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LEARNING THEORIES APPLIED TO TEACHING TECHNOLOGY:
CONSTRUCTIVISM VERSUS BEHAVIORAL THEORY FOR INSTRUCTING
MULTIMEDIA SOFTWARE PROGRAMS.
by
Cajah S. Reed
CARLOS CONTRERAS, PhD, Faculty Mentor and Chair
EVAN STRAUB, PhD, Committee Member
KEITH CIANI, PhD, Committee Member
Dean Ginther, PhD, Dean
Harold Abel School of Social and Behavioral Sciences
A Dissertation Presented in Partial Fulfillment
Of the Requirements for the Degree
Doctor of Philosophy
Capella University
December 2012
All rights reserved
INFORMATION TO ALL USERSThe quality of this reproduction is dependent upon the quality of the copy submitted.
In the unlikely event that the author did not send a complete manuscriptand there are missing pages, these will be noted. Also, if material had to be removed,
a note will indicate the deletion.
Microform Edition © ProQuest LLC.All rights reserved. This work is protected against
unauthorized copying under Title 17, United States Code
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P.O. Box 1346Ann Arbor, MI 48106 - 1346
UMI 3548893Published by ProQuest LLC (2012). Copyright in the Dissertation held by the Author.
UMI Number: 3548893
© Cajah Reed, 2012
Abstract
This study sought to find evidence for a beneficial learning theory to teach computer
software programs. Additionally, software was analyzed for each learning theory’s
applicability to resolve whether certain software requires a specific method of education. The
results are meant to give educators more effective teaching tools, so students ultimately get
the most out of any particular software program. The study’s value comes from additional
significant information added to the established constructivist and instructivist debate, which
is important to psychologists and educators.
The design of the study was a randomized quantitative experiment with an analysis of
covariance design employing four groups, gathered using convenience sampling, in a pretest,
posttest model to analyze multiple independent variables. Further design parameters included
a 2 X 2 Factorial Design, .05 significance, large post hoc Cohen f effect size for learning
theory, and 89% power. The sample was 167 students enrolled in Digital Image
Manipulation, Digital Layout, Digital Illustration, or Digital Typography classes during two
quarters of 2012. The participants were analyzed in their normal classroom environment
using an online test/lesson/test exercise. The instrument was Photoshop CS5 and InDesign
CS5 uCertify Adobe Certified Expert (ACE) exam preparation guides.
Research Question 1 stated: Is constructivist or behavioral learning theory more beneficial
when teaching multimedia software? A significant finding for Research Question 1 indicates
a difference between the learning theories behaviorism and constructivism. The behaviorist
group scored higher than the constructivist group. Research Question 2 stated: Is there a
difference in the effectiveness of learning between Photoshop and InDesign when teaching
multimedia software? There was no significant finding for Research Question 2; therefore,
no difference was found between Photoshop and InDesign.
Research Question 3 stated: Are there interactions between learning theory and software with
regards to teaching multimedia software? No interaction was found between learning theory
and software. According to the current study, teachers who instruct their courses through a
problem-based constructivist method should consider a behaviorist approach. A behavioral
learning curriculum is especially important if the class is instructing Adobe software.
iii
Dedication
I dedicate my dissertation to my Grandmother. Thank you for pushing me to get a
great education. I will try not to be so smart that I can’t have a normal conversation.
It is also dedicated to my family, who have sacrificed time with me and kept quiet
during nap-time so I could do “homework.”
iv
Acknowledgments
First and foremost, I must acknowledge Michael Reed, whose support was
endless. His masterful work on the experiment website was genius. The study would not
have been as successful without his hours spent recreating Photoshop and InDesign. I
want to thank Tommy Sullivan for listening, reading, testing the website, and spending
the time bouncing ideas around. His encouragement helped me to develop and fine-tune
many of the ideas floating in my head. Danielle Sullivan Kelly was instrumental in,
specifically, teaching me grammar. I appreciate the time, patience, and skill needed to
read my work.
Catherine Chauvin deserves acknowledgement for lending me a quiet place to
work, proofreading, driving to DTC, and testing the website. I appreciate the kindness
shown to my children and being an overall great friend. Thank you Logan and Evalyn
Reed; your patience and continual encouragement were vital to the completion of my
degree. I want to thank Susan Branch for testing the website and listening to my
exhaustive talk of school. Acknowledgement should also go to Marie Sullivan for being
so vocally proud of all my accomplishments.
During the course of my dissertation, Don Powers provided excellent statistical
explanations and advice. Matt and Angela Baca watched my children while I conducted
research. Michael Kelly tested the study’s website. Ken, Anne, and Sharon Reed listened
and gave encouragement. The family I developed at Four Mile Historic Park bestowed
unlimited support.
I want to thank the kind administration and faculty at the testing site for allowing
me into their school and classrooms. In particular, I want to thank those who both helped
v
as expert panelists and with the research exercise: Michael Chavez, Sharon DiIorio,
Joshua LeConey, Steve Pierce, Edward Popovitz, and Roger Rios. Thanks to those who
kindly tolerated my class disruption, Todd Debreceni, Daniel Levine, Kim Tempest,
Wesley Price, and John Wilbanks. A special thanks to Jon Kerbaugh and Chris Chen
Mahoney, and Lansford Holness for granting permission to conduct the study and
ensuring I had all the information needed to make it happen.
Thanks to Namrata Gupta, Mark Gupta, and Betsy Rivers for allowing me to use
the great preparation guides created at uCertify.com. A special extra thanks to Mark
Gupta for believing in my research, when I could not get any other company to listen. I
would like to acknowledge Carlos Contreras, Evan Straub, and Keith Ciani for providing
direction through the dissertation process. Finally, to the wonderful hardworking team of
advisors at Capella University, I could not have survived without you. In particular, thank
you Farrah Fossum and Michael Franklin for expert guidance and support.
No matter how large or small the help, your love and support has gotten me to the
title of Doctor of Philosophy.
vi
Table of Contents
Acknowledgments iv
List of Tables viii
List of Figures ix
CHAPTER 1. INTRODUCTION 1
Introduction to the Problem 1
Background of the Study 2
Statement of the Problem 5
Purpose of the Study 5
Research Questions 8
Significance of the Study 8
Definition of Terms 9
Assumptions 11
Limitations 13
Nature of the Study 15
CHAPTER 2. LITERATURE REVIEW 16
Theoretical Framework 18
Review of Research on the Topic 22
Review of Methodological Literature 52
CHAPTER 3. METHODOLOGY 85
Purpose of the Study 85
Research Design 86
Target Population and Participant Selection 89
vii
Procedures 93
Instruments 98
Hypotheses 106
Data Analysis 107
CHAPTER 4. DATA COLLECTION AND ANALYSIS 108
CHAPTER 5. RESULTS, CONCLUSIONS, AND RECOMMENDATIONS 119
Discussion of Results 124
Discussion of the Conclusions 128
Limitations 131
Recommendations 135
Conclusion 137
REFERENCES 138
APPENDIX A. PHOTOSHOP EXPERT PANEL HANDOUT 153
APPENDIX B. INDESIGN EXPERT PANEL HANDOUT 158
APPENDIX C. PHOTOSHOP INSTRUMENT 163
APPENDIX D. INDESIGN INSTRUMENT 165
viii
List of Tables
Table 1. Research Design 86
Table 2. Results of the Photoshop Expert Panel 104
Table 3. Results of the InDesign Expert Panel 105
Table 4. Frequency of Sample Participants for Each Degree Program 111
Table 5. Software Descriptive Statistics by Class 112
Table 6. Descriptive Statistics 113
Table 7. Levene’s Test of Equality of Error Variances 114
Table 8. Homogeneity of Regression–Tests of Between-Subjects Effects 114
Table 9. Factorial Design Analysis–Tests of Between-Subjects Effects 115
ix
List of Figures
Figure 1. Comparing Posttest Means of Software and Noting Theory 116
Figure 2. Comparing Means of Theory and Noting Software 117
1
CHAPTER 1. INTRODUCTION
Introduction to the Problem
There is a growing list of professions (especially those in design) using multimedia
software, which has brought about an increased prevalence of college courses teaching
computer programs such as Photoshop, InDesign, Flash, and After Effects (U.S. Department
of Labor, 2008). Students of such classes are expected to learn generalities of the programs,
while understanding finer details, so they can apply these skills in the workplace once
training is complete (as shown in the testing site’s online profile for 2009). The type of
learning described requires an instructor well trained in the software and equipped with
adequate teaching methods. This influx of students seeking computer software knowledge, as
well as the need for suitable instruction, gives cause to an exploration of the validity of
specific learning theories (McKenna & Laycock, 2004).
Accredited colleges educating students on computer software recognize the need for
teachers who have constantly updated training on ever-changing programs (Accrediting
Commission for Community and Junior Colleges [ACCJC], 2002; Commission on Colleges
[COC], 2010; Commission on Institutions of Higher Education [CIHE], 2005). Colleges
achieve up-to-date instruction by employing individuals from the technology industry, which
ensures relevant education in the discipline and daily usage of the software. While this
implies the person has knowledge on the software, it does not necessarily translate to
teaching ability. Good instructional skills are imperative; a major effect of nonconstructive
2
teaching methods is the failure of information transferring to long-term memory (Kirschner,
Sweller, & Clark, 2006). This is seen in the inability of students to learn, retain, and apply
techniques used within the software. Consequently, it is important to pinpoint adequate
methods of instruction for the students, to aid teachers not formally trained to educate. The
following sections will illustrate this study’s intentions to identify and evaluate particular
learning theories, which may assist multimedia software instructors in their endeavor of
instructing college level students.
Background of the Study
Learning theories have dominated throughout history, as people sought to teach
themselves and others about the world. Within the realm of this study, two learning theories
(constructivism and behavioral learning theory) have been chosen for research because of
their distinct characteristics, and existing prevalence in the education system. The debates
over constructivist, as opposed to behavioral (instructivist) theories, are well published. Some
articles comparing the theories analyze them theoretically, in the context of scheduling,
instructing mathematics, and teacher education (Baylor & Kitsantas, 2009; Boghossian,
2006; Hackmann, 2004; Mvududu, 2005). The articles weigh the options of each
philosophy’s teaching methods, many going beyond conjecture with experimentation, and
most deriving dissimilar results or determinations. While the published information is helpful
in identifying the particulars of each learning theory, it does not pinpoint the essence of this
proposed study.
Reviewing the previously stated studies, it would seem a significant result between
the two learning theories depends highly on what is being studied. This could give great
3
comfort, as well as a fair amount of confusion to instructors. There is no absolute right or
wrong answer to the best general learning theory to use. Each learning situation is different,
due to the information taught, and thusly, the most appropriate learning theory may be
distinctive because of this divergence.
The instructivist method of instruction is the traditional manner of teaching
information in a sequential style and a focus on the end goal, which is assessment (Baylor &
Kitsantas, 2009). The behaviorist model is known as a teacher-centered learning
environment. In short, the teacher’s primary mission is to provide knowledge, while the
student must acquire the knowledge (Boghossian, 2006). This approach is successful because
it relies on clearly defined goals, based on rigorous instruction, and subsequent assessment.
The constructivist learning theory is based on a student-centered environment (Baylor
& Kitsantas, 2009). This method uses engaging instruction to provoke higher order thinking,
which facilitates knowledge construction. The approach employs realistic learning
environments, social classrooms that encourage multiple perspectives, and self-awareness of
one’s own learning capabilities. Contrary to behavioral learning theory, the goal of the
constructivist instructor is to provide support, while the student engages in the active process
of constructing knowledge (Boghossian, 2006). This method is successful because it focuses
on the process of learning.
An article that greatly influenced the variable selection used in this study is a
publication by Stephanie Clemons from 2006. Seeking to accommodate the increased
demand of technology, Clemons (2006) constructed a case study designed to modify a
4
college Computer Aided Design (CAD) software course. Once properly altered, a single
course instructs twice the number of students previously held in the class.
Prior to Clemon’s (2006) change in curriculum and teaching methodology, per the
case study, the CAD course was taught using behavioral learning theory. The traditional
method utilized demonstrations of CAD techniques, exercises, and weekly assignments.
Conversely, the constructivism-based class was broken into three modules: learning the
software, plotting documents, and three-dimensional drawings. All modules were self-paced,
multi-week learning experiences encouraging each student to seek knowledge based upon
their own learning style.
The results of the case study noted a greater engagement of the student, increased
knowledge of the subject matter found within the three modules, more content learned during
the course, and successful understanding of problem-solving (Clemons, 2006). The results
were based upon an assessment of final projects, which provided an evaluation of CAD
skills. The findings of this study were derived from an immersion of the entire class in a
single specific learning theory.
While the article provides an excellent resource of constructivist learning, a strict
quantitative approach evaluating both constructivism and behavioral learning theories is
warranted (McKenna & Laycock, 2004). A measurable method analyzing the specific
knowledge a student acquires through a particular teaching method will give an accurate look
at the techniques used. In addition, quantitative analysis allows the student’s prior knowledge
to be accounted for in order to sift out inaccurate results (Frederickson, Reed, & Clifford,
2005).
5
Statement of the Problem
The research problem explored was the suitability of constructivism versus behavioral
learning theory, regarding teaching multimedia software. Due to the fact multimedia software
encompasses a large variety of computer applications, this study also analyzed whether
differing software packages accounted for any learning differences. For example, Photoshop
and InDesign software may have similar users, but generate completely different documents
made for dissimilar projects. In particular, Photoshop’s primary objective is to edit
photographs and create graphics, whereas InDesign is used for page layout and publishing
(Adobe Systems Incorporated [Adobe], 2009). With this reasoning in mind, the study sought
an answer to the question: since the software itself evokes differing ways of thinking, does it
require a particular learning theory?
Purpose of the Study
The purpose of the study was to analyze and find evidence for a beneficial learning
theory to teach computer software programs. This included testing students’ knowledge on
particular software before and after a lesson to accurately conclude whether the students
tested higher after a constructivist or behavioral lesson. Furthermore, due to the variety of
software available, establishing a single learning theory’s applicability for a specific program
was beneficial. This could reveal a learning theory’s favorable use across multiple programs,
general detriment to software instruction, or whether certain software requires a particular
method of education.
An example of potential results and meaning would be the behavioral learning theory
producing the highest scores for participants when tested through Photoshop, and
6
constructivism demonstrating the most beneficial learning theory when teaching InDesign. In
this case, one could speculate that every software program must be tested to verify the most
advantageous learning theory. Alternately, if the constructivist theory resulted in the highest
scores for both Photoshop and InDesign, then the single learning theory could potentially be
equally beneficial for most types of computer software instruction. Furthermore, the results
will support the use of particular learning theories or demonstrate a need for further research.
With regards to the study’s benefits to education and instructors in general, collegiate
institutions strive for accreditation to demonstrate competency within their organization;
therefore, schools voluntarily take note and abide by accreditation standards (Higher
Learning Commission [HLC], 2010). Regional accreditation is provided, according to
locations, by six associations. Although the accrediting bodies are independent, they work
together to ensure consistency. The purpose of accreditation is to ensure the educational
excellence of students’ learning through continuous improvement of quality, effectiveness,
and accreditation standards compliance (Accrediting Commission for Community and Junior
Colleges [ACCJC], 2002; Commission on Colleges [COC], 2010; Commission on
Institutions of Higher Education [CIHE], 2005).
A standard pertinent to the current research problem is faculty qualifications.
Analyzing some of the regional accrediting agencies will reveal a thread of consistency, but
slight differences in approach. The Higher Learning Commission (2010), which gives
regional accreditation to North Central States, asserts that faculty should have at least a
degree higher than they wish to teach, or terminal degree in the case of graduate education. A
considerable amount of the possessed degree should be within the discipline the instructor
7
wishes to teach. Other required knowledge includes curriculum design and successful
pedagogy strategies.
The Commission on Colleges (2010) accreditation association of Southern States
places the burden of proof in the hands of the school, requiring justification of each
instructor’s qualifications to acquire accreditation. The assessment criterion for a professor
primarily focuses on his or her earned degree. Additional aspects considered are field
experience, licensure, certification, and teaching accomplishments. The Commission on
Institutions of Higher Education (2005), which accredits North Eastern States, considered
New England and its surrounding areas, briefly affirms the need for schools to take into
account the level and particular field the educator wants to teach to determine qualification.
With this knowledge, appropriate measurements of degree, teaching ability, professional
experience, and other credentials are apparent.
In compliance with faculty standards, colleges with computer related classes will seek
instructors with a background in the discipline they are teaching. Consequently, many
technology software teachers do not have a formal educational background, because it is not
required for accreditation (ACCJC, 2002; COC, 2010; CIHE, 2005; HLC, 2010). These
teachers are often sought after, because of experience within their career in using a range of
software packages, or a distinct focus and background within specific software. For example,
a web designer with extensive knowledge of Flash and ActionScript (Flash scripting
language), may be the perfect candidate for a technology college. Unfortunately, knowledge
within one’s field does not automatically translate into being an effective teacher.
8
The outcome of this study should give educators more effective teaching tools, for
students to ultimately get the most out of any particular software program. This was achieved
by researching two widely used learning theories within the realm of natural learning (the
classroom). In narrowing to specific software, the study may identify whether differing
applications of learning theories are required for precise focuses of learning (Lawless &
Pellegrino, 2007). Furthermore, the results found will give instructors of the software
programs a defined and successful teaching direction, while also translating to a wider
understanding for them to build upon. Armed with this study’s results from a real classroom,
the computer software instructor can build his or her class curriculum around the proper
learning theory for the software being taught.
Research Questions
Research Question 1: Is constructivist or behavioral learning theory more beneficial
when teaching multimedia software?
Research Question 2: Is there a difference in the effectiveness of learning between
Photoshop and InDesign when teaching multimedia software?
Research Question 3: Are there interactions between learning theory and software
with regards to teaching multimedia software?
Significance of the Study
The value of this study comes from additional significant information added to the
established constructivist and instructivist debate, which is important to psychologists,
educators, national education associations, and governmental groups concerned with
education (Cronjé, 2006, Kozma, 2003; Lunenberg, 1998). While there may never be a
9
definitive answer on whether the constructivist or behavioral theory is better, as seen with the
multitude of conflicting results found in articles, this study intended to find evidence on
whether the discrepancy is due to the variability of subject matter (Baylor & Kitsantas, 2009;
Boghossian, 2006; Hackmann, 2004; Mvududu, 2005; Saljo, 2009). No one learning theory
has been accepted to teach; this may be due to the lack of a single theory’s suitability to teach
all subjects (Lawless & Pellegrino, 2007; Saljo, 2009). While a single theory may not be
blanketed to teach all, this does not rule out a theory’s validity for a specific subject. In
researching several learning theories’ appropriateness for specific use, the general question of
range of applicability will be addressed.
The continued quest for knowledge on specific subjects always calls for a reflection
on previous literature; hence, the research found in this study could provide a jumping-off-
point for further research. Moreover, the blending of learning theories specific to psychology
and educational values with technology makes this study quite relevant to the field of
educational psychology (Lawless & Pellegrino, 2007). Since no study is absolutely free of
errors, the quality features and shortcomings will add information to the existing education
and technology body of literature. Additionally, this study imparts a firm basis for further
research on teaching technology software.
Definition of Terms
The first construct is learning theory. This relates to the broader sense of differing
methods used to turn information into knowledge, but is specifically looked upon as the
informational delivery scheme used by an instructor in a classroom setting (Cooner, 2010;
10
Harris, Mishra, & Koehler, 2009; Zhang, 2010). A multitude of variables can fall under the
construct learning theory; therefore, the amount had to be narrowed for the study.
Constructivism and behavioral learning were chosen for learning theory, because of
their seemingly opposing methods of instruction. Constructivism encourages learning by
interacting with the information, since knowledge is individually constructed based on
personal interpretation (McKenna & Laycock, 2004). Alternately, behaviorists believe
knowledge is objective and can efficiently be learned through drill-and-practice exercises.
Manageable units of information can easily be communicated to the learner because
knowledge is seen as independent of the student’s subjective mind.
The construct learning theory will be measured as a choice of constructivism or
behavioral learning. These nominal variables will be assigned according to the random group
placement of the participant.
The second construct is multimedia software. The construct is a broad category of
programs written for specific design operations on the computer (Adobe, 2009). This
construct could have many variables as well, but only two were chosen for this study. A
number of software packages are taught through the selected college, but Photoshop and
InDesign exemplify programs used by many, often in conjunction, but are utilized for very
different purposes (Adobe, 2009). The construct multimedia software will be measured as
either Photoshop or InDesign. These nominal variables will be assigned according to the
random group placement of the participant.
The last construct is knowledge, which is the measurable amount of retained
information on any particular subject matter within one’s knowledge base (Cooner, 2010).
11
Knowledge is split into two variables. Post-lesson assessment, the first variable, is the
student’s comprehension of information given through the lesson. The second variable is pre-
lesson assessment, which represents the student’s understanding of the subject prior to taking
the lesson.
The construct knowledge was measured using a portion of the uCertify Adobe
Certified Expert exam study guide. The exam, in its entirety, is an industry standard used to
measure an individual’s competency in a particular Adobe software package (Adobe, 2009).
The measurement is scored based upon correctly answered questions and requires an
accuracy of at least 70% for an individual to pass the exam (Adobe Partner Connection
[APC], personal communication, October 28, 2009). The portions of uCertify Photoshop
ACE and uCertify InDesign ACE exam study guides used will specifically measure the
subject’s ability with elements of those computer software programs.
Assumptions
For the first assumption, it is important to understand the interpretation of learning
and the experimental study of learning to comprehend the field of learning (Hill, 2002). This
theoretical assumption directs the belief that lessons and experimentation in the classroom
should lead to a better understanding of the student’s learning as a whole.
A topical assumption for this study is the general materials within the lessons given
via the computer and those in the classroom setting are essentially the same. The difference is
only seen through the application of learning theory, which renders the delivery method
inconsequential. The assumption is made with the knowledge of potential differences, but the
belief that the study’s focus renders the disparity insignificant. This assumption should stand
12
valid because Frederickson, Reed, and Clifford (2005) found the quality of the instruction
outweighs the course delivery.
Due to the varying features, intended uses, and breadth of software currently
available, the assumption that some software may be more demanding to learn is a factor.
Due to this topical assumption, multiple software packages were tested to identify any
differences.
The quantitative methodology dictates any data reported as truth must be void of
researcher subjectivity (Taylor & Kermode, 2006). This methodological assumption,
objectivity absent of human distortion, shaped the research design of the study.
The second methodology assumption is the belief there is a cause to every event,
which is influenced by recognized or unknown conditions (Cohen, Manion, & Morrison,
2007). Furthermore, connections between these non-capricious, natural world causes and
conditions can be found and studied. This identification and understanding allows for the
development of scientific laws on what to expect in such an event. The expectation of
determining cause and event influenced this study’s research design.
The last methodology assumption is reliable knowledge as the result of experience
(Cohen, Manion, & Morrison, 2007). In the realm of science, this experience is interpreted as
empirical evidence for a theory or hypothesis. Empirical evidence is derived by research,
classification, quantification, relationship discovery, and the approximation of truth. The last
assumption guided the research design choices within quantitative research and
experimentation.
13
Limitations
The first limitation of this study was the use of non-probability sampling. In order to
test the subjects in their normal classroom environment and ensure as little disruption to the
class as possible, convenience sampling was utilized. The sampling procedure tested multiple
sections of Digital Image Manipulation, which was the introductory Photoshop course.
Additionally, various sections of the Digital Layout (InDesign), Digital Illustration or Digital
Typography (Illustrator) classes were employed. Digital Illustration and Digital Typography
were used as additional classes since they were prerequisites for Digital Layout. Utilizing the
students from the Illustrator class ensured the study achieved the required amount of subjects.
Non-probability sampling is a limitation because it affects the study’s external
validity. To ensure generalizability, it is important for relationships among variables to
remain robust (Hultsch, MacDonald, Hunter, Maitland, & Dixon, 2002). Typically, a suitable
representation is accomplished by using randomized sampling, which yields a broad
illustration of the population. Since this study is not using random sampling, it is difficult to
determine whether the chosen sample actually represents the population as a whole.
Using computer mediated instruction for lessons and quizzes may also be seen as an
additional limitation. An argument might be made that instruction given via computer has a
closer resemblance to online learning than traditional classroom learning. This opens a
debate with the intention of proving the instructional delivery methods may not be
comparable. The question over online versus traditional learning is well established and
conclusions run the gamut. Some authors report in favor of traditional, whereas those in
opposition support online learning, while others dispute any difference between the two
14
(Edmonds, 2006; Poirier & Feldman, 2004; Waschull, 2001). An assumption, stated earlier,
was made to account for this particular limitation, which notes the only difference in learning
as the application of learning theory (Frederickson, Reed, & Clifford, 2005).
