spoken modalities and

lack a style that is best addressed in a step-by-step manner.

Questions on the inventory ask respondents to rank their contextual

preference and, from the scores obtained, several learning styles were strongly

prevalent in the results of the ILS. Of the population tested, Table indicates that

most of the respondents—20 participants or 80%—ranked themselves as being visual

learners. Seventeen (68%) responded to indicate that they learn more adequately

when they are able to demonstrate their knowledge either by discussion, teaching or

application. Other rankings on the LSI do not show the same degree of variation but

again serve to define aspects of the participant population in this study.

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Table 6.8 Distribution of Preferred Felder LSI Scores

ACT REF SEN INT VIS VRB SEQ GLO

# of responses 17 8 15 10 20 5 11 14

Percentage of population

68% 32% 60% 40% 80% 20% 44% 56%

In order to determine if the differences between these paired rankings were

significant, the Mann-Whitney U for non-parametric data was used. There were no

significant differences found for the SEN-INT or SEQ-GLO scales with P values of

0.131 and 0.608 respectively; consequently, participants, as a whole, favoured neither

style in a statistically significant manner. However, as 60% rated themselves as more

strongly SEN and 56% as more strongly GLO, a slight directional trend does exist.

For the ACT-REF and VIS-VRB rankings, the Mann-Whitney U test found

statistically significant differences. Where p ≤ .05 the result of 0.0201 on the ACT-

REF dimension indicates that more of the tested population preferred an active

style—that is they preferred demonstrating what they have learned. On the VIS-VRB

dimension, the preference towards the visual was significant at the p ≤ 0.05 level

with a result of 0.0093 and indicates that population ranked themselves much more

strongly towards a visual learning preference.

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DIMENSION STRENGTH, TIME AND GROUP

In order to understand the specific effects of time on task as related to a

Felder learning style preference, participants scores on the LSI were also grouped,

when a prominent trend existed, into the test’s ranking categories of 1-3 (balanced to

mildly preferential), 5-7 (moderate preference) and 9-11 (strong preference). Table ,

below, shows the average times for each dimension and group. The following

discussion will consider breakdown of response frequencies, strength of preference

and time on task with respect to the scales of the ILS.

Table 6.9 Times on Individual Felder Dimension: Experimental and Verbal Groups

(No Categorical Breakdown)

ACT REF SEN INT VIS VRB SEQ GLO

Experimental 19.00 21.20 19.00 21.20 15.0 17.0 17.57 16.0

Control 13.20 17.00 14.71 11.00 17.06 12.0 12.0 14.0

VIS-VRB Dimension

VIS learners performed more quickly in the visual group with their

scores decreasing some 3.3 minutes from the visual group average (0 =

18.333) of all participants.

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VIS times in the verbal group rose to 17.06 minutes and increased by

nearly four minutes over the verbal group average (0 = 13.167).

Those participants with no rank on the VIS dimension scored on its opposite,

the VRB dimension. Of the five participants who did score on the VRB, no scores in

the 9 to 11 category were recorded nor were any scores in the high-moderate (7)

range; instead the scores spanned the balanced to low-moderate categories (1 – 5) and

indicate these individuals would function somewhat more effectively in a verbal

mode.

VRB in the verbal group performed the task in an average time of 12

minutes or approximately 1.3 minutes faster than the verbal average.

VRB in the visual group completed the task in an average of 17

minutes or approximately 1.3 minutes faster than the verbal average.

This difference between VRB and VIS reflects a 40% increase in completion

time and again lends support to the assertion that pairing individuals with their

preferred input modality can significantly improve their performance. However, it is

important to note that although the VRB breakdown shows a difference, VRB time,

regardless of group is still faster than the population’s average for each group.

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ACT-REF Dimension

On the Felder scale, the next most frequently scored learning style was the

ACT (Active) dimension of the ACT-REF scale. Active learners best understand

information by working with it in ways that include discussing the material,

performing a task or teaching others about the task; they are also a cohort that does

well in collaborative processes. REF (Reflective) learners, as per the label, like to

consider a task before starting it; they also work alone well. Seventeen of the 25

participants ranked on the ACT dimension.

ACT participants across all groups averaged a task performance time of

19 minutes.

ACT in the verbal group completed the drawing task in 13 minutes

based on a moderate preference for this style.

On the REF dimension, most of the variance in time on task was seen in

breakdowns by sub-categories as shown in

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Table on page Error! Bookmark not defined.. REF results are as follows:

REF average across all groups was 17 minutes for completion of the

drawing task.

REF average in the visual group was 21 minutes to complete the

drawing task.

REF average in the verbal group was 12 minutes.

That the REF time for the visual group is also quite high, as compared to the

verbal group, again brings forward the question of what a visual does to slow down

processing rather than enhancing it. While it might be, as surmised with the ACT

scores above, that the visual instruction somehow increases engagement with the task

and results in a better artifact, the visual instruction could also be acting as a

confound and reducing the quality of the artifact produced.

SEN-INT Dimension

Fifteen participants rated themselves as having a preference along the SEN

dimension of Felder’s scale while 10 rated on its opposite dimension, INT. Sensors,

according to Felder, like learning facts and are well-suited to follow established

procedures; Intuitors, on the other hand, prefer to discover possibilities and can

develop their own innovative strategies for problem solving.

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In comparing the two dimensions across the SEN/INT ranked participants,

neither dimension had a significantly faster time on task.

SEN across all groups had an average time of 16.07 minutes.

INT across all groups had an average time of 16.10 minutes.

The INT dimension, however, demonstrated noted differences in time. INT

profiles were significantly differentiated with the verbal group completing the task in

11.0 minutes and the visual group taking 21.0 minutes. A Mann-Whitney U

calculation indicates that the difference between the verbal and visual groups of INT

where p ≤0.05 is statistically significant at 0.0079.

That the INT participants slow down dramatically when presented with a

visual learning stimulus brings several thoughts to bear on our understanding of how

visuals and learning intersect. In the verbal group, the INT participants exhibited a

very rapid average completion time on the task. One early answer may be that as this

cohort learns through innovating, a visual may trigger a more exploratory type of

processing for this profile and, in turn, engagement with the task increases resulting

in a slow time to completion.

GLO-SEQ Dimension

On the GLO/SEQ dimension, 14 of the participants ranked as GLO while 11

ranked as SEQ. Global learners create ‘big picture’ understanding by their seemingly

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disconnected jumps between ideas; sequential learners, by contrast, learn best in a

deliberative step-by-step sequence. It should be noted that both GLO and SEQ meet

or exceed the average performance times in both the visual and verbal groups

indicating that this ‘processing dimension’ may be key to performative differences

between the visual and the textual. Breakdowns into verbal and visual groups

revealed, again, more similarities between the groups than differences as shown in

Error! Reference source not found., on page Error! Bookmark not defined..

GLO, as a whole, completed the task in 18 minutes while SEQ did so

in 15.

GLO in the verbal group reported a 12-minute time on task.

GLO in the visual group completed the drawing in just less than 18

minutes.

In this population of college students, 80% have at least some leaning towards

being visual learners with respect to how they best take in information. The results

from considering time on task between the visual and verbal groups indicate that

where VIS is a moderate to strong preference, participants perform the task more

quickly when their assignment was to the visual group. In particular, those in the high

preference rating (9-11) were 25% faster than their counterparts in the verbal group.

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For the INT learning style, there was also a marked increase in completion

time for the task with 11.0 minutes logged for the verbal group; yet, like the REF

dimension, INT slowed significantly when in the visual group. In terms of

significance, this was a notable difference and one that indicates a particular dynamic

about the INT and REF styles when in a visual group. When compared to other

measures in the study and the final artifact, it is presumed more of this dynamic will

be understood via additional analyses.

GLO and SEQ dimensions contribute to speed—regardless of group—and,

while the analyses here are only partially complete, it may be that this dimension

assists significantly with processing either a visual or textual modality. Further

analyses may also demonstrate that a higher GLO or SEQ preference can help a

weaker VIS or VRB style perform better.

In most of the Felder dimensions, however, the verbal group (text) did

perform faster than the visual group, which is seemingly in opposition to the overall

assertion that most participants rated themselves as having some degree of preference

towards visual learning. Consequently, the elements that make a visual effective for

an experiential task may not be entirely related to time on the task. If the visual, in

most cases, slows down completion time, it may be that the visual increases

participant engagement with the task and, especially within specific learning styles,

activates the experiential and ultimately results in the production of a superior

artifact. In order, however, to understand more about the interactions of experiential

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styles with learning styles, the next phase of the results will look for significant

intersections between the LSI and the ILS. In making further recommendation for

study later in this dissertation, it will be important to understand the successes and

failures seen in the final artifact and how the visual provided may have mitigated

those outcomes.

Intersecting Kolb, Felder and Artifacts

In order to understand how time and learning styles function with respect to

the differences between artifacts produced via visual and textual instructions, this

research developed a minimalist visual instruction and tested it as compared to a

minimalist verbal instruction. Initially, the research questions from Chapter One

framed the hypothesis that a visual would increase the completion speed of a simple

computer task; however, the results presented so far indicate that the visual group

results in a significantly slower time on task. Breakdowns of Kolb’s experiential styles

(LSI) across the participants studied here also show that some styles are, regardless of

their inclusion in the visual or verbal group, are not well suited to the task of working

with a simple computer program.

As an example, the Diverging style performed very slowly, on average, in both

the visual and verbal group while the Assimilating style was a poor performer in the

visual group. These differences in experiential styles were ones Carroll would not

necessarily have anticipated when he originally developed his text-based version of

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minimalism. Carroll may also not have considered how individual learning style

preferences could have impacted the model he developed. At the time of his original

work, visual learners may not have been such a significant percentage of the

population; however, the work conducted here on a population with may provide

insight into the need for more work on visual instructions.

To provide a richer understanding of how experiential learning and learning

styles may interact—and ultimately manifest themselves in the creation of the

drawing artifact—the results of this study examine combinations of data to find

associations, patterns and trends that may interact to produce effects that differ

between the verbal and visual groups. In particular, data mining can provide a means

to develop predictive measures with respect to the effectiveness of a visual in specific

learning styles. This section examines both the profiles measured from the Kolb and

Felder inventories and presents this information in conjunction with the artifacts

produced. The individual strategies of participants will be included in Chapter

Seven—Discussion, to follow.

