Creating Effective Data Visualizations for Online Learning

CREATING EFFECTIVE DATA VISUALIZATIONS FOR ONLINE LEARNING

Dr. Shalin Hai-Jew

Kansas State University

4th Annual Big 12 Teaching & Learning Conference

Texas Tech University

June 8 – 9, 2017

PRESENTATION DESCRIPTION

• Virtually every type of online learning involves some type of data visualization.

Some common data visualizations include timelines, process diagrams,

linegraphs, bar charts, pie charts, treemap diagrams, dendrograms, cluster

diagrams, geographical maps, network graphs, word clouds, word networks,

scatter diagrams, scatterplot matrices, intensity matrices, decision trees, and

others. Indeed, there is also data in screenshots, photos, drawings, videos, or

other types of visuals. Online dashboards contain rich data visualizations to

convey dynamic data. Some data, such as big data, may only be conveyed in

visuals for human understanding and interpretation; in raw form, the meaning is

obscured and elusive. Data visualizations highlight salient aspects of data, and

they have to be aligned for particular multi-uses: (1) user awareness and

understanding, (2) data analytics, and (3) decision-making.

2

PRESENTATION DESCRIPTION(CONT.)

• This session defines some best practices for informative and engaging data

visualizations for online learning. Original real-world examples are provided

from modern software programs.

3

OVERVIEW

• Oversimplification about Data,

Information, and Data Visualizations

• Data Visualization Sampler (and Audience

Interpretations)

• Data as Visualization

• Data Visualizations in Online Learning

• Defining “Effective” Data Visualizations

• Human Visual Perception

• Cognitive Theory of Multimedia Learning

• Steps to Creating Data Visualizations

• Conventions of Data Visualizations

• 2D

• 3D

• 4D

4

OVERVIEW (CONT.)

• Sequencing Data Visualizations

• Contextualizing Data Visualizations

• User Interactivity with the Data

Visualizations

• About Data Visualizations and Decision-

making

• Some Quick Takeaways

• A Note about the Software

• Contact and Conclusion

5

OVERSIMPLIFICATIONS ABOUT DATA, INFORMATION,

AND DATA VISUALIZATION

6

ABOUT DATA

• Anything that contains raw information

• May be structured (labeled data, such as in data tables)

• May be unstructured or semi-structured (such as imagery, text, audio, video, mixed

media, and other non-traditional contents that contain informational value /

extractable meaning)

• Structured data tend to follow the basics of row data as individual records and

column data as attributes or variables

• Matrices may contain similar attributes in the columns (banners) and rows (stubs)

depending on the type of matrix

7

ABOUT DATA (CONT.)

• Raw master files of data should be kept

• Data are generally parsed in the following ways: classification, frequency (or

intensity), relationship [null, associational (negative or positive), causal, time

relation (slice-in-time, over time, discrete time or continuous time, predictive

or future-focused), space relation, and others]

8

ABOUT DATA (CONT.)

• Only as good as the sourcing and methods for collection

• Should be legally sourced and collected

• Should be accurately maintained and handled (with whatever levels of confidentiality required)

• Is only “good” for a particular time (but also valuable in time for historical purposes and base-lining and possible trend-line analysis)

• Is the base material on which research assertions and analyses are made

• May be grounds for fresh research hypothesizing

• May be a renewable resource in some circumstances and one-offs in others

9

ABOUT PUBLICLY SHARED DATASETS

• Full dataset may be shared online at the time of publication per grant funder

requirements and some practices in some domains

• Shared dataset needs to be properly documented in terms of sources and

methods and in terms of crediting

• This is often in a README file accompanying the data or included at the top-level of

the dataset as a text box

• Data limitations and qualifiers need to be acknowledged

10

ABOUT PUBLICLY SHARED DATASETS (CONT.)

• Data need to be cleaned:

• Repeated information should be omitted

• Outliers should be deleted (or mitigated)

• Data norming should be applied so that the meanings of disparate terms may be

captured, and others

• Data may need to be re-structured for different types of data analytics in

different software programs

11

ABOUT PUBLICLY SHARED DATASETS(CONT.)

• Shared dataset data need to be properly labeled; the data need to be

structured in conventional ways for ease-of-use and professionalism

• Columns as variables, rows as individual data entries

• Data need to be versioned in multiple formats for download and sharing

• Data need to be de-identified and made robust against re-identification

(avoidance of data leakage)

12

ABOUT INFORMATION

• Is an extraction from raw data, and is more processed (filtered, cleaned,

selective) than raw data

• Contains some interpretation

• Contains applied value for human use and benefit for awareness, decision-

making, and other applications

• Should be accurate and avoid any sort of mis-representation, even by nuance

or false inference

13

ABOUT DATA VISUALIZATIONS

• Is a purposive and selective data summarization (of the underlying data), and it

generally contains particular dimensions or facets of the underlying data

• May be linked to the underlying data (for reproducibility)

• Involves titles, shape labels, data labels, and keys / legends

• Includes visual aesthetic style elements

• Is usually stand-alone but also used in an original context (so may have

dependencies)

• Follows particular data visualization conventions

14

ABOUT DATA VISUALIZATIONS (CONT.)