The next limitation this study may have faced was learners with a non-computer
oriented focus might have greater difficulty learning the software due to inexperience. A
student with very little knowledge of computers might face a dramatic learning curve by
simply learning the operating system, without the additional mental effort needed to learn in-
depth software. This is due to the amount of errors experienced by novice computer users
versus more computer-literate students (Kay, 2007). Errors are found to disrupt learning;
therefore, the more errors that occur, the harder it is to learn the software.
The last limitation found was the use of the same testing method for all groups. It
could be argued the assessment, modified uCertify Adobe Certified Expert (ACE) study
guide exam, was conducive to the instructivist views of teaching and testing, but
counterintuitive for constructivist beliefs (McKenna & Laycock, 2004). The appropriate
assessment format for the constructivist instruction would be authentic testing, applicable to
the information taught. To apply the assumption, a behaviorist exam would be used to test
the behaviorist lesson and constructivist exam for the constructivist lesson. Regrettably,
employing tests with a contradictory basis brings about the questions: Is the difference in
scores caused by the variables or a divergence in the tests? Are the tests actually equal? Is
there a way to make such dissimilar tests equivalent?
The ACE assessment was used to ensure consistency in testing by implementing an
industry standard exam. This exam was not available in a constructivist relevant format.
15
Furthermore, the decision to utilize the ACE exam would stand no matter its basis, since it
was the only accepted exam on the market for gauging Adobe software knowledge.
Ultimately, this was the test all students would take for certification in the design field.
Regardless of the method of gaining knowledge, the Adobe Certified Expert exam was the
standard design certification employers expected to see on a resume.
Nature of the Study
The study of learning follows a belief, which denotes understanding and meaning are
derived from the structure, organization, and delivery of information (Fardanesh, 2002).
While learning theories are resources that can guide an individual to an area of solutions,
these theories cannot determine the actual solution. Accordingly, the experimental study of
learning was born of necessity to assess theoretical learning systems, and derive appropriate
applications to deal with those theories (Hill, 2002). The interpretation of how individuals
learn and experiments concerning the study of learning are a necessary pair for the
understanding of learning.
Learning theories include a myriad of philosophies that individually highlight a
particular process of learning (Hill, 2002). Remaining mindful of the specific theory,
experiments, as well as the larger picture as a whole, the researcher will have a better
understanding of learning conditions and possible solutions to learning problems. The
comprehensive definition over the many facets of learning theories and experimentation
drives the conceptual framework of this study. Thus, the particular structure and basis of
research, which connects the concept of this inquiry, is a learning theory framework.
16
CHAPTER 2. LITERATURE REVIEW
No matter the subject, theme, or method of delivering information, educators are at
the heart of learning (Merriam, 2008). The transcendence across setting and student
population leads to a determination to understand the act of learning. The more the
educational community understands how students learn, the better each instructor becomes at
structuring learning activities to facilitate knowledge. Popular beliefs understand learning in
a myriad of different ways. Some theorists consider learning a purely cognitive process,
where the mind takes in information and converts it to knowledge. This knowledge can then
be observed as a behavioral change. In opposition, learning is seen as a widespread endeavor,
including the individual’s mind, body, and emotions.
Theories on the act of learning have seen fluctuations of favor as the modern world
and educational system have changed (Aguilera & Lahoz, 2008). Teaching techniques have
evolved in adaptation of newer resources and learning environments. Technological advances
have created new tools for teaching and learning to the extent that government agencies
heavily invested monetarily to encourage the use of technology in schools (Lawless &
Pellegrino, 2007). This overt encouragement is also a response to the enormous movement of
technology in the workforce.
The weight of an ever-changing world is felt by all who have an association with
education (Aguilera & Lahoz, 2008). In response, researchers have conducted studies
implementing various learning strategies. Unfortunately, it becomes apparent when analyzing
each study’s results that no single inquiry has the breadth to adequately reflect an
17
instructional approach to handle all subjects, situations, and students (Lawless & Pellegrino,
2007). As a result, the current study focused on particular applicable theories with relevance
to actual teaching situations. Accordingly, the examination of two specific learning theories’
appropriateness for teaching distinct multimedia programs was conducted within a college
environment.
The literature review chapter will give a look into the study’s structure, theoretical
framework, as well as constructs to be analyzed. The constructs include learning theory,
multimedia software, and knowledge. Furthermore, a review of relevant literature
contributing to the discussion of methodological choices will be discussed. This involves
common and alternative methodological approaches to research on the topic, as well as the
current study’s approach. Additionally, instructional delivery and assessment will be
examined.
The strategy used to gather data for this study primarily rested with a review of
published journal articles, but also utilized books to fill in gaps of information. Individual
resources were also acquired by consulting relevant articles’ references. The Denver Public
Library system was used to access books, which includes Prospector and WorldCat
interlibrary loans. The articles were derived from multiple electronic databases: Academic
Search Premier, Business Source Complete, CINAHL, ERIC, Health and Psychosocial
Instruments, Library Information Science & Technology, psycARTICLES, psycBOOKS,
psycINFO, Regional Business News, socINDEX, and Mental Measurements Yearbook with
tests in print. Additional databases include: ABI/INFORM Global, Dissertations and Theses,
18
ProQuest Educational Journals, ProQuest Medical Library, and ProQuest Psychology
Journals.
The search criteria used to explore the databases can be categorized by theoretical
framework and constructs. The search phrases used to find information about the learning
theory framework was: learning, education, instruction, teachers, instructional systems,
instructional technology, pedagogy, instructional design, learning sciences, teach, and
learning theory framework. For the construct learning theory the following words were
searched: behaviorism, constructivism, cognitivism, cognitive theory, cognitive science,
construct, learning theory, objectivism, direct instruction, and instructivism. The construct
multimedia software employed: software, computer, technology, Flash, Adobe, computer
software, software packages, Photoshop, InDesign, design software, computer programs,
computer software industry, e-learning software, and computer systems. These search
statements were additionally used within the multimedia software category: multimedia,
multimedia materials, multimedia instruction, media programs education, multimedia
software, multimedia systems in education, computer-aided design, informed design,
communication systems, multimedia systems. Lastly, the following phrases were used for the
construct knowledge: theory of knowledge, knowledge, prior knowledge, thought and
thinking. All searches explored the given expressions by using both the title and subject
filters.
Theoretical Framework
Theories within a field can be as important as the discipline itself, since models and
frameworks resulting from them are vital for the area to remain viable and credible (Gorsky
19
& Caspi, 2005). If results are not grounded in theory, they are simply data gathered around a
particular subject matter. The theoretical framework explains events, structures questions,
and allows researchers to test their study empirically. Consequently, to understand the human
behavior and practice associated with education, one must turn to a learning theory
framework. Accordingly, the theoretical framework will be discussed, as well as the pertinent
definition of learning for this study.
Learning Theory Framework
In a society consumed with acquiring knowledge, learning has become quite visible
(Saljo, 2009). With this apparent visibility, many individuals across disciplines and traditions
of research have come forth, each offering their own opinions and insights. The multitude of
learning concepts also means a large amount of potential ways to analyze each model. The
unit of analysis and level of inquiry ranges from the molecular examination of neuroscience
and surveys in social science, to the complex testing instruments of psychology.
Moving briefly away from technical studies of learning, it is also important to note
the concept is quite common in day-to-day language. Learning is frequently used to describe
an individual’s experiences (Saljo, 2009). Any student may be casually overheard saying
they learned a lot from their lesson of the day. The student’s statement can be taken as a
report of their experience, and recognition that learning is important within the role of human
speech. This is significant because the beliefs a person holds about learning and educational
settings plays a part in how the person approaches actual learning tasks (Loyens, Rikers, &
Schmidt, 2007a; Saljo, 2009).
20
Bringing traditional and researchers’ perspectives together shows the concept of
learning is used in many practices, contexts, and language exchanges (Saljo, 2009).
Identifying these facts and examining them within the realm of human practices leads to a
more complete picture of learning itself. Developing this understanding allows researchers to
see what qualifies as learning within their theoretical perspective and ultimately reveals what
is occurring and why.
There are two essential elements at the focal point of the learning theory framework,
teaching methods and the focus of learning, which is the student. Teaching methods are
largely personal to the instructor. Each set of methodology is composed of the teacher’s
beliefs, assumptions, and knowledge of learning and instruction (Young, 2008). These
conceptions are developed through learning experiences, interactions, and studies; thus, an
educator’s perception can shape views and facilitate the creation of his or her approach. To
encourage growth within teaching methods, the instructor must be shown the validity of a
particular method, as well as commit to consideration and integration of the new technique.
The individual learner is distinguished by many variables, which includes the ability
to learn, prior knowledge, goals, and motivation (Gorsky, & Caspi, 2005). These attributes
are important in determining the effectiveness and quality of learning occurring within the
student. The highly unique process each student engages as purposeful learning must be
taken into account when assessing whether learning has actually occurred (Gorsky & Caspi,
2005; Saljo, 2009). This structured manner of looking at learning provides the organizational
dynamics with which to research teaching methods used in an educational environment
(Young, 2008).
21
Learning Defined
Theoretical perspectives on learning are fragmented due to the immense diversity
within education (Saljo, 2009). While some see the dissimilar views as detrimental,
recognizing these differences gives researchers a frame of reference for significant
epistemological traditions of knowing and learning. Various contexts are required to
understand the many needs and priorities in a learning environment. Consequently, the
definition of learning is elusive and often conflicting. Settling on a particular definition
involves sorting through the variety of notions ranging from simple acts of observation to
complex explorations of language, memory, and comprehension.
Research within scholarly texts reveals many explanations of learning based upon a
change of behavior. Whether the modification of behavior is determined by the potential,
stable, or enduring form of change, the definition distinctly states it as purposeful, as opposed
to accidental learning (Saljo, 2009). This stance of learning works on a cycle where
information is internalized, then behavior is externalized to show the change in knowledge
(Conradi, 2000). Alternate explanations note learning as making sense of information. The
act of creating meaning requires learners to assimilate experiences into existing knowledge
(Fox, 2001). The view of learning as understanding takes into account the structure of an
individual’s knowledge.
Beliefs on learning have been oversimplified in such a way as to explain it as
memorization or understanding (Fox, 2001). The simple views can be slightly expanded upon
to include acquiring practical skills or the understanding of a particular topic, but it stands to
argue that remembering the learned concept is also important. Furthermore, it should not be
22
seen in categories of learning, such as driving, language, brickwork, or alphabetizing files.
Learning involves the transformation of an individual and activity (Saljo, 2009). As a result,
learning is defined as a person’s ability to advance his or her results based upon newly
acquired knowledge (Conradi, 2000).
Review of Research on the Topic
Learning Theory
For at least a century, learning has been a major element of psychology, which
involved varying presentations and outcomes of education (Valsiner, 2009). When studying
learning, the processes must be analyzed within the many fields of research (Saljo, 2009).
These traditions of research have complex relationships with each other; therefore, bridging
them is often impossible. This is due to the immense variation of what is believed to be
learned within a particular learning theory (Zito & Schout, 2009). Some theories focus on
simple changes in the individual, while others look for a complex or expressive
transformation.
A learning theory simply for theory’s sake is pointless, but theories with sound
theoretical foundations, which improve curriculum and evaluation, are invaluable (Hean,
Craddock, & O’Halloran, 2009). Learning about useful theories requires research into their
assumptions, epistemologies, and nature of existence to understand the compatibility to
specific aspects of education (Saljo, 2009). Many theories of learning have influenced and
enriched psychology’s study of education, but two of the most recognizable are behavioral
learning and constructivism (Hean et al., 2009; Zito & Schout, 2009). This section will
analyze these important learning theories.
23
Constructivism. The educational community has seen a fluctuation in popularity for
many learning theories, but none so much as the enormous growth in the status of
constructivism over the last few decades (Al-Weher, 2004; Colburn, 2000). The
constructivist point of view spreads throughout a student’s school life to influence standards,
values, and practices (Al-Weher, 2004). Additionally, learning, knowledge, and teaching are
also distinctive within the realm of constructivist thinking. Knowledge is personal to the
learner. Consequently, what one person perceives as reality, may not be what another sees as
true (Al-Weher, 2004; Colburn, 2000). In order to construct a new idea, the student must
actively transform information by creating hypotheses and making decisions (Connolly,
Stansfield, & Hainey, 2007).
In constructivist learning environments, it is important for the instructor to mediate
the student through the process of learning (Al-Weher, 2004; Mvududu, 2005). This structure
is relevant for any activity or social setting, and takes into account the student’s prior
knowledge, what can be accomplished, as well as how a state of knowing can be achieved
(Mvududu, 2005). Furthermore, constructivism is a theory with many facets. The current
study allows many different views of the theory, while distinctly turning away from any
social learning aspect of constructivism to use a more cognitive approach. This allows for an
even comparison with behavioral learning, which is a theory focused on the individual. By no
means does this limit the study’s use of constructivism, since it is a vast theory centered on
knowledge that is distinctive to the learner. The sections within this heading will explain the
principles of constructivist learning theory in further detail.
24
Personal construction of reality. At the root of constructivist beliefs is the vastly
intricate human mind. Within the mind is knowledge, which is developmental, internally
constructed, and nonobjective (Herring, 2004). Accordingly, knowledge cannot be passively
absorbed; the individual must actively construct his or her own knowledge (Lunenberg,
1998). Students cannot be information recorders. Instead, they must build structures of
knowledge. As a result, students are responsible for learning within an educational
environment.
Students in constructivist educational atmospheres are young scientists, actively
testing and exploring the world around them to develop understanding (Edwards, 2005).
These active participants are playing the part of the knower in the spectator theory (Phillips,
1995). An example of the spectator theory is learning ballet. The spectator seeks to learn
ballet movements by watching a performance from the seats in a theatre. Alternately, the
knower dons ballet shoes and learns while performing. The dynamic interaction with the
process of movements makes the student an organic part of learning.
The actual construction of knowledge is an intellectual transformation, which occurs
in a unique process within each individual (Gordon, 2009). The student must interpret any
new information by relating it to previously held knowledge on the subject (Loyens et al.,
2007a). This significant process of elaboration reconciles instructional encounters with
existing knowledge (Gordon, 2009; Loyens et al, 2007a). It is this struggle between current
personal models and new insights that causes the meaning–making endeavor to be distinctive
for each person (Cooner, 2005; Herring, 2004). An individual uses his or her own unique
25
mental structures, previous experiences, and beliefs to construct a personal understanding
(Clemons, 2006; Herring, 2004). This, in turn, creates an individual reality.
A person’s truth created through experiences, learning, and understanding can only be
viewed as his or her current reality (Henry, 2002). As a result, it is important for educators to
realize the marked change that must occur to accommodate learning. The constructivist
perspective of knowledge alters a student’s pursuit of objective truth to a search for the
consensus of valid perspectives (Cooner, 2005).
Teacher and student roles. Constructivist learners create meaning from their own
experiences. Each person’s subjective experiences are equally as valid as other’s encounters,
which gives no single person a privileged viewpoint (Boghossian, 2006). This idea is
changing traditional rules in the classroom to reflect that the knowledge one person possesses
might not be the same as what someone else holds true. The roles held in a constructivist
classroom both by the teacher and student are quite altered as compared to traditional
classroom responsibilities (Dalgarno, 2001; Sutinen, 2008).
In order to learn, the constructivist student must build on his or her prior experiences,
which is different from all other previous experiences of learners in the class. To facilitate an
opportunity for all students to relate to their own experiences, the students should be in
charge of what they are learning, account for differing learning styles, and the information
given within a context the students can easily relate (Dalgarno, 2001). Since the process of
learning is active, the focus should veer away from formal instruction to student’s activity.
The student-centered learning environment predominant among constructivist
classrooms develops meaningful learning, which promotes higher order thinking. This type
26
of setting is achieved by providing multiple perspectives and modes of representing
information, immersing the student in realistic learning situations, and encouraging self-
awareness and ownership of the learning within the knowledge construction process (Baylor
and Kitsantas, 2005). These independent students actively participate in learning by
exploring knowledge, problem solving, discussion, as well as designing and executing
projects (Al-Weher, 2004). In addition, it is important for learners to respect others’ views
even though they are different from their own.
The optimal student produced from a constructivist environment is a self-regulated
learner (Loyens et al., 2007a; Loyens, Rikers, & Schmidt, 2007b). Self-regulating one’s own
learning is successful for future knowledge in and out of school. This type of inner directive
is typified by setting and achieving goals, as well as taking responsibility for assessing,
observing, and reinforcing your own learning (Loyens et al., 2007b). Additionally, the
individual must understand which learning strategies are the most appropriate for what he or
she is studying (Loyens et al., 2007a). The self-regulation must permeate all areas of
educational activities including the underlying beliefs, cognitions, and intentions to reach the
full potential of achievement (Loyens et al., 2007b).
Students in a traditional classroom are not accustomed to real-world learning
activities or self-regulation; instead, the teacher controls the direction of class interest and
learning in general with an emphasis on achieving the correct answers (Mvududu, 2005).
Conversely, primary sources serve as a conduit in constructivist learning, which provide raw
materials for the student to relate to in his or her own way (Henry, 2002). Traditional
instructors present students with predigested information from a point of view based on their
27
experiences. On the other hand, primary sources supply the authenticity needed for a true
understanding of the materials.
A constructivist teacher does not hold the key to knowledge. Alternately, the
instructor becomes the facilitator as he or she supports the construction of knowledge, and
provides experiences with which students’ develop critical thinking and problem solving
skills (Neo & Neo, 2010). Instead of providing ready-made results, the teachers encourage
the students to orient their own path of exploration and resolution to knowledge construction
(Mvududu, 2005; Simpson, 2002). In the role as a facilitator, instructors must be prepared to
allow their students to expend energy struggling with problems, which may or may not have
right solutions (Mvududu, 2005). The students’ temporary state of confusion leads to the
confidence needed to achieve understanding. The mental experimentation learners engage
allows them to experience new ideas, interpret, reason and reflect on the encounters, as well
as the process of reasoning itself (Gholson & Craig, 2006).
As a facilitator, the teacher must be mindful of students’ growth and learning needs.
As such, authentic learning situations should be provided in a non-threatening environment,
which encourages free thought without hesitation (Al-Weher, 2004; Sutinen, 2008). Lastly,
instructors should also reflect on their own learning approaches to thoroughly implement
constructivist teaching and learning (Al-Weher, 2004).
Thinking and experience. Constructivism began as a human development theory, but
has been integrated into education and the nature of learning itself (Clemons, 2006). When
concepts and information are presented in a constructivist learning environment, the student
is responsible for evaluating the information and directing the process of inquiry. The unique
28
stance on knowledge is also worth noting, which is viewed as a working hypothesis since
knowledge is formed from within, as opposed to information forced from outside the
individual. Accordingly, the transmission of information from an instructor to student is
inadequate (Al-Weher, 2004). More appropriately, the student maneuvers through a process
of interpretation allowing information to be compared and integrated with prior knowledge.
Thinking is the result of a perceived incomplete event within a situation (Sutinen,
2008). The unfinished occurrence incites the process of inquiry, thinking. Once a problem
emerges, the person must interpret it according to his or her previous experiences. Next,
problem analysis begins, and a personal hypothesis is formed. Lastly, the hypothesis is
tested, which produces the problem’s solution. Essentially, thinking is the process of deriving
significance from doubt and uncertainty.
Thinking is not mechanistic; instead, it is a creative activity enabling an individual to
produce multiple solutions for a myriad of problems with the integration of ideas (Sutinen,
2008). The final outcome of each person’s recurrent functional experiments, also called
thinking, is often never the person’s original intention. The new line of cognitive activity
then reinserts itself into the mind as an experience. An experience, which can be a passive or
active element, is the connection between the person and the outside world.
We experience the world around us by acting upon things and enduring the
subsequent consequences (Sutinen, 2008). As a result, all experiences are distinctive to each
individual. People learn from these experiences, but an additional factor is needed to achieve
understanding. Memory keeps each encounter stored, so past experiences continually direct
the person’s actions towards the future. Ultimately, knowledge is gained from imperfect
29
events, causing the individual to think and subsequently acquire a new experience (Al-
Weher, 2004; Sutinen, 2008).
Problem-based learning. Learning and achievement within the constructivist
movement is the product of knowledge construction and self-regulation (Loyens et al.,
2007b). In order to encourage these qualities, the information must first be meaningful to the
student (Fyrenius, Bergdahl, & Silen, 2005). This awareness comes from the student’s belief
that data is related to previously acknowledged phenomenon. These criteria give context and
motivation for a new relevant experience. Reality based scenarios provides the relevance
needed to push the learner to become active in the learning process, which leads to the
integration of meaningful knowledge.
The goal of problem-based learning (PBL) is to connect learning, which occurs in the
school, with problems rising in the real world (Al-Weher, 2004). The authentic situation used
within PBL naturally integrates problem solving, inquiry, and action research. Additionally,
these situations encourage the wait time needed to produce multiple answers. This type of
learning environment uses real tasks and specific objectives to support meaningful learning
and build problem solving skills (Fyrenius, Bergdahl, & Silen, 2005; Loyens et al., 2007b;
Neo & Neo, 2010).
The authentic challenges found in PBL are ill-structured problems used to facilitate
learning (Loyens et al., 2007a). These circumstances mimic those found in professional
situations, essentially confronting students with problems potentially found in their own
future professions (Loyens et al., 2007a; Loyens et al., 2007b). Problem solving builds
reasoning, while the students develop a better understanding of the subject as a whole. This
30
type of learning is also seen when experienced people in a given field generate and utilize
gained knowledge (Loyens et al., 2007b).
As the constructivist discourse has grown strong, the educational community has seen
a powerful model emerge for producing meaningful knowledge, as well as explain how
students learn (Gordon, 2009). Since knowledge does not merely exist from a constructivist
standpoint, each angle a phenomenon is viewed changes the values a researcher considers
important. Consequently, each individual’s viewpoint coupled with his or her previous
knowledge has the potential for countless results. Eloquently stated, reflections of nature can
be seen in simple ideas, but only the human mind can construct complex ideas (Phillips,
1995).
Behavioral learning. The main principle of behavioral psychology is all changes
occurring within a person manifest themselves through their behavior (Mvududu, 2005). For
this reason, learning is a change in observable behavior due to reinforcement of a person’s
reaction to stimuli within an environment. Behavioral learning theory is a teacher-directed
approach, where students seek to accumulate knowledge, and instructors aim to convey
knowledge. It is the teacher’s responsibility to fill the empty vessels, which are their students.
The reliable knowledge found in the world must be translated by instructors, which is
then replicated and structured in the mind of the learner (Mvududu, 2005). This type of
structured instruction has been invaluable in improving the education of disadvantaged and
disabled people (Kozioff, LaNunziata, Cowardin, & Bessellieu, 2001). Since behavioral
learning works where other learning theories have failed, it is thought the theory is only
appropriate for those populations. On the contrary, behavioral learning has been field tested
31
and found effective with a myriad of populations, which includes average, challenged, and
exceptional students. The remainder of the behavioral learning sections will discuss
important concepts surrounding this philosophy.
Behavioral learning history. Behaviorism had many important contributors, which
helped shape the theory; one being a completely separate philosophy and the other was
influential theorists within behaviorism itself. Firstly, the philosophical movement positivism
had a strong impact (Boghossian, 2006). Positivists only acknowledge natural occurrences
and characteristics of knowable phenomena, as well as the conformity and orderly sequence
of empirical truth. They also believed experimentation and observations were the only true
methods of determining relationships. If only externally viewed phenomena can be accepted,
then any subjective ways of ascertaining understanding is discredited.
Early behaviorists also shaped the theory with a firm stance on what can be learned
from the behavior of humans and animals. Two of the most popular theorists were John
Watson and B. F. Skinner (Overskeid, 2008). Watson (1913) took psychology from the study
of consciousness and analysis of mental states, to the deconstruction of complex states into
simple elements. Furthermore, he believed the straightforward factors, an organism’s
stimulus and response, should be analyzed. Shaking off the strongly held need felt by other
psychologists to examine consciousness, Watson realized habit formations and integrations
were the means of adjustment to an environment. This indicated a particular stimulus led to a
certain response because of hereditary and habits, which changed the viewpoint of
psychology to see the science of behavior could stand as independent.
