Discuss how the problem statement informed the development of the purpose statement in this study.
Relationships Between Learners’ Personality Traits and Transactional Distance
within an e-Learning Environment
Submitted by
Burton Alexander Casteel, III
A Dissertation Presented in Partial Fulfillment
of the Requirements for the Degree
Doctorate of Philosophy
Grand Canyon University
Phoenix, Arizona
August 26, 2016
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GRAND CANYON UNIVERSITY
Relationships Between Learners’ Personality Traits and Transactional Distance
within an e-Learning Environment
by
Burton Alexander Casteel, III
Approved
August 15, 2016
DISSERTATION COMMITTEE:
Audrey Rabas, Ph.D., Dissertation Chair
Julie Nelson, Ph.D., Committee Member
Nathan Griffith, Ph.D., Committee Member
ACCEPTED AND SIGNED:
________________________________________ Michael R. Berger, Ed.D. Dean, College of Doctoral Studies
_________________________________________ Date
GRAND CANYON UNIVERSITY
Relationships Between Learners’ Personality Traits and Transactional Distance
within an e-Learning Environment
I verify that my dissertation represents original research, is not falsified or plagiarized,
and that I have accurately reported, cited, and referenced all sources within this
manuscript in strict compliance with APA and Grand Canyon University (GCU)
guidelines. I also verify my dissertation complies with the approval(s) granted for this
research investigation by GCU Institutional Review Board (IRB).
_____________________________________________ ______August 4, 2016_____ Burton A. Casteel, III Date
Abstract
The relationship between personality traits and learner outcomes has been demonstrated
within a variety of environments. However, the extent of the relationship between Five-
Factor Model personality traits and transactional distance had not previously been
examined within the asynchronous video e-learning environment. It was not known if
personality traits were predictive of transactional distance in this environment. This
question was addressed through a quantitative correlational study conducted online using
an interactive course. Participants (N= 98) were recruited online from across the U.S.
All participants completed the Big Five Inventory, three modules of a video-based
communications course, and the Structure Component Evaluation Tool (SCET), a
measure of transactional distance (TD) in which high scores indicate more desirable or
small transactional distance. Pearson correlation analysis was conducted between each
personality trait and SCET values to measure the relationship. It was found that
Openness (r = .25, N = 98, p = .02) and Extroversion (r = .28, N = 98, p = .005) exhibited
significant positive correlations with SCET scores; therefore, as the strength of these
personality traits increased, the transactional distance decreased. Regression analysis
demonstrated that personality traits were predictive of TD (F(5, 92) = 3.99, p = .003, R2 =
.18, Adjusted R2 = .13) and that Extroversion (R2 = .08, p = .005) and Openness (R2 =
.062, p = .01) independently explained 14.2% of transactional distance variance. Based
upon the findings, instructional developers should consider the role of personality traits
during the creation of video-based instructional material.
Keywords: Five-Factor Model of personality traits, Transactional Distance
Theory, video, e-learning, Big Five Inventory, Structure Component Evaluation Tool
vi
Dedication
To my wife and best friend, Jenny. Thank you for your love, patience, and
encouragement. And for playing Mario Kart with me.
vii
Acknowledgments
Over the course of my doctoral journey, I have received support and
encouragement from a great number of individuals. I am grateful for my dissertation
committee, Dr. Audrey Rabas, Dr. Julie Nelson, and Dr. Nathan Griffith, for their
tremendous guidance, encouragement, and accountability throughout my research and
writing. Your counsel throughout the study process exemplified the spirit of the learning
journey, and I am a better scholar for it. Thank you to Dr. Andree Robinson-Neal and
Dr. George Bradley for your thorough reviews of my writing. Thank you to my fellow
doctoral students for your support, feedback, and friendship. I am also thankful for my
good friend, Dr. Kurt Peters, who helped me greatly by authoring some of the code
within my study instrument. I am thankful for my TNS friends, Kelly, Scott, Tim, and
Kyle, who challenged me to squeeze tighter and aim. Last, but certainly not least, I
would like to thank my family, especially my wife, Jenny Casteel, and daughters Katie,
Megan, and Sydney Casteel, for your love, encouragement, and the late night cookies.