CORRELATIONS BETWEEN THE KOLB AND FELDER MEASURES

Multiple breakdowns across the individual dimensions of Kolb’s LSI and

Felder’s ILS demonstrate interesting linkages between these two measurement

devices. However, the most revealing associations between Kolb’s experiential styles

and Felder’s learning preferences are shown through composite profiles of individual

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participant’s ILS scores as they occur in LSI profiles. Examining the differences

between Accommodators, Convergers, Assimilators and Divergers as shown by their

Felder attributes provides a multi-dimensional analysis of the strengths and weakness

of the experiential styles. Additionally, these profiles provide a means to understand

how experiential style and learning style engage a participant to produce the drawing

artifact in this study. As discussed earlier, engagement with the task is measured by

the participants’ tool use, attention to detail and approximation of the original.

ACCOMMODATORS

The Accommodators in this study all ranked as having a high VIS profile—

they take in and process new information best if it is presented in a visual format.

Across the four Accommodators in this research, the profile for three participants is

very consistent with varying degrees of ACT-SEN-VIS-SEQ comprising their

profiles. While this sample size is small, it may be that Accommodators are a

relatively consistent profile across the Felder dimensions and have a tendency to be

highly visual learners as shown in Figure 6.3.

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Figure 6.3 Accommodators

Accommodators, according to Kolb, are an experiential style that has a strong

propensity towards active experimentation—this is a style that enjoys ‘doing’ and it

reflects that with most of the profiles containing a strong ACT component.

Accommodators are sufficiently exploratory so that problem solving—as aided by

minimalist instructional materials—should come easily. However, for the

Accommodators studied here, their preference to a high VIS style and assignment to

the groups produced very different artifacts as shown in Error! Reference source

not found.. As evidenced by participant #11271, a high VIS Accommodator (11) in

the verbal group performed the drawing task quickly at 11 minutes but with

extremely poor execution. Examining tool use in this drawing shows that participant

#11271 used far fewer tools (2) than would be expected. The drawing is, at best,

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rudimentary and not what one would imagine as the product of a 20-year old college

student.

In contrast, #13774 a high VIS Accommodator (9) assigned to the visual

group, took much longer to produce the final drawing; however, the attention to

detail, tool use, colour matching and overall execution of the product are far superior.

As an example, this participant used 14 tools to complete the drawing, which is

evidenced by the quality of the completed product as shown in Figure 6.4. While the

visual group does result in a slower performance on the task in terms of time, it

increased engagement with the task and facilitates the necessary exploratory skills

required to use the program.

Participant #: 11271 Group: Verbal Time: 11 minutes Tools used: 2

Participant #: 13774 Group: Visual Time: 19 minutes Tools used: 14

Figure 6.4 Accommodator/High VIS Artifact Sample

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CONVERGER

Like Accommodators, Convergers are Kolb’s other more experiential style

and the profiles are predominantly ACT based. In this study, the Converging style

performed the fastest on both the visual and verbal groups but unlike the

Accomodators, these composite profiles are much more diverse as shown by the

greater variety of Felder dimensions distributed across the participants in Figure 6.5.

The Convergers show strengths in the areas of ACT, VIS and SEQ—they work well

with visuals, they like to ‘do’ and they demonstrate a learning style that progresses

well via an incremental process.

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GLO (Felder)

SEQ (Felder)

VRB (Felder)

VIS (Felder)

INT (Felder)

SEN (Felder)

REF (Felder)

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Figure 6.5 Converging Style Composite

In the samples from participants 10016 and 14525 a difference in both time

and the quality of the final artifact, as shown in Figure 6.6, is demonstrated.

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Participant 10016 completed the task very quickly but showed no attention to detail

or exploration of the tools with only one tool used; the final product is primitive at

best.

Taking nearly twice the time of 10016, participant 14525 completed the task in

15 minutes and produced an artifact that demonstrates increased engagement. While

this participant had difficulty in selecting the correct colour for several objects, many

of the shapes and detail elements of the original drawing—as created by tool use (12

tools)—are clearly evident in this artifact.



Figure 6.6 Accommodator/High VIS Artifact Sample

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ASSIMILATOR

The Assimilating style was the fastest experiential style with respect to the text

group of the study. A ‘think and watch’ profile, Assimilators were also more likely to

be VRB rather than VIS with three of the five verbal-ranked participants in this

experiential style as shown in Figure 6.7. Unlike the Accommodating and Converging

styles, the Assimilating style contains a predominantly REF component rather than

ACT. In other words, this style prefers to think rather than do which is supported by

both Kolb’s and Felder’s measures.

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GLO (Felder)SEQ (Felder)VRB (Felder)VIS (Felder)INT (Felder)SEN (Felder)REF (Felder)ACT (Felder)

Figure 6.7 Assimilating Composite Profile

While the Assimilating profile produced the fastest average completion speed

on the verbal group, the visual group this group ranked third with a speed of 19.2

minutes. With respect to completion of the drawing artifact, Assimilators are not

inclined towards the active experimentation styles of Convergers or Accommodators;

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therefore, it would be safe to assert that the artifact created by this profile would lack

attention to detail and the necessary exploratory skills to find and utilize the

program’s tools.

Figure 6.8 shows a verbal and visual sample from this profile. Again, the visual

group is completed in more time and demonstrates better placement on the page and

attention to detail. Participant 5009—a VIS learner—is also much better with the use

of scale, colour and shape; overall, this piece is a reasonable replication of the

original.

The artifact produced by participant 13006—a VRB learner—is less skillfully

executed and reflects the amount of time taken to complete it. Colour and shape are

not well managed and the placement of items on the page indicates a lack of attention

on the part of 13006 to the details of the original image. However, tool use of this

participant is above average with ten tools used.

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Figure 6.8 Assimilator Artifact Sample

DIVERGERS

Divergers are a ‘feel and watch’ profile and the least inclined of Kolb’s four

styles towards experientialism. Like the Accommodators, the individual profiles of

the Divergers are quite consistent across all participants. All Divergers have a VIS

component and most contain ACT and GLO dimensions; it is the predominance of

the GLO, as shown in Figure 6.9 that distinguishes this profile from that of the

Accommodators, a mostly SEQ style.

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Figure 6.9 Diverging Composite Profile

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The ‘feel and watch’ profile is not well adapted to active tasks such as working

with a computer-based activity and of all the Kolb profiles Diverging was the slowest

in both groups of the study; provides an example of the artifacts created by this

experiential style.

Participant 13806, as both a Diverger and a high VIS profile, took 38 minutes

to re-create the sample drawing. With only a few exceptions, the placement and

attention to detail is obvious in the final artifact—it is a good replication of the

original and demonstrates an exploratory engagement with the program. In contrast,

participant 9890 took less time to produce their version of the drawing but produced

a vastly cruder replica of the original. The text-based instructions, it would seem, did

not engage with this style well as shown in Figure 6.10; however, the visual

instructions worked well in that the increase in time on task resulted in a better

product.

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Figure 6.10 Diverger Sample Artifacts

ILS VIS/VRB PREFERENCE, EXPERIENTIAL STYLE AND GROUP

From the results presented in this chapter, experiential style and a text-based

instructional set interact to produce a shorter time on task. However, the experiential

styles and resultant artifacts, as discussed previously, demonstrate that this

combination of factors produces drawings that are vastly inferior, in most cases, to

those created in the visual group. At a glance it is evident that a visual instruction

engages a more involved and exploratory response on the part of participants and, in

turn, they produce a drawing similar to the sample they were asked to replicate. It

also appears that the experiential nature of the Kolb profile assists participants to

some degree with this endeavor.

High VIS, Strong Experiential Style and Group

Other than the distinct differences between experiential style, group and

learning preference shown previously, other results provide additional assertions

regarding the effectiveness of visual as compared to textual instructions. A stronger

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LSI experiential style and a higher ranking on either SEQ or GLO of the ILS can

help offset some of the problems associated with placement into a learning style not

suited to an individual’s preference. While not evident when the VIS ranking is high,

low to moderate VIS rankings coupled with a more experiential style and a better

developed SEQ or GLO can produce a more substantial artifact as shown in Figure

6.11.

Participant #: 13914 Group: Verbal Time: 17 minutes Kolb Profile: Convergent Felder: VIS, 5; SEQ, 7 Tools used: 13

Figure 6.11 High VIS, Strong Experiential Style and Contraindicated Group

Problems arise most significantly with the interaction between a high VIS and

a weaker experiential style. With lower rankings on the VIS, a moderate (7 and above)

SEQ or GLO can provide a way for a participant to function better on the task even

when assigned to the text-based verbal group; ultimately, the stronger GLO or SEQ

is a processing mechanism that assists in overcoming some of the limitations posed

by an incompatible learning style.

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High VIS, Weak Experiential Style and Group

However, where the less experiential assimilating or diverging style occurs in

conjunction with a high VIS (9-11), the dominance of the VIS score is such that it

outweighs the effect of a substantive SEQ or GLO score. Succinctly, the requirement

for a visual so strong that other processing abilities are unable to compensate. Figure

6.12 demonstrates the result of a weaker experiential style coupled with a high VIS

and moderate SEQ. The artifact created by participant #14021—even though it took

19 minutes to complete—is a poor rendition of the original as shown in Figure 6.12.

As none of the participants in the Divergent category had a VRB score, testing this

assumption with the inverse style was not possible. It is, however, interesting to

consider that a VRB modality may not be a frequently occurring dimension in this

profile.

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Participant #: 14021 Group: Verbal Time: 19 minutes Kolb Profile: Divergent Felder: VIS, 11; SEQ 7 Tools used: 6

Figure 6.12 High VIS Preference with Moderate SEQ

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Moderate VRB, Weak Experiential Style and Group

As only five participants ranked as having a VRB learning style preference in

this study, very little can be asserted about their performance. However, the

Assimilating style had three of the five VRB learners and all in the study were low to

low-moderate in range. Consequently, how higher VRB modalities would function is

not determined from this study but some ideas of VRB performance can be

ascertained from the available data.

The Assimilating style is like the Diverging experiential style—it is not a

strong experiential modality and will be limited in terms of active experimentation. In

the case of participant #6180, as shown below in Figure 6.13, a low VRB style

occurred in conjunction with the verbal group of the study. The drawing artifact was

completed in 13 minutes and is a good rendition of the original in terms of

placement, colour, scale and detail.

As per participant 13914, 6180 had a GLO style strength that exceeded the

strength of the VRB score. While undoubtedly being in the group compatible with

learning style assisted 6180, the stronger GLO score appears to have contributed

some degree of success to the artifact produced.