32
Skinner furthered Watson’s legacy by moving beyond prediction and controlling
behavior to integrating understanding as the goal (Overskeid, 2008). He made headway in the
field of behaviorism with operant conditioning, which is associative learning where the
response is contingent on the appearance of the reinforcement (Costall, 2004). The
relationship between a behavior and the environment is important to determining the
meaning behind the behavior (Overskeid, 2008). No matter the particular contributor to
behavioral learning theory, the consensus remains within the field that private motives for an
organism’s actions is speculation compared to observable empirical research.
Behavior defined. B.F. Skinner thought of himself in the same way as those he
studied (Skinner, 1983). He further noted his behavior was nothing more than the result of
his genetics, personal history, and current setting (Boghossian, 2006; Skinner, 1983).
Behavior is simply what a person is doing (Costall, 2004). In particular, behavior is the part
of a person, which is engaging, acting upon, or communing with the world.
Sensory input, which motivates, shapes, or brings forth behavior, is comprised of
reinforcement and stimuli (Overskeid, 2008). While the input guides a person’s actions, it is
first changed and expanded before incorporating into a behavior. The possibility of what will
happen as a result of the reinforcement is often equally as important as the actual sensory
input. This is due to individuals’ response to feedback, which allows for problem solving and
in extreme circumstances, survival.
A person’s behavior is constantly evolving (Magliaro, Lockee, & Burton, 2005).
Useful behaviors are strengthened by subsequent consequences; because differing
consequences are found in different environments, even with the same behavior, they must
33
be expected only within the particular context in which it occurred. It is only the consequence
restricted to context, not the reason the behavior was initiated. For example, deep cavernous
termite hills are not the cause of an anteater’s long tongue. Conversely, the evolution of the
animal’s tongue has enabled it to reach termites in deeper burrows.
In a learning environment, there are two categories of behavior, which are lower
order and higher order. Lower levels of behavior involve memorization or rote learning of
basic concepts; whereas, reflection and problem solving is considered higher order behavior
(Kozioff et al., 2001). Everyday learning activities involve both types of behavior (Kozioff et
al., 2001; Magliaro et al., 2005). For instance, multiple levels of behavior are seen in a
chemistry class where students must learn the periodic table abbreviations (memorization)
and be able to set up a scientific station (rote), before creating an experiment (problem
solving) and determining limitations after the study is completed (reflection). Instructors of
all subjects in each grade level must begin teaching basic skills before students can move on
to higher levels of learning (Magliaro et al., 2005).
Teacher’s role. Learning is a perceived change in an individual’s behavior as a result
of interaction with the environment (Kozioff et al., 2001). Accordingly, teachers must
understand generalities on how people learn to properly develop appropriate curricula and
instruction. The teacher is responsible for delivering well-organized knowledge in the form
of instruction (Wang, 2007). In this traditional form of instruction, the teacher is seen as the
authority figure by which students are expected to obey. It is anticipated all students will
succeed, and when this does not occur, it is assumed there is an instructional problem
34
(Kozioff et al., 2001). This belief is derived from the fact that students are capable of
learning; thusly, there are no faulty children, merely defective instructional methods.
Changes in behavior related to learning should be documented to track proficiency
within the educational environment (Kozioff et al., 2001). Identifying mistakes must be the
instructor’s highest priority because learned errors take a tremendous amount of time and
effort to correct. The timely correction of errors encourages students to examine and improve
their own behavior. In turn, the exercise builds persistence, confidence, and patience.
Instructors often teach by modeling behaviors, which is more effective than trial and
error, since it avoids unnecessary mistakes (Chen & Shaw, 2006). Modeling trains students to
learn a new behavior by evaluating their own actions in favor of the instructor’s and properly
implementing the newly learned behavior. This is accomplished by attention, retention,
physical or mental imitation, and motivation combined with reinforcement.
Achievement is gained by using organized, supervised, and responsive teaching
methods (Ryder, Burton, & Silberg, 2006). This is implemented by directing the students’
instruction, pacing lessons, as well as emphasizing and supervising seatwork. Additionally, a
routine should be constructed, which utilizes a review of previously learned material,
presentation of new information, practice, feedback, and an incorporation of weekly
assessments. Ultimately, it is important for the teacher to learn the format of instruction
(Kozioff et al., 2001). By committing to the educational design, it is easier for each teacher to
make it his or her own. Once this has occurred, the teacher is more apt to express creativity
within the lessons.
35
Organization of information. One of western history’s greatest accomplishments has
been the organization of the world’s knowledge rationally structured by subject and
independent of any learner (Boghossian, 2006). In order to adequately educate students, the
teacher’s task is to clearly deliver the structured knowledge with little additional
accommodation. This instruction begins with the goal of a specific behavior, which is then
split into smaller, more manageable tasks (Ryder et al., 2006). The target behavior
components are then taught by modeling, providing practice, feedback, and reinforcement, as
well as assessment (Magliaro et al., 2005; Ryder et al., 2006).
Behavioral learning instructional practices are analytical and dogmatic, advocating
delivery of chunked information and immediate practice, all within a framework of goals and
tasks (Hackmann, 2004). The activities are structured so the students can achieve mastery of
the practices and transfer knowledge to more advanced learning techniques (Hackmann,
2004; Magliaro et al., 2005). Each lesson, which is formed of precise presentations and
examples, is designed for the most logical communication (Kozioff et al., 2001). The
faultless transfer of information encourages generalizations and distinctions, so the concepts
may be used properly.
The sequential manner in which information is taught and frequently practiced is a
systematic approach purposefully guiding students to their goals (Baylor and Kitsantas,
2005). This approach should not be seen as mindless drill, but practice designed to improve
skills and confidence (Kozioff et al., 2001). The usefulness of repetitive performance can be
seen in a myriad of professions, such as dancers, writers, athletes, and cooks; thus, useful
practice enhances accuracy and retention. Furthermore, academic achievement improves
36
student’s confidence, self-esteem, and increases motivation for further learning (Magliaro et
al., 2005). This follows the notion success begets more success. Consequently, the
opportunity for practice allows students to connect with the knowledge and feel as sense of
accomplishment.
Opposing views. There is an ongoing debate in education on the utilization of
behavioral learning theory and constructivist practices. The support for each learning theory
is on a pendulum that swings back and forth, favoring one then the other (Cronje, 2006). The
theories in question are plotted on opposite ends and described as extremes on the continuum
of internal to external reality. By accepting one learning theory model, it is understood the
other is rejected, since their underlying assumptions appear to contradict each other. The
main points of contention between the learning theories will be discussed as the opposing
views are analyzed.
Science of inquiry. Many fields of education have become dominated by the
constructivist view of learning (Fox, 2001; Kozioff et al., 2001). Outside the circle of
constructivists, the theory is often considered a guiding myth or general idea, instead of a set
of clearly stated practices (Fox, 2001). Frequently, constructivism is only articulated as the
opposite of behaviorism. Unfortunately, the educational viewpoint has been integrated into
curricula for mathematics, English, teacher education, and early childhood education
(Kozioff et al., 2001). Consequently, a decrease in students’ proficiency of writing, reading,
and math occurs, as well as achievement discrepancies between affluent and minority
learners.
37
Educators are rediscovering that understanding of behavior is important for efficient
interactions within the classroom (Overskeid, 2008). Behavioral learning theory offers
significant facts and theories on daily operations of learning, as well as long term
applications. Conversely, with regards to instruction, constructivism seems vague at best; it
explains internal processes, not teaching practices (Cronje, 2006). The theory of
constructivism asserts only active construction can lead to knowledge, which is incomplete
and misleading (Fox, 2001). Due to the unclear nature of the theory, it can be skewed in
differing ways, becoming a detriment to others.
There is a distinct difference between learning and practicing a learning theory, which
becomes confused when using the discipline as inquiry. The disparity is among the utilization
of the theory’s research processes as the starting point for curricula design and using the
research processes as instructional methods for learning (Kirschner, Sweller, & Clark, 2006).
The procedures used within a discipline may be fine for the researcher’s methods, but are
inappropriate for novice students new to a subject. To gain critical knowledge of a topic,
scientific inquiry uses methodical investigative abilities through formal instructional
methods. This process cannot be equated with constructivist methods of self-instruction or
open ended instruction, which is considered a misuse of inquiry.
Those who stand in alliance with constructivism see it as a learning theory that can be
enacted, an explanation of learning, and a useful set of instructional practices (Colburn,
2000). Furthermore, a specific philosophical position does not have to be executed, because
different settings and learning tasks may require differing perspectives and applications of
instruction. An explanation of learning should morph according to time, culture, place, and
38
subject matter. Accordingly, constructivist teaching models are generally suggested instead
of giving specific authoritative guidelines and processes (Hackman, 2004).
Prior to entering a classroom, students have accumulated many unique experiences,
which are transformed into beliefs and knowledge of the world (Colburn, 2000). Some of
these viewpoints are in line with the scientific community and others are not. These students,
who are not empty vessels, may have current knowledge that can be hard to change.
Constructivist teaching methods help students understand why some generally accepted ideas
better predict and explain occurrences than a student’s own beliefs. This is achieved by
encouraging a deep understanding of material, instead of giving students superficially
predigested information (Hackman, 2004). While admittedly the move from theory of
practice to widespread effective approaches has been slow to emerge within the educational
realm of constructivism, successful constructivist-inspired learning strategies and principles
are abundant (Hannafin, 2006).
Unguided versus guided. An instructor’s guidance during instruction is a hot topic in
education, and this is especially seen with both constructivists and behavioral learning
theorists. Constructivists believe students learn most efficiently through a minimally or
unguided learning situation. In this learning environment, a student discovers and constructs
his or her own information (Kirschner et al., 2006). In opposition, behaviorists provide direct
guidance during instruction, so students are not left perplexed in navigating information by
themselves.
In constructivist education, students are placed within a context of learning and
allowed to discover their own knowledge by engaging in activities as a professional
39
researcher (Kirschner et al., 2006). This heavy reliance on the discovery of important
concepts fails to impart a strong proficiency in a broad array of competencies (Kozioff,
2001). Moreover, it favors well prepared affluent children, which worsens the divide of
knowledge from the underprivileged. Additionally, constructivism shifts away from teaching
a body of knowledge, to students only accumulating the information they can experience
themselves (Kirschner et al., 2006). While instruction through practical application and
problem-solving skills can be helpful, it is unreasonable to think teaching should only use
these methods.
An expert working within his or her field is quite dissimilar to classroom learning
(Kirschner et al., 2006). Seasoned workers develop their skills over time and through
experience within their line of work. Giving the great responsibility of learning without
guidance does not create little scientists, but causes confusion, anxiety, uncertainty, and leads
to misconceptions (Kirschner et al., 2006; Loyens et al., 2007b). Furthermore, it can make
students doubt they have the capacity to learn (Loyens et al., 2007b). Conversely, when a
student is given adequate information, most have no difficulty assembling knowledge
(Kirchner et al., 2006). When a complete representation is given, accurate knowledge is
easily gained.
Constructivists argue learning is based on context, as well as the student’s attitudes
and beliefs (Mvududu, 2005). When an instructor attempts to teach students, the teacher may
be inadvertently working against the students’ expectations and susceptibility to effectively
integrate the information. In essence, guided instruction interferes with the learner’s natural
process of constructing newly situated information based on prior experiences (Kirschner et
40
al., 2006). While guidance might produce an acceptable imitation during immediate practice,
it hinders performance when the student attempts to reconnect the information at a later time.
What’s more, the acceptance of one’s responsibility of learning builds great confidence when
moving forward through further education (Al-Weher, 2004).
Teachers who embrace constructivist teaching methods may not fully understand the
learning theory, or its proper applications (Gordon, 2009). Facilitating learning experiences is
only part of employing constructivist learning; an instructor must also understand why active
learning is important and how the implementation is different from traditional learning.
Without understanding key principles, the teacher cannot effectively associate objectives
with the appropriate activity or assessment. Teaching in a constructivist environment is
complex and unpredictable, which means the instructor must concentrate on embracing more
academic responsibilities, than a teacher who simply assigns seatwork.
Active versus passive. A constructivist view of learning accepts communication as a
complex process; therefore, an instructor cannot simply deliver information to learners with
the expectation of understanding (Phillips, 1995). When communicating concepts, the
instructor should present a model within context and assist with a restructuring of views, so
they are logical to the student, as well as the teacher. The emphasis on the constructive
process allows constructions to be modified through reflection and action. Using activity
methods in the classroom for potential masterminds is a stark contrast to the view of passive
receptacles, students, waiting to be filled with knowledge. The distinction is also seen in the
chosen environment for learning. While constructivists encourage experimentation,
communal projects, outdoor research, libraries, and laboratories, behaviorists require an
41
ordinary classroom with crowded geometric rows of desks and bare walls only made for
listening (Dewey, 1899; Phillips, 1995).
Constructivist students confront and create understandings by taking into account
what is revealed in a learning situation (Mvududu, 2005). If the encounter conflicts with prior
knowledge, the understanding can be altered to accommodate the new experience. Through
the active process a learner can modify knowledge based on judgment. Constructivist
learning does not imply students are always actively constructing and reflecting, there must
also be time for experiencing, learning by listening, practice, and thinking. These activities
encourage the construction of many kinds of knowledge.
The act of building on students’ current thinking is the key to helping them
understand new information (Mvududu, 2005). Even if a student’s ideas seem unproductive,
it is the beginning of the knowledge construction process. Each student may see a different
pathway to a solution, but the goal is to make sense of the result within the community of
accepted explanations. When this is accomplished, all efforts can be reflected upon, while
remaining aware some answers are superior to others.
Behavioral learning theorists oppose the constructivist view of relic teachers of the
past, with bored students assembled in neat rows of seats (Simpson, 2002). Students do learn
by acting upon their environment, but are also reactive once acted upon (Fox, 2001).
Behaviorism accounts for the whole child by looking at distinct behaviors and reinforcement
contingencies (Strand, Barnes-Holmes, & Barnes-Holmes, 2003).
The physical activity required for constructivist learning doesn’t always translate to
mental activity (Clark & Mayer, 2008). Furthermore, there are many cases where activity
42
hinders learning or viewing is simply more effective. Firstly, applicable modeled examples
are more accurate than a student’s uninformed attempts. Next, lectures are equally as
effective, if not more, as a collaborative discussion, because lectures provide the whole
picture of a subject. Lastly, still graphics provided by an author are more preferable than
graphics created by students or animations, which can be distracting.
While active learning is quite popular as a new tool in education, demonstrating its
superiority has been difficult (Michel, Cater, & Varela, 2009). Due to the non-unified
practice of constructivism, a range of activities are classified as active learning; therefore,
accurate quantitative comparisons of effectiveness are difficult to achieve. Conversely, the
traditional approach of imparting knowledge to students is a well documented method of
instruction (Fox, 2001).
Knowledge as independent or subjective. In a constructivist learning environment,
students learn by interacting with their surroundings. This interaction leads to the
construction, interpretation, and modification of previously held knowledge (Sutinen, 2008).
The construction of one’s own understanding is an internal process that cannot be influenced
by outside elements. The students are placed at the center of knowledge, instead of an
instructor (Boghossian, 2006). This gives the students’ experiences and perceptions a unique
meaning and educational value. The constructivist view of individually constructed
knowledge implies there are multiple realities, since each person’s own reality is constructed
in his or her own mind.
Knowledge is not a reflection of an independent reality; therefore, there is no shared
reality (Boghossian, 2006; Fox, 2001). Each reality is unique and only lives in the mind of
43
the individual (Cronje, 2006). The God’s eye view behaviorists hold that truth is objective,
does not exist (Fox, 2001). Knowledge is perceived from a historical and sociocultural
context and is the result of the human mind. Although conceptual viewpoints may be limited,
constructivists do not believe the existence of concepts or things should be cast aside. It is
impractical to think each individual can know all, so people adapt to accepted explanations
within the population (Fox, 2001; Mvududu, 2005).
Behavioral learning theory dictates learning can be seen as an external observation;
more specifically, learning is achieved through the interaction between discernible stimuli
and the subsequent response (Boghossian, 2006). Knowledge is readily observable and
mental states are just another visible behavior. Moreover, most modern psychologists base
evidence on empirical testing and viewable behavior (Costall, 2004). These researchers
meticulously detail outside stimuli and a person’s response, as well as consider only impartial
supported evidence as scientific. If we only recognize truth in this way, we are behaviorists.
Moreover, people from all walks of life have tried to understand reality and gained shared
knowledge by organizing it into systems such as, science, history, mathematics, and literature
(Kozioff et al., 2001).
If individuals only accept the existence of their own mental states as true, they can be
reduced to thinking their own mind is the entire world (Fox, 2001). This seems to dispute any
other person’s existence or the reality of the natural world itself, which leaves the individual
in isolation. This ideology is irrational and calls its soundness into question (Kozioff et al.,
2001). Due to the constraints of a person’s surroundings, knowledge may result from our
own perceptions, but there is also feedback obtained from that world (Fox, 2001).
44
Multimedia Software
Today’s classrooms are typically equipped with computers, general programs, and
multimedia software (Deal, 2004). Technology labs are standardized with specialized
software like, graphic design, desktop publishing, computer aided design (CAD), computer
numerical control (CNC), or video editing. Additionally, multimedia packages are used for
instructional support, which provides learning activities, informational content, as well as
hardware and software training. This section will discuss computers in the classroom, define
multimedia, history of software, and the specific software company utilized in this study,
which is Adobe Systems Incorporated.
Technology and multimedia. A problem in America’s schools is ensuring all
children’s potential by enabling them to effectively learn and carry the ability to ascertain
information into the future; this is marked by change, growth, and constantly evolving
technologies (Peng, Su, Chou, & Tsai, 2009). This is brought about by the significant
increase in the educational use of computers, which now guides instructional methods and
the technology itself (Peng et al., 2009; Winn, 1999). The ever-present machines are
powerful tools providing learning opportunities for all students in terms of communication,
work, learning, and life (Peng et al., 2009). The rapid change and frequent updates seen in
hardware and software requires expanded knowledge of computer skills to adapt to new
technology, synthesize creative solutions, and work effectively with others (Mbarika et al.,
2010). This ability to readily adjust is the product of academic achievement, retention, self-
esteem, and social ability.
45
The frequent evolution of software also provides a challenge for teachers as well. It
becomes a cycle of updating software to gain new features and having to modernize
hardware to accommodate the software (Clemons, 2006; Hill, 2004). Furthermore, teaching
materials and curriculum must be brought up to date, even though textbooks are often a step
behind (Clemons, 2006). It is also important for instructors to continue to renew their own
knowledge on technology; this prevents students from entering the classroom more computer
literate than their teachers (Clemons, 2006; Hill, 2004).
Technology has affected the manner in which students’ are taught, the setting it takes
place, as well as what they learn (Wang, 2009). Computers, internet, and multimedia
capabilities have brought about the dramatic change in education (Buckley & Smith, 2007;
Wang, 2009). Multimedia is the presentation of information through more than one process
(Buckley & Smith, 2007). For example, any combination of audio, animation, text, graphics,
or video used together in an application would be considered multimedia (Buckley & Smith,
2007; Mandernach, 2009). The integration of more than one media type makes materials
dynamic and more efficient. Consequently, this format has been found to have positive
effects on students by maintaining their interest and more thoroughly meet their specific
learning needs (Buckley & Smith, 2007).
Software. In the early success of commercial computers, software was developed by
individuals within a business who understood their company’s software needs (Damsgaard &
Karlsbjerg, 2010). Software manufacturing was formed several decades later as the creation
of specialized software was outsourced. In the beginning, the software industry had very little
standardization and each software package was designed as a unique system for specific
46
businesses. This was later changed as software companies began holding exclusive rights to
the software they produced and distributed to multiple customers. Proprietary systems that
were once able to stronghold companies into a single producer were released to publicly
available software. Standardization lowered the cost of purchase, increased functionality, and
gave consumers more variety in choosing programs.
The influx of new resources encouraged software producers to generalize the purpose
of an application by increasing the amount of features a product could perform, which led to
packaged software (Damsgaard & Karlsbjerg, 2010). Packaged software is a type of
application possessing common functionalities for all who use it. A package is standard
because all core components are the same across installations, although it can be configured
to fit a customer or organization’s requirements. Software used as initially installed are often
referred to as off-the-shelf packages; these need limited adjustment before using.
Customization is achieved by changing program parameters, purchasing add-on components,
or connecting with compatible software systems.
Multimedia software is versatile applications used to develop static or dynamic
creations including multiple text, video, graphics, or audio elements (Mandernach, 2009).
Certain types of multimedia software are used to create specific products. Examples of work
generated with this software are: websites, animations, computer training, print materials,
kiosks, and graphic design (Buckley & Smith, 2007). Software companies currently
producing multimedia software include Microsoft, Adobe, and TechSmith.
Adobe. Adobe Systems Incorporated is a leader in setting the standard for interaction,
collaboration, and the exchange of ideas through technology (Adobe, 2010). This impact can
47
be felt working, socializing, or transacting online, as Adobe has utilized its technology to
increase creativity, reduce paper, secure information, improve online learning, and streamline
work procedures. The socially responsible company began with a mission to solve the
problem of accurately translating text and images from the computer to print, which was
accomplished with Adobe PostScript. Continuing the role of solving technology problems,
Adobe Illustrator and Adobe Photoshop were created to perfect the quality of images used in
print, video, and the internet. The trend persisted with the creation of Portable Document
Format (PDF), as well as the acquisition of Dreamweaver, Flash, and several other software
applications.
Customers of Adobe range from individuals and small businesses to industries and
global brands like, The New York Times, eBay, and Sony (Adobe, 2010). These customers
have the shared experience of adapting to the technological needs of working within and
outside of the organization or communicating with others. The once impersonal tool called
the computer is now imperative for work, playing, and staying connected. This can be seen in
daily life as Adobe products are used to create billboards, television shows, movies,
magazines, multimedia presentations, and websites.
InDesign. The first version of InDesign went on sale in 1999 and was advertised as
professional design software with a creative environment to work with layouts, typography,
and graphics (Adobe, 2010). The software was meant to update the old concept of single
textual columns into flexible layouts and sophisticated digital design (Dabbs, Concepcion,
McMahon, & Martin, 2005). InDesign is a technology supporting multi-line organization,
OpenType, Unicode, PDF exportation, and scripting support (Kvern & Blatner, 2006).
48
Furthermore, the standalone multiplatform program is also offered within Adobe’s Create
Suite, which is a myriad of programs bundled for the creation of print and Web designs
(Johnson, 2008).
In digital publishing history, Adobe PostScript was the first printing language to
provide graphics and text, not using traditional paste-up (McClelland, Futato, & Futato,
2008). Using this language and new functions like transparency and Portable Document
Format (PDF), Adobe’s freeform program PageMaker became the most popular publishing
software (Gruman, 2009; McClelland et al., 2008). Two years later, QuarkXPress appeared
on the publishing market with great success (McClelland et al., 2008). Its achievement was
due to the program’s what-you-see-is-what-you-get (WYSIWYG) structure and easily
adjustable functionality (Gruman, 2009). With the appearance of InDesign came the ability to
choose a manual layout or guided approach, as seen with the previous publishing programs,
in one software application.
InDesign’s workflow and integrated tools give the user an efficient publishing tool to
create digital, print, or online documents (Adobe, 2010). As a page layout program for print,
InDesign can be used to produce large items such as books, magazines, and newspapers or
smaller pieces like flyers and newsletters (Gruman, 2009; Johnson, 2008). Alternately, the
electronic publishing side of the application allows for documents to be sent directly to print,
or electronically distributed using PDFs (Gruman, 2009). In addition, an InDesign file can
also be exported for use in Adobe Flash or Adobe Dreamweaver to be converted into a
website (Johnson, 2008). For a single designer or a publishing team, InDesign simplifies
49
page layering, which makes the delivery of error-free appealing documents easy to achieve
(Adobe, 2010).
Photoshop. The complex software, Adobe Photoshop, offers a straightforward
interface, sophisticated filtering, and image editing features, which draws many different
types of users for differing applications (Cole & William, 2010). This industry-standard
image manipulation program is used by photographers, graphic designers, artists, web
designers, and many other professionals for film, video, architecture, science, product
design, and medicine (Adobe, 2010; Cole & William, 2010). Introduced to the public in 1990
Photoshop was originally used to edit photography (Adobe, 2010; Perkins, 2009). The
software, which can be used on Macintosh or Windows platform, is a stand-alone program
that can also be purchased through Adobe’s Creative Suite (Johnson, 2010).