viii
Table of Contents
List of Tables ……………………………………………………………………………………………………… xii
List of Figures ……………………………………………………………………………………………………. xiii
Chapter 1: Introduction to the Study ……………………………………………………………………….. 1
Introduction ……………………………………………………………………………………………………. 1
Background of the Study ………………………………………………………………………………….. 6
Problem Statement …………………………………………………………………………………………. 10
Purpose of the Study ………………………………………………………………………………………. 13
Research Questions and Hypotheses ………………………………………………………………… 16
Advancing Scientific Knowledge …………………………………………………………………….. 19
Significance of the Study ………………………………………………………………………………… 23
Rationale for Methodology ……………………………………………………………………………… 25
Nature of the Research Design for the Study …………………………………………………….. 28
Definition of Terms ……………………………………………………………………………………….. 34
Assumptions, Limitations, Delimitations ………………………………………………………….. 38
Summary and Organization of the Remainder of the Study …………………………………. 40
Chapter 2: Literature Review ……………………………………………………………………………….. 44
Introduction to the Chapter and Background to the Problem ……………………………….. 44
Theoretical Foundations and Conceptual Framework …………………………………………. 48
Review of the Literature …………………………………………………………………………………. 56
Characteristics of learning ………………………………………………………………………. 58
Learning environments ………………………………………………………………………….. 64
Psychological constructs in the e-learning environment ……………………………… 78
Personality and learning …………………………………………………………………………. 81
ix
Methodology …………………………………………………………………………………………. 91
Instrumentation. …………………………………………………………………………………….. 99
Summary …………………………………………………………………………………………………….. 105
Chapter 3: Methodology …………………………………………………………………………………….. 110
Introduction ………………………………………………………………………………………………… 110
Statement of the Problem ……………………………………………………………………………… 111
Research Questions and Hypotheses ………………………………………………………………. 111
Research Methodology …………………………………………………………………………………. 114
Research Design ………………………………………………………………………………………….. 119
Population and Sample Selection …………………………………………………………………… 123
Instrumentation ……………………………………………………………………………………………. 126
Validity ………………………………………………………………………………………………………. 128
Reliability …………………………………………………………………………………………………… 129
Data Collection and Management ………………………………………………………………….. 130
Data Analysis Procedures ……………………………………………………………………………… 135
Ethical Considerations ………………………………………………………………………………….. 140
Limitations and Delimitations ……………………………………………………………………….. 141
Summary …………………………………………………………………………………………………….. 142
Chapter 4: Data Analysis and Results ………………………………………………………………….. 147
Introduction ………………………………………………………………………………………………… 147
Descriptive Data ………………………………………………………………………………………….. 148
Tests of linearity and normality ……………………………………………………………… 155
Test of homoscedasticity ………………………………………………………………………. 156
Data Analysis Procedures ……………………………………………………………………………… 156
x
Research Question 1 and hypotheses ………………………………………………………. 162
Research Question 2 and hypotheses ………………………………………………………. 162
Additional analyses ………………………………………………………………………………. 163
Results ……………………………………………………………………………………………………….. 163
Research Question 1 and hypotheses ………………………………………………………. 164
Research Question 2 and hypotheses ………………………………………………………. 165
Additional findings in Chapters 4 and 5 ………………………………………………….. 168
Summary …………………………………………………………………………………………………….. 171
Chapter 5: Summary, Conclusions, and Recommendations ……………………………………. 173
Introduction ………………………………………………………………………………………………… 173
Summary of the Study ………………………………………………………………………………….. 175
Summary of Findings and Conclusion ……………………………………………………………. 178
Research Question 1 and hypotheses ………………………………………………………. 178
Research Question 2 and hypotheses ………………………………………………………. 181
Additional findings in Chapters 4 and 5 ………………………………………………….. 184
Implications ………………………………………………………………………………………………… 186
Theoretical implications ……………………………………………………………………….. 186
Practical implications ……………………………………………………………………………. 188
Future implications ………………………………………………………………………………. 191
Strengths and weaknesses ……………………………………………………………………… 192
Recommendations ……………………………………………………………………………………….. 196
Recommendations for future research …………………………………………………….. 196
Recommendations for future practice ……………………………………………………… 199