• May be 2D (x- and y-axes), 3D (x, y, and z axes), and 4D (x and y axes and time;

x, y, and z axes and time as the 4th dimension)

• Should follow all laws

• Should respect intellectual property and not contravene IP rights

• Should also give credit where it is due

• Should respect privacy rights and not contravene privacy rights

• Should have legal and signed media releases for all depictions of people’s likenesses

• Should be accessible, with the information available in multiple modalities

15


• May be drawn from different sources:

• raw data: structured, unstructured, semi-structured

• synthetic (faux) data

• processed information

• theory(ies)

• model(s)

• projection(s)

• concepts

• A data visualization may be drawn from a combination of sources

• The underlying sources and the visuals inform understandings of the data visualization and the confidence that may be applied

16


• May be created in a number of ways:

• manually drawn with diagramming tools, note-taking tools, tablet drawing programs

• drawn by machine based on both data and various computer algorithms

• statistical analyses (correlations, chi-square test, simple regression, multiple

regression, t-tests, ANOVAs, sign tests, and others)

• cluster (similarity / dissimilarity) analysis

• machine learning or computational identification of patterns in data

17


• drawn by computer program (cont.)

• agent-based modeling

• data modeling

• simulation

• virtual immersive worlds, and others

• and often created with a mixed sequence, such as some computational data

visualization augmented by manual data labels and other visual overlays

18

DATA VISUALIZATION SAMPLER (AND AUDIENCE INTERPRETATIONS)

19

WHAT DO THE FOLLOWING DATA VISUALIZATIONS SHOW?

• The following data visualizations are based on education-seeded datasets and

various software programs.

• The data sources include the following: curated text sets, LMS data portal

data, social media datasets, crowd-sourced encyclopedias, non-consumptive

text analysis data, and others.

• The data visualizations are labeled by the following: (1) data, (2) data

visualization type, and (3) software technology.

20

GENERAL STEPS TO RESEARCH AND THE ROLES OF DATA VISUALIZATION

21

22

Concept

Process Diagram

Microsoft Visio

10 STEPS TO CREATING DATA VISUALIZATIONS

23

24

Concept

Process Diagram (sequential

and recursive)

Microsoft Visio

@USEDGOV TWEETSTREAM

25

26

@usedgov Tweetstream

Geographical Map

NVivo 11 Plus

27


Sociogram

NVivo 11 Plus

28


Dendrogram (horizontal)

NVivo 11 Plus

29


2D Cluster Diagram

NVivo 11 Plus

30


Ring Lattice Graph / Circle Diagram

NVivo 11 Plus

31


3D Cluster Diagram

NVivo 11 Plus

32


Bar Chart (by month)

NVivo 11 Plus

33


Pie Chart (by month)

NVivo 11 Plus

34


Treemap Diagram

NVivo 11 Plus

35


Sunburst Diagram

NVivo 11 Plus

36


3D Bar Chart

NVivo 11 Plus

37


Intensity Matrix

NVivo 11 Plus

38


Node Structure

NVivo 11 Plus

39


Treemap Diagram

NVivo 11 Plus

40


Sunburst Diagram

NVivo 11 Plus

41


3D Bar Chart

NVivo 11 Plus

42


Word Cloud

NVivo 11 Plus

43


Treemap Diagram

NVivo 11 Plus

44


Word Tree (interactive)

NVivo 11 Plus

45


Dendrogram (vertical)

NVivo 11 Plus

46

@usedgov Tweetstream and

@educationgovuk Tweetstream

(based on word similarity)

3D Cluster Diagram

NVivo 11 Plus

HTTPS://EN.WIKIPEDIA.ORG/WIKI/EDUCATION ARTICLE

47

https://en.wikipedia.org/wiki/Education

48

Education Article on Wikipedia

Spider / Radar Chart

LIWC2015 and MS Excel 2016

96.40

58.96

23.08

46.70 0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

Analytic

Clout

Authentic

Tone


49


Treemap Diagram


50


Histogram (based on function words)


EDUCATION, TRAINING, ONLINE LEARNING, DISTANCE LEARNING /

GOOGLE BOOKS NGRAM VIEWER

51

52

Education, Training, Online Learning, Distance Learning

Line Chart

Google Books Ngram Viewer

“EDUCATION” RELATED TAGS NETWORK ON FLICKR (2 DEG.)

53

54

“Education” Seedingg Term

Related Tags Network on Flickr (1 deg.)

NodeXL

55

“Education” Seeding Term

Related Tags Network on Flickr (1.5 deg.)

NodeXL

56

“Education” Seedingg Term

Related Tags Network on Flickr (2 deg.)

NodeXL

@USEDGOV USER NETWORK ON TWITTER

57

58

@usedgov user network on Twitter

Social Network Graph

NodeXL

“EDUCATION” ARTICLE NETWORK ON WIKIPEDIA (1DEG.)