Photoshop’s creative uses include image compositing, special effects, illustration, and
text-formatting (Johnson, 2010; McClelland, 2010). Additionally, it can surpass simple
image editing to construct digital artwork from nothing more than a blank document. There
are thousands of manipulations that can be made with Photoshop including color correction,
removing dust or scratches from a scanned photograph, as well as eliminating or adding
entire elements, like taking out a tree or placing a person in the image (Johnson, 2010). This
is why at least 90% of design professionals use Adobe Photoshop (Adobe, 2010).
Knowledge
For a person to become educated, the individual must learn a body of knowledge,
principles, and skills to become competent enough to contribute to society and develop his or
her potential (Kozioff et al., 2001). To accomplish this, educators must provide an abundance
50
of pertinent information and learning opportunities, so the student can move from a
beginning reasoning state to understanding a knowledge domain (Derry, 2008). This
knowledge is different from everyday learned experiences because the deeper knowledge is
not seen in daily life, although it is important for its normal existence. Knowledge acquired
through education requires purposeful and conscious involvement on the part of teachers and
learners.
How do students come to know information? If a person’s knowledge is discovered,
the knowledge is preset and independent of the individual (Simpson, 2002). This theory
accepts objectively correct knowledge that should be consistently held by all (Dalgarno,
2001). Alternately, if a person’s knowledge is made, then this creation occurs within the
human mind by way of experiences and beliefs (Simpson, 2002). The belief of many equally
valid knowledge representations results from the contradictory theory (Dalgarno, 2001).
Moving past knowledge acquisition is the actual knowledge itself, which will be discussed in
this section.
What is knowledge? Central to educational psychology is developing a science of
instruction to understand how individuals learn and improve the process (Mayer, 2008).
Instruction is comprised of the manipulations an instructor uses to modify the student’s
knowledge. Consequently, the matter of interest is how to present information in such a way
to achieve the expected cognitive processing. Furthermore, the learning taking place is a
change in knowledge, which can be attributed to experience.
Knowledge is the combined learned principles, facts, and truths gained from an
educational setting, research, or analysis functioning for the individual (Conradi, 2000).
51
Knowledge embraced by the person is concrete and can often be linked with emotions
(Ignatow, 2007). This is because knowledge can be independent and dependent of
perceptions and sensations like hearing, taste, touch, vision, and smell. Accordingly, reality is
carved into meaningful units, or bits of information stored within the mind through thought
styles and traditions. These categories of long-term memory knowledge are derived from all
areas of a person’s life and are cued by modes of thought.
While it is not possible to generally know, it is feasible to know a particular thing; in
knowing the object or concept is to understand it independently (Ilyenkov, 2007). Thinking is
to intelligently deal with the object within its context in nature, not in a capricious or fanciful
manner. Thinking is knowledge dealt with functionally. Consequently, it is absurd to state a
person has knowledge of something, but cannot apply the knowledge; if the individual
processes the knowledge, he or she should be able to relate the knowledge to reality.
Conversely, when the individual does possess knowledge on a subject, an endless
loop of thinking, new knowledge, and further thinking begins (Cabrera & Colosi, 2009).
Furthermore, knowledge is noticeable and tangible, while the process of thinking is invisible
and elusive. Therefore, to understand how individuals think and learn, knowledge must be
studied and understood. The discernible structure of knowledge allows instructors and
researchers alike to view the pattern for creating additional knowledge by way of thinking.
Prior knowledge. Prior knowledge is the student’s procedural, content, or declarative
knowledge of a particular subject before applying a new instruction or learning task (Gurlitt
& Renkl, 2010; Hailikari, Nevgi, & Komulainen, 2008). Previously learned information on a
subject is important for further learning (Gurlitt & Renkl, 2010). For example, prior
52
knowledge can compensate for lower aptitude, but higher intelligence cannot counteract
limited previous knowledge. Hence, the influence of prior knowledge on the learning process
is significant (Horsley, 2010). The previous knowledge can come from an educational
setting, work, or an individual’s life experiences.
When a person already possesses information on a specific subject, any new
knowledge gained on the topic is influenced; therefore, the processing of any further
knowledge on the matter is also effected (Hailikari et al., 2008). Consequently, once prior
knowledge is accounted for in classroom activities, it can be used to predict achievement and
facilitate learning in other related subjects (Clapper, 2007; Hailikari et al., 2008). As such,
prior knowledge is a central variable with regards to learning since it consistently impacts
knowledge attainment by focusing the student’s concentration on relevant elements of the
subject (Gurlitt & Renkl, 2010; Hailikari et al., 2008). This framework of knowledge
improves organization and assimilation of subject matter (Gurlitt & Renkl, 2010).
Review of Methodological Literature
Approaches to Research on the Topic
Each research performed has a history of various approaches. These approaches may
be reproduced exactly to judge accuracy, or completely changed to discover truths behind
alternate decisions. The important matter to understand is each study shapes new research in
some way. Bearing the inevitable influence in mind, this author wishes to discuss outside
approaches, which inspired methodology, as well as summarize the current study’s design
with reference to significant previous methods and findings.
53
Common and alternate approaches. The design and direction the current study has
taken was particularly impacted by the articles reviewed in this section. As such, the design
model and research of five significant articles are outlined. The weaknesses or strengths
shown in the studies are also considered.
Clemons article. In the 2006 peer-reviewed article “Constructivism Pedagogy Drives
Redevelopment of CAD Course: A Case Study,” Stephanie Clemons (2006) was faced with
the requirement to increase technology use in the classroom due to higher technological
literacy among students. The integration of more computer labs within higher education
schools has also caused enlarged class sizes. A boost in physical presence in classes spurred
Clemons to redesign her Computer Aided Design (CAD) curriculum to better accommodate
the doubled class size, while requiring less direct instruction from the teacher. This need to
change student roles within the classroom led Clemons to conduct a case study utilizing
constructivism.
Clemons (2006) revised the previous traditionally taught CAD class, which used
demonstrations, exercises, and weekly assignments. The modified constructivist class was
taught with the goal of students becoming problem-solvers rather than merely learning the
CAD software. This was achieved with the encouragement of students to become self-paced,
self-regulated, and to use self-discovery while completing three progressive modules. The
task of students constructing their own meaning at their own pace prevents the frustration of
keeping up or being dragged behind by the class. In addition, the instructor is freed to help
those who express a need.
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The end of the semester assessment brought the results of Clemons’ study. The
evaluation showed a “sophisticated grasp” of the software’s commands, an increased
capability of drafting, and modification of drawings, as well as the understanding of model to
paper space (Clemons, 2006, p. 21). Furthermore, students connected with the software due
to their responsibility for learning and had more collaborative interactions with peers and the
instructor, than seen in a traditional class. As an additional note, Clemons remarked the
decrease in pressure for the instructor to sustain comprehensive knowledge of the software,
but the need for an open mind in creating learning opportunities.
The discussed study certainly has elements working for and against accurate results.
The most favorable component is the absolute immersion of the entire class in the
constructivist learning theory. Both the curriculum and physical rooms were completely
altered to accommodate the theory’s success. Alternately, the incredible effort can also be
seen as a negative. The presentation of constructivist views and applications, compared to the
nonexistent discussion of previous instructional endeavors, suggests a potential bias in design
towards the constructivist instruction. Although the brief summary of results does not
outright state the comparison of the constructivist class to the previous traditional class, it is
implied. One can reason, if such energy were put towards the traditional class, its success
could also be attained.
The largest disadvantage to the study was the organization of results. In addition to
being quite brief, the findings summary section seems to use a highly subjective assessment
to rate student success. Clemons (2006) based student performance and understanding of the
software on an evaluation of his or her final project. An additional encouragement of
55
accomplishment was the students’ ability to get through more content than the previous class.
The evaluation of students’ projects by the researcher (instructor) of the study (class) could
demonstrate a bias. Furthermore, the simple act of delving further into the class material does
not equal comprehension.
Al-Shammari, Al-Sharoufi, and Yawkey article. The Kuwait educational system,
overseen by Kuwait Ministry of Education (KMOE), provides learning opportunities for
instructors through their teacher education program (Al-Shammari, Al-Sharoufi, & Yawkey,
2008). In the article “The Effectiveness of Direct Instruction in Teaching English in
Elementary Public Education Schools in Kuwait: A Research Case Study,” Zaid Al-
Shammari, Hussain Al-Sharoufi, and Thomas Yawkey noted that although the direct
instruction method was taught through the education program, it was not ultimately
integrated into the teachers’ classrooms. Consequently, the authors sought to develop a case
study to demonstrate the success of direct instruction. In order to execute their research, two
elementary level English classes were designated as group one and two. Group one was
taught using direct instruction, while the group two teacher did not use direct instruction. The
direct instruction class's objectives, instructional approach, and modeling procedures were
altered in accordance with the educational model; however, the control class remained
unchanged.
The authors used SPSS statistical software to analyze the data gathered through a test
based on the direct instruction method (Al-Shammari et al., 2008). A T-test and subsequent
Mann-Whitney test was performed, both results were in agreement. The results indicated the
direct instruction group achieved higher scores than the control group. Due to the findings of
56
the study, the researchers sought to integrate direct education into the Kuwait educational
system.
A flaw within the research, which affects the results, is the test used to collect the
data. The authors stated the test was similar to those used in other direct instruction studies.
This indicates a test specifically designed for a particular learning methodology. In other
words, the assessment may not be applicable to the teaching techniques used in the control
classroom. This inconsistency could cause the results to show increased learning in the
experimental section, because the assessment is simply a more appropriate testing method for
the group.
Much effort was applied in designing and executing the direct instruction lessons. For
example, the researchers explained and demonstrated direct instruction teaching methods to
the group one instructor; in turn, the teacher was required to practice the methods, while the
researchers acted as the class. As seen in the previous article, it is difficult to conclude
whether the alternative instructional method would have been met with greater success if it
received as much attention. The nondirect instruction class was the control, which should
remain untouched. Therefore, it is understandable to restrain from enhancing the curriculum,
but was such care taken in the initial development of the class? In addition, how long had the
current curriculum been taught and was it ever updated? These unanswered questions lead
the reader to wonder whether the lessons were equivalent, no matter the instructional method.
Neo and Neo article. With a changing educational landscape on the horizon, the
Malaysian Government called for higher education facilities to focus on learning instead of
pedagogy and teaching (Neo & Neo, 2010). The concentration on learning included students
57
retaining, synthesizing, and applying information. This was to be accomplished by
encouraging the students to utilize multimedia through constructivist project-based learning.
Since traditional teaching techniques were typically used in all subjects of instruction, the
shift to constructivist methods would be quite a transformation. As a result, Neo and Neo
(2010) developed a study to examine student perceptions and learning when multimedia is
used in a constructivist learning situation. In turn, the outcome should reveal a student's
ability to attain and apply learned materials to a real-world workplace.
The authors used 53 second year students from management, information technology,
and engineering degrees, who were registered for an interactive multimedia class (Neo &
Neo, 2010). The 14-week course encouraged the students’ development in multimedia skills
and ended in a group project to be authored through the software package Director. The final
project was fashioned as a realistic task to create a Malaysian culture themed application for
the tourism board. The students' learning environment required reflection, critical thinking,
collaboration, problem solving, as well as decision making.
The data for the study was derived from specific expected outcomes on the students'
final projects, as well as a course perception survey taken by the students. The survey's
purpose was to measure the students' attitude towards the group project and was based on a
Likert scale. A factorial analysis was conducted through the statistical program SPSS on the
gathered data from the questionnaire. The statistical analysis showed that students primarily
agreed or strongly agreed with the asked questions. The student approved items included
questions on teamwork, project motivation, perceptions of learned skills, educational
environment, and the application of skills. From the statistical results and high ranking
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grades students' achieved on the final projects, the authors concluded positive attitudes
towards the project were relational to the motivation to acquire and appropriately apply
learned skills.
A strong aspect of this study was the thorough effort put into designing the
constructivist curriculum. Since the entire study was focused on a single learning theory, it
was necessary to effectively teach and test within the scope of the approach. Neo and Neo
(2010) successfully did this by creating a hands-on authentic setting for learning, which
encouraged students to take responsibility for their knowledge. In addition, imitating a work-
place project to test skills is directly in line with constructivist techniques.
McKenna and Laycock article. Educational software has been traditionally created
with behavioral principles, which results in structured instruction, tests, and feedback. The
ability to create opulent multimedia environments has caused a deviance into the realm of
constructivist techniques, such as interaction and animation. The authors, McKenna and
Laycock (2004) designed a study, discussed within the “Constructivist or Instructivist:
Pedagogical Concepts Practically Applied to a Computer Learning Environment” article, to
analyze the use of multimedia within the scope of behaviorism and constructivism, as well as
preferences of the learning theories among students taught through these methods. To attain
this research goal, two approaches were developed based on the chosen learning theories to
test a single multimedia concept, which was waveform sampling.
In testing their research, McKenna & Laycock (2004) implemented four groups using
constructivist, instructivist, and a hybrid, which employed both constructivist and
instructivist techniques. Lastly, the fourth group was an untouched class taking the normally
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offered multimedia module. Data was collected through an assessment questionnaire, as well
as an interview that explored each student's understanding of the materials. An average was
also calculated from the students’ performance earlier in the course for comparison purposes.
Students in the instructivist group resulted in the most improved scores and overall the
groups indicated a preference for the instructivist or hybrid learning environment.
The design of McKenna and Laycock's study was especially strong, due to their use
of varying degrees of instruction. The inclusion of a hybrid and control group enables the
reader to analyze many possible aspects of the research. The additional groups are
enlightening since many studies do not expend the effort to equally test opposing learning
theories; however, McKenna and Laycock have applied identical awareness to the main
theories, as well as offered a control and blended theory group. This creates confidence in the
reader that such thoroughness has extended through the remainder of the study.
Kay article. Human errors are unavoidable; thusly, much effort has been expended to
test and lower the danger of mistakes in high-risk domains. Extensive literature research on
the subject drew Robin Kay (2005) towards the creation of a study on errors in the process of
learning computer software. Within this area of investigation, the focus has been on system
development, operating systems, and software design faults. Those studies specifically
sought to decrease errors found in the programming itself. Conversely, the goal of Kay's
study was to reduce user mistakes. While errors are seen when humans complete any tasks,
blunders are quite frequent when learning through computers. Understanding the cause of
such miscalculations can lead to more effective instructional techniques.
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The subjects used in the study were gained through convenience sampling (Kay,
2005). The sample, split evenly between genders, possessed a step level of computer
proficiency. The individuals divided into 12 intro-level, 12 intermediate, and 12 skilled
computer users making a total of 36 subjects from ages 23 to 49. Additionally, education
level spanned from college diploma to a doctorial degree.
To begin the actual experiment, the subjects were given an interview and computer
survey to establish computer skills (Kay, 2005). It was decided that while a range of skills
was seen through the subjects, none of them had previously operated the spreadsheet
software used in the study. Next, each subject was given five learning activities to carry out
through the software. During the exercises the person was asked to speak his or her actions
aloud, while being filmed. A time period of 55 minutes were given to complete all tasks.
To gather data for the study, a think-aloud protocol was utilized (Kay, 2005). This
procedure is a method of requiring subjects to verbalize their tasks. The technique is meant to
reveal the internal talk or mental processes of learning. The information was collected based
on 627 behaviors identified as relating to errors while learning. Furthermore, the procedure
enacted a six step process for encouraging the subject to continually talk without reserve.
The results of the study found that all individuals consistently made errors throughout
the learning process (Kay, 2005). Additionally, the frequency of errors increased
dramatically when the subjects engaged in knowledge processing, seeking information, and
software interaction. The witnessed mistakes show a weakness in model building, memory,
and observation errors. The author noted her findings were consistent with prior studies'
claims of the inevitability of errors.
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The innovative process used to collect data was a distinct strength within the study.
The ability to hear the unfiltered thought process of learning certainly gives a look into each
student’s unique discoveries. Additionally, the act of recording the knowledge progression
allowed the author to ensure all behaviors were documented, as well as provide the
opportunity to re-examine subjects for comparison between individuals. The utilization of
this technique presented a concrete method of establishing data.
The author noted there was little difference in errors between the levels of computer
proficiency (Kay, 2005). This may be due to the actuality the advanced users were not skilled
in the particular software, but computer usage as a whole. In general, all users were novices
with regards to the employed software. Expanded knowledge of the actual program may have
produced different results.
Current study’s design approach. Humans strive to comprehend the world they live
in, which includes an understanding of actions, expressions, natural occurrences, emotions,
and all other matters of human activity (Smeyers, 2001). This typically takes place in two
ways, the first is a general knowledge about the physical world in basic principles. The
comprehension gained from this general insight allows individuals to fit objects and
experiences into a broader scheme. The second way humans understand their world is to
study the interworking of events and phenomena, which ultimately develops causal
explanations. This permits the recognition of an occurrence so an individual can account for
what will happen next.
Causal explanations are a part of daily life, as it pervades human thinking and is
fundamental to the understanding of intellectual and practical existence (Smeyers, 2001).
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Perhaps this method of deriving information is so important because once an individual can
establish cause and effect, the cause can be manipulated to achieve specific outcomes.
Furthermore, the current study seeks to understand cause and effect within the parameters
explained thus far and the rationale for the design approach will be described.
Qualitative versus quantitative. Significant debates surround the merits of qualitative
and quantitative approaches with regards to which deserves respect or less suspicion
(Smeyers, 2001). To contribute proper knowledge, the branches of social science are
pressured to utilize the correct methodology. Satisfying the demand to understand everything
with a law-like explanation, as seen in natural sciences, has left researchers grappling with
the correct path. Avidly avoiding a false homogeneity by placing phenomenon and concepts
side-by-side for comparison, while refusing to prematurely formulate a theory leads
researchers to wonder if the effort exerted is in vain.
The reliance on research and belief in its ability directs policy makers to push for a
science of education (Smeyers, 2001). Those who challenge this idea argue that such
aspirations are undeliverable, and focus instead on unique situations and participant
perceptions. The concept of an indefinite number of ways to interpret the human mind and
social life brings about a different way of viewing research, and as such, skews the abilities
of each methodology (Bartsch et al., 2008; Smeyers, 2001). Quantitative research is praised
for condensing complex phenomena into simpler, more manageable parts, which is
invaluable when dealing with a multitude of influences within a study (Christ, 2007;
Valsiner, 2009). This chosen method employs systematic manipulations of variables to
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support a cause and effect relationship (Christ, 2007). The goal of experimental control is
achieved when potential influences are systematically ruled out.
Pre and posttest. Internal validity and experimental control are highly dependent on
accurate and robust data (Christ, 2007). Ongoing or repeated measures in a study affect the
internal validity regarding testing and instrumentation. When considering instrumentation,
measurements need to be as similar as possible to rule out inaccurate results due to an
inconsistency (Bartsch et al., 2008; Christ, 2007). For example, if two different
measurements are used within a study and the second test (posttest) is easier than the first, is
the resulting higher score due to simpler questions and not the manipulation. To avoid this
threat to internal validity, the same test can be utilized for both the pre and posttest (Bartsch
et al., 2008).
Accounting for instrumentation, internal validity with respect to the testing is called
into question. The repetitive testing that occurs with pre and post-testing can create an
increase in results due to practice (Christ, 2007). The practice effect takes place because the
subject becomes experienced with the measurement (Bartsch et al., 2008). It is possible to
prevent this validity threat by using different tests, but it may still persist due to testing
procedure familiarity.
Thusly, the opposing potential threats to internal validity lead this study’s design to
be aware of both, but fully account for instrumentation. The decision is due to the dissimilar
ways each test results will be used. The pretest will be used as a covariant to account for
prior knowledge when analyzing the results of the posttest. Reasonably, an individual’s
previous experience with the software tested should affect the amount of errors he or she
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performs (Kay, 2007; Kopcha & Sullivan, 2008). In general, society expects an expert to
achieve more than a novice (Kay, 2007). As a result, accounting for previous knowledge will
help eliminate an increase in results due to an influence other than the dependent variable.
Power. Jacob Cohen, the father of power analysis, awoke the research world to
statistical power in 1962 (Borkowski, Welsh, & Zhang, 2001). His argument for the
importance of a study’s awareness of power was due in large part to the overreliance on
significance. While preventing a Type I error or the acceptance of an effect when none
actually occurs is critical, it is also imperative to avoid a Type II error. A Type II error is the
failure to reject a null hypothesis within a statistical test, when it is indeed false (Borkowski
et al., 2001; Devane, Begley, & Clark, 2004; Faul, Erdfelder, Lang, & Buchner, 2007). The
probability of a Type II error is connected to a test’s power, which is determined by
significance level, sample size, and effect size.
Statistical power analysis is the capacity to properly identify statistically significant
results among groups; in other words, the capability to correctly reject a null hypothesis
(Cohen, 1988; Devane et al., 2004). The analysis of power is administered within multiple
disciplines such as education, medicine, psychology, and sociology (Borkowski et al., 2001).
While power analysis is important, it does not take the place of significance testing
(Borkowski et al., 2001; Faul et al., 2007). Instead, the analysis should be used to balance a
test of significance in such a way the researcher has the best possible chance in identifying
correct results (Borkowski et al., 2001).
To create an optimal opportunity for accurate detection, a priori analysis is used to
achieve control over a study’s power prior to executing an experiment (Faul et al., 2007;
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Faul, Erdfelder, Buchner, & Lang, 2009). Examining the ratio of significance level, effect
size, anticipated power, and sample size prior to executing an experiment can avoid low
power and ambiguous results (Cohen, 1988). With the goal of 70 to 90% power, the a priori
test can reveal needed adjustments to the research and subsequent accurate data conclusions.
Examples of an a priori test may shed light on the researcher’s need to analyze before
execution, as well as the careful selection of parameters. Using G*Power, the first example
will employ a small Cohen d effect size (.10), small significance (.001), and large power
(.90). This results in a needed sample size of 1,897 subjects, which is a substantial number of
participants. The same test using more conventional parameters – medium Cohen d effect
size (.30), alpha of .05, and 80% power, requires a sample size of 64 subjects (Faul et al.,
2007; Faul, Erdfelder, Buchner, & Lang, 2009). Taking the time to understand the
dependence these parameters have on one another, prior to experimentation, can relieve the
heartache of uncertain results (Cohen, 1988).
Effect size. The effect size of a study demonstrates the size difference between the
null and alternate hypothesis, which is the extent the regarded phenomenon exists
(Borkowski et al., 2001; Cohen, 1988). It can also be used to illustrate the relationship
strength amongst variables; in other words, how strongly the null is false (Cohen, 1988).
With an increase in effect size also brings an increase in the prevalence of the study’s
phenomenon. The actual effect size of a study is not often known prior to the execution of
research, but a subjective determination must be made to obtain power and sample size.
(Borkowski et al., 2001; Devane et al., 2004). Unfortunately, when predeterminations such as
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an a priori analysis are not made, the study often results in low power, which renders it
useless.
The effect size essentially measures how wide of a mark the null hypothesis has, and
represents the least effect that is still observable and important (Devane et al., 2004). The size
will vary between studies since it signifies the smallest effect needed to be meaningful, and
also depends on the measureable outcome, conditions severity, and intervention convenience.
Often misunderstood, the definitions of effect size categories small, medium, and large are
dependent on the statistical test being used and the study itself (Cohen, 1988). Consequently,
an experiment that results in reducing mortality by a percentage would be more clinically
important than one reducing the percentage of stress. As such, research conducted in
psychology and education can utilize a larger effect size than pharmaceutical or medical
research, given that death and injury are not connected with the results (Borkowski et al.,
2001).
New research endeavors typically encounter small effect sizes due to less than perfect
experimental conditions or measurement. This increases the likelihood of variable noise,
which makes the phenomenon harder to identify (Cohen, 1988). A medium effect size is
representative of an effect that is visible to a careful observer (Borkowski et al., 2001). A
large effect size occurs when the phenomenon in question is grossly discernible.
The various effect size conventions most often used are those associated with Cohen
d, which are small .2, medium .5, and large .8. The Cohen d effect sizes should be
specifically used when executing a t test to find differences between independent means.
Alternately, the f test model used for testing fixed effects, the analysis of variance or
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covariance, such as ANOVA, ANCOVA, or Factorial Design, require the use of Cohen f.
Within the scope of Cohen f, small is .10, medium .25, and large .40. Furthermore, those who
practice behavioral science generally utilize “smaller” effect sizes within the .00 to .40 range
(Cohen, 1988, p. 284).
Significance. Inferential statistics uses sample data from a population to deduce
information about the population as a whole (Devane et al., 2004). In order to accomplish
this, an appropriate test is executed and the results are compared according to a probability
table, which gives the significance level. If the null hypothesis is accepted, meaning no group
difference, there may still be small fortuitous differences found between the groups.