References ……………………………………………………………………………………………………….. 202
xi
Appendix A. IRB Approval Letter ………………………………………………………………………. 228
Appendix B. Informed Consent …………………………………………………………………………… 229
Appendix C. Copy of Instruments and Permissions Letters to Use the Instruments …… 232
Appendix D. Recruitment Script …………………………………………………………………………. 241
Appendix E. Recruitment Materials …………………………………………………………………….. 243
Appendix F. Tables and Charts for Statistical Analyses …………………………………………. 246
Appendix G. Statistical Analyses ………………………………………………………………………… 259
xii
List of Tables
Table 1. Online Course and Survey Continuation and Completion Data …………………… 151
Table 2. Participant Demographics (N = 98) …………………………………………………………. 154
Table 3. Descriptive Statistics of Participant Personality Traits and TD Measures …….. 155
Table 4. Reliability of Big Five Inventory Scale ……………………………………………………. 161
Table 5. Comparison of Personality Traits for Sample and General Populations ……….. 161
Table 6. Pearson Correlations between FFM Personality Traits and SCET Values ……. 165
Table 7. Multiple Regression Analysis of SCET Values by FFM Personality Traits ….. 167
Table 8. Hierarchical Regression Analysis for FFM Personality Traits and SCET Values ……………………………………………………………………………………………………………. 167
Table 9. Independent Samples t-Test of Internet Experience with SCET Values ……….. 169
Table 10. Independent Samples Test of Gender with SCET Values …………………………. 169
Table 11. Analysis of Variation between Device Type and SCET Values ………………… 170
Table F1. Tests of Normality for Participant Personality Traits and TD Measures …….. 257
Table F2. Test of Homogeneity of Variances ……………………………………………………….. 258
Table F3. Personality Trait Collinearity Statistics …………………………………………………. 259
Table G1. Group Statistics of Internet Experience with SCET Values ……………………… 259
Table G2. Group Statistics for Gender with SCET Values ……………………………………… 259
Table G3. Descriptive Statistics for Device Type with SCET Values ………………………. 259
xiii
List of Figures
Figure 1. Workflow describing learner path and data collection ……………………………… 133
Figure C1. SCET permission letter. ……………………………………………………………………. 239
Figure E1. Ad #1 of Google AdWords campaign ………………………………………………….. 243
Figure E2. Ad #2 of Google AdWords campaign ………………………………………………….. 243
Figure E3. Ad #3 of Google AdWords campaign ………………………………………………….. 244
Figure E4. Ad for paid Facebook social media recruitment campaign ……………………… 244
Figure E5. Front side of recruitment postcard ……………………………………………………….. 245
Figure E6. Back side of recruitment postcard ……………………………………………………….. 245
Figure F1. Histogram of trait Openness within sample population …………………………… 246
Figure F2. Box chart for trait Openness from sample population …………………………….. 247
Figure F3. Normal Q-Q plot of trait Openness from sample population …………………… 247
Figure F4. Histogram of trait Conscientiousness within sample population ………………. 248
Figure F5. Box plot of trait Conscientiousness from sample population …………………… 249
Figure F6. Normal Q-Q plot of trait Conscientiousness from sample population ………. 249
Figure F7. Histogram of trait Extroversion within sample population. …………………….. 250
Figure F8. Box plot of trait Extroversion from sample population …………………………… 251
Figure F9. Normal Q-Q plot of trait Extroversion from sample population ………………. 251
Figure F10. Histogram of trait Agreeableness within sample population ………………….. 252
Figure F11. Box plot of trait Agreeableness from sample population ………………………. 252
Figure F12. Normal Q-Q plot for trait Agreeableness from sample population …………. 253
Figure F13. Histogram for trait Neuroticism within sample population ……………………. 253
Figure F14. Box plot for trait Neuroticism from sample population. ……………………….. 254
Figure F15. Normal Q-Q plot for trait Neuroticism from sample population …………….. 254
xiv
Figure F16. Histogram for SCET values within sample population with observed right skewness (1.02) …………………………………………………………………………………….. 255
Figure F17. Box plot for SCET values from sample population. ……………………………… 256
Figure F18. Normal Q-Q plot for SCET values from sample population with nonparametric values. …………………………………………………………………………….. 256
Figure F19. Linearity test using scatterplot for personality traits and SCET values.. ….. 257
1
Chapter 1: Introduction to the Study