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Participant #: 6180 Group: Verbal Time: 13 minutes Kolb Profile: Assimilating Felder: VRB, 3; GLO, 5 Tools used: 12

Figure 6.13 Low VRB Assimilator Artifact

From parsing the results so far with both learning styles inventories, it can be

asserted that the strongest common denominator at work in completing the drawing

task quickly—regardless of group—is that of the ‘thinking’ dynamic identified in

participant styles. Thinking before acting, possibly as a result of studying the text or

visual instructions prior to drawing may, in further analysis, be key to performance.

The association with RO and VIS may also be indicative that thinking and

experiential performance can be enhanced by a visual; as well, the visual—a more

abstract representation of instructions than text—is sufficient to engage those work

best with facts and details (typically domains of the written).

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Does the visual developed and tested in this study assist individuals in

completing the study’s task? In particular, does it stimulate more engagement with the

task and does it result in a better final artifact?

Qualitative analysis—as gathered from observations and post-test questions

taken during each participant’s session with the drawing task—added richness to this

study and especially to this, the 4th research question. While the visual group for this

study took extended amounts of time to complete the drawing task, studying a visual

instruction prior to starting work increased participant engagement with the task;

consequently, the visual did assist with focusing participants on the details of the

program and the stimulus picture. Participants were, on the whole, moving through

the menus more frequently and checking back between the “Big Bank” picture, the

printed instructions and the screen when given the visual instruction. Their rate of

cross-checking between the screen, “Big Bank” and the instructions was more

pronounced when they were assigned to the visual instructions. Additionally,

participants in the visual group were generally more focused on finding the correct

tool and matching the elements in the “Big Bank” drawing. The visual instruction

also stimulated the experiential styles that are not overly exploratory to take more

time and work through the drawing task with better precision and completeness.

Chapter Seven will discuss the results in more detail and, in particular, will

elaborate more on the findings from the qualitative portion of this study.

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CHAPTER VII

DISCUSSION

This chapter discusses the findings from the preceding results section. In

particular, the discussion that follows will examine what was found in light of the

research questions posed in Chapter One. Additionally, this chapter will synthesize

what trends emerged from the two psychometric inventories with respect to

visual/textual modalities and learning preferences. The results from these inventories

will be vital in understanding not only the population studied, but also how this

population cognitively processed different modalities of information. This

information, when considered with the participants’ final drawing artifacts, will

provide additional insight into the question if a minimalist visual can function as a

form of instruction. Finally, possible applications of this study and the directions for

future research will be highlighted.

Goals of the Research

The goal of this research was to address what many contributors to Minimalism

Beyond the Nurnberg Funnel saw as the most sizeable gap in John Carroll’s minimalist

model—the design, role and success of a minimalist visual instruction. Specifically,

the study conducted and documented in this dissertation developed a minimalist

visual instruction and compared it to a minimalist verbal instruction. By utilizing an

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experiential style inventory and a learning style inventory to assess what factors

contribute to the success or failure between groups on the task, this study has

focused on the objective of providing an impetus for further research in minimalist

documentation, especially as it relates to the refinement and inclusion of a visual.

The aim of the study discussed here is not to present a panacea for all of the

difficulties noted by several of the contributors to Minimalism Beyond the Nurnberg

Funnel. A visual-only approach to minimalist documentation may not address the

issues of legal completeness and liability nor may it reduce the issues of risk

associated with a documentation model that is lighter in content but more substantial

in meaning. It may not help advanced learners with complex tasks; however, what a

visual can do, as demonstrated in the upcoming discussion, is provide a mechanism

that shows promise towards engaging a new generation of documentation users and

activating their experiential preferences.

As mentioned above, the research questions that shaped the original goals of

the study also guide this discussion. The place of the visual for both experiential

learning and minimalist documentation was, until this study, undefined; therefore,

one important question focused on what kind of visual would act as minimalist

documentation. A second point of inquiry focused on how or if that visual could

better activate the experiential. While John Carroll had sought, through his minimalist

model of documentation, to activate the experiential capacities of individuals using

instructions, he had only considered the visual in the most marginal terms. As well,

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Carroll had conceived of the experiential nature of documentation users in a one-

dimensional way; he had not considered variance in experiential profiles and how

they may manifest themselves in terms of success on a task.

Learning styles formed the third question posed in this study as they were area

referred to by several commentators in Minimalism Beyond the Nurnberg Funnel. One of

the shortcomings of minimalist documentation was its perceived inability to address

the variety of cognitive processing styles. As with the experiential element, no other

specifics on learning styles were provided by Carroll’s commentators; however, the

study conducted here established the goal of gaining both clarification and insight

into this question by administering a well-known learning styles inventory.

The fourth and final question asked in this study asked how the effectiveness

of the theoretically derived artifact—as a minimalist visual instruction—would be

measured. Time on task—where less time was taken—was presumed to be the

primary indicator of success; however, while an important metric, time proved to

have an inverse quality in this study. More time on the task, as demonstrated by the

visual group in their completion of the “Big Bank” drawing, was indicative of more

engagement with the task.

Research Question #1

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If we consider the term, “minimalist visual” as based on a static graphic

like the screen capture, what are its physical requirements—

appearance, function, colour and other—to engage users?

To determine if the minimalist visual instruction was more effective than the

verbal instruction for engaging participants in the drawing task, some of the

qualitative comments from the study’s participants provide additional information

about the success of various physical features. As discussed previously, all participants

felt the instructions—verbal or visual—were helpful; however, with more prompting

about the specifics, participants were able to shed additional light on what they felt

were some of the more effective features.

That the minimalist visual provided an indication of where key features were

was one of the most frequently occurring comments from the verbal group.

Participants #12352 and 10761 noted that the most helpful feature of the visual

instruction was in pointing out the location of the colours. In particular, these

participants thought they might not have been able to find this feature easily without

some direction. Accessing and using the colours was, according to #12352, important

to completing the drawing accurately.

Participant #11221 said that positioning the tools on the far left of the

instructions made her look at them first. She thought this location made sense as the

tools were the integral to working with the program; as well, this placement of the

tools with the other three palettes being smaller and to the right was also meaningful

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for this participant. Her assumption was that these smaller palettes were sub-

categories for the tools, so she “knew to look for line size and shapes” in the sub-

menus.

Highlighting key features of the program did prove to be successful in

assisting the participants with the task. Establishing a visual syntax—or ‘reading’—of

the major functional areas helped establish a hierarchy of functions. As well, placing

key operations prominently (such as colour) assisted in making it a more salient

feature in the minds of the participant.


What will a study comparing minimalist text and visual instructions

yield in terms of speed and engagement with the task?


Hypothesis #2: the visual will engage participants as measured

by detail, accuracy, tool use and overall completeness of the

drawing artifact.

At the beginning of this study, it was assumed the visual condition would

result in shorter completion times for the drawing task. That is, the visual would

facilitate the participants’ understanding of Imagination Cubed’s functions and would

enable them to move more quickly within the program and complete the ‘Big Bank’

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image rapidly. It was also assumed that a college population, such as the one studied

here, would be skewed significantly towards learning from visuals. This cohort is,

almost exclusively, a generation who grew up with MTV©, video games, computers

and other visually-based media; therefore, extrapolating their preference for learning

from visual media was a logical step. With nearly 20 years of exposure to their credit,

it was expected that the experimental group of participants would excel with their

instruction set and complete the task quickly.

Conversely, the other initial assumptions of this study were that the textual

condition would result in excessively slow rates of completion time. The visual

preference of the population studied, it was presumed, would not lend itself well to a

textual condition; as well, the very confound of receiving a written set of instructions

would frustrate the participants to such a degree that they would again, slow down

and lose interest in completing the task. Ultimately, the textual condition and levels of

annoyance would increase the time on task and the final artifact would be marginal at

best.

As presented in the results section of this dissertation, there was a statistically

significant difference between the visual and verbal groups. However, the

directionality of this difference and its statistical strength was unexpected, if not

enigmatic; the original assumptions about performance time for both conditions were

soundly refuted by the data collected. The hypothesis that reduced time on task

would result from working with a visual was refuted. With the visual condition

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average being 18.3 minutes on task—5.2 minutes slower than the text condition

average of 13.1—the question of what affect a visual has in terms of cognitive

processing becomes relevant.

Without yet examining the results from Felder’s ILS—an inventory that would

establish the visual or verbal preference of the participants—one of the first

considerations regarding this cohort related to the earlier assumptions of their

partiality to visuals. In reviewing the time on task for both conditions, it initially

appeared that the participants were not the visual learners they were presumed to be;

one possible theory is that the educational structure of college with its lecture-based

focus and emphasis on textbooks may appeal only to verbal learners. Another

possibility could be that visual learners eschew their preferred visual learning style for

a verbal one in order to succeed in college classes. Either hypothesis appeared to be a

reasonable explanation for the differences in time on task.

Another theory for the significant difference between the verbal and visual

groups related less to innate abilities as to the familiarity of text instructions. An

instructional visual may be such a foreign construct for the participants, especially in

combination with a computer-based task that it hindered their ability to work with

the program. The visual condition may have frustrated the participants in some way

that delayed their time on the task. This outcome would eliminate the role of the

visual as being able to function as a minimalist instruction set and leave minimalism

where it languished in the late 1990s.

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To obtain some qualitative indicators of why the minimalist visual instruction

could be effective, one of the questions asked in the post-test session referenced how

effective the participants felt the instructions were at familiarizing them with the

program. Regardless of assigned group in the study, all of the participants felt the

instructions they received were of some help in understanding the program.

Participant #9890 (verbal group) stated the verbal instructions provided a good

“overview” of the program; however, when the participant was asked how the

drawing s/he produced in the study compared to sample “Big Bank”, the participant

noted a less than satisfactory replication and stated that s/he “should have looked

around more” at what was on the screen.

Participant #11271, who was also in the verbal group for this study, noted

that the verbal instructions were “OK” but that in retrospect reported that “not

looking” was a problem. Specifically, this participant found that the verbal

instructions “…made me just think of following directions and getting the picture

finished”. Several other participants in the verbal group also felt the written

instructions were effective but in considering what they produced as an imitation of

“Big Bank”, it is a fair summary to say that the verbal instructions served to constrain

their exploratory responses to the task.

In considering the same question with the visual instruction group, most

participants were pleased with the results of their final “Big Bank” drawing and not

necessarily as reflective and articulate in their responses. Several participants felt the

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visual instruction provided a good ‘feel’ for the program; no one reported, as per the

verbal group, feeling limited in any way by the visual instructions. It may be the case

that visual instructions are less authoritative in the minds of participants and thus

result in a more exploratory approach to working with a simple drawing program.