Consequently, the P value is the likelihood of observing a group difference if the null is true.
The acceptance or rejection of a null hypothesis is ascertained by comparing the P
value and the study’s chosen alpha (Devane et al., 2004). For example, if the P value is less
than the alpha, the null would be rejected; conversely, if the P value is more than the alpha,
the null hypothesis would be accepted. The current study is using the conventional alpha
level of .05, which indicates an acceptance of a 5% chance that the null will be falsely
rejected. This is indicative of a Type I error, signifying the researcher believes a difference
has occurred between the groups, although it has not.
Critics of statistical testing state the results are often wrong due to misinterpretation
or the researcher’s disregard of Type II errors (Borkowski et al., 2001). In general,
researchers are more concerned with preventing Type I errors, so literature is not filled with
erroneous effects. The flawed findings are then perpetuated when the next researcher builds
upon false results. To control Type I and Type II errors, the power and effect size of a study
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should be taken into account, in addition to significance testing (Devane et al., 2004). The
combination of knowing the additional statistical details about the study can improve the
design and reporting, since the added statistics strengthen conclusions deduced from the
results (Borkowski et al., 2001; Devane et al., 2004).
Factorial design. In the field of psychology, statistical power analysis has gained
attention over the last decade, which has given way to journal articles, books, and statistical
programs devoted to the subject (Maxwell, 2004). Even with its prominence, the power of
studies has not increased as much as expected. While it may seem that researchers are
ignoring power analysis, the persistent underpowered studies may be due to tests conducted
with multiple hypotheses. Since a study’s power is highly dependent on the test used, it
stands to reason that each individual test has a separate level of power attached. The problem
arises when a study with multiple hypotheses treat the compounded tests as having one level
of power. Although the researcher may believe their study has an acceptable power, it may
have decreased dramatically because of numerous testing.
Factorial design is one of the many commonly used methods of analysis in inferential
statistics. The main benefit of a factorial design is the ability to correctly analyze multiple
hypotheses within a study by comparing all levels of factors while controlling for errors
(Trochim & Donnelly, 2008). The power of a factorial design can be calculated with
knowledge of the study’s intended sample size, effect size, and statistical significance
(Borkowski et al., 2001; Devane et al, 2004; Faul et al., 2007). The difference between
analyzing power through this design and the problematic studies mentioned earlier, is a study
utilizing factorial design has already accounted for several alternate factors being tested;
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however, other designs are intended to examine one hypothesis, although they often conclude
by testing many (Maxwell, 2004; Trochim & Donnelly, 2008).
Sampling and sample size. Methodological concerns in behavioral research most
often refer to generalizability and sampling procedures (Hultsch, MacDonald, Hunter,
Maitland, & Dixon, 2002). Due to the enormous cost and impracticality, researchers cannot
observe an entire population. Therefore, a portion of the population must be secured through
a variety of strategies, which is aptly labeled sampling. Generalizability comes into question
when an inference is made about a group or phenomenon’s relationships, and whether the
assumptions can rightfully be applied to the larger population. Consequently, methods have
been designed by statisticians to effectively gain true values of a population, which includes
random sampling and non-probability sampling (Guo & Hussey, 2004). Additionally,
discerning the necessary sample size is also important with regards to sampling and requires
statistical knowledge of the typically unknown sample error. To obtain the essential
information of sample size, a statistical power analysis is employed.
As the desire for a higher level of power goes up, the sample size must also increase
(Borkowski et al., 2001). The amplification of power and sample size causes the distributions
of standard deviations for the null and alternate hypotheses to decrease, which creates less of
an overlap in the sampling distribution. If the researcher uses too small of a sample size it is
unlikely that a delineation can be made between the effects of intervention and chance;
moreover, the study may be too small to show a difference at all (Devane et al., 2004).
Conversely, it is also a waste to recruit too many participants, due to costs and potential
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ethical issues. Justifiably, this study has conducted an a priori analysis to conclude the proper
sample size while maintaining adequate power and effect size.
For reasons noted earlier, behavioral research typically engages a sample for use in a
study as opposed to census like observations of a population (Hultsch et al., 2002). Random
sampling consists of indiscriminately selecting subjects from a population, with the goal of
creating an accurate representation of the chosen population. Alternately, non-probability
sampling does not use probability to select subjects; instead, they are collected based on
availability, subjective judgment, as well as research purpose (Guo & Hussey, 2004). An
example of non-probability sampling is convenience sampling, which will be used in the
current study. This method usually gathers subjects by recruiting volunteers, while
attempting to maintain the chosen inclusion and exclusion criteria (Hultsch et al., 2002).
Random assignment. Research studies would be easier and more precise if unknown
causal variables did not exist (Krause & Howard, 2003). To understand the effects variables
have on a study, each should be accounted for, but it seems impossible to know even a
portion of the causal variables. Random assignment is currently the best method for
controlling unknown or unmeasured variables (Brooks, Miles, Torgerson, & Torgerson,
2006; Krause & Howard, 2003). This type of assignment is accomplished by randomly
dispersing subjects across all groups (Devane et., 2004; Ferron, Foster-Johnson, & Kromrey,
2003). For example, a sample of 30 subjects would be evenly assigned using a randomization
procedure into Intervention A – 10 subjects, Intervention B – 10 subjects, and Control Group
– 10 subjects (Enders, Laurenceau, & Stuetzle, 2006; Ferron et al, 2003).
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Random assignment is effective because it distributes potentially confounding
variables throughout the study’s groups (Brooks et al., 2006). As long as the factors are no
more attributed to one group than another, any differences found should be caused by the
intervention performed within the study (Krause & Howard, 2003). The use of random
assignment should increase a study’s internal validity (Bartsch et al., 2008). Accordingly, it
is often used when a study possesses an analytical difficulty, such as a small sample size,
abnormal distribution, or non-random sampling (Ferron et al, 2003).
Instructional Delivery
To maintain consistency and overcome inter-rater reliability issues, the current study
will administer lessons and exams via the computer. While the proctor overseeing each class
of subjects will physically be in the room, the process of the experiment closer resembles an
online classroom than traditional face-to-face instruction. As such, a look at these types of
instructional delivery systems is warranted.
The particular method of instruction for each learning theory (behavioral learning
theory and constructivism) will also be discussed. Demonstrating the framework for each
lesson is necessary because of the diverse nature of each learning theory. For example, a
constructivist learning environment uses realistic contexts and relevant tasks to encourage
understanding; however, the behaviorist model utilizes a logical sequential method of
delivering information (McKenna & Laycock, 2004; Rodrigues, 2000). Consequently, each
lesson must be carefully created within the scope of principles of the given theory.
Online versus face-to-face. The traditional classroom consists of an instructor who
teaches a room of students; this is referred to as the face-to-face (F2F) method of instruction
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(Moneta & Kekkonen-Moneta, 2007). Alternately, online learning occurs through the World
Wide Web on a platform designed to administer course materials possibly containing
graphics, text, audio, videos, and tests. Due to the drastic physical difference, educational
researchers have sought to ascertain whether a learning difference occurs as well. Results
have varied greatly, wavering back and forth with regards to the method producing more
effective learning.
Frederickson, Reed, and Clifford (2005) compared the learning of two different
courses, educational psychology and statistics, each with online and F2F instructional
delivery. The study demonstrated similar results between the classes and delivery systems.
Waschull (2001) altered her inquiry into two sections; study 1 and study 2 consisted of both
online and F2F classes taught on the subject Intro to Psychology. The modification was that
the subjects in study 1 chose either the online or F2F class, whereas the delivery methods of
study 2 classes were chosen after the students were registered for the course. The results
gained from the studies differed from one another. In study 1, students in the F2F class
performed significantly better, while the subjects in study 2 showed no significant difference
between the delivery methods.
Poirier and Feldman (2004) noted a divergence between student achievements on
class assignments versus exams. Although equal success was seen through online and F2F
paper assignments, students completing coursework via computer received higher exam
grades. Lastly, Wang (2009) conducted a study using university students learning computer
aided design (CAD) software. The study employed a group physically in the classroom
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learning on a multimedia platform and another group taught traditionally. No difference was
found between students taught F2F or using computer instruction.
Many researchers have stated flaws in studies conducted on F2F versus online
learning. One of the problems noted is allowing subjects to choose their own groups. When a
student purposely chooses an online or traditional class, it may be due to prior experience
with the type of class, or overall ease with the teaching method (Poirier & Feldman, 2004).
This non-probability method can certainly create inequality between the groups. A further
example of learner characteristics producing variances is the fact that adult learners have
been shown to perform better with online learning, as compared to younger more
inexperienced students (Frederickson, et al., 2005).
Another issue is the fundamental differences between the techniques could create
difficulty in ensuring each method of instruction and examination are similar enough for a
proper comparison (Waschull, 2001). For example, students in a traditional setting would
have an instructor overseeing the exam administration, while online learners privately take
unsupervised tests (Poirier & Feldman, 2004). Bearing this in mind, any differences that are
found may change with a modification of the lacking delivery method (Edmonds, 2006). A
student’s success may depend less on the delivery system as the actual nature of activities
within a given course (Poirier & Feldman, 2004).
Lecture. The behavioral learning theory method of instruction is a well-structured
way of transmitting information and skills to students (Wang, 2007). In the classroom, the
teacher acts as the authority, and the students follow all given instructions. The conveyed
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facts are part of a body of knowledge independent from the student’s minds (McKenna &
Laycock, 2004). Furthermore, the data is broken into distinctive parts for logical delivery.
Before the transmission of knowledge occurs, the information must be dissected into
small units (McKenna & Laycock, 2004). Each unit of knowledge has an objective and clear
learning outcome (Mayer, 2008). An objective is comprised of an action description of the
concept to be learned and the performance expected from the behavior (Simms & Knowlton,
2008). Consequently, objectives hold the criteria for determining successful behaviors
resulting from learning the material, which are categorized as either application or recall.
Establishing the components of learning helps to facilitate the student’s clarity and retention
of the material (Jackson, 2008). The resulting measurable output demonstrates learning has
occurred. Once the units of learning have been resolved, the correct sequencing of material
should be established (Rodrigues, 2000).
Within the scope of this study, presentation of information will be written in lecture
form. The origin of lecture emerged with human language and was essential for survival
(Jones, 2007). The transmission of replicated information initially included animal behavior,
food resources, human relationships, and general dangers. This type of oral communication
occurred through poetry, storytelling, rituals, and has since evolved into the educational
speeches seen today. The basic foundation remains the same; individuals with knowledge
reproducing information or skills in the minds of those without experience. In other words,
lecture is the method of presenting simultaneous data to a varying group size, where a
qualified person transmits information through a prepared oral presentation (Jones, 2007;
Morgan, Whorton, & Gunsalus, 2000).
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The last elements of behaviorist instruction are practice and immediate feedback.
Placing newly gained information into practice is an important part of instruction, so the
learner has the opportunity to exercise and appreciate the knowledge (Dalgarno, 2001).
Practice could be in the form of simple quizzes utilizing one or all of the following: multiple
choice, matching, and questions requiring a single number or word answer (Dalgarno, 2001;
McKenna & Laycock, 2004). Following up a practice session with immediate feedback both
reinforces right answers, and corrects inaccurate responses while the subject matter is still
fresh. This method of presentation, practice, and feedback provides a predictable learning
environment that ensures clarity (McKenna & Laycock, 2004).
The lecture method used in the study will include a presentation of information, in
conjunction with images and animations. For example, one of the techniques taught in the
Photoshop lesson is making and aligning shapes. Textual information on the creation of
shapes will be displayed, as well as an animation demonstrating the use of shape tools in
Photoshop. Additionally, an image of the align panel will be shown as its use is explained.
The informational material provided for the lessons reflect data found in the software’s help
reference (Adobe, 2009).
Since practice is an important aspect of behavioral learning, a short one question quiz
will be administered after each section is completed to ensure understanding. The correct
answer will be displayed along with a short explanation, in order to provide feedback and
reinforce the materials. All behavioral learning lessons will be taught using the visual lecture
method via the website developed for this study.
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Problem-based learning. Learning is a cumulative evolutionary process of
understanding, constructing, and applying ideas over time (Fardanesh, 2002; Gordon, 2009).
It is not a simple additive method of acquiring knowledge, because many active changes
must occur within the individual to achieve optimal learning (Fardanesh, 2002). Ultimately,
the result of a constructivist environment is profound long-lasting knowledge (McKenna &
Laycock, 2004). One method in the constructivist repertoire is problem solving, which is the
use of a problem to incite a student’s thinking (Webster, Campbell, & Jane, 2006). This type
of instruction employs analytic and creative thought to encourage the discovery of a solution
(Cote, 2007; Webster et al., 2006).
Taking problem solving a step further is the constructivist approach problem-based
learning (PBL). This particular method has seen an increase in educational application over
the last three decades, since it greatly increases a student’s problem solving capabilities
(Cote, 2007). The student-centered manner of instruction places the learner in a real-world
context with the focus of addressing and solving problems within the simulated situation
(Cote, 2007; Simms & Knowlton, 2008). The ill-structured problems typically used to mimic
complicated real-world situations are a stimulus for students to learn while researching and
determining resolutions (Mettas & Constantinou, 2008). The key to this approach, as with
other constructivist methods, is actively engaging students in situated learning (McKenna &
Laycock, 2004; Mettas & Constantinou, 2008).
Firstly, a PBL problem should be a challenge the student would ordinarily face in
everyday or professional life (Cote, 2007). Next, it should be presented in the same manner
an individual would normally encounter the problem. Subsequently, the student takes over
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learning by determining any needed additional information and seeking answers (Cote, 2007;
Mettas & Constantinou, 2008). Finally, the instructor can act as a facilitator, if additional
leadership is needed by providing supplementary resources, which is known as scaffolding
(Cote, 2007; Kozma, 2003). The student is in charge of setting his or her goals, planning
activities, and maintaining a level of mastery needed for further learning (Kozma, 2003).
This learning situation of active critical thinking and creativity pushes students to generate
ideas and theories, instead of seeking a single answer (Zhang, 2010).
Professionals who construct structures know scaffolding as a vital tool used to erect
buildings. Scaffolding supports a person so he or she can get to parts of a building that may
be difficult to reach without help (Holten & Clarke, 2006). The same idea applies to
scaffolding used within a constructivist problem-based learning environment. Teachers can
put cognitive scaffolding into place that allows students to achieve knowledge they may not
have easily attained on their own.
Scaffolding is a tool, guide, or strategy that assists students through a cognitive
activity that may be beyond their individual ability (Doering & Veletsianos, 2007;
Pentimonti & Justice, 2009; Simons & Klein, 2007). For the support to be effective, the
scaffolding must be an advancement of the current skill level (Bibok, Carpendale, & Muller,
2009; Pentimonti & Justice, 2009). Accordingly, a student’s ability relates to tasks completed
independently, whereas potential ability is associated with tasks that require great struggle or
minor assistance (Pentimonti & Justice, 2009). Additionally, independence must be the
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goal; therefore, a gradual withdrawal of support ensures the student masters his or her own
knowledge (Kozioff, LaNunziata, Cowardin, & Bessellieu, 2001; Pentimonti & Justice,
2009).
There are a myriad of lessons, techniques, and concepts that can be learned through
PBL; in turn, many scaffolding ploys may be used to aid students in understanding and
knowledge creation (Holten & Clarke, 2006). Another important method of scaffolding is
controlling parts of a task that are beyond a student’s capability (Holten & Clarke, 2006;
Simons & Klein, 2007). This includes direction maintenance and frustration control.
Direction maintenance ensures that problem solving activities remain on-task for a solution
(Bibok et al., 2009; Holten & Clarke, 2006). Frustration control is meant to regulate negative
reactions when faced with difficulty, which can lead to a lack of commitment in completing
the activity (Bibok et al., 2009). Ultimately, by controlling certain elements, the student is
allowed to focus on aspects within his or her range of competence (Hsu & Roth, 2009).
Lastly, scaffolding is used to reduce ambiguity and maximize learning opportunities
(Simons & Klein, 2007). Uncertainty can be decreased with the use of hints and cues, which
encourages the students to consider certain ideas within a problem. This technique should be
employed when the student shows a particular need and must be relevant to the specific
dilemma (Doering & Veletsianos, 2007; Simons & Klein, 2007). To maintain the learner-
centered strategy required by constructivism, the success of scaffolding must meet each
student’s needs (Doering & Veletsianos, 2007). Furthermore, scaffolding is a gateway to
elements of knowledge required for students to satisfy a problem.
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In line with problem-based learning, the study’s constructivist lesson provided a
problem for the students to solve. For example, the Photoshop lesson required the student to
create shapes and align them in a certain style. The scenario was presented as a professional
giving a logo to the designer for creation in Photoshop. A hand-drawn logo was shown on-
screen with a written description of the logo requirements from the client. As in a real-world
situation, the student needed to figure out how to use the program and create the logo.
Once the student read the problem, they were taken to a simulated Photoshop
software screen. While the screen looked like the program, all of the elements were not
functional. Only the tools needed to create the logo were available, which kept the student
focused on the problem and decreased frustration. When the student clicked on any tool, a
one-line explanation (raw information) was provided. All lessons taught through the
constructivist learning theory used the simulated problem-based learning environment
through the website developed for this study. Additionally, the informational material
provided for the lessons were directly taken from the software’s help reference (Adobe,
2009).
Assessment
A study’s measurement is an important element to the success of the research project
as a whole. To understand the applicability and effectiveness of the chosen evaluation, the
varying types of assessment methods will be discussed, as well as the particular system used
within this study. Due to limitations of subject contact time, the entire uCertify ACE practice
exam cannot be utilized; thus, the validity of questions is called into account. Consequently,
validity and the utilization of an expert panel will be examined.
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Testing and assessment. Tests and assessments are used to appraise a student’s
understanding, application of a particular subject matter, or range of information (Deal,
2004). Whether educational or research based, these evaluations typically relate to objectives,
goals, or content of a lesson or class. There are three main types of tests and assessments that
will be discussed: formative, summative, and performance. Firstly, formative testing
illustrates a student’s progression of learning. This measure can confirm the student
understands a subject, either prior to or after instruction, to ensure the quality of the learning
environment.
Next, a summative examination encapsulates the student’s understanding at a fixed
date or milestone, such as midterm or the end of a semester (Deal, 2004). This type of
assessment is primarily used to establish overall comprehension of the generalized subject
matter; therefore, it is better for measuring retention of information. Lastly, a performance
assessment allows students to demonstrate the knowledge and skills they gained by
constructing products or answers. An example of this testing medium can range from a
simple answer, complex demonstration, or a collection of work. Performance assessments are
most often seen in constructivist learning environments.
The Adobe Certified Expert (ACE) exam was a summative test used to verify a
person’s general knowledge of an Adobe computer software program (Adobe, 2009). While
summative in its complete form, distinct parts of the uCertify ACE study guide practice exam
were extracted to use as a formative assessment. Consequently, the subjects were only asked
questions from the practice exam on the particular subject taught in the lesson.
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ACE. The Adobe Certified Exam (ACE) was created so designers of all types could
provide tangible proof of their proficiency in one or many Adobe software products
(Johnson, 2010). This is achieved by becoming qualified in one of three levels of
certification. Single product certification is given to a person who passes the ACE exam of
one Adobe software program. Specialist certification requires expertise in several software
programs within a focused collection, such as video or print. To acquire master certification
an individual must pass all exams in an Adobe product suite. Once certified, the person is
authorized to use the official ACE logo as a credential.
In the development phase of ACE exams, studies were carried out specifically
analyzing office environments utilizing Adobe software (Johnson, 2010). Observations made
through these studies led to detailed objectives based upon standard usage. The questions on
each exam were created using the objectives as a guideline. Furthermore, the ACE exam is
exclusively administered at Pearson VUE testing centers, which includes over a thousand
facilities nationwide. The multiple choice questions found on the exam are given via
computer and can take one to two hours to complete. At the conclusion of the exam, a pass or
fail score is given, and data is automatically sent to Adobe.
Validity. Assessment has become a hot topic due to the push for proof that students’
are attaining their goals (Bartsch et al., 2008). Whether in research or classroom, the
objective is demonstrating a change, or quantifiable measure of students’ learning.
Regrettably, low instances of validity are found throughout many assessments. To increase
the validity of an instrument, the face, content, and construct validity should be analyzed.
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First, an instrument is said to have face validity if the assessment appears to evaluate
what it is intended to measure (Hardesty & Bearden, 2004). If a measurement has face
validity, then it can be evaluated for content. Content validity evaluates how well the
questions embody the substance of the construct. When an instrument contains both face
validity and content validity, then it can usually also be deemed to possess construct validity.
The last validity concept is defined by the ability to measure all elements of the construct it
represents (Wang, Wang, & Shee, 2007).
Each of the mentioned types of validity is important to the confidence of an
assessment. For example, while face validity seems superficial, it is important for ensuring
the validity of the instrument as a whole. The initial approval of a measures apparent validity
allows the researcher to move deeper, but is not sufficient enough to conclude the quest
(Hardesty & Bearden, 2004). Alternately, if an assessment has construct validity, it is
assumed to be face and content valid.
Expert panel. The instrument used to collect data for a research study can greatly
influence results despite whether the purpose is explanatory, descriptive, or exploratory in
nature (Davis, 1992). The origination of instruments varies, such as through clinical
observations, theoretical models, or a revision of previously used instruments. Accordingly,
valid methods used to enhance data collection are significant. A process that is often used to
assess and improve measures is an expert panel. A panel of experts is used to gain insight
into an instrument through the expertise of those in the appropriate field of study. Utilizing
this vital element of the development process strengthens the content and face validity, as
well as ensuring a well-constructed instrument.
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The first step in using an expert panel is determining the characteristics needed to
deem an individual worthy of providing suggestions and knowledge to the instrument (Davis,
1992). The professional must have certification, education, published papers, or ample
experience on the subject. Only a person with a great deal of awareness on the topic can be
expected to analyze the content validity of a measure. Next, the reviewers should be given
information on the study, so they are oriented with hypotheses, definitions, and the usage of
the measure. Adequate background information will place the experts in the proper state of
mind for a comprehensive instrument review.
Once the basis of a measure is completely clear, the panel can be asked a myriad of
conceptual and theoretical questions to agree on the appropriateness of the many facets of the
instrument (Davis, 1992). There are many methods that can be employed with an expert
panel; for example, asking reviewers to rate the relevance of each individual question
contained within an instrument (Davis, 1992; Hardesty & Bearden, 2004). Utilizing an item
rating scale for this task gives an easily quantifiable rating to each question (Davis, 1992).
This type of scale involves appraisal on a degree of clearly representative to not
representative, or very good to poor, in order to account for what the concept signifies
(Hardesty & Bearden, 2004).
Another method used with expert panels is the allocation of questions or concepts
under the proper construct (Hardesty & Bearden, 2004). Through this procedure each review
has the duty of assigning each question to a construct category. In doing so, the professional
is acknowledging the suitability of each question within a certain focus. This particular
technique is useful for a study with many or multifaceted constructs. The last popular system
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of assessment when using an expert panel is concluding whether the scoring format is
conducive for the type of results expected (Davis, 1992). Scoring and interpretation of results
is important for yielding a correct outcome; therefore, the experts should be used to evaluate
and decide if the format is proper for the level of measurement.
Lastly, it is the author’s job to take the information gained from the expert panel and
ascertain the questions that should be used in the instrument, or whether further testing is
needed (Hardesty & Bearden, 2004). The effective use of reviewers should clearly show
unrelated questions, as well as those that are redundant or ambiguous. Whether utilized to
partition questions into categories or the general evaluation of a measure, the use of expertise
is an excellent method of improving an instrument.
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CHAPTER 3. METHODOLOGY
Purpose of the Study
The purpose of the study was to analyze and find evidence for a beneficial learning
theory to teach computer software programs. This included testing students’ knowledge of
software before and after a lesson; thus, concluding whether the students tested higher after a
constructivist or behavioral learning lesson. Furthermore, due to the variety of software
available, establishing a single learning theory’s applicability for a specific program is
beneficial. This could reveal a learning theory’s favorable use across multiple programs,
general detriment to software instruction, or whether certain software requires a particular
method of education.