59

60

Article Network on Wikipedia

Article-Article Network Graph (1 deg.) / grid layout algorithm

NodeXL

AN INFORMATION TECHNOLOGY (IT) SATISFACTION SURVEY

61

62

An IT Satisfaction Survey

Cross-Tab Analysis (with chi-square scores)

Qualtrics

THIRD PARTY TOOLS ACTIVATED IN K-STATE INSTANCE OF CANVAS LMS IN DESCENDING

ORDER

63

64

Third-Party Tools Activated in Canvas LMS at K-State

Statistic Chart

MS Excel 2016

65


Packed Bubble Diagram

Tableau Public

66


Treemap Diagram

Tableau Public

SELECTED POPULAR ACTIVATED THIRD-PARTY SOFTWARE IN CANVAS LMS AT K-

STATE

67

68

Canvas LMS Data Portal Data

Streamgraph (a time-based filled-area chart)

MS Excel 2016 with Streamgraph Add-on

(by Microsoft Research)

COURSE WORKFLOW STATES IN THE K-STATE CANVAS LMS INSTANCE

69

70

Canvas LMS Data Portal Data

Doughnut

MS Excel 2016

VARIANT BILL-OUTS FOR SET OF INSTRUCTIONAL DESIGN PROJECTS ON A

UNIVERSITY CAMPUS

71

72

Billing Data for Instructional Design Projects

Scattergraph with Lines

MS Excel 2016

73

Billing Data for Instructional Design Projects

Treemap

MS Excel 2016

TIME-TO-EVENT ANALYSIS (FORMERLY “SURVIVAL ANALYSIS” ) OF INSTRUCTIONAL DES IGN PROJECTS AND TIME WHEN A PROJECT ACHIEVES EVENT ( IS PAID OUT) OR IS CENSORED

(DOES NOT ACHIEVE EVENT DURING THE RESEARCH PERIOD)

74

75

Instructional Design Billing Data

Kaplan-Meier Curve /

Line Graph (based on “survival analysis”)

IBM’s SPSS Statistics

76

Instructional Design Billing Data

Line Chart (based on “survival analysis,”

non-descending stepwise curve)

IBM’s SPSS Statistics

MASTERY OF SOFTWARE CONCEPTUALIZATION

77

78

Conceptual Way to Understand Whether One

Understands a Software Program

Decision Tree

MS Visio

“VIRUS ON A NETWORK” MODEL[AGENT-BASED MODELING (WITH 4D)]

79

http://ccl.northwestern.edu/netlogo/models/community/

80

“Virus on a Network” (by Stonedahl & Wilensky, 2008)

Agent-based Modeling (over Time, into 4D)

NetLogo

http://ccl.northwestern.edu/netlogo/models/community/

EDX.ORG ACADEMIC YEAR 2012 – 2013(PUBLIC DATASET)

81

82

edX.org Academic Year 2012-2013

Map of Learner Locations of Origin

Tableau Public

https://public.tableau.com/s/resources?edition=public&version=10.2.0&__full-version=10200.17.0216.1925&platform=windows&status=buy&qt-overview_resources=1#qt-overview_resources

CLASSIC OPEN-SOURCE IRIS DATASET

83

84

Classic Iris Dataset (open-source)

Decision Tree (autocreated through

machine learning)

RapidMiner Studio

EXAMPLE CUSTOMER DATASET (AS PROVIDED IN RAPIDMINER STUDIO)

85

86

Example Customer Dataset

Decision Tree

RapidMiner Studio

DATA AS VISUALIZATION

87

DATA

• What / Entity

• Frequency / Intensity (How Much?)

• Relationships (Association, Causation,

Hierarchical, and Others)

• Slice-in-Time

• Changes over Time

• Shape

• Size, Thickness, Height

• Connected Lines, Scatter in Space, Tree

Structure Diagrams, and Others

• Time Label, Time Indicator

• Line / Scatter over the X-axis

VISUALIZATION

88

DATA -> VISUALIZATION

DATA VISUALIZATIONS IN ONLINE LEARNING

89

COMMON FORMS

• Timelines

• Bar Charts, Pie Charts, Line Charts, and

Others

• Models (Venn Diagrams, Figures)

• Geographical Maps

• Photos / Imagery

• Simulations

• 3D Immersive Virtual Worlds

• 4D Immersive Virtual Worlds

• Games

• Video

90

ONLINE LEARNING CONTEXTS

• Online learning includes both term-length courses and short courses (such as for trainings).

• In online learning, instructors maintain a level of telepresence through interactions and intercommunications with learners.

• Learners maintain some level of inter-communications with their peers. They co-create learning communities to support each other’s learning.

• In an online learning context, learners have to be somewhat self-driven and self-directed.

• Given that online learning occurs via the Web and Internet, learners have easy access to online resources: digital libraries, websites, immersive virtual spaces, online datasets, and other contents.

• Depending on the sociability of others, they will have access to experts and peers to engage with about various topics.

91

ONLINE LEARNING CONTEXTS (CONT.)

• The nature of the online learning context means that online learners will have

access to other datasets and data visualizations related to the same

information…and other perspectives and points-of-view.