The measures of accomplishment for a visual in terms of filling the gaps

perceived in Carroll’s model were never rigourously defined through the

establishment of quality measures. Several of the contributors to Minimalism Beyond the

Nurnberg Funnel noted issues with respect to legalities, risks and omitted content;

David Farkas, however, in framing the deficiencies of minimalist documentation

provides an inverse structure for success. Succinctly, he defined the shortcomings of

minimalism as stemming from the following:

abandonment of the documentation

inability to complete the task successfully

expenditure of too much time or energy

incomplete development of a mental model

Farkas’ concerns with the model provide an evaluative rubric for the visual

studied here. However, it is important to keep in mind that neither Farkas nor any of

the other critics of the original minimalist model hypothesized what would be the

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exact indicators of success for this element. If time on task were an important

measure, the critics would undoubtedly be disappointed in the finding that a visual

slows down processing on a simple computer-based task.

As a singular measure of success, however, the time taken by the participants

was not a sufficient metric for establishing the success of the visual developed in this

study. It should be added that time alone was not expected to be the defining

measure; it would only provide a rudimentary indicator of the visual’s benefits. The

experiential styles inventory of Kolb and the learning styles inventory of Felder were

included in this study as a mechanism to elaborate on the differences between the

conditions and to understand individual factors that contribute to the findings.


Is there any significance as shown from the results of Kolb’s Learning

Styles Inventory (LSI) and the success (or failure) of a visual element?

What do these inventories reveal about the population observed in this

study and how will this information intersect with experiential learning?

Kolb’s LSI inventory provides one means to parse some of the contributing

factors to time on task and, ultimately, the measure engagement with the task via

detail, tool use, placement and colour choice as shown in the final artifact. As well,

Kolb’s inventory affords the opportunity to examine what individual experiential

factors contribute to the participants’ engagement with the visual or the textual. Time

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difference, as seen through the lens of experiential styles, did provide considerable

insight into the functioning of experiential styles. The range of completion times

across styles varied as presented in Chapter Six; in summary, some styles, as expected,

were quicker than others were and one style laboured to create the drawing artifact

regardless of condition. Thus, as asserted in Chapter Six, time and experiential style

do demonstrate differences between those styles that are, more or less, inclined

towards active experimentation.

The LSI provides a measure of experiential style and successfully delineates

how some styles are poorly suited to tasks requiring high levels of self-directed

exploration—this is especially evident with respect to computer tasks. Across all four

experiential styles, times on the verbal group were faster than the visual group;

consequently, regardless of experiential style, the visual instruction increased the

amount of time on task. This is consistent with the general findings between the two

groups.

BREAKDOWNS ON TIME

However, the two more experiential styles—Accommodating and

Converging—showed only a marginal difference in time between the two conditions

in the study. At a glance, it then might be reasonable to assert that these two styles

could function reasonably well with either a text or visual condition. However, what

is important to note is not the difference in time between conditions within an

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experiential style but the variation in time as measured against the averages from the

primary analyses of the verbal and visual groups.

The average time in the verbal group was 13.1 minutes; the Accommodating

and Converging styles are either at or above the average time (slower) for completion

of the task. Consequently, the text condition is either fairly neutral or somewhat

detrimental for the more experiential styles. More importantly, however, is the

completion times for these two styles when in the visual group. The average time for

the visual group was 18.3 minutes; at 16.5 and 14.3 minutes respectively, it is clear

that the Accommodating and Converging styles are working more quickly when given

an instructional visual. Thus, it may be that a visual does activate innate experiential

tendencies more and this is manifested in better performance on task in terms of

time.

The Assimilators and Divergers performed almost as expected in the study in

terms of their time on task. More or less, as styles not as predisposed towards active

experimentation, their times on the drawing task were generally slow. The

Assimilators did show a marked decrease in time on task in the verbal group,

exceeding the average by two minutes. The anomaly of this finding did not become

evident until later in the analysis when Felder’s results and the artifacts were

considered.

The time for Divergers in either condition was very slow but again,

considering this metric against the average times for completion is more telling.

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Divergers took seven minutes beyond the average time to complete the task when

they were assigned to the verbal group. On the other hand, they were slower by just

over four minutes above average when assigned to the visual group. While this style

clearly struggled with the task regardless of condition, the visual condition

demonstrated some degree of promise for facilitating the Diverging experiential style

in the completion of a computer-based task.

For Carroll and the success of minimalist documentation, the fact that these

stylistic differences exist at all is problematic for his model and the kind of computer-

based tasks he perceived as being ideal for it. With only two of the four styles

generally predisposed towards technology and exploration, predicating a

documentation model on the assertion that people learn by active engagement was

risky at the outset. Interestingly, Carroll and his contributors to Minimalism Beyond the

Nurnberg Funnel had some consideration of learning styles and their adaptability to a

minimalist model of documentation; yet, they had not conceived of the experiential as

having four unique profiles that could have varying degrees of success with

computer-based tasks.

TOOL RECALL

Another important disparity, though, between the verbal and visual groups for

Accommodators and Convergers is that of their tool recall. Asked in the post-test

questionnaire to describe what each of the major tools did, participants in the visual

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condition, from these more exploratory experiential profiles, were better able to

explain the function of each tool. Participants identified at least five of the six tools in

the palette and some could explain the function of other operations in the program

that were not part of the drawing test. The verbal group for these two profiles

produced less accurate recall of the tools’ functions and, as participant #10016

commented when asked about the tools, “I’m not really sure what they’re all for. I

don’t think I used a lot of them.”

The Assimilators were extraordinarily quick with the text condition of the

study yet this is contraindicated for their style; as a ‘think and watch’ profile, their

degree of speed was the first indicator that an effect other than time was present but

until further analysis, this effect was undefined. Most telling from the control cohort

were their responses to the post-test questionnaire; when shown a picture of the tool

palette and asked to indicate each tool’s function, participants had more difficulty

than the Accommodators or Convergers. Participant 6180 noted that he had

“ignored” what the tools meant and just focused on getting the drawing done. Yet,

when a subsequent question asked if this participant felt he had understood the

program, 6180 stated he was very knowledgeable.

The same recall problem occurred with the Diverging style—the least

experiential of the four and the cohort who struggled most substantially in terms of

time. The disjunction between the verbal and visual groups with respect to tool recall

permeated all four of Kolb’s experiential learning styles. With very few exceptions,

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participants had better tool recall and could provide more accurate descriptions of

each tool’s function when participant assignment was to the visual group.

The implications of better recall from interacting with a visual instruction

relate to the concept of learning versus knowledge building; in Farkas’ terms, learning

may also be classified as the development of a mental model. Defining learning was

problematic for several of the contributors to Minimalism Beyond the Nurnberg Funnel as

well as Peter Jarvis, a critic of Kolb’s model of the experiential. In short, concern

regarding minimalist documentation practices focused on whether the goal was to

merely aid a user to complete the task or to instill a greater understanding of the

program (Draper 352, 362). Carroll had hypothesized that learning and doing were

conjoined and that once the experiential was activated, users would do both; Kolb’s

model also asserted that learning, doing and creating knowledge go hand-in-hand as

defined by his views on the cyclical nature of the experiential. However, neither had

considered learning as having degrees of engagement or if there were situations where

no learning occurred.

Peter Jarvis, however, problematized Kolb’s assertions as being overly

reductive and postulated in his version of the experiential that there was non-learning

(no knowledge), non-reflective learning (rote memorization) and reflective learning

(knowledge building). Jarvis, as discussed in Chapter Two, envisioned learning as a

more complex model that allowed a learner to enter a learning situation and leave

either unchanged or incrementally changed by the experience.

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From examining the experiential styles, the conditions and the post-test

questionnaires, what participants are able to articulate after the test is equally, if not

more compelling than their time on task. Those participants in the visual condition

and especially those in the more experiential Accommodating and Converging

profiles had understood the program and its tools much better than their verbal

counterparts had. While the time differences for Accommodating and Converging

vary by only a minute between conditions, the details and descriptions supplied as

part of the post-test questions were significantly richer.

This difference in the quality of recall indicates the visual condition is building

a better knowledge in users of the system and its basic functions. The more

experiential styles in particular mirror this in their responses and, reflect a deeper

process of knowledge development. Even for the less experiential styles of

Assimilating and Diverging, the visual group participants were more knowledgeable

when reflecting on the tools they had used to complete the drawing. In Jarvis’s terms,

participants emerged from the process as changed and more experienced—they

demonstrate the kind of knowledge indicative of what he calls ‘reflective learning’.

In contrast, shorter times on the task, especially as they occurred in the verbal

group and in less experiential styles, demonstrated poorer recall of the tools and their

functions. Engagement with the task and an understanding of the program—as

measured through the post-test questionnaire—was limited for many participants in

this condition. The text-based instructions, especially as they interact with less time

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on task, function detrimentally for participants and leave them, in many cases, with

what Jarvis would call a non-learning situation.

THE SUCCESS OF A MINIMALIST VISUAL INSTRUCTION

The second part of the research question posed here asked if experiential style

would provide an indication if a visual would be sufficient for successful engagement

with the task and result in its completion. Experiential styles by themselves served to

confirm that different profiles would complete the task more or less quickly

depending on their propensity towards active experimentation. However, in more

detailed analyses, two important considerations become evident: the differences

between experiential styles and conditions with respect to a visual and the ability of

recall ability based on style and condition.

One metric for validating the success of the visual in conjunction with

experiential styles was found between styles and conditions. As discussed earlier in

this chapter, the differences within the conditions in each style were not particularly

illuminating. They conveyed, more or less, what would be expected from each of the

styles. However, the success of the visual when task completion times were compared

to averages for the verbal and visual groups indicated where processing gains were

achieved.

The two more dominantly experiential conditions reported completion times

faster than average in the visual group. Synoptically, the more dominant experiential

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styles perform more quickly when given a visual instruction. The two less experiential

also showed promising gains when working in the visual condition of the study.

While their times were above the overall average for the visual group, the cognitive

processing gap was indeed narrowing indicating that these profiles were making some

inroads with respect to working with a visual instruction.

Another outcome that proved to be telling about experiential styles and visuals

is the degree to which each style could recall information about the function of tools

in the Imagination Cubed program. The visual condition, especially in the ‘do’

profiles, demonstrated a better understanding of the tools; however, even in those

profiles associated with think/feel/watch, the visual condition proved to provide

motivation for completing the task.

From these two findings, there is evidence that to affirm the productive

interaction of experiential learning styles and a visual instruction. A visual assists all

experiential styles in completing the task conducted in this study. In particular, more

experiential styles perform very well in terms of time on the task and even the less

experiential styles showed more promise towards completing the drawing task when

they were in the visual condition.