The current study meant to give educators more effective teaching tools, so students
would ultimately get the most out of any particular software program. This was achieved by
researching two widely used learning theories within the realm of natural learning (the
classroom). In narrowing the research to specific software applications, the study sought to
identify whether differing applications of learning theories were required for precise focuses
of learning (Lawless & Pellegrino, 2007). Furthermore, the results should give software
educators a defined and successful teaching direction, as well as translate to a wider
understanding for the instructors to build upon. Armed with this study’s results from an
actual college classroom, the computer software instructor can build his or her class
curriculum around the proper learning theory for the software taught.
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The following are the research questions for this study:
Research Question 1: Is constructivist or behavioral learning theory more beneficial
when teaching multimedia software?
Research Question 2: Is there a difference in the effectiveness of learning between
Photoshop and InDesign when teaching multimedia software?
Research Question 3: Are there interactions between learning theory and software
with regards to teaching multimedia software?
Research Design
The aim of this study was to carry out a randomized quantitative experiment with an
analysis of covariance design employing four groups, gathered using convenience sampling,
in a pretest, posttest model to analyze multiple independent variables (see Table 1).
Table 1 Research Design
Photoshop InDesign R O1 X1 O1 R O1 X2 O1 R O2 X3 O2 R O2 X4 O2
O1 – Photoshop exam O2 – InDesign exam X1 – Photoshop constructivism lesson X2 – Photoshop behavioral lesson X3 – InDesign constructivism lesson X4 – InDesign behavioral lesson
The nature of the research questions in this study calls for a quantitative approach.
Firstly, each of the research questions seek to discover whether any difference between the
variables can be found (Bartsch, Bittner, & Moreno, 2008). Furthermore, to find evidence of
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a relationship between the variables, a manipulation of the independent variables must occur
(Bartsch et al., 2008; Trochim & Donnelly, 2008). Lastly, the goal of the study was to
conclude which learning theory was more conducive for learning multimedia software;
therefore, the approach taken needed to have the ability to generalize to the larger
educational community (Trochim & Donnelly, 2008).
The sample of students used was analyzed within their natural environment to
encourage an ordinary learning atmosphere; therefore, the students were tested in the
classroom during their scheduled class time (Trochim & Donnelly, 2008). The class itself
was either assigned by the Department Chair or chosen by the student during mid-session of
the previous quarter. Due to the class distribution, the sample was obtained through
convenience sampling. To increase internal validity and place subjects in probabilistically
equivalent groups, random assignment was utilized. Since the assignment was random, the
study was considered a randomized experimental design (Campbell & Stanley, 1963;
Trochim & Donnelly, 2008).
A pretest was used to measure each student’s knowledge of the materials prior to the
lesson given. The pretest was ultimately used as a covariate, which removed the effect of
prior knowledge from the students’ posttest scores (Trochim & Donnelly, 2008).
Furthermore, a 2 X 2 factorial design was implemented to test the effects of learning theory
and software, as well as any interactions occurring between the factors (Campbell & Stanley,
1963; Trochim & Donnelly, 2008).
Every study faces a multitude of potential causes to the phenomenon being researched
(Christ, 2007). Quantitative empirical research is a strict statistical way of looking at
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measured values and utilizes independent and dependent variables in a systematic approach
to seek evidence of the existence of a causal relationship (Christ, 2007; Krause & Howard,
2003). The cause and effect relationship can be justified when other influences have been
discredited. This method facilitated an examination of each question in an unbiased and
calculated manner, to resolve the most sufficient technique to teach multimedia software.
This study’s research questions sought differences between many variables in accordance
with a quantitative analysis design.
The covariant, pre-lesson assessment, served a very important purpose in
strengthening the internal validity of the study (Bartsch, Bittner, & Moreno, 2008). Internal
validity is the degree the proposed cause has actually resulted in the stated effect (Christ,
2007). For example, in a posttest only design, the likelihood of an alternative cause to a
study’s results is high (Bartsch, Bittner, & Moreno, 2008). Consequently, a posttest only
study has a low level of internal validity since no comparison, whether group or alternate
reason, is made other than the targeted cause. Incorporating a pretest allows the experimenter
to rule out the probability of at least one other potential source for the given result. In the
case of the current study, the covariant took the pretest one step further by using the factor to
account for a named cause in the results (Trochim & Donnelly, 2008).
Within the scope of this study, the covariate was warranted to eliminate any inflation
in the post-lesson examination score (Bartsch, Bittner, & Moreno, 2008). Due to the fact the
chosen classes were required for several majors, it was important to note each student’s
previous computer skills (as shown in the testing site’s online profile for 2009). For example,
a web design interactive media student may enter the class with no prior knowledge of
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Photoshop or InDesign, whereas a video production student might have advanced
comprehension of the software programs. Thusly, without a pretest (covariate) to assess the
students’ knowledge of the program, prior knowledge could not be ruled out as the reason for
a high test score after the lesson has been administered.
In addition, the use of factorial design enabled this study to determine which aspects
of the results, or combination of features, could have produced an effect (Trochim &
Donnelly, 2008). The study’s 2 X 2 factorial design analyzed the main effects of learning
theory and software separately, as well as the interactions occurring between the two factors.
This type of design decreased the error resulting from multiple independent studies and
increased efficiency by eliminating the need for a series of studies. It also encouraged
interaction examination, which would have been difficult to resolve through other designs.
Target Population and Participant Selection
The general population of this study was American collegiate level students
participating in any course including software. This population excluded very few students
because, at the very least, colleges supply remedial software instruction in the required
introduction to the school course. Across the United States, thousands of students enroll for
college each term; therefore, the population encompassed a tremendous number of
individuals (National Center for Education Statistics [NCES], 2009). In narrowing the
population, the demographics of college students collected from The National Center for
Education Statistics (2009) are presented. The enrollment status of students across the United
States was undergraduate-86%, graduate-13%, and professional–2%. The statistics
categorized by sex was female-57% and male-43%. Attendance status was full-time-61% and
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part-time-39%. Finally, the United States student population’s ethnicity was White-63%,
Black-14%, Hispanic-12%, Asian or Pacific Islander-7%, American Indian/Alaskan Native-
1%, and Nonresident Alien-3%.
The sample was students enrolled in Digital Image Manipulation, Digital Layout,
Digital Illustration, or Digital Typography classes during two quarters of 2012. The testing
site was an open admission, for-profit private college. The demographics of the testing site’s
students were (A. Black, personal communication, February 15, 2012): the enrollment status
of testing site was undergraduate 100%, graduate 0%, and professional 0%. This reflects that
individuals enrolled at the testing site are either undergraduate or certificate program
students. The sex was split into female-51% and male-49%. Attendance status was full-time-
56% and part-time-44%. The ethnicity of the school was reported to be White-42%, Black-
4%, Hispanic-17%, Asian or Pacific Islander-2%, American Indian/Alaskan Native-1%,
nonresident alien-0%, and unknown-35 %.
The recruiting procedure began by contacting the Dean of Students to request
permission to carry out an experimental lesson on the Digital Image Manipulation, Digital
Layout, Digital Illustration, and Digital Typography classes. Next, the teacher of each class
was asked for consent to carry out the experiment in his or her classroom, as well as specific
time and dates that were convenient. Last, each class of students was asked to participate in
the experimental lesson.
The selection procedure included any students in the chosen classes who agreed to
participate and excluded individuals under 18 years of age. The main concern with regards to
ethical issues was voluntary consent. According to the Belmont Report, a subject must
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voluntarily agree to participate, for the consent to be valid (Department of Health, Education,
and Welfare [DHEW], 1979). Furthermore, an individual considered an authority figure may
influence the subject unjustifiably; thusly, calling into question whether the participant has
voluntarily given consent.
This study was conducted in a college classroom environment. Undue influence may
have been present if the students felt their instructor, department chair, or school
administrator had a stake in the outcome of the study, or the subject’s general involvement.
The pressure could make the subject feel as if he or she had no choice but to participate,
causing a stressful situation for the student. Consequently, the subject’s participation would
not be voluntary.
To overcome a potential problem, the instructor was asked to make a statement at the
beginning of class. “I have no involvement in the outcome of this study and there will be no
class penalty or reward for participation.” The direct statement made by the instructor
ensured the students’ participation was voluntary, not due to a feeling of any expectation.
A strong external validity depends on a broad representation of the population, which
is typically ensured by random sampling (Hultsch, MacDonald, Hunter, Maitland, & Dixon,
2002). While convenience sampling does not employ random selection, it is commonly
utilized to gain an adequate range of subjects when randomness is either too expensive or not
a suitable option. In this study, convenience sampling increased the likelihood of gaining
permission of class time by minimizing the general disruption of class.
The experiment within this study was comprised of 4 groups: Photoshop taught with
behavioral learning, Photoshop taught with constructivism, InDesign taught with behavioral
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learning, and InDesign taught with constructivism. Groups were randomly assigned using a
computer-generated randomized code, which was built into the lesson website. The code was
activated and assignment occurred when the student logged onto the lesson through a
password-protected site. By using random assignment, the varying characteristics of the
students were uniformly applied across groups, which equalized any confounding variables
(Enders, Stuetzle, & Laurenceau, 2006).
In determining the anticipated sample size for the study, the computer program
G*Power was utilized. G*Power is primarily used for power analysis and includes a priori,
post hoc, and compromise analyses (Faul, Erdfelder, Lang, & Buchner, 2007). A priori
analysis, which is conducted before the execution of the study, is highly recommended to
control statistical power (Faul et al., 2007). By using G*Power for this type of analysis, the
user can adjust statistical parameters and instantly view their effects.
Specific to resolving the anticipated sample size of this study, an ANCOVA main
effects and interactions-a priori power analysis was calculated. Input parameters used for
G*Power are based on the structure of the study and standard considerations in the
psychology discipline. As such, the information used to ascertain sample size was: Cohen f
medium effect size (.25), 5% significance (.05), and 80% Power (.80). In addition, the
degrees of freedom (1) were entered, as well as number of groups (4) and covariates (1).
When calculated the result was a total sample size of 128 subjects (Buchner, Erdfelder, Faul,
& Lang, 2008; Cohen, 1988).
The samples for the Photoshop groups were taken from Digital Image Manipulation
classes and the InDesign groups originated from Digital Layout, Digital Illustration, and
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Digital Typography classes. Each class had an average of 15 to 20 students, which meant the
expected amount of students approached for recruitment was 200 to 250 subjects. While the
expected size was slightly larger than the sample size calculated with G*Power, it was
justified because all subjects selected did not choose to participate.
Procedures
To ensure a complete understanding of the procedure, a step-by-step analysis has
been provided.
1. A meeting time was set up with the instructors of the classes used at the beginning of the
quarter for about an hour of his or her time.
• One instructor may teach several of the targeted classes.
• The researcher explained the purpose of the study, the importance of the
experiment, and the procedure followed during the experiment.
• The instructor was given the opportunity to ask any questions.
• The instructor gave the researcher the date and time his or her class was
scheduled.
2. On the day of the experiment, the researcher wrote her first name and important points to
stress on a dry-erase board in the classroom, in order to reinforce possible ethical issues.
The information below was covered by the projector screen, which could be rolled up or
extended open over the grease board, until the researcher’s official introduction.
• Cajah
• Exercise at beginning of class.
• Normal class lesson will take place.
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• Under no obligation to participate.
• Excuse yourself from participation at any time
• Discount code
• Information and responses are confidential.
3. The researcher sat at the front of the class as the students entered the classroom.
• The particular courses chosen were taught in classrooms where every student had
his or her own computer station.
4. Once the instructor completed his or her “beginning of the day” duties, the teacher
introduced the researcher and stated to the students: “I have no stake in the outcome of
the study being conducted and there will be no class penalty or reward for participation.”
• Thus, the teacher verbally declared his or herself neutral regarding the study.
5. The students were given a typed informed consent form that included their role in the
study.
6. The researcher orally informed the students:
• “My name is Cajah and I am an alumnus of the testing site. I will be guiding you
through this exercise, if you choose to participate. You have the informed consent
form in your hands, which provides information about the study and your
participation. I would like to highlight a few parts and then I can answer any
questions you may have.”
• “The study is a part of an educational psychology dissertation that is testing
learning theories, which is any number of teaching methods used to educate
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students. Particularly, the study will be looking at a few learning theories to teach
software. The chosen software is Photoshop and InDesign.”
• “The exercise will be conducted at the beginning of today’s class, giving the
instructor the opportunity for regular class afterwards.”
• “All students will have the normal class lesson, taught by the instructor, after the
research study session. Whether or not you choose to participate, you will still
have the normal instruction as if I were not here.”
• “There is no obligation to participate in this study; it is completely voluntary.”
• “If you choose to participate, you can excuse yourself from participation at any
time during the exercise. The study is completely voluntary.”
• “If you complete the study, you will be given a 25% discount code for any
uCertify preparation kit. The uCertify company provided the questions used in the
exercise. The company develops study guides and preparation kits for many types
of exams, including Adobe Certified Expert exams. The discount code can be
used to purchase any study guide or preparation kit at a discounted price, if you
choose to pursue certification in a product. There is no requirement to use the
coupon.”
• “All information gathered in the study will remain confidential. No personal or
identifying information will be gathered.”
• Then the exercise was explained to the students.
a. “The exercise will be carried out on the computer through a website”
b. “First, you will be asked your degree program.”
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c. “Then a short 10 question quiz will be given to measure your current
knowledge of the software.”
d. “Do not be alarmed if you are unable to answer the questions. If your
experience with the software is limited, then you will probably not be
familiar with the questions.”
e. “Next you will be given a lesson about the software.”
f. “You will have another 10 question quiz to test the understanding of the
lesson.”
g. “Once everyone is finished, the researcher will leave.”
• “Do you have any questions?”
7. After all questions were answered, the students were instructed to take a break to ponder
their participation and attend to personal matters.
8. Additional questions were answered and the consent forms were collected.
9. Any students who did not wish to participate were instructed to work quietly on a
previously given homework assignment, class assignment, or explore the software
program.
10. Once the non-participating students were attended to, the web address and password for
the study was given to the participating students.
• The students were told to inform the researcher when they completed the exercise
by raising their hand, and they were instructed to work quietly until everyone was
finished.
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a. While this may be seen as potentially disruptive, it was important to
ensure the subject had exited the website to prevent any database
tampering.
11. When a student logged onto the website and entered the password, they were randomly
assigned to one of two groups.
• Once the specified number of students (determined by the amount of signed
consent forms received) logged on to the website, the password was disabled.
This prevented additional students’ access to the exercise.
• While there were 4 groups total, only the groups dealing with the software taught
during the class time were assigned. For example, if the instructor informed the
researcher he or she would be teaching Photoshop, only the Photoshop
constructivism and behaviorism were assigned.
• The assigned groups were constructivism and behavioral learning.
a. The constructivism group was given the appropriate software exam,
constructivist lesson, and the same exam again. For example, the class
instructed on Photoshop was only given the Photoshop exam.
b. The behavioral group was given the appropriate software exam, behavioral
lesson, and the same exam again. For example, the class instructed on
InDesign, typography, or Illustrator was only given the InDesign exam.
12. Once each student finished, the researcher made sure the website was closed by a visual
inspection of the computer. The student was then handed a participation certificate with
the uCertify discount code.
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13. After all students completed the experiment, the researcher exited.
There were a few ethical considerations accounted for, to preserve the subjects’
privacy and minimize any discomfort. Firstly, there was very little personal information
taken from the subjects. The researcher was aware that revealing test scores to others would
be a violation of privacy or at least embarrassing. This was avoided by each student alerting
the researcher when he or she was finished with the exercise. The researcher visually
inspected the computer to ensure the experiment browser window was closed.
A further consideration regarded the actual testing of materials. The pretest was
designed to assess any prior knowledge the student might have possessed on the computer
software. The fact the student was unable to answer any questions on the pretest could have
caused anxiety. This was minimized by informing the subjects not to be worried if the
questions were unanswerable. A prior warning and the inclusion of an “I don’t know” answer
option on the pretest should have eased apprehension during the test.
Instruments
All research questions used the same data collection instruments. The data was
assembled through questions from two of the uCertify Adobe Certified Expert (ACE) exam
preparation guides. The specific prep guides used by this study were Photoshop CS5 and
InDesign CS5.
The ACE exam measured an individual’s proficiency level of a specified Adobe
product (Adobe, 2009). A single Photoshop CS5 ACE exam consisted of 64 questions,
whereas the InDesign CS5 exam had 72 questions, and both were administered via computer
at a testing facility. The test time was limited to 90 minutes and required a score of 74 to pass
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the Photoshop exam and 78 for InDesign. The data type was a ratio score based upon the
amount of correct responses. The targeted audience for the ACE exam was any person well
versed in an Adobe product who wished to establish documentation for that knowledge.
Due to the highly sensitive nature of the ACE exam, test questions could only be
released to authorized testing sites for the purpose of certification. Consequently, exclusive
practice ACE exam questions developed by uCertify were utilized. The uCertify company
specialized in study guides and practice exams for a myriad of certifications and assessments
(uCertify, 2011). Some of these included: college admission exams, IT certification,
Microsoft, Adobe Certified Expert, Cisco certification, and information security certification.
UCertify was a trusted company for original study material, as well as a legal reseller for
practice tests (CertGuard Inc, 2009).
In general, study guides were powerful tools because they communicated the most
pertinent information required of students (Khogali, Laidlaw, & Harden, 2006). In turn,
students were able to manage their education with an emphasis on relevant material.
Additionally, practice exams provided the opportunity for students to work through
significant data (Dickson, Miller, & Devoley, 2005). The multiple-choice and matching
questions typically found in practice tests were excellent for focusing students’ efforts on
understanding the concepts instead of memorizing them.
Each uCertify preparation kit covered the same expansive amount included in the
actual ACE exam, but only a portion of each guide was used within the study. For example,
the Photoshop CS5 ACE exam tested all facets of the Photoshop program, such as retouching
images, using layers, managing color, and many other tasks that can be completed with the
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application (Adobe, 2009). The small amount of time given with the study’s subjects
suggested a focused lesson on one topic each software piece featured. Thus, the questions
asked of the students needed to be applicable to the lesson given.
With regards to the Photoshop CS5 adapted uCertify assessment used for this
experiment, the primary concentration was working with layers (Adobe, 2009). The focus
included the creation and arrangement of layers, as well as layer effects and styles.
Additionally, it also contained questions on working with multiple layers of an image and
layer blending options. This category comprised 16% of the overall original exam.
The InDesign CS5 adapted uCertify assessment was created with questions on laying
out a document (Adobe, 2009). Included within the class of questions was queries on
working with master pages, quick applying styles, layers, arranging document windows, and
controlling text on a path. Furthermore, questions on transforming objects, information panel,
object styles, and smart guides were also incorporated. The original ACE exam consisted of
18% of this grouping.
The information generated from each modified uCertify assessment was a ratio data
score of correctly answered questions. Two scores were gathered from each subject using the
same assessment, one prior to testing and another after the lesson. The pretest score,
covariate, was compared to the posttest score to establish any prior knowledge on the subject.
The assessment was designed to reveal knowledge about the given material, even if the
understanding predated the experiment.
A panel of experts was convened to evaluate the intended instrument for this study.
The Adobe Certified Expert (ACE) exam as a whole was an industry standard, therefore, it
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was relied upon to certify an individual’s proficiency within the scope of a single Adobe
software (Adobe, 2009). While the instrument was widely used, it was a product of the
design industry and had not been subjected to psychometric evaluations (Adobe Partner
Connection [APC], personal communication, October 28, 2009). Due to the lack of
established data on the original test or uCertify preparation guides, the expert panelists
evaluated potential questions for face validity and logical coherence.
A request was made to uCertify that called for access to 60 questions on particular
factions of the Photoshop CS5 and InDesign CS5 ACE exams. The researcher analyzed the
30 questions per software application, in order to organize related questions that could be
taught in a single class period of an introductory course. Subsequently, an expert panel of
instructors evaluated the questions for each exam. An instructor must have at least one year
experience teaching either Photoshop or InDesign software to be considered an expert. The
goal of both expert panels was to find the 10 best questions for each exam, which was
achieved.
The instructors used for the expert panel were contacted via a letter placed in the
teacher’s mail box at the testing site. Five instructors received information on the Photoshop
expert panel and four were given InDesign expert panel documents. Two instructors were
presented with both the Photoshop and InDesign panel letters. Subsequently, four completed
panels were returned for Photoshop and three for InDesign. The panelists answered questions
directly on the document provided by the researcher and were returned to the testing site’s
office. The expert panelists were presented with the respective uCertify questions, as well as
three assessment inquiries (see Appendix A and Appendix B). Each evaluation query was
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meant to bring the most appropriate questions to the forefront, while identifying any
unsuitable or redundant questions (Trochim & Donnelly, 2008). The following questions
were utilized for the expert panel:
1. Please choose 10 out of the (15/14) questions you believe best assess a student’s
knowledge of (the Photoshop or InDesign construct). Mark the checkbox next to
the 10 appropriate questions.
2. Are there any questions that stand out as not measuring (the Photoshop or
InDesign construct) or would not be covered in an introductory class? List the
number corresponding to the unsuitable question.
3. Are any of the questions redundant? List the number corresponding to the
redundant questions.
The results of the Photoshop expert panel revealed seven modified uCertify Adobe
Certified Expert (ACE) exam questions that all four panelists agreed were perfect to measure
introductory students’ knowledge of Photoshop layers (see Table 2). Next, two exam
questions were selected by three panelists, and a single question was approved by two expert
panel members, which completed the top 10 questions. Although a few of the chosen exam
questions were marked as possibly being repetitive, none received more than two votes in
this area. Additionally, no exam question that was used in the experiment was marked for the
expert panel question 2; hence, none were indicated as questions not measuring the
Photoshop concept or inappropriate for an introductory class. The questions used for the
Photoshop instrument were exam questions 1, 4, 9, 10, 11, 12, 13, 5, 14, and 3 (see Appendix
C).
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Five modified uCertify ACE exam questions were commonly agreed upon by all
three panelists as the best questions to measure students’ knowledge of laying out an
InDesign document (see Table 3). Furthermore, five exam questions were chosen by two
panelists as being great inquiries about the InDesign concept. All 10 exam questions noted
had no votes for expert panel questions 2 (marked as an inappropriate query) and 3
(redundant question). Using these top 10 questions would require five sections within the
experiment’s InDesign lesson, and three of the lessons would be taught for only one exam
question each.
To minimize the sections administered in the InDesign lesson and encourage more
modified uCertify ACE questions per lesson section, exam question 14 was substituted for
exam question 8. As shown in Table 3, the two questions scored the same for expert panel
questions 1 (best question for the InDesign concept) and 2 (inappropriate question), but the
modified uCertify ACE exam question 14 was voted by one panelist as redundant (expert
panel question 3). The substitution decreased the sections in the InDesign lesson to four, and
each section included multiple questions. The questions used for the InDesign instrument
were exam questions 5, 6, 7, 12, 13, 3, 4, 10, 11, and 14 (see Appendix D).
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Table 2 Results of the Photoshop Expert Panel
Exam
question
number
Expert panel question scores
1a 2b 3c
1 4 0 1
2 1 2 0
3 2 0 0
4 4 0 0
5 3 0 2
6 1 0 1
7 1 2 0
8 0 3 0
9 4 0 0
10 4 0 0
11 4 0 2
12 4 0 0
13 4 0 0
14 3 0 0
15 1 0 2
Note. The values represent exam questions chosen by expert panelists. a Higher values indicate the exam question best assesses a student’s knowledge of the
concept. b Higher values indicate the exam question does not measure the student’s knowledge of the
concept or is not taught in an introductory class. c Higher values indicate the exam question is redundant.
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Table 3 Results of the InDesign Expert Panel
Exam
question
number
Expert panel question scores
1a 2b 3c
1 0 0 0
2 0 2 0
3 2 0 3
4 2 0 0
5 2 0 0
6 3 0 0
7 3 0 0
8 3 0 0
9 2 0 0
10 1 0 2
11 2 0 0
12 2 0 0
13 3 0 0
14 3 0 0
Note. The values represent exam questions chosen by expert panelists. a Higher values indicate the exam question best assesses a student’s knowledge of the
concept. b Higher values indicate the exam question does not measure the student’s knowledge of the
concept or is not taught in an introductory class. c Higher values indicate the exam question is redundant.
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Hypotheses
HA1: The hypothesis for the study’s first research question indicated a difference
would be found between the constructivism and behavioral learning lesson assessment mean
scores. This was thought to be true because of the distinctly different methods of instruction
possessed by each learning theory. In particular, the polar opposite beliefs held by behavioral
learning and constructivism theorists would suggest a divergence when both are equally
applied to a subject.