• Ostensibly, they’ll be able to see if data visualizations are borrowed and reproduced

from elsewhere (through reverse image search, through basic Web image search).

• They’ll be able to access public datasets.

• They’ll be able to see if there are different datasets, data visualizations, and different

understandings and interpretations of the issue.

92

REQUIRED LEARNER RESPONSES

• The data visualization(s) need to be designed so that learners do the following:

• Pause, not just blitz past

• Engage with the visualization (and interact for the interactive visualizations)

• Extract accurate meaning for the learning

• Reflect

• Follow-through on learning activities

• Experience inspiration

93

DEFINING “EFFECTIVE” DATA VISUALIZATIONS

94

EFFECTIVE DATA VISUALIZATIONS…

• represent the selected underlying data accurately based on the inherent form and structures in the underlying data and on user needs (and control against misperceptions and misunderstandings);

• highlight relevant aspects of the underlying data;

• convey information in an aesthetically pleasing way (to attract human attention and to increase the memorability of the visualization and the underlying information);

• align with conventions of the respective data visualizations (directionality of reading, respective sizes of elements, placement of elements in relation to each other, naming and labeling protocols, perspective, and other aspects);

95

EFFECTIVE DATA VISUALIZATIONS…(CONT.)

• maintain consistency both within and across related data visualizations;

• are accessible in terms of element labeling, text readability, image resolution,

and uses of color [proper contrast, proper color palettes, and way(s) to convey

information beyond color];

• are presented in a contextualized way, including access to information about

the underlying research, data collection, and data cleaning;

• avoid unnecessary (read: purely decorative) elements, and

• occasionally connect to the underlying data (data portals, interactive web-

based data visualizations), among others.

96

HUMAN VISUAL PERCEPTION

97

VISUAL PERCEPTION

• The human visual perception system includes the eyes (cornea, lens, and

retina), the optic nerves, and visual paths in the brain to process light

information.

• The retina contains 150 million light-sensitive rod and cone cells

• In the brain, there are hundreds of millions of neurons that process visual information

(“and take up about 30 percent of the cortex, as compared with 8 percent for touch

and just 3 percent for hearing”)

• Optic nerves consist of “a million fibers” each (Grady, June 1, 1993, “The Vision Thing:

Mainly in the Brain,” Discover)

98

http://discovermagazine.com/1993/jun/thevisionthingma227

BEYOND THE PHYSICAL

• Perceptual signals do not only come from the world but also from the mind

and body (internally).

• Vision, though, is informed by the prior experiences (prior observed patterns)

of the individual.

• One researcher suggests that in visual perception: 40% comes from visual signals, and

60% comes from prior experiences and memory (Catmull, 2014, Creativity, Inc.:

Overcoming the Unseen Forces that Stand in the Way of True Inspiration, p. 178).

99

http://www.penguinrandomhouse.com/books/216369/creativity-inc-by-ed-catmull-with-amy-wallace/9780812993011/

COGNITIVE THEORY OF MULTIMEDIA LEARNING

100

MAIN THEORISTS AND THEORIES

• Richard Mayer’s Cognitive Theory of Multimedia Learning (2002):

Engaging cognitively involves costs to the learner.

• (1) Intrinsic cognitive load is related to the difficulty of the topic-to-be-learned.

• (2) Extraneous cognitive load is based on how information is designed and

presented.

• (3) Germane cognitive load is dependent on “the processing, construction and

automation of schemas” (schemas being frameworks for understanding parts of the

world). There are ways to design multimedia to align with human cognitive limits to

lighten cognitive loads to enhance learning.

101

https://en.wikipedia.org/wiki/Cognitive_load

MAIN THEORISTS AND THEORIES (CONT.)

• John Sweller’s Cognitive Load Theory (1988): “Means-ends analysis”

requires high cognitive load on people, and those who teach can lighten the

load by offering organizing schemas and “worked-examples” and “goal-free

problems.”

• Allan Paivio’s Dual-Coding Theory (1960s / 1971): Humans process

information through separate auditory and visual channels. Verbal (word,

symbolic) and non-verbal (visual image) information is processed in different

channels.

102

IMPLICATIONS ON DATA VISUALIZATION DESIGN

• Cognitive Theory of Multimedia Learning

• Complex topics should be unpacked in a clear way to limit intrinsic cognitive load.

• Data visualizations should never be decorative alone. They should be informative, so

as to align with extraneous cognitive load insights.

• Data visualizations should have main relevant aspects highlighted and noted, to lower

germane cognitive load. Learners should not be given confounding data visualizations

without clear meanings.

103

IMPLICATIONS ON DATA VISUALIZATION DESIGN (CONT.)

• Cognitive Load Theory

• Data visualizations should be placed in the context of a relevant framework in a

particular learning domain or context. The data should be presented in the context

of accepted schemas.

• Dual-Coding Theory

• Data visualizations presented to learners should not only be on purely verbal or non-

verbal channels. There should be a balance in the information, so learners can process

the information appropriately. There are contested ideas about how much

redundancy across channels should be deployed to convey information.