Is there any significance as shown from the results of Felder’s Inventory

of Learning Styles (ILS) and the success (or failure) of a visual element?

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What do these inventories reveal about the population studied in this

experiment and how will this information intersect with experiential

learning?

The Felder Inventory of Learning Styles (ILS) was clearly one of the most

revealing psychometrics administered as part of this research. It served to provide the

study with data that both confirmed expected trends and clarified performance

outcomes; as well, it supports one of the original assertions of this research—that the

next generation of learners will require a medium other than text due to their

preferences for the visual.

Equally as important though, the ILS provides a composite model of learning

styles; it showed how participants prefer to take in information, how they like to

process it and finally, what their preferred method is for assembling that information

into knowledge. This inventory extends Kolb’s experiential model with its ‘do’ or

‘reflect’ dichotomy and provides richer analyses of how individuals learn and

ultimately build knowledge.

John Carroll’s second book, Minimalism Beyond the Nurnberg Funnel, called for,

from several contributors, a way to develop the model to address a broader array of

learning styles. The authors asked if minimalist documentation could reach both

sequential and holistic learners, those who are exploratory versus those who prefer

direct instruction and, in the vaguest of requests, if minimalist documentation could

address specific learning styles. No single contributor calling for work on learning

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styles considered them as significantly more than monolithic constructs whereby an

individual is either ‘X or Y’ and rigidly predisposed to act within defined parameters.

How learning occurs—as this section will further articulate—is more nuanced

and multifaceted than the above-noted contributors may have imagined. Many

models address only limited conceptual frameworks and these models inform most

thinking about learning styles; however, where Felder’s model distinguishes itself is in

its provision of learning styles across four unique dimensions. Particularly, the Felder

model stands distinctly apart from Kolb’s when the issue of how individuals choose

to received information—visually or textually—is considered. The Kolb LSI

considers experiential style and, in particular, how individuals prefer to process on the

‘think/do/feel/watch’ quadrants of the model; however, for learning scenarios in

particular, the Kolb psychometric has no provision for the mode of presentation. It

does not consider intake, only variations on processing.

There are, without a doubt, commonalities between the Kolb and Felder

models in terms of learning style assessment; later, in the breakdown of Felder

profiles as juxtaposed to Kolb profiles, the overlap will be evident. Reflective, for

example, is one term with comparable meanings in both inventories—it is the

contemplative, passive style of processing new information. Active, too, has some

overlap but it is not a wholesale overlap. Yet, other aspects of the Felder scale have

no direct counterpart to a Kolb measure but form a significant part of that profile. As

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the verbal-visual provides the compelling information with respect to performance

on the drawing task, this dimension will be discussed in below in some detail.

The other dimensions of Felder and their composite relationship to the Kolb

profiles are better explained in conjunction with the artifacts produced in the drawing

task. Examining how these aspects of learning can conflate with the experiential style

and ultimately the final artifact takes this discussion towards how the data collected

here may frame future research that could ultimately guide technical communication

towards re-visiting minimalist documentation.

Intake Preference for Instructions—Visual or Verbal

The question becomes one not of styles, per se, but of how people prefer to

take in new information. The first important finding from the ILS was that most of

the learners in the study were visual. This supported one of the initial suppositions

about a population of college students and refuted alternative speculations; they were

strongly inclined towards visual learning based possibly on their exposure to multiple

forms of visual media. The ILS determined 80% of the participants ranked on the

visual dimension over the verbal, which was a statistically significant finding. Of these

20 participants ranked as visual, more than half were categorized as strong visual

learners—they would have difficulty in learning in situations without visuals.

Performance, as a reduction in time on task, was also an important finding

from the ILS results on visual-verbal. Where visual ranked learners were in the visual

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group of the study, the amount of time to complete the drawing task dropped—

notably so if they were in the high visual category. Thus, what becomes evident is the

importance of developing an instruction that best meets the needs of the intended

audience—seen here as a cohort with a strong preference for the visual. Minimalist

documentation, as Carroll envisioned it as a text-based form, may no longer be

adequate for new generations of instructional users. If this study is indicative of a

greater trend, successful instructions will need to address a population that could very

well be more inclined to learn from visual instructions.

As well, such a call for visuals is important for not only the visual learners but

for their opposite, the verbal. With only five identified verbal learners in this study

and none of those ranking in the upper ranges of scores on the ILS, the question of

both the frequency and strength of verbal learning in any population becomes

relevant. While the scope of the study presented here is small, it is important to note

this apparent trend in verbal learning does not mean text should be eschewed in

favour of a visual-only model; however, addressing verbal learners in an increasingly

visual world will become more problematic. In terms of experiential style, it is also

important to note that in this study three of the five verbal learners were in the

Assimilating style—one that is not vested with a strong ‘do’ component. If, in a

general population, Assimilators have a higher number of verbal learners, finding

effective ways to meet their needs will be paramount.

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THE SUCCESS OF A VISUAL

Certainly, meeting the needs of those who use instructional materials is

paramount and, based on the results presented here regarding preferences for visual

learning, a call for more research is required. With a significantly high percentage of

visual learners, there is a clear want for visuals to address this type of learner

effectively. To that end, the design of the visual—or types of visuals—will be an area

rich in research opportunities. The theoretically derived artifact created for and tested

in this study may provide some preliminary directions for research on this topic.


Does the visual developed and tested in this study assist individuals in

completing the drawing task? In particular, does it stimulate more

engagement with the task and does it result in a better final artifact?

The adage “A picture is worth a thousand words” acquires even more

meaning when the visual artifacts from the drawing task are analyzed. In examining

the pairing between conditions, and grouped by experiential style with learning style

as an overlay, the differences are, in most cases, marked. In order to explicate the

variations between conditions for each experiential style, the discussion will overlay

Kolb’s profile with the composite Felder profile and discuss these findings and the

efficacy of the visual for instructional purposes. The discussion will be broken down

by trends in the results that serve to explain the major findings of the study.

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Strong Experientialism and High Visual Learning Preferences

As noted in Chapter Six, the Accommodators in this study all rated as visual

learners; they were, in fact very consistent in their composition of Felder profiles with

three of the four participants having an active versus reflective style on the Felder

inventory. It is important to keep in mind that the active dimension of Felder is not

exclusively linked to that of active experimentation or an exploratory nature. active

engagement can also mean the individual enjoys discussion, teaching and working in

collaborative processes—a profile by which 75% of the population studied here self-

rated. This group is also mostly sensing ranked which indicates Accommodators in

this study tend to be methodological and detailed oriented.

Their execution of the drawing where the visual style occurred in the visual

group of the study was well done as shown in Figure 5 of Chapter Six. Participants in

general managed position, colour, shape, and detail well and demonstrated their

engagement with the task via the final product. While the time on the visual condition

is slower than the textual condition, it is important to consider what was gained other

than time. In studying the drawing artifact as coupled with the tool recall from the

previous section, it is reasonable to assert that the visual did achieve its goal.

Exploration was enhanced, understanding of the program was enhanced and, as an

end result, the product reflected those gains on the part of the participant.

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Of significant importance though for the visual learners—found

predominantly in the population tested in this study—is that of placement into the

verbal group. While Accommodating is a strong experiential style, a dominant visual

learner in a verbal group was not able to perform the task with any degree of skill.

The sample shown in Table 15 of Chapter Six, demonstrates a reduced time on task

and a drawing that represents no engagement on the part of the participants. The

contraindicated style limits the engagement of the learner, reduces activation of the

experiential and ultimately thwarts successful completion of the drawing task. Text,

therefore, does not meet the needs of visual learners nor can any more dominant

experiential skills compensate for receiving information in an unsuitable form.

Lower Strengths of Visual Preference and Strong Experiential Style

Convergers were the second of the two styles more inclined towards active

experimentation in the Kolb model. As well, this group was almost consistently active

on the Felder scale and ranked as visual and sensing across most of the participants—

a composite profile similar to the Accommodators. Recurring consistently as per the

Accommodating style is that of time on task and quality of the final artifacts. In the

verbal group, the drawing task was completed in somewhat less time but, more

significantly was the quality of the artifact produced.

When a visual learner—even a moderate one as shown in the example in

Chapter Six—is in a verbal group, the predisposition towards active experimentation

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is not engaged. As well, reduced time on the task is more indicative of diminished

performance and lessened involvement than any other measure. In contrast, a mild

visual learner, as shown in the same example, was able to complete the drawing with

a reasonable degree of success and in less time than the average for the experimental

group as a whole. While styles such as Convergers do have some verbal learners in

the profile, certainly the two more experiential styles in Kolb’s model are strongly

inclined towards visual learning and engage more with the task when presented with a

visual.

Verbal Learners and Weaker Experiential Style

The Assimilating style was the only one of Kolb’s four dimensions to

demonstrate any promise with respect to working with text-based instructions. While

this is a small study, it is worthy to note that three of the five verbal profiles occur

within the Assimilating style. As well, they are the one group to exhibit a reflective

tendency in their composite Felder profile—all other Kolb styles were predominantly

active. With both a verbal and reflecting component in this profile, it may be that

written materials appeal strongly to the style preferences of this cohort.

One difficulty in making parallel assertions about visual and verbal learners in

this study relates not just to the lack of verbal participants but the strength of the

verbal preference found in those five participants. No participants ranked above a

five (lower-moderate preference) on verbal whereas 11 of the 20 visual learners were

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in the high (9-11) category. One explanation for this may be that strong verbal

learning skills are not necessary (and therefore not developed) due to the

preponderance of visual materials. Regardless, the cluster of verbal learning

preferences in the Assimilating profile does however; explain the 11-minute

completion time in the verbal group.

As a weaker experiential style, the Assimilating cohort should have some

degree of difficulty with the task conducted in this test. The ‘do’ portion of this

study—as working on a computer-based task—would not fit well with such an

experiential style; however, in the visual group, participants again, were able to

complete the task with reasonable skill. The sample shown in Chapter Six has

indications of unsure starts with respect to tool use and placement but overall the

artifact is a good representation of the original. The visual artifact as combined with a

visual learner can help assist, it would seem, to counterbalance some of the effects of

a diminished experiential style. Therefore, it appears the visual group is able to

address both visual learners and non-visual learners with varying degrees of success.