HA2: The hypothesis for the second research question affirmed a difference would be
found between Photoshop and InDesign lesson assessment mean scores. While the software
programs might both be utilized by one individual, they are used to create different types of
projects. Users of Photoshop create and manipulate graphics; however, InDesign produces
documents for print. The differing mindset needed to maneuver each piece of software may
create a variation in results when assessing the software programs.
HA3: The hypothesis for the third research question asserted at least one interaction
would be found between learning theory and software. The dissimilar learning theories and
software compared within this study might show particular combinations of instruction and
programs that are more beneficial than others. For example, the results may show evidence
that Photoshop is helpful for teaching behavioral learning or constructivism is favorable for
InDesign. Conversely, the interactions could also verify learning theory and software
groupings that should not be used together.
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Data Analysis
First, descriptive statistics were calculated to gain the mean and standard deviation of
each condition. This information was especially valuable after significance was ascertained,
as well as in the documentation of results (Yockey, 2008). Next the pretest scores (covariate)
and posttest scores (dependent variable) were examined to check the homogeneity of
regression slopes (Tabachnick & Fidell, 2007). The covariate and dependent variable were
compared using one-way between subjects ANCOVA, as well as a scatter-plot to identify a
linear relationship (Brace, Kemp, & Snelgar, 2006).
Levene’s Test of Equality of Error Variances was used to conclude whether the
homogeneity of variances assumption was upheld (Leech, Barrett, & Morgan, 2008;
Tabachnick & Fidell, 2007). Finally, the main analysis was conducted using a two-way
between subjects factorial analysis of covariance. This allowed the researcher to ascertain the
results of each main effect (software and learning theory) and the interaction (software *
learning theory) (Tabachnick & Fidell, 2007). Additional post hoc tests and visual
representations of the data were determined based upon results.
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CHAPTER 4. DATA COLLECTION AND ANALYSIS
The current study sought to find evidence to support whether any single learning
theory was favorable for teaching software. To achieve this, the following research questions
were asked: Is constructivist or behavioral learning theory more beneficial when teaching
multimedia software? Is there a difference in the effectiveness of learning between
Photoshop and InDesign when teaching multimedia software? Are there interactions between
learning theory and software with regards to teaching multimedia software?
The data collection and analysis chapter provides the findings of the study discussed
in previous chapters. As such, Chapter 4 will reveal many data aspects gained from the
execution of the study. Firstly, a description of the sample will be discussed, which includes
power analysis and demographic information of the sample participants. Next, the summary
of results, according to each research question, will be explained. Finally, a presentation of
the analysis details with a breakdown of all statistical analysis performed, as well as
appropriate tables and graphs will be illustrated. Overall, this chapter will provide specific
statistical aspects needed to justify the discussion of results found in Chapter 5.
The execution of the study spanned two quarters. Interaction with the participants
occurred in a single class during the first or second week of the quarter. Each course was
chosen because it was either a prerequisite for InDesign or a class the research topic would
normally be covered (Photoshop or InDesign). Consequently, there was approximately a 12
week lapse of time between gathering data from the first 100 participants and the rest of the
data collected, which coincided with the end of the first quarter to the beginning of the
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second. The same procedures were utilized both quarters as outlined in the procedures
section of Chapter 3.
The Academic Department Director for Graphic and Web Design provided dates and
times for the approved classes, and the instructors of those courses were contacted. The
instructors were given general information about the study and procedural details. Questions
about the researcher’s time in the classroom were answered and confirmation was gained
from the instructors to conduct research in the classroom. Once in the class, the recruitment
procedure was enacted. After writing pertinent talking points on the grease board, consent
forms were handed out and discussed; then, the students were given time to ponder
participation.
Very few questions were asked by the students, but those with inquiries generally
wanted to ensure the study was independent of the testing site and continual participation was
not required. Furthermore, 59% of all students asked to participate gave consent and
proceeded with the exercise. An explanation of not wanting to participate was neither asked
by the researcher, nor given by the students. After the consent form was signed, the
participants entered the website to take the pretest, lesson, and posttest. Participants who
completed the exercise received a coupon certificate for uCertify.com.
Some technical issues arose, but most regarded logging-on to the testing site’s
computer system. Students were required to log-on the computer with a username provided
by the school and a password set up during enrollment orientation. Some of the selected
classes were introduction courses; therefore, an influx of new students required guidance
during the log-in process. The assistance was provided by the class instructor. A single
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instance occurred in which a student could not view the exercise website, because the
computer had a JavaScript plug-in error. The student decided to withdraw from the study,
and no data was gathered.
The study amassed 167 participants across four different groups. With regards to
software, the Photoshop group had a population of 82 and 85 participants for InDesign. The
theory category had 85 subjects for the behaviorist group and 82 for constructivist. A priori
testing through G*Power with a conventional significance (.05), medium Cohen f effect size
(.25), degrees of freedom (1), number of groups (4), and covariate (1) allowed for a
determination of power versus sample size (Faul et al., 2007; Faul, Erdfelder, Buchner, &
Lang, 2009). The results revealed that a sample size needed to achieve an 80% power was
128 participants. A post hoc power analysis using G*Power showed the actual power of the
study was 0.8946, which was due to a slightly larger sample size.
Out of the 20 degree program options offered at the testing site, 16 were represented
in the sample taken during the course of the study. The greatest prevalence of degree
programs were Graphic Design (20%), Media Arts & Animation (14%), Game Art & Design
(8%), and Digital Film Making & Video Production (8%). The programs that did not have
participant representation were Audio Production, Digital Image Management, Fashion
Retailing, and Web Design & Interactive Communications. The full list of degree programs
accounted for in the sample is shown in Table 4.
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Table 4 Frequency of Sample Participants for Each Degree Program
Degree Program Frequency Percent Valid Percent
Cumulative Percent
Animation & Special Effects 2 1.2 1.2 1.2 Digital Film & Video Production 14 8.4 8.4 9.6 Design Management 9 5.4 5.4 15.0 Design & Technical Graphics 5 3.0 3.0 18.0 Fashion Design 7 4.2 4.2 22.2 Film Production 3 1.8 1.8 24.0 Fashion Retail Management 3 1.8 1.8 25.7 Game Art & Design 13 7.8 7.8 33.5 Graphic Design 34 20.4 20.4 53.9 Interior Design 1 .6 .6 54.5 Media Arts & Animation 24 14.4 14.4 68.9 Photography 6 3.6 3.6 72.5 Visual Effects & Motion Graphics 10 6.0 6.0 78.4 Video Production 4 2.4 2.4 80.8 Web Design & Development 11 6.6 6.6 87.4 Web Design & Interactive Media 21 12.6 12.6 100.0 Total 167 100.0 100.0
The study was tested through multiple classes over the course of two quarters. The
Digital Image Manipulation class was exclusively used to test Adobe Photoshop because it
was the primary software utilized in the course. Digital Layout was chosen since the
curriculum included instruction on print layout through InDesign. Due to few courses of
Digital Layout taught per quarter, Digital Typography and Digital Illustration were also
included, since they were prerequisites for the InDesign class. A table of the descriptive
statistics of these classes sorted by software has been included in Table 5.
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Table 5 Software Descriptive Statistics by Class class software Mean Std.
Deviation N
Illustration Quarter 1
InDesign 52.50 19.008 32
Illustration Quarter 2
InDesign 57.65 15.624 17
Image Manipulation Quarter 1
Photoshop 47.02 20.843 47
Image Manipulation Quarter 2
Photoshop 47.43 21.467 35
Layout Quarter 1
InDesign 57.78 19.869 18
Layout Quarter 2
InDesign 46.36 17.477 11
Typography Quarter 1
InDesign 73.33 20.817 3
Typography Quarter 2
InDesign 47.50 17.078 4
Total Photoshop 47.20 20.981 82 InDesign 54.35 18.609 85 Total 50.84 20.073 167
The analysis of Research Question 1 revealed a statistically significant finding
between the learning theory behaviorism and constructivism. Research Question 2 found no
significance between the computer software Photoshop and InDesign. Lastly, there were no
statistically significant interactions found between the learning theories and software, as
asked in Research Question 3.
Looking at a detailed analysis of the data, the basic information originated from the
descriptive statistics (Table 6). The behaviorist Photoshop mean was 56.90 with a standard
deviation of 20.776 and population of 42. The behaviorist InDesign group mean was 60.93
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with a standard deviation of 18.233 and a population of 43. The constructivist Photoshop
category had a mean of 37, standard deviation of 15.884, and 40 for the population. The
constructivist InDesign cluster presented a mean of 47.62, standard deviation of 16.647 and a
population of 42. The mean for the complete behaviorist section was 58.94 with a standard
deviation of 19.519, and a population of 85. Lastly, the constructivist exercise presented a
mean of 42.44, standard deviation of 17.037, and total population of 82.
Table 6 Descriptive Statistics software theory Mean Std. Deviation N
Photoshop Behaviorist 56.90 20.776 42 Constructivist 37.00 15.884 40 Total 47.20 20.981 82
InDesign Behaviorist 60.93 18.233 43 Constructivist 47.62 16.647 42 Total 54.35 18.609 85
Total Behaviorist 58.94 19.519 85 Constructivist 42.44 17.037 82 Total 50.84 20.073 167
Note. Dependent Variable: post.
Next, assumptions of homogeneity must be tested to ensure the populations were
equal and the covariant was independent. An assessment of the homogeneity of variance
assumption was conducted using Levene’s test (Table 7). A non-significant result was
produced (.079), which indicated the assumption was upheld. The analysis of covariance
assumption of the Homogeneity of Regression was used to test the independence of the
covariate from the study’s factors. The analysis of interaction between theory and pretest
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(Table 8) showed no significance P (.269) > a (.05). F (1, 161) = 1.229, p = .269. The
analysis of interaction between software and pretest also showed no significance P (.546) > a
(.05). F (1,161) = .367, p = .546. The results demonstrate no violation of the assumption;
therefore, the covariate is independent.
Table 7 Levene’s Test of Equality of Error Variances a
F df1 df2 Sig. 2.300 3 163 .079
Note. Dependent Variable: post. a. Design: Intercept + pretest + software + theory + software * theory
Table 8 Homogeneity of Regression-Test of Between-Subjects Effects Source Type III Sum
of Squares df Mean Square F Sig.
Corrected Model 22189.537a 5 4437.907 15.987 .000 Intercept 99713.112 1 99713.112 359.201 .000 theory 5819.965 1 5819.965 20.966 .000 software 275.306 1 275.306 .992 .321 pretest 8131.756 1 8131.756 29.293 .000 theory * pretest 341.174 1 341.174 1.229 .269 software * pretest 101.781 1 101.781 .367 .546 Error 44693.097 161 277.597 Total 498500.000 167 Corrected Total 66882.635 166
Note. Dependent Variable: post. a. R Squared = .332 (Adjusted R Squared = .311)
The results of the factorial analysis of covariance for Research Question 1 is a
statistically significant main effect for the amount of knowledge gained between learning
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theories F (1, 162) = 37.730, p < .05, partial η2 = .189. Consequently, the null hypothesis
stating no difference exists between behaviorism and constructivism was rejected. The main
effect of theory only possessed two levels; therefore, no post hoc or contrasts were
performed. Instead, the descriptive statistics (Table 9) and line graphs (Figure 1) were
analyzed to conclude results. As such, a determination was made that students in the
behaviorist group scored higher than the constructivist group.
Table 9 Factorial Design Analysis–Tests of Between-Subjects Effects Source Type III Sum
of Squares df Mean
Square F Sig. Partial Eta
Squared Corrected Model 21863.048a 4 5465.762 19.668 .000 .327 Intercept 100129.960 1 100129.960 360.311 .000 .690 pretest 7842.728 1 7842.728 28.222 .000 .148 software 266.148 1 266.148 .958 .329 .006 theory 10485.217 1 10485.217 37.730 .000 .189 software * theory 92.228 1 92.228 .332 .565 .002 Error 45019.587 162 277.899 Total 498500.000 167 Corrected Total 66882.635 166
Note. Dependent Variable: post. a R Squared = .327 (Adjusted R Squared = .310)
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Figure 1. Comparing posttest means of software and noting theory.
Research Question 2, the main effect software, produced a statistically non-significant
result F (1, 162) = .958, p=.329 partial η2 = .006. No difference was found between
Photoshop and InDesign, so the null hypothesis was accepted. The interaction between
software and theory, which refers to Research Question 3, also resulted in a non-significant
effect F (1, 162) = .332, p = .565 partial η2 = .002. No interaction was found between
learning theory and software; therefore, the null hypothesis of question three was accepted.
Furthermore, non-parallel lines on a plot graph usually indicate a statistically significant
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interaction effect. There is no statistical significance shown in Figure 2, nor was any
significance found through factorial data analysis.
Figure 2. Comparing means of theory and noting software.
A medium Cohen f effect size (.25) was chosen for the study as a whole through an a
priori analysis, which also determined the power, significance, and sample size. A post hoc
partial Eta squared (Table 8) was calculated for each of the main effects, as well as the
interaction. The results were partial η2 = .006 for software, partial η2 = .189 for theory, and
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partial η2 = .002 for the interaction (software * theory). In order to compare effect sizes,
partial eta squared has been converted to Cohen f effect size. The Cohen f effect size is 0.078
for software, 0.482 for theory, and 0.045 for the interaction (Faul et al., 2007; Faul,
Erdfelder, Buchner, & Lang, 2009).
The general research analysis explanation initiates with a significant finding for
Research Question 1. This indicates a difference between the learning theories behaviorism
and constructivism, as well as a rejection of the null hypothesis. Furthermore, there was no
significant finding for Research Question 2 or 3, which means no difference between the
software Photoshop and InDesign. Additionally, no interaction was found between learning
theory and software. Consequently, both research questions two and three accepted each of
the null hypotheses. These results were based on a .05 significance, 89% power, and overall
medium Cohen f effect size. The raw findings provided within this chapter will be utilized for
the elaboration and discussion of the study in Chapter 5.
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CHAPTER 5. RESULTS, CONCLUSIONS, AND RECOMMENDATIONS
Chapter 5 delves into the study’s results with regards to the explanation,
interpretation, and effectiveness of answering the research questions. The raw data has been
presented in Chapter 4; accordingly, it is now important to understand the implications of
significance. Additionally, a consideration of the study as a whole will reveal shortcomings
and potential improvements, which could be used for replication of the study. As such, the
chapter will present a summary of the findings, as well as discussions of the results and
conclusions. The limitations will also be reported and recommendations for further research.
The study’s main research goal was to identify whether a particular learning theory
(behaviorism or constructivism) would be beneficial for instructing multimedia software.
Moreover, multimedia software covers a large array of subjects, methods, and depth;
therefore, an additional objective was initiated to find evidence of any differences between
software packages with relation to instruction. The results of such research would not only
add to the expansive behaviorist and constructivist debate, but would also provide tested data
for those associated with education. In particular, instructors of software technology could
analyze the study and results to help direct their own instructional methods.
In order to accomplish the daunting research task, a learning theory framework was
utilized. This theoretical framework is comprised of two distinct elements, teaching methods
and the focus of learning. While the focus of learning is typically the student, teaching
methods have a wide range of options that are often quite personal to the instructor. Each
teacher’s methodology stems from study, experiences, as well as interactions, and creates his
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or her assumptions, beliefs, and body of knowledge. The framework provides a structured
manner of analyzing an educational environment’s instructional methods.
Many articles and books were used as inspiration for certain elements of the
dissertation research, but a few pieces of literature heavily influenced the structure and
methodology of the current study. Firstly, Stephanie Clemons (2006) discussed her case
study that converted a Computer Aided Design (CAD) software class to a constructivist
based curriculum. The class change included self-paced and self-regulated students utilizing
progressive modules. Results were gathered through a semester assessment, which showed
an increase in knowledge of software commands, drafting, and spatial design. While the
immersion of students in software instruction through a particular learning theory was well
formed, the comparison to the previous (traditional) method was not articulated. The
Clemons article drove the current study’s need for an accurate comparison between the
chosen learning theories.
Al-Shammari, Al-Sharoufi, and Yawkey’s (2008) article sought to support the
hypothesis that direct instruction was successful by comparing a class taught through direct
instruction with an unchanged control class. Using a test based on the direct instruction
method, the results of the two groups showed a higher score for the direct instruction
students. Unfortunately, the test itself could have produced a confounding effect in the
results. The test may have been conducive for assessing knowledge for one group, but not the
other. This is also a dilemma found in the current study, since a behaviorist test was used to
assess both the behavioral and constructivist groups, which is discussed in the study’s
limitations.
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Neo and Neo (2010) published an article that dealt with the higher education
conversion from traditional methods to constructivist project-based multimedia activities.
Students from management, technology, and engineering degrees, who were enrolled in a
multimedia course, were given a realistic task project to be completed in groups. Grades from
the project and student perceptions were used as data, which resulted in positive student
attitudes and high marks on the project. The current study gained an excellent example of
executing problem-based learning in an authentic classroom setting. Equally important, Neo
and Neo’s study further reinforced the need for an accurate comparison of multiple learning
theories.
Next, McKenna and Laycock’s (2004) article compared the traditional behaviorist
methods of multimedia educational software with constructivist techniques of instruction. In
addition to evaluating the two theories, the authors also included a mixed behaviorist-
constructivist (hybrid) learning environment and the module as it was normally taught
(control). Through data gained from an assessment and a comparison from earlier instruction,
it was found that the behaviorist group’s scores were the most improved. This article was the
best example of equally comparing multiple groups in order to find evidence of a beneficial
learning theory. The inclusion of a hybrid and control course created a complete picture in
the reader’s mind that the authors sought to analyze all aspects to create an improved
teaching environment.
Although human error is predominant in every area of life, it seems especially
prevalent when learning through computers. Consequently, Kay (2005) researched the
reasoning behind such elevated amounts of errors in order to improve instructional
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techniques. Using a think-aloud protocol, behaviors were identified and recorded while the
participant learned. The author noted that computer skill experience was all across the board,
but none had used the particular software utilized through the study. The results showed that
all participants consistently made errors, chiefly during times that required knowledge
processing, seeking information, and software interaction. The measurement of familiarity
was based on general computer knowledge, which potentially caused very little difference
between the students’ quantified computer software experience. Information gained through
Kay’s article instigated the current study’s recognition of the importance of knowing the
users experience with the specific software; therefore, a pretest was enacted as a covariant to
account for prior knowledge.
Lastly, math fluency is the ability to swiftly and effortlessly respond to math tasks.
Students with good math fluency are more capable of accurately completing advanced tasks
concerning math. Conversely, those who are not adept at math fluency are less likely to
attempt everyday math, which impacts daily functioning, such as balancing a bank account or
making change. Accordingly, Poncy, McCallum, and Schmitt (2010) created a research study
to find support for an appropriate learning theory for teaching math fluency.
Poncy, McCallum, and Schmitt’s (2010) study compared constructivist and
behavioral learning methods with an untouched control group. The results showed no
significant difference between the constructivist and control group; however, the behaviorist
group showed an increase in math fluency. The authors’ reasoning was the directness of
behavioral methods of instruction, which included modeling, active responding, and
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immediate feedback. The article further strengthens the similar outcome derived by the
current study.
With regards to this dissertation’s methodology, the general population of the study
was American collegiate level students participating in any course including software. The
sample was students enrolled in Digital Image Manipulation, Digital Layout, Digital
Illustration, or Digital Typography classes during two quarters of 2012. The sample was
analyzed within their natural environment to encourage an ordinary learning atmosphere;
therefore, the students were tested in the classroom during their scheduled class time. The
selection procedure included any students in the chosen classes who agreed to participate and
excluded individuals under 18 years of age. The data was assembled through questions from
two of the uCertify Adobe Certified Expert (ACE) exam preparation guides. The specific
prep guides used by this study were Photoshop CS5 and InDesign CS5.
The aim of this study was to carry out a randomized quantitative experiment with an
analysis of covariance design employing four groups, gathered using convenience sampling,
in a pretest, posttest model to analyze multiple independent variables. To increase internal
validity and place subjects in probabilistically equivalent groups, random assignment was
utilized. The experiment was comprised of 4 groups: Photoshop taught with behavioral
learning, Photoshop taught with constructivism, InDesign taught with behavioral learning,
and InDesign taught with constructivism. The pretest was used as a covariate to remove any
inflation of prior knowledge from the students’ posttest scores. The study’s 2 X 2 factorial
design analyzed the main effects of learning theory and software separately, as well as any
interactions occurring between the two factors.
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Finally, the results of the study are stated along with the study’s research questions.
Research Question 1-Is constructivist or behavioral learning theory more beneficial when
teaching multimedia software? There was a significant main effect for the amount of
knowledge gained between learning theories. Consequently, the null hypothesis stating no
difference exists between behaviorism and constructivism was rejected. Students in the
behaviorist group scored higher than the constructivist group.
Research Question 2-Is there a difference in the effectiveness of learning between
Photoshop and InDesign when teaching multimedia software? No difference was found
between Photoshop and InDesign, so the null hypothesis was accepted. Research Question 3-
Are there interactions between learning theory and software with regards to teaching
multimedia software? No interaction was found between learning theory and software;
therefore, the null hypothesis of question three was accepted.
Discussion of Results
Research Question 1 stated: Is constructivist or behavioral learning theory more
beneficial when teaching multimedia software? The hypothesis suggested a difference would
be found between constructivism and behavioral learning theory on the basis that the two
methods of instruction were so distinctively different. Indeed, the hypothesis was correct in
the fact that a significant main effect was found between the two learning theories. The
behaviorist group scored higher than the constructivist group.
A significant result means the learning theories showed a difference in software
knowledge among the students. Due to the use of a covariant, which accounted for any prior
knowledge on the subject, the learning variance was caused by the software lessons
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dispensed to the participants. Analyzing the result on the specific level of this study reveals
that in the case of teaching Photoshop and InDesign, behavioral learning theory was found to
be more beneficial than constructivism. At this level, there is a difference in learning
theories; therefore, it could be generalized to say that behaviorism may be preferable for
instructing all software. Again, it could also be reasoned that because there is a difference
between the learning theories used in this study, there may also be a difference in other
learning theories. As such, a different theory may be more or less favorable in teaching
software than behaviorism.
The potential reasoning behind the results could be many or few. If the research and
experiment was expertly and accurately executed, then the results logically suggest that the
methods behind behaviorism were more useful than constructivist techniques. In particular,
the specific facet of behaviorism (visual lecture) was preferable to the constructivist practice
(problem-based learning). Conversely, the difference found between the learning theories
could derive from a skewed variation of the lesson. In other words, the behaviorist lessons
may have been easier to understand and gain information from than the constructivist
lessons. The lesson divergence could be caused by beneficial behaviorist techniques or an
error of development within the lessons themselves.
A possible flaw in the lessons could have been the level of difficulty between the
constructivist and behaviorist lessons. The opposite nature in which the learning theories are
built upon makes it problematic to conclude whether the lesson difficulty was equal. An
additional limitation was the use of a behaviorist test for both learning theory lessons. The
design industry standard for testing Adobe software products is the Adobe Certified Expert
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(ACE) exam, which is a multiple choice answer inherent behaviorist test. While determining
whether the behaviorist test accurately measured knowledge accumulated from a
constructivist lesson was difficult, the design industry only recognized the employed test no
matter the method of learning software.
The implications of the results should cause software instructors to assess their
method of instruction. If strictly constructivist methods are used, according to the results, a
move to behaviorist techniques may be helpful. As noted, there are some learning benefits to
using behaviorism, although neither learning theory group did especially well. The outcome
showed a difference in acquiring knowledge between the learning theories; therefore,
additional learning theories should be tested. Consequently, another implication may suggest
a hybrid class that weighs heavily on behaviorist techniques, while also employing other
learning theories as well.
Research Question 2 stated: Is there a difference in the effectiveness of learning
between Photoshop and InDesign when teaching multimedia software? The hypothesis for
Research Question 2 also anticipated a difference between the two groups, because of the
varying ways the software is utilized. While a single designer may employ both software
pieces, each is used for different types of end-products. The hypothesis was rejected and the
null hypothesis was accepted. No significance was found between Photoshop and InDesign.
The results indicate that no matter the method of instruction, there was no difference
in acquiring knowledge between the selected software. In other words, any learning theory
used would result in the same amount of learning between Photoshop and InDesign. If the
results were generalized, one would expect any instructional technique to work equally well
127
across all software. Relating back to the results found in this research, the implications could
be the educational technique is more important than the software being instructed.