104

STEPS TO CREATING DATA VISUALIZATIONS

105

REVIEW: 10 STEPS TO CREATING DATA VISUALIZATIONS

1. Analyze the data

2. Clean / process the data

3. Select the data aspect(s) to highlight

4. Structure the data for the visualization

5. Create initial data visualizations

6. Analyze the data further

7. Add data labels, title, key / legend, and other elements

8. Pilot-test the data visualizations (stand-alone)

9. Pilot-test the data visualizations (in context)

10. Finalize the data visualizations 106

(as seen on Slide 23)

DEBRIEFING THE SEQUENCE

• A data visualization begins with intimate knowledge of the underlying data.

• Data often has to be processed in the correct format for visualization.

• Data visualizations are used partially as a data exploration method.

• Data are often processed in multiple different methods…and even in multiple different software programs in order to see what may be learned from the data.

107

• Depending on aesthetics, some may process data in one tool and export the resulting data tables and / or other digital artifacts for final processing in other software programs.

• There are data visualization drafts created before a final one is output (for presentation).

• Data visualizations have to be human-readable and human-usable.

MORE TO THE STORY…

• To create relevant data visualizations, those who would design data visualizations need to understand the following:

• the underlying data and prior research

• the statistical assumptions

• the conventions of the particular data visualizations

• the target audiences (and the incidental audiences)

• the socio-cultural and geographical backgrounds of the target and incidental audiences (in order to avoid miscommunications and potential offense)

• the requirements (color processing, resolution, and others) and technical versions of the imagery needed for digital distribution and print

108

RESEARCH STANDARDS

• Following legal standards for research and data collection, including oversight, informed consent, candor, benevolence, and others

• Following legal standards for data handling and storage

• Following legal standards for privacy protections of research participants (and data)

• Following legal standards for information accuracy (and controlling for negative understandings)

• Creating original contents using materials and data that one has legal rights to use

• Using software that is legally acquired

• Giving credit where it is due (such as in cases of open-source and / or Creative Commons-released materials)

• Avoiding contravening others’ intellectual property

• Doing due diligence to identify ownership of works (even for “orphaned” works)

INTELLECTUAL PROPERTY (IP)

109

QUALITY STANDARDS FOR DATA VISUALIZATIONS

PRIVACY PROTECTIONS

• Acquiring informed consent from all participants in research (and maintaining accurate and up-to-date documentation of these permissions)

• Acquiring media releases for uses of people’s likenesses (such as for audio, video, and other recordings and captures)

• Protecting data (both in transit and at rest)

• De-identifying data where necessary (to the standard that re-identification is not possible)

• Ensuring that all data visualizations are available to users in multi-modal channels (visual, textual / audio)

• Ensuring that 4D data visualizations (with the time element) may be controlled by users (some the timing may be slowed or stopped, for easier usage)

• Ensuring that data tables may be read coherently by screen readers

• Ensuring that color is not used as the only channel for information conveyance (for those with color-blindness)

• Using high-contrast colors to enable accurate visual uptake of information

ACCESSIBILITY

110

QUALITY STANDARDS FOR DATA VISUALIZATIONS (CONT.)

ADDITIONAL COMMON ERRORS IN DATA VISUALIZATIONS

The Data

• Introducing error in data handling, data processing, and / or data cleaning

• People who work too quickly will accidentally delete information or corrupt it if they

are non-thinking in their work

• Using an unaligned data visualization type for the underlying data

• It’s easy to get a software program to output a data visualization without actually

understanding what is going on with the data or in the software

111

ADDITIONAL COMMON ERRORS IN DATA VISUALIZATIONS (CONT.)

The Data (cont.)

• Using high-density data that may overpower the data visualization

• Excessive nodes in a network will make the network unreadable

• Insufficient understanding of the limits of assertions that may be made with

that visualization

• Not remembering that data visualizations are summary data, not comprehensive (in

most cases)

• Not remembering that data visualizations are inherently ambiguous and polysemous

(multi-meaninged) and can be interpreted in different ways by different beholders

112


The Data (cont.)

• Labeling data visualization elements incorrectly

• Insufficient labeling of visual elements (such as data labels) in the data visualization

• Incorrect labeling of data elements (confusing rates over time with set amounts)

• Using mixed measures in data

• Not using consistent time measures

113


The Data (cont.)

• Not considering language

• Simple English reads better and translates better

• Parallel construction should be applied to all language use in a data visualization

especially since language is so sparse and powerful in a data visualization

• Spelling should be correct

• The language used should be consistent and aligned with the research context

114


The Software

• Using software without understanding the software

• Researchers will use software programs without reading the manuals and the underlying documentation (or they’ll go to forums before they go to the actual documentation)

• Of course, some software makers do not document as well as they should (most will not reveal underlying algorithms, for example)

• Researchers need to understand the software programs they’re using, particularly for coding and analysis

• They need to represent what they learned while using the software, not just mention that they used the software (as if that would lend their work credibility)

• Shabby work reads as shabby, and name-dropping a software tool will not make things better