As per the example in Chapter Six, the verbal group in the case of the

Assimilating style was a verbal learner. Since the grouping and the learning style of

the participant matched, the artifact—while by no means perfect—is adequate. While

colour selection and detail are absent in portions of the drawing, this is a fair

rendition of the stimulus drawing, especially considering that it was completed in nine

minutes. This artifact can be seen as a good indicator that verbal learning styles will

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perform better when given verbal instructions; however, if these verbal styles—even

moderate ones—do occur commonly in less experiential styles such as Assimilating, it

may be that certain tasks will always be difficult for individuals to complete.

Addressing the Issue of Time

The Divergent style in this study was very similar in composition to that of the

Accommodating—all were visual learners and nearly all profiles contained a moderate

degree of active in their profile. One of the most significant questions though, is why

this style was incredibly slow at completing the task regardless of random assignment

to either the visual or the verbal groups. While they are a less experiential style and

should therefore, be unsuited to the task, their time of 20 minutes and above was

noteworthy.

What may be an important factor for time is the dimension on which

individuals connect ideas and form knowledge. In the Divergent style, five of the six

participants have some measure of global in their Felder profile. Global learners

move through ideas in a seemingly disparate manner and connect ideas together more

in the style of an epiphany—the Eureka moment. The opposite dimension,

sequential, as expected, connects ideas one after the other and builds knowledge

incrementally.

As mentioned above, the Accommodators and the Divergers have almost the

same Felder composite measures of visual-active-sensing; however, where these two

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styles break apart is at the dimension of global-sequential processing style. The

Accommodators, as an experientially inclined style, demonstrate in this study that

three of the four participants have a sequential modality in terms of assembling and

understanding information. The Divergers, on the other hand, are global in all but

one instance. Therefore, it may be that certain experiential styles are predisposed to

processing information distinctly on one of either sequential or global. A preference

such a global, then, can be viewed as a processing style that is more time intensive.

The strength of the global or sequential dimension of an individual may also

play a significant part in how they perform a task such as the one conducted in this

study. Where the strength of a verbal or visual learning style is moderate or minimal

and the processing style of global or sequential is high, this ability to assemble and

process knowledge appears to assist in task completion. Consequently, while intake as

either visual or textual is important, how well an individual is able to assemble and

make sense of information is equally as critical for knowledge building.

In terms of the artifact results for Divergers, they replicate what has occurred

with Accommodators and Convergers quite well. As the Divergers were all visual

learners, their artifacts when created in the visual group were well done. The images

showed shape, colour, placement and detail as factors that had received significant

amounts of attention from the participant. When, of course, the Divergent visual is

placed in the verbal group, again, the reduced time on task and degraded quality of

the final artifact are evident.

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Perceptions of Time on Task

Another question from the post-test interview asked participants to estimate

how long they had spent on the task. This question was designed to determine if

there were differences between the groups in their general awareness of time spent on

the drawing task. If participants were more engaged with the task, then they might be

less conscious of how long they had been working. In the verbal group, participants

were more aware of the amount of time they had spent on the task and were able to

estimate times that were very close to the actual number of minutes taken on the task.

On the other hand, the visual group appeared to be less aware of time’s

passing. While they took more time on the task and produced a better final artifact,

this group was not as accurate at providing an estimate of the time they had spent

drawing “Big Bank”. When told afterwards how long they had taken to complete the

drawing most of the verbal group participants remarked that they did not realize—in

some cases—that nearly 20 minutes had elapsed. This finding may be an indication

that a visual instruction can encourage exploration and involvement with a drawing

task.

THE SUCCESS OF A VISUAL

Overall, this research can conclude that there is sufficient preliminary evidence

to substantiate the need for additional work in the area of a minimalist visual. The

learning and experiential styles inventories, including as part of the research design,

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have helped to articulate where the original model might not have met the

requirements of users. The results of the study highlighted here show promise for the

visual as an instructional modality. With a population classified as mostly visual

learners, meeting the needs of this group will be an important area of research for

academia to undertake.

Summary

Before continuing to Chapter Eight where the conclusions of this study and

the directions for future work will be outlined, this is an excellent opportunity to

reflect on the outcomes of the research. Small studies such as the one conducted here

provide results that are indicative of trends rather than of conclusive results. The

visual—as developed and tested as part of this research—has shown promise towards

revitalizing the work of John Carroll and minimalist documentation. Certainly, what

has added depth to our understanding of the function of a visual are the inventories

of both Kolb and Felder. These two psychometrics have provided the research with

an alternative view as to how individuals cognitively process information; specifically,

these instruments provided insight into composition of varying styles and how these

styles may be helped or hindered by instructional materials.

Minimalist documentation, as originally conceived, did not, as we know now,

meet the practical needs of the people who would have used it and taken the model

forward into standardized documentation practices. The theory was popular; yet,

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there was something about the practical aspect of minimalist thinking that resonated as

incomplete with those who evaluated it. Many who commented saw problems

stemming from completeness, risk/liability issues, complex tasks, advanced users, to

name but a few. The dissension at this level with the model was focused on valid and

concrete concerns; yet, they are over-extensions of the model’s intent and were as

unlikely to be resolved then, as they are today.

However, other concerns about minimalist practices were far less concrete;

rather, the question of ‘something’, as noted above, had more of an intuitive quality

about it. Those who commented with respect to less tangible concerns framed their

ideas as relating to the lack of a visual and the question of how learning styles would

be addressed. Undoubtedly, this intuited sense of the shortcomings with the model

may have negatively permeated, at some level, its broader acceptance into the canon

of documentation practices. Ultimately, there was a vote of non-confidence that left

minimalist documentation as an archival feature of the early 1990s.

One outcome from this research is a new beginning for the model. It is

important, however, to contemplate the scope of this study before declaring that

minimalist practices have been resuscitated singularly by a visual. There is preliminary

evidence that a visual meets the requirements of a population such as the one tested

here based not only on the artifacts they produced but also on measures of their

visual preferences and experiential style. For more substantive claims to be made

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about the facility of the visual for instructional purposes, additional research—

especially with respect to understanding how individuals process information—will

be important for the development of future iterations of a graphical element.

Hopefully, the study conducted here presents some starting points for later work.

In concluding this dissertation, Chapter Eight will reflect on the achievements

of the research and revisit the initial research questions as a means to assess what was

gained and what might shape future study. As well, the next chapter will review if the

outcomes of this study may address the doubts expressed by several individuals with

respect to the viability of minimalist practices. Finally, Chapter Eight will examine

John Carroll’s goals for this model of documentation and determine how those have

been or may be better achieved by continuing research on the visual.

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CHAPTER VIII

CONCLUSION

The goals of this dissertation were to use John Carroll’s minimalist

documentation as a model and consider the type and design of a minimalist visual

that could activate experiential learning and function as a minimalist type of

instructional documentation. Aside from investigating form and function, designing

the theoretical visual and testing it in a study, this research sought to understand

more about the cognitive processing abilities of a population in terms of experiential

and learning styles. In particular, as Carroll had, as a goal, the intent of activating the

experiential learning tendencies of people with his text-based model, the research

done here chose to examine experiential styles in more detail to determine where a

visual may fail or succeed for the population that uses it. As well, gaining more

insight about learning styles was important for the assessment of whether or not a

visual could meet the experiential learning needs of its intended audience.

Running an actual study—comparing a verbal group to a visual group—put

the ideas regarding a visual in a scenario where a controlled environment could be

used to gather data. Measuring time, noting participant comments, and considering

the final test artifact in conjunction with the data obtained from the experiential and

learning styles inventories resulted in a rich body of data that allowed multiple

measures to assess the success of the visual as an experiential instructional document.

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Ultimately, it was the participants who defined the success of the visual by their very

need for it. The predisposition of the population towards visual learning and their

engagement with a visual was demonstrated by the artifacts they created.

Reviewing the Research Questions

In considering the preceding summary then, the research questions that

prefaced the study can be reviewed and responded to in light of its completion.

Those initial questions from Chapter One were: 1. If we consider the term, “minimalist visual” as based on a static graphic like



2. What will an study comparing minimalist text and visual instructions yield in





artifact.




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4. Can a visual, such as the one developed and tested in this study, assist

individuals in completing the experimental task through the activation



In the case of the first research questions, the visual designed for this study

did achieve the goal of engaging the participants more in the drawing task as

evidenced by the artifacts produced. Following best practices as outlined in Chapter

Three, the overview visual was designed to frame the look and feel of the screen in

addition to providing a locator for only the major elements of functionality. This

visual was not a duplication of the screen; rather, it focused on the placement and

connection of key elements via colour and line to conceptually link the principles of

Imagination Cubed’s functionality.

The artifact developed and tested here focused on core elements of

functionality for the program. As per Carroll’s original tenets, minimalist

documentation’s philosophy was based on the idea that users needed less, not more

information to support them in task completion. Without a doubt, the “less” concept

for instructions still holds true; however, isolating and highlight core tools and

articulating some (but not all) of their functionality provides sufficient detail to frame

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what the program does and encourage the active experimentation so key to

minimalist practices.

Ultimately, the theoretically derived artifact set the stage for starting quickly—

a mandate of Carroll’s—and then relied on the participants to explore additional

concepts and thereby build their own knowledge of the program. The visual provided

enough information in an understandable form that participants became engaged in

the task and the active experimentation needed to complete it. In the post-test

questions, the efficacy of the visual as a knowledge builder was confirmed via

participant descriptions.

Certainly, more work on the theory of design for the visual instruction is

called for, especially as that design might vary with the complexity of the task and the

skill level of the users. As Barbara Mirel noted in her comments about the textual

form of minimalist documentation, it did not always fit well with complex tasks or

advanced users (182). Research focused precisely on this cohort would be a rigourous

test for a theoretically designed visual. It may arrive as an outcome that visual design

becomes a tiered schema with best practices for novice, intermediate and advanced

users being codified.

Such best practices would undoubtedly be centered on the area of detail as it is

necessary for the task. Stephen Draper and others in Minimalism Beyond the Nurnberg

Funnel questioned what would be required with respect to detail in order to assist

users with tasks. Some activities would necessitate a granular approach while others

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might function well in chunks; additionally, the question of how to present

information—as steps or relationships—comes into focus at this junction (Kearsley

398). Establishing ways and means to manage specific presentation situations would

certainly be a valid avenue of research. The visual—created here as a theoretically

derived artifact—would be an excellent mechanism through which to explore these

ideas and address the more complex issues associated with the model.