The lack of significance between the software may be due to the fact that both were
Adobe products, which possess similar interfaces and tools. Even though the software
produces dissimilar projects, the overall use of the software has a parallel design, as featured
in all Adobe products. A further test would be needed with software not made by the same
company. Results from such a study would either support the type of software being taught is
not important or that more studies on a variety of software are needed.
Research Question 3 stated: Are there interactions between learning theory and
software with regards to teaching multimedia software? The last research question also
asserted an anticipated difference between the various interactions. The hypothesis was
rejected and the null hypothesis was accepted. No significant interactions were found
between learning theory and software.
The results mean there was no combination of research variables that were more or
less beneficial than others. For example, after observing the test/lesson/test exercises
conducted, the author believed that a significant interaction may be found between
constructivist and InDesign groups. This belief was due to the apparent participant struggle in
completing the problem-based learning (constructivist) lesson. As stated, not only was no
difference found between the constructivist InDesign and Photoshop groups, no other
divergence was apparent.
128
Discussion of the Conclusions
To initiate the discussion of conclusions, reflections on observations made during the
test/lesson/test exercises are necessary. While the participants were maneuvering through
each exercise, the researcher stood at the back of the room, monitoring the activities. This
allowed the researcher to quickly assist any problems that occurred (predominantly log-in
help), as well as hand out the participation certificate as soon as the student was finished.
Additionally, the researcher had a vantage point to notice how easily the students moved
through the tests and lesson.
The exercises consisted of four groups, but only two were studied in a single class. As
such, the Photoshop classes investigated the Photoshop constructivist and Photoshop
behaviorist groups. The InDesign classes evaluated the InDesign constructivist and InDesign
behaviorist groups. While the researcher’s aim was to make the groups’ lessons equal in
difficulty, the differences in software and learning theory were problematic. Consequently,
the researcher’s observations may illuminate disparities in the equality of groups.
In a comparison of behaviorist software lessons, the instruction was near identical.
The format of written lecture was conducive to easily arranging the Photoshop and InDesign
information in the same manner. Each followed the same pace of written and visual
information, video modeling, as well as one question quizzes with immediate feedback. For
the behaviorist Photoshop and InDesign combination, it is strongly believed that any results
found would be due to the students learning. This is further confirmed by the observation of
apparent equal time spent on the two lessons. Additionally, there was no overt frustration
129
noticed in the behaviorist participants, although some did speed through the material not
reading all of the text or watching the entire videos.
Next, there seemed to be a difference between the constructivist Photoshop and
InDesign lessons. As stated, the participants generally spent the same amount of time for
both behaviorist lessons; therefore, the researcher used that time as a baseline. Overall, the
constructivist Photoshop groups completed their lessons before the behaviorist Photoshop
groups; however, the constructivist InDesign groups finished after the behaviorist InDesign
groups. This was the researcher’s first red flag that a difference in difficulty may have
occurred. In watching the two software constructivist groups, another informal observation
was that Photoshop lessons were more complete before moving to the posttest than the
InDesign lessons.
The constructivist lessons were created using the constructivism problem-based
learning techniques. As such, the participants were given a real-world design scenario to
complete within a simulated Photoshop or InDesign environment. The simulated software
was a replica of the chosen software, but only featured functional tools that dealt with the
task to be accomplished. The Photoshop constructivist task was to modify the graphics on the
Photoshop stage per the given instructions. The InDesign constructivist mission was to create
a new document and construct graphics as shown in the hand-drawn example and written
directions. The difference between manipulating preexisting graphics and creating something
from a blank stage may have created the difficulty gap.
In keeping with the problem-based learning method, the completion of the
constructivist lesson required a finished project created in the simulated environment that
130
reflected the instructions. As the behaviorist groups intently viewed the information
displayed on the computer screen, the constructivist groups struggled through the lesson
project trying to create and align the graphics on the stage as seen in the instructions. The
reference material provided with the constructivist lesson reflected the information given
through Adobe’s software help section, so reading and experimental attempts with the tools
were required for accomplishing the tasks. Some students were observed thoroughly reading
the reference files before beginning the task, others looked at the material only when they
were unable to complete a portion of the instructions, while many never utilized the
information at all. With regards to the completion of each problem-based learning task, the
researcher noticed many more constructivist Photoshop lessons were completed as instructed
than the constructivist InDesign projects.
Comparing constructivist and behaviorist lessons for equality was a difficult and
subjective duty. The design of each lesson fully reflected the belief system each learning
theory was built upon. To say one lesson was easier than another equates to stating one
learning theory was easier than the other. Each theory’s stance was that its basis of learning
was more thorough than the other; therefore, the lessons were created in the image of the
theory it portrayed. Due to the strict adherence to each learning theory’s assumptions within
the current study, it was strongly believed that any results found were a direct result of
student learning.
As shown in the literature reviewed within this dissertation, both behaviorism and
constructivism have prevailed in separate studies. Furthermore, this study was neither
supported nor disconfirmed by prior research, since one can find as many studies proving the
131
value of constructivism as behaviorism. Specific to software instruction, the findings of this
document do provide support for the use of behaviorism when instructing multimedia
software, which is in opposition with Clemon’s (2006) case study that found constructivism
more beneficial for instructing Computer Aided Design (CAD). Clemon’s research design
did not provide a direct comparison with the traditional method used in previous classes, nor
did her article describe what the traditional method may have been. As noted earlier, further
testing may be required to support the hypothesis that all software (especially non-Adobe
products) is best taught with behavioral learning theory. No other study was as closely
related to the one carried out through this dissertation; thus, seen from the myriad of results
across studies, the subject being taught has a great impact on the appropriateness of the
learning theory used for instruction.
Limitations
The first limitation may be subjective. Due to the informal observations made by the
researcher, it was noticed that the constructivist lessons were much harder to complete than
the behaviorist lessons. The requirement of completion for the behavioral learning theory
lessons was a mouse click-through, with occasional quiz, until the participant reached the
second test. Conversely, the constructivist lesson asked the participant to create or
manipulate graphics within a simulated software environment, using tools that might be
previously unseen. The potential limiting factor was the constructivist students were left
wondering what to do and frustration set in before learning could occur.
The lessons were created by closely following the tenets of each learning theory’s
widely published principles. It could be argued, the limitation was actually an inadequacy of
132
the learning theory, rather than the design or execution of the study. Again, the simplified
theory of behaviorism is the delivery of information to the student and the use of quizzes
with immediate feedback, which was accomplished by the behaviorist lesson. Constructivism
stated simply, it is essential for the student to construct his or her own knowledge by
participating in real-world work projects and situations. This type of learning situation was
achieved with the simulated learning environment previously described.
The second limitation was the use of a behaviorist measure of learning for
constructivist lessons. The instrument used for all lessons was based on the Adobe Certified
Expert (ACE) exam. The certification received when passing the ACE exam was a design
industry standard in signifying proficiency in a single Adobe software or an Adobe Suite.
The exam was a multiple choice test designed to measure an individual’s comprehensive
knowledge of Adobe software. Due to the limited time with this study’s participants, the
uCertify ACE study guide was honed down for examination of one area of each software
package selected.
Theoretically, the improved behaviorist groups could be due to the behaviorist
assessment, instead of beneficial teaching techniques. To accurately test the theories we
should match behaviorist learning with a behaviorist assessment, likewise, constructivist
learning with a constructivist assessment. This would then reveal the best method of
instruction. If the findings of such a study showed evidence that constructivist instructional
methods were preferable, then teaching with these techniques would provide students with
the knowledge they need to succeed.
133
Practically, American society dictates that most assessable moments in life can be
measured with behaviorist evaluation methods. In elementary school, teachers are pushed to
explore alternate instructional methods to encourage learning; yet, fourth and fifth grade
students are tested through behavioral methods, such as the Colorado Student Assessment
Program (CSAP). The culmination of grade school, whether public, private, or home
schooled is analyzed through a Scholastic Aptitude Test (SAT). While continuing into the
workforce without such a test is normal, it is required for acceptance into any college. Lastly,
after college or trade school, certification is necessary to work in many fields; for example,
the licensing of nurses, electrician certification, and software certification are all behaviorist
exams.
Matching the instructional and assessment learning theory may not be enough. To
provide teachers with an adequate theory of instruction, the method must be able to withstand
the expected standardized testing. Barring anomalies, the consistently used knowledge
assessment model is distinctly behaviorist. Are the behaviorist tests actually reflecting the
knowledge we want students to possess?
The Adobe Certified Expert exam measures the test taker’s knowledge of how to
maneuver through the Adobe program. Examples of Photoshop ACE exam questions are:
How do you align objects to the left? What do you select to merge two layers? One would
like to believe that an individual, who could pass an entire exam with task oriented questions
such as this, could then go on to create projects in the software program. Is this assumption
true? Focusing on a learning outcome such as standardized testing is important, but
remaining aware of what the test actually measures is also vital.
134
The next limitation for this study was the use of only Adobe products for comparison
of software. Each Adobe product was developed to fulfill a need within the design industry,
which is why each of Adobe’s software is quite dissimilar in the producible products and
focus of use. While the end-product of each software program is different, the interface and
toolbars are nearly identical. This is beneficial for feeling comfortable in the Adobe
environment and maneuverability between programs. Conversely, users feel a deceptive
comfort in thinking they can perform the same tasks in each program, which is quite untrue.
Even though the similar tools are found in Adobe Photoshop and Adobe InDesign they are
used in differing ways to create different effects.
The fact that Adobe products are similar may have created a comparison of software
that was excessively alike. If this is the case, the generality cannot be stated that a single
learning theory should have the same rate of success across all software. Instead, it may be
true that a single learning theory should have the same rate of success across all Adobe
software. However, if completely different software was used, an additional limitation may
arise in creating lessons equal in difficulty. Additionally, an alternate instrument of
measurement would be needed, since the Adobe Certified Expert exam only tests Adobe
products.
Lastly, a point that might have bearing on the results, but was not accounted for, was
the degree focus of the student. Some of the degrees found in the classes were similar, such
as Web Design & Development and Web Design & Interactive Media, but others are quite
different, like Fashion Design and Film Production. The focus of a student’s degree can have
a large impact on the way a software program is used. For example, a Photography student
135
may utilize Photoshop for the color adjustment of a photo, while a Fashion Design student
might create a graphic mockup of a shirt. For the examples given, differing tools, approach,
and levels of depth within the software is used. Accounting for degree focus may reveal a
significant interaction between learning theory, software, and degree that was previously
unknown.
Recommendations
The first recommendation is to ensure every technique or educational method added
to a learning theory lesson is indeed reflective of the theory. This important step erases any
doubts of compromised elements when significance is analyzed. In addition to fully
understanding the general beliefs that make up each theory, it is also noteworthy to
understand the opposing viewpoints. While those that firmly stand behind a particular theory
tend to overlook certain aspects, generally negative, others who have taken a differing
viewpoint are quick to point out any lacking elements.
A problem that was informally observed many times during the course of the
test/lesson/test exercises was the frustration level experienced by those struggling through the
constructivist project-based learning assignments. The constructivist theorists believe such a
struggle to be the active construction of knowledge a student must experience to embrace
learning. Conversely, the researcher noticed more students giving up and opting to not
complete the project-based learning task, rather than struggling through the task in order to
learn. The recommendation for this problem is to integrate more scaffolding methods into the
constructivist environment, which should encourage completion of the project.
136
If a true classroom environment were utilized, a face-to-face teaching situation, the
instructor or facilitator might provide enough help to encourage the completion of problem-
based learning tasks. A change would need to be made for both the behaviorist and
constructivist groups for consistency. Such instruction would modify the study enough to
assume alternate results may be expected. This introduces more of the social aspect of
constructivism; therefore, further research on the subject would also be required.
The second recommendation regards the test used in this study. The knowledge
gained from both constructivist and behaviorist lessons were measured with a multiple choice
behaviorist exam. While the researcher utilized the Adobe Certified Expert (ACE) exam
because it is a design industry standard, differing results may have been produced if separate
tests were used. Consequently, it is recommended that a study is performed to support the
hypothesis a difference exists between constructivist and behaviorist tests when taught
through constructivist techniques. The same question can also be applied to other learning
theories, since many standardized tests administered today are distinctly behaviorist. If a
student is taught through a learning theory other than behaviorist, can it be adequately
measured using a standardized test?
The next recommendation suggests a comparison of learning theories utilizing at least
one non-Adobe software product. The Adobe products have many similarities, including
nearly identical interfaces and tool bars. This likeness may have skewed this study’s
generality to reflect a lack of significance found in Adobe products instead of all software.
As such, a comparison between an Adobe product and software made by a different company
137
may produce a significant result. Alternately, further research might confirm there is no
difference in learning among all software.
Lastly, due to the differing degree focuses found in the research classes, it is
recommended that the degree a student is majoring be accounted for in a study. It may be
argued that some degrees are very logical and linear like Web Design & Development, which
is a programming/coding based degree; however, Film Production is a highly creative degree
that often relies on the imagination of the student. Since each student’s mindset can be so
different, it is likely that a certain degree program may benefit from a particular method of
instruction. Accordingly, testing learning theories against degree programs may be quite
beneficial in determining the proper theory for instructing students.
Conclusion
Due to the push of technology in today’s society, many college courses are providing
instruction on a range of software. While students are required to gain knowledge of current
software, the instructors are also charged with the duty of keeping up with ever-evolving
technology. Consequently, colleges often recruit instructors of software courses from the
technology industry, such as graphic or web designers to teach an Adobe Photoshop class.
The influx of students seeking computer software knowledge, and the need for suitable
instruction, gave cause to an exploration of the validity of specific learning theories.
138
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153
APPENDIX A. PHOTOSHOP EXPERT PANEL HANDOUT
Dear Colleague:
I am working on a doctoral quantitative dissertation, entitled, “Learning Theories
Applied to Teaching Technology: Constructivism versus Behavioral Theory for Instructing
Multimedia Software Programs.”
My research questions:
Research Question 1: Is constructivist, behavioral, or current instructional methods
more beneficial when teaching multimedia software?
Research Question 2: Is there a difference in the effectiveness of learning between
Photoshop and InDesign when teaching multimedia software?
Research Question 3: Are there interactions between learning theory and software
with regards to teaching multimedia software?
The research problem to be explored is the suitability of constructivism versus
behavioral learning theory with regards to teaching multimedia software. The purpose of this
study is to give educators more effective teaching tools in order for students to ultimately get
the most out of any particular software program. In narrowing the research to specific
software applications, the study may identify whether differing applications of learning
theories are required for precise focuses of learning. Furthermore, the results found will give
instructors of the software programs a defined and successful teaching direction and translate
to a wider understanding for them to build upon.
154
To properly execute the study, an appropriate measure of content knowledge must be
attained. The included questions are derived from the uCertify Adobe Certified Expert
Photoshop practice exam. As such, the general appropriateness of the questions is assured,
but their use within the scope of this study is called into question. Thusly, your participation
in this expert panel is greatly needed and appreciated.
Only a portion of the practice exam will be used, due to restricted time with students.
Consequently, only questions relating to “working with layers” have been obtained. The
focus will include the creation and arrangement of layers, as well as layer effects and styles.
Additionally, it will also contain questions on working with multiple layers of an image and
layer blending options. Since the exam will be administered in an intro level class, only
questions relating to a novice to intermediate stage should be used
The purpose of the field test is to ensure that the interview questions are appropriate
for the population and will not unnecessarily put participants through distress or discomfort.
In other words, if there is a better way to ask the question to get at what is needed to answer
the research question, or if there are questions that are simply not needed and would be
unnecessarily stressful for the participant or inappropriate to ask that particular population.
155
It is required that I contact 3-5 experts in the field to conduct a field test on the
proposed interview questions, which I plan to ask of the participants in my study, to ensure
that the interview questions are related to the main research question and to make sure that
the questions are clear.
As an identified expert in the field, I would very much appreciate your expertise and
feedback on the proposed interview questions.
1. Please choose 10 out of the 15 questions you believe best assess a student’s knowledge of
working with Photoshop layers. Mark the checkbox next to the 10 appropriate questions.
2. Are there any questions that stand out as not measuring a student’s knowledge of working
with Photoshop layers or would not be covered in an introductory class? List the number
corresponding to the unsuitable question.
________________________________________________________________________
3. Are any of the questions redundant? List the number corresponding to the redundant
questions.
_______________________________________________________________________
4. Please complete the questions on the last page to document your background on the
subject.
Thank you again for your time and input.
Sincerely,
Cajah Sullivan Reed
156
o Question 1. You want to align objects on different layers. Which of the following steps
will you take before selecting the any alignment method?
o Question 2. Which of the following statements are true about the Auto Blend Layers
command?
o Question 3. You are working with the shape layer in an image of your project. You want
to preserve transparency of the shape layer while working with other layers. Which of the
following options are correct in this scenario?
o Question 4. You have selected two layers in the Layers panel. What will you do if you
want to create a group so that the selected layers automatically become its members?
o Question 5. You have a Photoshop file that includes two layers. You need both the layers
to be aligned to the right. Which of the following is the best method to accomplish the
task?
o Question 6. You have multiple layers to align with each other. Which of the following is
NOT a layers alignment option?
o Question 7. You have many layers that you need to align to a reference layer using the
Auto-Align Layers option. What does the Auto-Align Layers option allow you to do?
o Question 8. Which of the following blending modes darkens the base color by increasing
the contrast to reflect the blend color?
o Question 9. James is creating an image with several layers. What will he do to convert
one of the regular layers to the background layer?
157
o Question 10. You have selected a portion of an object in a layer and want to convert it
into a new layer. Which of the following commands will you use if you do not want to
remove the selected portion from the original layer?
o Question 11. You want to reduce the opacity of a layer's contents. You want to ensure
that the layer styles applied to it are not affected. What will you do to accomplish this
task?
o Question 12. Which of the following statements about the background layer are true?
o Question 13. You create an image in Photoshop. The image contains several layers, some
of which are visible and others are hidden. You use the Layer > Flatten Image command.
What will it do?
o Question 14. You have linked several layers in an image. Which of the following
statements about linked layers are true?
o Question 15. What will you do to reduce the opacity of the content of a layer without
affecting the appearance of the styles applied to the layer?
Note. Answers to the uCertify questions and instructor background documentation have been omitted from the appendix.
158
APPENDIX B. INDESIGN EXPERT PANEL HANDOUT
Dear Colleague:
I am working on a doctoral quantitative dissertation, entitled, “Learning Theories
Applied to Teaching Technology: Constructivism versus Behavioral Theory for Instructing
Multimedia Software Programs.”
My research questions:
Research Question 1: Is constructivist, behavioral, or current instructional methods
more beneficial when teaching multimedia software?
Research Question 2: Is there a difference in the effectiveness of learning between
Photoshop and InDesign when teaching multimedia software?
Research Question 3: Are there interactions between learning theory and software
with regards to teaching multimedia software?
The research problem to be explored is the suitability of constructivism versus
behavioral learning theory with regards to teaching multimedia software. The purpose of this
study is to give educators more effective teaching tools in order for students to ultimately get
the most out of any particular software program. In narrowing the research to specific
software applications, the study may identify whether differing applications of learning
theories are required for precise focuses of learning. Furthermore, the results found will give
instructors of the software programs a defined and successful teaching direction and translate
to a wider understanding for them to build upon.
159
To properly execute the study, an appropriate measure of content knowledge must be
attained. The included questions are derived from the uCertify Adobe Certified Expert
InDesign practice exam. As such, the general appropriateness of the questions is assured, but
their use within the scope of this study is called into question. Thusly, your participation in
this expert panel is greatly needed and appreciated.
Only a portion of the practice exam will be used, due to restricted time with students.
Consequently, only questions relating to “laying out a document” have been obtained. The
focus will include the creation and arrangement of layers, as well as layer effects and styles.
Additionally, it will also contain questions on working with multiple layers of an image and
layer blending options. Since the exam will be administered in an intro level class, only
questions relating to a novice to intermediate stage should be used.
The purpose of the field test is to ensure that the interview questions are appropriate
for the population and will not unnecessarily put participants through distress or discomfort.
In other words, if there is a better way to ask the question to get at what is needed to answer
the research question, or if there are questions that are simply not needed and would be
unnecessarily stressful for the participant or inappropriate to ask that particular population.
160
It is required that I contact 3-5 experts in the field to conduct a field test on the
proposed interview questions, which I plan to ask of the participants in my study, to ensure
that the interview questions are: related to the main research question and to make sure that
the questions are clear.
As an identified expert in the field, I would very much appreciate your expertise and
feedback on the proposed interview questions.
1. Please choose 10 out of the 15 questions you believe best assess a student’s knowledge of
laying out an InDesign document. Mark the checkbox next to the 10 appropriate
questions.
2. Are there any questions that stand out as not measuring a student’s knowledge of laying
out an InDesign document or would not be covered in an introductory class? List the
number corresponding to the unsuitable question.
________________________________________________________________________
3. Are any of the questions redundant? List the number corresponding to the redundant
questions.
________________________________________________________________________
4. Please complete the questions on the last page to document your background on the
subject.
Thank you again for your time and input.
Sincerely,
Cajah Sullivan Reed
161
o Question 1. For which of the following tasks is a page tool used?
o Question 2. Which of the following steps should be taken foremost in order to create a
new document?
o Question 3. What does the More Options button change to when it is clicked?
o Question 4. Which of the following can be chosen in the Internet pop-up menu to create
a document?
o Question 5. Which of the following objects are not affected by the Align panel?
o Question 6. Which of the following objects can be created with In Design’s object-
creation tools?
o Question 7. Which of the following tools can be used to create basic frames?
o Question 8. Which of the following shortcuts will you use to apply a paragraph style and
remove overrides from a selected item?
o Question 9. Which of the following commands is used to open a new document?
o Question 10. Which of the following panels is used to distribute objects horizontally or
vertically along the selection, margins, page, or spread?
o Question 11. Which of the following commands is used to open an Align panel?
o Question 12. Which of the following tools CANNOT be used to rotate an object in a
document?
o Question 13. You have drawn a curve path using Pen tool. Which of the followings is
NOT a method to adjust the shape of the curve?
162
o Question 14. Which of the following tools is used to reposition an anchor point of a
path?
Note. Answers to the uCertify questions and instructor background documentation have been
omitted from the appendix
163
APPENDIX C. PHOTOSHOP INSTRUMENT
1. You want to align objects on different layers. Which of the following steps will you take
before selecting the any alignment method?
2. You are working with the shape layer in an image of your project. You want to preserve
transparency of the shape layer while working with other layers. Which of the following
options are correct in this scenario?
3. You have selected two layers in the Layers panel. What will you do if you want to create
a group so that the selected layers automatically become its members?
4. You have a Photoshop file that includes two layers. You need both the layers to be
aligned to the right. Which of the following is the best method to accomplish the task?
5. James is creating an image with several layers. What will he do to convert one of the
regular layers to the background layer?
6. You have selected a portion of an object in a layer and want to convert it into a new layer.
Which of the following commands will you use if you do not want to remove the selected
portion from the original layer?
7. You want to reduce the opacity of a layer's contents. You want to ensure that the layer
styles applied to it are not affected. What will you do to accomplish this task?
8. Which of the following statements about the background layer are true?
9. You create an image in Photoshop. The image contains several layers, some of which are
visible and others are hidden. You use the Layer > Flatten Image command. What will it
do?
164
10. You have linked several layers in an image. Which of the following statements about
linked layers are true?
The Photoshop instrument is a modified version of uCertify’s Adobe Certified Expert
Photoshop CS5 practice exam. Cajah Sullivan Reed was granted permission by uCertify LLC
to use 60 copyrighted questions for the purpose of this dissertation. The content and
copyright for the questions remain the sole property of uCertify.
165
APPENDIX D. INDESIGN INSTRUMENT
1. What does the More Options button change to when it is clicked?
2. Which of the following can be chosen in the Internet pop-up menu to create a document?
3. Which of the following objects are not affected by the Align panel?
4. Which of the following objects can be created with In Design’s object-creation tools?
5. Which of the following tools can be used to create basic frames?
6. Which of the following panels is used to distribute objects horizontally or vertically along
the selection, margins, page, or spread?
7. Which of the following commands is used to open an Align panel?
8. Which of the following tools CANNOT be used to rotate an object in a document?
9. You have drawn a curve path using Pen tool. Which of the followings is NOT a method
to adjust the shape of the curve?
10. Which of the following tools is used to reposition an anchor point of a path?
The InDesign instrument was a modified version of uCertify’s Adobe Certified
Expert InDesign CS5 practice exam. Cajah Sullivan Reed was granted permission by
uCertify LLC to use 60 copyrighted questions for the purpose of this dissertation. The
content and copyright for the questions remain the sole property of uCertify.