115


Data Visualization Conventions

• Not understanding the conventions of a data visualization

• Spatial relationships in 2D, 3D, and 4D planes

• Shapes and meanings

• Color applications

• Lines: thickness, color, interruptions, line ends, and others

• Symbology

• Textures and patterns

116


Contextual Details

• Not offering sufficient contextual details to fully understand a data visualization

• Not including data parameters for data processing in a data visualization

• Not indicating that a data visualization is conceptual vs. empirical

• Not labeling synthetic or faux data as such

• Misrepresentations of information

117


Going Glam / Not Going Glam

• Using data visualizations that are glamorous (read: 3D) but which misrepresent data

• Misplacement of data on the x, y, or z axes

• Occlusion of visual data

• Not considering aesthetics

• Using mixed color palettes (or using colors without any consistency or strategy)

• Using poor aspect ratio (stretching data visualizations)

• Not designing for white space (by overloading a data visualization)

118


Audience Needs

• Not fully considering audience needs [such as by running pilot tests; such as building for both expert and general audiences (simultaneously)]

• Visual perception needs; cognitive and symbolic processing needs (symbols, language); accessibility needs

• Learner developmental stage needs (with implications for the data visualization and the sequence of related data visualizations)

• Informational needs

• Technological needs (such as viewing data visualizations on mobile devices and smartphones with small screens)

119


Designing for Usage Contexts

• Incomplete consideration of various contexts in which the data visualizations

may be used

• Insufficient consideration for both stand-alone (the disaggregation of elements in

online learning) and in-context usages of the data visualization

• And others

120

TEMPLATING

• For those working on projects, it helps to…

• define comprehensive data visualization

standards early in a project stylebook

• use prototypes of data visualizations and

images and test these with people who are

similar to those who will ultimately consume

the data visualizations

• create evolving data visualization templates for

use during the lifespan of the project

• It is important to keep clear

documentation of all work and how the

data visualizations were created

• It is important to keep all raw files,

especially data ones, for re-do’s as

needed

CLEAR DOCUMENTATION

AND STORAGE OF RAW FILES

121

QUALITY APPROACHES

CONVENTIONS OF DATA VISUALIZATIONS

122

SOME COMMON DATA VISUALIZATION CONVENTIONS

• There are senses that there are optimal amounts of data for a particular data visualization. Excessive data makes a data visualization hard-to-read or confusing; too sparse data makes a data visualization feel incomplete.

• Data visualizations are generally read from top-to-bottom and left-to-right.

• Timelines are read either from top-to-bottom or left-to-right, for example.

• If there is a sequence of data visualizations, these are usually presented from simple to complex.

• In linear regressions, the x-axis is usually time, and the y-axis is the variable. Or, both can be variables.

• Data may be engaged with with varying levels of granularity. Less specific data may not be labeled, but at finer levels of granularity, data labels are often used.

123

SOME COMMON DATA VISUALIZATION CONVENTIONS (CONT.)

• Time is often an important part of a data visualization, whether time is treated

as discrete (slice-in-time), periodic (in phases), or as continuous. Time is an

important variable in all research.

• Data visualizations are usually named for easier reference.

• If external data is used, the data source should be cited.

• Some data visualizations are offered along with underlying datasets that inform

the data visualization—for reproducibility of the data visualization (and for

enriched research using the shared data).

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SOME COMMON DATA VISUALIZATION CONVENTIONS (CONT.)

• Aesthetically, data visualizations are created with a proper balance of filled-in

spaces and white spaces.

• Color palettes are deployed for both aesthetics and for accessibility (proper

color contrasts).

• Font types tend to be sans serif for easier readability.

• Font sizes tend to be within a certain size range, so that there are not huge

differences in sizes, particularly for shared and similar types of data. (The font

sizes of titles may be quite a bit larger than other fonts used in a data

visualization because of its role in the visualization.)

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2D

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BASICS ABOUT 2D DATA VISUALIZATIONS

• 2D data visualizations exist on a flat two-dimensional plane.

• The planes are usually squares or rectangles (quadrilaterals). Within the area,

various types of data visualizations may be displayed.

• Generally 2D data visualizations are understood to have an x-axis and a y-axis

(such as linear regression graphs, bar charts, and others). In some cases, the x-

and y- axes do not apply since the visualization may be rotated and maintain

the same meaning (such as some forms of network graphs, bubble diagrams,

and others).

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3D

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• Three-dimensional (3D) data visualizations are drawn on a space that involves

more than area (also volume) and three dimensions: x-axes, y-axes, and z-axes.

• In many software tools, the 3D effect is created with shading and the

appearance (illusion) of a third dimension.

• Such visualizations tend to be rotate-able and zoomable for clarity.

• People are not thought to process 3D data very well because of challenges

with occlusion and visual ambiguity.

• Often, 3D visualizations may also be offered in 2D.

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4D

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• The fourth dimension is conceptualized as time. For data visualizations, this means changes over time.

• Changes over time may be seen in spaces that are two-dimensional or three-dimensional.

• Time may be discrete (a particular slice-in-time), phased (into periods), or continuous.