In this study, the hypothesis that time on task would be reduced by a visual

was refuted. Time—as framed in the second research question—was a telling

measure here as it was, with rare exception, indicative of a lack of engagement on the

part of the participants. Shorter times on task occurred in the verbal group and

resulted in drawings that were astoundingly poor; many of these ‘works of art’

appeared to be the products of crayon-clenching toddlers rather than young adults in

their early twenties. Quality measures of the artifacts such as placement, tool use and

colour choice were marginal to absent in the case of shorter completion times;

however, their presence in longer completion times supported the hypothesis

regarding the measurements of engagement. Ultimately, this population spoke very

clearly, yet without words, regarding their facility with the written word—it did not

provide what they needed in order to complete the drawing task. The question that

naturally followed, of course, was “Why?”

In terms of understanding the participants more, the purpose of administering

both the Kolb Learning Styles Inventory and the Felder Inventory of Learning

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Styles—as per the third research questions—was to obtain data on their cognitive

processing preferences; it was hoped these inventories would help answer the

“Why?” noted above. To this end, using data mining techniques to group inventory

scores and look for trends proved a valuable methodology for understanding how the

studied population functioned with respect to visuals and text. Both of these

inventories were vital in establishing baseline facts about the population tested; as

well, they gave valuable insight into other factors not previously considered about the

population.

Experiential styles—as measured by Kolb’s LSI—revealed another facet of

“Why?” for this study in that they added a dimension not previously considered by

Carroll or others. The stronger experiential styles of accommodating and converging

are almost exclusively comprised of visual learners and the two verbal learners in the

converging profile are not strongly verbally oriented. It can therefore be asserted

from this study that the success of experiential learning seems to be strongly tied to a

visual form of input. We cannot, however, rule out learning styles and determine that

a visual instruction is the global answer to the question of what type of artifact will

support the experientialism that underpins minimalist documentation.

While the visual instruction in this study did help the three verbal learners in

the weaker assimilating condition, it is important to note that their strength as verbal

learners was only moderate. To rule out the effects of a preferred intake modality

such as verbal or visual and declare the visual as the over-arching solution, studies

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would need to be done on high (9-11) scoring verbal learners to determine how they

functioned with the visual. At this point it is unknown if high verbal learners would

perform well in the visual grouper not. It might be however, that college age

people—the current and future users of instructional materials—may be more

disposed to visual versus verbal learning. Therefore, research focusing on visuals for

instructional purposes may be one way to effectively address the majority of a

population.

From the results and earlier discussion in this study, it is clear the population

who volunteered for the test is predominantly visual in their preferred learning style.

This was expected, for the most part, but not to the magnitude for which visual

learning would be a statistically significant style. If this finding were to hold true for a

larger sample of a college-age population, it would be telling for the future of

instructional materials including their design and composition.

To meet the needs of new generations of instructional users, the necessity for

further study is evident. More work is required on larger samples to determine if the

patterns here for visual learning are consistent with other college-age populations.

Should this be the case, researchers are obligated to consider in more depth how a

visual will address the styles by which individuals process information. Determining

the extent of visual learners in college and comparing them to a cohort 20 or 30 years

older will be instrumental in ascertaining if a significant shift has occurred in the

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population. It may be that so little work was done in the field that other generations

of learners have been instructionally mismatched.

Addressing the Concerns from 1998

By no means can a study of this size manage to overcome every situation seen

to impede the instantiation of minimalist documentation practices. At this point in

the conclusions, however, it is possible to assess if the results obtained here meet the

criteria set by others to improve the model. David Farkas, as cited in Chapter 7, had

noted that his concerns about minimalist documentation stemmed from four major

themes: abandonment of the documentation, unsuccessful task completion, too much

time or energy required, and incomplete development of a mental model. These four

points, as discussed earlier, form an excellent rubric on which to assess the success

Carroll’s practices or any subsequent revision of the model such as the one developed

in this research.

Based on the textual version of minimalist documentation Carroll had

originally developed, Farkas’ concerns are highly relevant, especially in light of how

participants performed in the verbal group in this test. If we measure abandonment

of the documentation by lack of engagement with it, then certainly this concern of

Farkas’ is met. Evidence of limited success on the task has already been articulated in

this study and, if we consider tool and function recall key to the development of a

mental model, then again, Farkas’ fears are realized.

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Farkas would find the results from the visual group however, sufficient to

alleviate his concerns. Initially, he may be troubled to learn that a visual group

resulted in more time/energy taken on the task. At the time of minimalist

documentation’s inception, the textual version was not the subject of comparative

testing against non-textual conditions; therefore, no benchmarks regarding its

function against a visual existed. How, throughout the 1990s, researchers were

measuring success (or failure) when learning and experiential styles was dependent on

individual studies rather than agreed-upon principles. As discussed here, aptitudes

and preferences on both of these psychometric measures can have a profound impact

on how instructional materials should be designed. Therefore, without this

information, the minimalist model may have been dismissed too early due to a lack of

understanding regarding not just the text but more importantly, the variations in

cognitive processing styles of the people that would use it.

Farkas might be assuaged though about the time/energy issue once the

artifacts were considered. That the visual group does take more time to complete is

indicative of an engagement with the instructions and the task. The detriment of time

is quite insignificant considering the superior quality of the work produced. As well, a

better product the first time—in any work or professional situation—means less time

spent revising; therefore, the cost-benefit considerations are easily mitigated.

Others who queried minimalist practices also warrant mention in this

conclusion as their original input helped to shape the directions this research took.

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Patricia Anson had postulated that minimalist documentation as moved from theory

to practice was focused, in a simplistic manner, on “cutting words and adding

pictures” (94); Janice Redish queried how non-explorers would navigate a

documentation methodology designed for more inquisitive types (228), while JoAnn

Hackos considered the idea that a well-designed graphic might be able to function as

a minimalist text (176). Interestingly, all three asked about the question of learning

styles and how minimalist documentation would address them.

The answers, as framed from the results gathered in this study would, more

than likely provide a sense of promise, if not completeness for Anson, Redish and

Hackos. The focus of this research was not to eliminate standard frameworks for

audience and task analysis; rather, a goal was to determine the effectiveness of a

theoretically derived artifact—a minimalist visual—and supplement existing

documentation structures with the potential of being visual-only. Eliminating all

guidance, as Anson feared, has never been the intent of Carroll’s work or the work

carried out in this study. What has been accomplished here is determining if a visual

might work as a means to invigorate the model.

Redish had questioned exploratory styles and how in particular those less

inclined towards active experimentation would manage with minimalist

documentation. Without a doubt, this study has answered that question by including

an experiential learning inventory; the results show that weaker experiential styles

demonstrate performance gains when given a visual instruction. For the question

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posed by JoAnn Hackos, the answer is very promising regarding a well-designed

visual acting as a minimalist instruction—more work is needed but the preliminary

results from this study show that a visual is very capable of addressing the needs of

users. All three would undoubtedly be pleased to see the question of learning styles

addressed and a measure of what users need obtained from the results.

Most importantly however, is the question of how the visual developed and

tested here meet with John Carroll’s original requirements for successful minimalist

practices. Carroll had envisioned minimalist documentation as allowing users to start

quickly, read in any order and create, via their own experiential processes, an

individual understanding of an application. As determined in this small study, the

visual is very promising with respect to these goals.

Some of Carroll’s early goals such as coordinating the system and the training

and providing a means to identify and recover from error need to be re-framed in

2007. As Barbara Mirel commented in her 1998 contribution to Minimalism Beyond the

Nurnberg Funnel, much of what she and Carroll had discussed some 10 years

previously had already necessitated re-vamping some of minimalism’s early goals.

Today, systems are substantially more ubiquitous, users have internalized the core

concepts of computing at an early age and error is mitigated by sound and screen

messages. As well, multiple levels of “undo” and history palettes that permit an

iterative step-backwards approach to error recovery have changed how error is

perceived. Rather than as an instructional device, a minimalist visual may prove, in

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future research, to be a more meaningful medium for recovery than the existing text-

based messages that so often make ‘help’ an oxymoronic construct. Undoubtedly,

though, John Carroll could see the work presented here as re-establishing minimalist

documentation through the use of a visual.

Directions for Future Research

The study conducted in this dissertation has presented some preliminary

findings for research on minimalist visual instructions. This study, by virtue of its

scope and limitations, also provides multiple avenues for continued research. As

stated earlier in this chapter, one direction for future research would involve running

the study on a significantly older cohort of participants. It may be that this group is

less inclined to learn from visuals or that they are visual learners who have had to

adapt to textual instructions. Until more work is done, little is known about learning

preferences and older people.

A significant limitation in this dissertation-sized study related to the time and

money required to pre-test participants for experiential style and learning style.

Participants only reported once to the usability lab and in that hour session they

completed both the Felder and Kolb inventories, did the drawing task, and answered

post-test questions. In future iterations of this work, pre-testing participants on the

Kolb and Felder inventories, sorting them into specific groups and running more

strictly controlled categorical comparisons could yield more conclusive findings.

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In terms of what was tested—a drawing task and a computer-based drawing

program—there are also multiple opportunities to revisit this study with different

tasks. As completing a visual task with a visual instruction only provides a limited

form of assessment, future work should also address how a minimalist visual and

verbal instruction could work for tasks that are traditionally text based.

While this study followed Carroll’s original work and used a computer task,

there is no need to limit later work strictly to technologically-based tasks. Future work

could involve assembling an artifact such as a small piece of furniture or preparing a

recipe with only visual instructions. It may be realized that visual or verbal

instructions are optimal for specifics kinds of tasks; however, until more work is

done, little is known about other uses for a minimalist visual instruction.

From this study, what we are presented with in 2007 is an excellent

opportunity for further research. These findings have addressed perceived gaps and

shortcomings in John Carroll’s minimalist model of documentation and used them to

inform the design of a visual that may better address learning through its

understanding of both experiential and learning styles. Using visual theory from a

variety of sources—including picture theory and information design—the artifact

constructed for this research demonstrated that refining graphical elements through

the application of well-known principles results in the creation of an image that can

stand alone as an instructional device. Ultimately, the question of is it possible to

design a visual to support experiential learning has received some positive support.

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Visuals for instructional purposes can not, however, be designed in isolation

from the individuals who will use them. As with any product, testing is required to

determine how people interact with and use a visual to complete tasks. As a matter of

course, including other measures to evaluate the population and make assertions

about their preferences is key to developing a sound methodology for the design and

development of an instructional methodology using only visuals.

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APPENDIX A

“BIG BANK” DRAWING

The “Big Bank” drawing was given to participants in both the verbal and text

groups. Replicating this drawing with Imagination Cubed was the task each

participant completed.

THE “BIG BANK” DRAWING WAS GIVEN TO PARTICIPANTS IN BOTH THE VERBAL INSTRUCTION AND VISUAL INSTRUCTION GROUPS.

