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

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© 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,

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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).

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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.

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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.

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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

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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).

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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).

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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

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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.