• Time may be presented in sequential order or reverse-sequential order, in terms of phased or continuous time.

• In data visualizations, time may be run forwards or backwards in simulations, virtual immersive worlds, and video.

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SEQUENCING DATA VISUALIZATIONS

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SOME ORDER PREFERABLE

• Data visualizations may be presented in a particular order or sequence.

• The order may be somewhat serendipitous only in terms of placement in a slideshow,

in a book, on a web page, and so on.

• The order may be purposeful to highlight some macro-level observation about the

data.

• No matter how the presentation order comes about, it helps to have an

underlying rationale or logical trajectory for the sequence.

• This small section provides some ideas for the data visualization sequencing.

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TIME-BASED ORDER

• Data visualizations may show a phenomenon changing over time.

• The changes may be a factor of time, a factor of an intervention or multiple

interventions, a factor of a process, or other factors.

• Time itself may be discrete, phased, or continuous.

• The time may be in sequential order, reverse-sequential order, or some mix of

phasing.

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SIMPLE-TO-COMPLEX ORDER

• Data visualizations may be presented in a simple-to-complex way, to bring

observers along with the flow of the data revelations.

• Simple pieces may be offered first to build up to a complex summary data

visualization, for example.

• Or, the data visualizations may begin with a complex visualization and then

offer more simple zoomed-in views of to offer more in-depth discussion and

insights.

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THEORETICAL AND ACTUAL ORDER

• Another sequence may begin with a concept or model or some theoretical

conceptualization followed by empirical and actual data.

• This is the top-down approach, beginning with the general and moving to the specifics.

• Or, the sequence can go the other way, with observations from-world…and

moving to a more general data visualization.

• This is more of a bottom-up approach, beginning with specifics and moving to the

general.

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FEATURE-BASED ORDER

• Most datasets today are multi-dimensional and complex. One way to sequence

data visualizations is to focus on different aspects or features of the dataset.

• It may be helpful to create an over-arching structure of the dataset’s features

and use those to organize the data visualizations.

• For example, if datasets involve a learning management system, would it be

helpful to organize the data visualizations by the data dictionary? The various

features of the LMS from most commonly used to the least commonly used?

The features by role (student, faculty, advisor, instructional designer, librarian,

and administrator)?

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CONTEXTUALIZING DATA VISUALIZATIONS

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A DATA VISUALIZATION “SURROUND”

• Data visualizations may be presented not only as stand-alone visualizations but within a context or surround.

• A most close-in aspect of context may involve the data visualization directly.

• An important aspect of context involves the backstory behind the data visualization.

• Where did the data come from? What sort of research was conducted in order to capture the data? How was the data cleaned and processed?

• If datasets were mixed, where did the data come from? How were the datasets mixed? Who should be credited?

• What are some qualifiers that need to be applied to the data visualizations?

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SOME BENEFITS OF PROVIDING “CONTEXT”

• If designed properly, a context for a data visualization achieves the following:

• enriches the data

• provides direction for proper interpretation of the data (highlights what “story” the data are telling)

• suggests the relevance of the data in the real world

• raises interest about the data visualization(s)

• offers access to the underlying dataset

• provides ideas about where to acquire more relevant information about the related data

• gives credit where it is due for the data visualization, the dataset, the research, and other related information, and others

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ELEMENTS OF “CONTEXT”

• At a superficial level, data visualization “context” involves the lead-up and lead-away text surrounding the data visualization.

• This may include stories to “set up” the phenomenon under study.

• This may include table data and downloadable datasets.

• This may include captioning, credits, research citations, and other details.

• This may include qualifiers.

• This may include lead-up multimedia (audio, video, and others) to prime learners to understand the data visualization.

• There may be a lead-up or lead-away interview by the researchers or data analysts or others related to the work.

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“CONTEXT” BY ASSIGNMENT

• The learning situation offers some direction for the design of data visualization context. Especially in a learning context, the instrumental uses of the data visualization are important.

• The assignment should specify how learners should read / use the data visualization or the data visualization sequence.

• For cognitive scaffolding, it may help to let learners know what to pay attention to in the respective data visualizations. In a simple case, learners may only need to view the data visualization and interpret what its meaning is.

• Some assignments can be broadly open-ended, with the data visualization(s) as a jumping-off point for discussions, analyses, research, and other work.

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DATA VISUALIZATIONS IN ONLINE LEARNING CONTEXTS

• A slideshow

• A video

• A simulation

• A discussion board conversation

• A case for analysis

• A role play

• A group project

• A writing assignment

• A research assignment

• A field trip, and others

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“CONTEXT” BY ISSUE

• Another method to build a surround around a data visualization or series of

data visualizations is by contextualizing these as part of an issue.

• An issue may be an in-world phenomenon, with its own history, evolution,

present, and future. There may be particular dynamics with this phenomenon

and certain levers and mechanisms that may affect the changes to this

phenomenon.

• The data visualization(s) may be presented to highlight aspects of this in-world

issue.

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USER INTERACTIVITY WITH THE DATA VISUALIZATIONS

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WHY USER INTERACTIVITY?