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APPENDIX B

MINIMALIST VISUAL INSTRUCTIONS

The minimalist visual instruction detailed in Chapter Four is shown below.

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APPENDIX C

INSTITUTIONAL REVIEW BOARD MATERIALS

Items C.1. and C.2. are the two primary documents used to describe the study

to participants. These documents were created and filed in compliance with the

human subjects requirements of the Office of Research Services.

C. 1. TEXT FROM RECRUITING WEB PAGE

The following text is taken from the recruiting website for this study. Potential

participants were referred to this information after hearing the in-class recruitment

presentation.

RESEARCH PROJECT

Thank you for visiting this site. My name is Laura Palmer and I'm a Ph.D. candidate in Technical Communication and Rhetoric here at Texas Tech University. I'm using this webpage to further explain my research to people who are interested in participating in the study. You should find everything you need to know about my work on this page. If you're interested in participating in my study, my contact information is at the bottom of this page.

OVERVIEW OF THE RESEARCH

My research interest is focused on how people understand and gain knowledge from visual material. Traditionally, we've always learned from written materials such as books and instructions; however, I believe that due to the increase in the complexity, frequency and types of visual media—print, video games and TV, to name but a few—learning might also occur very well for many people through the use of well-designed visuals. As a result, I want to use two questionnaires designed to indicate your preferred learning style and I want to conduct a small test with a visual to see if it does promote learning.

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WHAT WOULD A RESEARCH PARTICIPANT DO?

In my research, I would need approximately 90 minutes of your time. In that hour and a half, you'll do three tasks:

1. complete two online learning styles inventories. Each one takes about 15 minutes and provides you with insightful information about how you best learn.

2. work on a small, specific computer task using the visual I've designed. You'd be participating in what's called a "usability test" and you'd have the opportunity to be involved in research carried out in the usability lab in the Department of English.

3. answer several questions about your experiences with the visual.

WHAT WILL HAPPEN? HOW ARE YOU COLLECTING DATA?

As I said above, part of the research involves answering two different multiple choice learning style questionnaires. These questionnaires are both online and provide an immediate score. In order not to bias the study, I'll give you your printouts and discuss them with you when we complete the session.

The usability test of my research involves working in the usability lab. This facility, on the third floor of the English Building, is like something like a music-recording studio.

This study has been approved by Texas Tech's Office of Research Services and conforms to all the requirements for working with human subjects in a non-risk experimental setting. Before we start our 90-minute session, I'll brief you on everything we'll cover and I'll have you sign a consent form. As with any experiment, you are always free to stop at any time.

To maintain your anonymity, I'll assign you a participant number during the briefing session. Your name will never be used in any materials and I will not use your transcript publicly unless I have your express written permission to do so. All materials gathered will remain my personal research property and will be kept completely confidential.

WHAT WOULD A RESEARCH PARTICIPANT RECEIVE?

First, you'd have the results of two very interesting learning style inventories. These inventories indicate your preferred learning style and give you guidelines as to how you can best perform in a variety of situations.

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Second, you'd have the experience of being involved in a usability test. Usability is very powerful method of gauging responses to products like websites and instructions; for anyone in psychology or computer science, understanding usability testing via being a participant would be useful.

Finally, you'll receive a $20 gift card for your time once we complete your 90-minute session. Researchers such as me appreciate your efforts to be involved in our studies and, while I do thank you for your participation, I know that as a student, a little extra is always helpful.

YOU'D LIKE TO PARTICIPATE?

If you're interested in being involved in this project, I'd be very glad to hear from you. You can contact me via email and we'll set up a convenient time for you to come to the English Building and participate in my research. Other than the times when the lab is booked for teaching usability classes, we should have a flexible schedule.

Please email me (Laura Palmer) at: [email protected] and I'll work with you to set up a time to meet. If you know anyone who would also be interested, please have him or her contact me via email.

OTHER QUESTIONS OR CONCERNS?

If there's anything else you'd like to know, please feel free to email me; I'll respond as quickly as possible. As I am a Ph.D. candidate and work under the immediate supervision of a faculty member, you may also contact Dr. Thomas Barker, Professor of English and Director of Technical Communication ([email protected]). Dr. Barker can also answer any questions you may have about this study.

Thank you for taking the time to read this page. I look forward to hearing from you should you wish to participate in my research.

Laura Palmer Ph.D. Candidate, Technical Communication and Rhetoric Department of English Texas Tech University

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C.2 . PARTICIPANT CONSENT FORM FOR THE STUDY

This form was signed by each participant prior to commencing the study. It

outlines the study, identifies the Principal Investigator and provides information on

participant rights and remuneration.

Reconsidering Minimalist Documentation: Developing a Visual for Experiential Learning

PARTICIPATING IN RESEARCH We understand you’re interested in being involved in the study “Reconsidering Minimalist Documentation: Developing a Visual for Experiential Learning”. Before we start today’s research, we’d like you to know what you’ll be doing in our 90-minute session. This consent form explains the study and your rights as a voluntary subject in our research. At the end, the form asks for your signature. By signing this consent form, it means you’ve read about the research, understand what we’ll be doing during the next 90 minutes and agree to be involved.

WHO’S IN CHARGE? This study is conducted by Dr. Thomas Barker, Professor of English and Director of Technical Communication. As the principal investigator (PI) for this research, you may contact him at any time by phone (806-742-2500 ext. 279) or by email ([email protected]).

OVERVIEW OF THE RESEARCH This study is investigating if a picture (visual) can provide enough information for people to complete tasks on a computer. In the past, we’ve all learned from books or written instructions but with all the visuals we see everyday, learning from a visual might be more effective. We know the visual might not work well for everyone, so the other part of this study involves determining a person’s learning styles. We’ll be using two learning styles questionnaires: one determines a basic learning style preference and the other indicates what type of learning-by-doing approach a person has. The goal will be to see if any particular learning style works well (or badly) with our test visual.

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PROCEDURES During the 90 minutes we’ve scheduled, you’ll do three things:

• complete the two learning styles questionnaires. They’re both online and should take no more than 15 minutes each to answer. The first questionnaire has 44 questions and you chose either answer A or B. The second one has 16 questions and you rank your choice by selecting a number (1 through 4). Our study isn’t interested in your individual answers; we want to know your learning style types for our research.

• work on a small computer task using the test visual. This should take 15 to 20 minutes total. You’ll be recorded via the audio/video equipment in the usability lab. There’s no measure of success of failure on this task; we’re testing the visual, not you, the person.

• answer several questions about what you liked or didn’t like about the visual. This is a follow-up to the test that helps us get your feedback about the visual, the task and anything else involved in the research.

• At the end of the study, you’ll receive a print out of your learning style results and an interpretation of what your score means. As well, you’ll get a $20 gift card to TTU’s Barnes and Noble Bookstore/Café as a thank you for your participation.

CONFIDENTIALITY To make sure you remain anonymous in this research, your name won’t be used. If you agree to participate in the research and sign this consent form, the first thing we’ll do is give you a participant number. We’ll put this number on your learning styles test results and on your audio/video recording file. In the write-up of the research findings, you’ll only be referred to by your participant number. For example:

Participant #ABC123 found that the green box was too small while #DEF456 could not see it at all.

The learning styles questionnaires and the recordings of your test of the visual will remain the confidential property of the researcher. All materials will be kept in a locked Texas Tech University office in a locked filing cabinet. No one else but the researcher will see your results.

RISKS AND BENEFITS Risks There are no known or predicted risks involved in this research study. Benefits As stated above, you will receive a $20 Barnes and Noble gift card once the 90 minutes of research is complete. As well, you get the results of two different learning styles questionnaires. You may find the results of these questionnaires

265


interesting and they may help you consider the different ways you, as an individual, learn.

YOUR RIGHTS AS A HUMAN SUBJECT People who voluntarily participate in research studies such as this one are protected by the university. Your participation in this study is voluntary and should you refuse to participate, there is no penalty. You may, at any time and for any reason, decline to continue in this study without penalty or loss of benefits. As well, you can also contact the following with any other questions or concerns you may have:

Dr. Thomas Barker will answer any questions you have about the study. He may be reached via email ([email protected]) or phone (806-742-2500 ext. 279).

For questions about your rights as a subject or about injuries caused by this research, contact:

Texas Tech University Institutional Review Board for the Protection of Human Subjects Office of Research Services Texas Tech University Lubbock, Texas 79409

You may also contact this office by phone at (806) 742-3884.

DURATION OF THE STUDY This consent form is not valid after June 30, 2007.

SIGNATURE By signing this consent form, you are indicating you have read the form and understand the nature of the research project. Your signature also indicates that you agree to participate in the research. Signature: _____________________________ Date: ____________________

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APPENDIX D

KOLB EXPERIENTIAL LEARNING STYLE INVENTORY (LSI) VERSION 3.1

The Kolb LSI is a copyrighted instrument owned by the Hay Group Inc. For

the study conducted in this research, the Hay Group graciously provided a paper-

based questionnaire and scoring sheet at no charge. As per the terms of use

agreement, the questionnaire and the scoring sheet may not be reproduced.

For information on the LSI, please contact the Hay Group at

http://www.haygroup.com.

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APPENDIX E

FELDER INVENTORY OF LEARNING STYLES (ILS)

The Felder Inventory of Learning Styles is available at no charge at

http://www.engr.ncsu.edu/learningstyles/ilsweb.html. The test automatically

scores participants’ responses once the submit button is clicked.

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APPENDIX F

SCRIPT READ PRIOR TO TASK

“In a few moments, you’ll begin work at the computer. You’ll be completing a

task with the help of a set of instructional materials. The task is easy: we’re asking you

to work with a computer-based drawing program and re-create the simple image we

give you.

Before we start our study with the drawing program, here’s a few things you

should know:

We’re not measuring your abilities as an artist

We’re not assessing your skill with computers

There’s no requirement to replicate the drawing with photographic

precision.

The program only allows for the creation of simple drawings—like those done

by a grade-schooler—you’ll see what we mean once you get started.”

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APPENDIX G

POST-TEST QUESTIONS

These questions are to be asked after the participant has completed the

usability portion of the research.

1. Overall, did you enjoy the task we did today in the lab?

2. In looking at the instructions we gave you, how useful were they at showing you

an overview of the program?

3. How long, in your estimation, did you work on the task?

4. What element(s) were most helpful to you?

5. Tell us what you now know about this program?

What’s “Info”?

What does “Grid on” do?

How do you invite a friend and what happens when you do?

What do the following icons do?

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RECONSIDERING MINIMALIST DOCUMENTATION: DEVELOPING …