• Data visualizations are not just static and flat files.

• Many enable various types of interactions:

• adjusting parameters of a model (such as data inputs and outputs);

• engaging time (speeding it up, slowing it down, stopping it);

• zooming in and out to disambiguate, and

• accessing underlying data.

• Interactions with data visualizations may enable easier learning (with lower

cognitive loads) and the creation of insights.

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DATA INPUTS AND OUTPUTS

• There are a number of data visualizations (built on NetLogo, Wolfram Language, and others) that enable users to change up the parameters of the data visualizations (including data) in order to see what will happen.

• Such data visualizations are focused on system effects of different parameters.

• Often, inputs may be emplaced with slider bars or forms.

• In some cases, it is important to design these with natural data limits (so as not to enable going beyond reality). In other cases, such interactive data visualizations are able to be informed by imaginary data ranges and others.

• Some of these data visualizations enable predictivity into the unknown and into the future. (Agent-based models can be played out into imagination realms by enabling hundreds of thousands of iterations or more, to see how systems change over time given theoretical parameters.)

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ENGAGING TIME

• Some data visualizations enable viewers to engage time…to start at particular

points of the 4D visualization, to pause, to restart, and so on. Data

visualizations may sometimes be slowed or speeded up.

• The phenomenon in such data visualizations include those that illuminate

systems and system effects.

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ZOOMING IN AND OUT TO DISAMBIGUATE

• Some data visualizations may be sufficiently complex that objects in data

visualizations may be occluded. To disambiguate complex data visualizations,

such as word networks or 3D cluster diagrams, many enable zooming in and

out to disambiguate the data.

• Many of these also enable the moving around of nodes and links in order to

enable clear visibility.

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ACCESS TO UNDERLYING DATA

• Another type of interactivity with data visualizations involves viewers accessing

the underlying data behind the data visualization.

• For example, a text set which has been coded for sentiment may be explored

by clicking on a bar on a bar chart, to access the coded data under that

particular level of sentiment. Or a node representing an interview subject may

be clicked to access the underlying transcript.

• This type of interactivity enables the individual to explore the related data

more deeply.

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ABOUT DATA VISUALIZATIONS AND DECISION-MAKING

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HUMAN DECISION-MAKING

• Data and data visualizations provide information about in-world phenomena and in-world potentials.

• There are computational methods that enable the surfacing of latent patterns from data that would be invisible otherwise.

• Data visualizations make latent insights visible and human-perceivable.

• Ultimately, it is the data behind the data visualization that should inform the decision-making.

• It’s rare that one data visualization or even a sequence or a set will be sufficiently informative or compelling to sway an important decision, but data visualizations may be powerful depending on how they are created and harnessed.

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SOME QUICK TAKEAWAYS

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THE UNDERLYING DATA

• Data visualizations reference underlying data in a data summarization way.

• This data may be descriptive, inferential, deductive, inductive, analytical,

conceptual, predictive, or some mix of the prior.

• Historically, structured and semi-structured data have particular ways that they

are explored and visually expressed.

• Data visualizations have conventions that they must follow based on prior practice

and common understandings.

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SOFTWARE TOOLS

• Software tools have to be used appropriately for accurate data visualizations.

Tools have differing strengths for data visualizations.

• Predictive analytics tools have tests of models…

• Manual drawing tools have grids, guidelines, templates, and pre-made shapes.

• Data visualizations should accurately represent the underlying data. The design

of the data visualization should be tailored to the targeted and potential

audiences and work against negative learning / misunderstandings.

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STAYING LEGAL

• Data visualizations should be based on solid research practices.

• Data visualizations should be created in legal ways. Relevant laws include

intellectual property, privacy protections, accessibility, and others.

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TECHNOLOGICAL FEATURES

• Data visualizations should be designed optimally for the following technological

features:

• accessibility

• human usability across platforms and devices

• machine readability

• preservation (future-proofing across time)

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A NOTE ABOUT THE SOFTWARE

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SOFTWARE

• The data visualizations were created with various types of data, various data

sources, various seeding terms and data parameters, and software.

• The software used for data visualizations include the following (in alphabetical

order): Google Books Ngram Viewer, IBM’s SPSS Statistics, LIWC2015,

Microsoft Excel 2016, MS Visio, NetLogo, NodeXL (Network Overview,

Discovery and Exploration for Excel by Microsoft and available on CodePlex),

NVivo 11 Plus (QSR International), Qualtrics, RapidMiner Studio, and Tableau

Public.

• Backup software include Gadwin PrintScreen and Adobe Photoshop.

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CONTACT AND CONCLUSION

• Dr. Shalin Hai-Jew

• Instructional Designer

• iTAC

• Kansas State University

• 212 Hale Library

• [email protected]

• 785-532-5262

• Note: The presenter has no professional

ties to any of the software makers

mentioned here.

• Thanks! I am grateful to the organizers

of the 4th Annual Big 12 Teaching &

Learning Conference at Texas Tech

University for including this presentation

in their lineup.

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mailto:[email protected]

Data & Analytics

Creating Effective Data Visualizations for Online Learning