Introduction
The extent of the fit between the learner and learning environment factors (Wu &
Hwang, 2010), such as a video instructor (Kim & Thayne, 2015), on-screen, multimedia
content (Calli, Balcikanli, Calli, Cebeci, & Seymen, 2013), or peer interaction (Wang &
Morgan, 2008), within each learning environment is a critical determinant in student
learning outcomes. The more satisfying, attractive, and useful the learning factors are to
the learner, the more likely the student is to interact with the learning environment, and
ask questions, clarify information, and remain open to new information, and,
subsequently, to perform well (Hauser, Paul, & Bradley, 2012; Wang, Chen, &
Anderson, 2014). Moore’s (1993) Transactional Distance Theory introduced three types
of learner interactions that occur within the distance-learning environment, which are
between learner and instructor, between learners, and between the learner and the
content. Chen (2001) identified the interaction between the learner and the technological
interface as a fourth interaction type. The intensity and quality of the learner’s
interaction experience with the learning environment is measured as transactional
distance (TD), which is the learner’s perceived psychological and communication
distance between the learner and the learning environment (Ustati & Hassan, 2013).
Environments in which the learner perceives easier communication and more comfortable
interactions are characterized by small TD, while environments in which the learner finds
it difficult to ask question or obtain the desired information are marked by large TD
(Moore, 1993). The desired relationship between the learner and the learning
environment is to have as small a TD as possible, a relationship that facilitates the
2
greatest opportunity for a learner to explore and clarify information (Benson &
Samarawickrema, 2009). Transactional distance is influenced by three design factors: the
structure of the environment, the amount and frequency of purposeful and valuable
communication between the learner and learning environment, and the learner’s
autonomy within the environment (Chen, 2001; Park, 2011).
Self-regulatory processes—those psychological characteristics that govern an
individual’s behavior—are also responsible in part for a learner’s interaction experience
and the resulting transactional distance (Moore, 1993). Psychological constructs that
influence self-regulation include personality traits (Legault & Inzlicht, 2013), self-
esteem, self-efficacy, motivation, and attitudes (Fishman, 2014). Individual learner self-
regulatory processes, including self-efficacy (Hauser et al., 2012), attitudes (Wu &
Hwang, 2010), and motivation (Byun, 2014), were correlated to the learner’s personality
traits (Tabak & Nguyen, 2013) and were shown to influence the learner’s propensity to
engage in dialogue and exhibit autonomy within the distance learning environment.
Current studies assessed the relationship between learner personality traits and the
learning environment. Five-Factor Model (FFM) personality traits have been shown to
correlate with learner-learning environment interaction quality and strength in some
distance-learning environments, including two-way video distance learning (Falloon,
2011), hybrid online and in-seat classrooms (Al-Dujaily, Kim, & Ryu, 2013; Murphy &
Rodríguez-Manzanares, 2008), asynchronous computer-assisted instruction (Kickul &
Kickul, 2006), and game-based learning (Bauer, Brusso, & Orvis, 2012). Studies such as
these contributed to a holistic view of the learner-learning environment interaction within
the e-learning environment by providing a map from the most basic of human
3
characteristics—one’s personality—to that person’s interaction preferences within a
learning environment. Additionally, considering the fit between personality traits and
various e-learning settings extended the conclusions of Benson and Samarawickrema
(2009) for instructional designers to determine the environment most preferred by the
learner to reduce communication difficulties and meet the designer’s desired level of
learner autonomy to include learner self-regulatory processes. Because the learner’s
natural tendencies tend not to change (Mōttus, Johnson, & Deary, 2012), the learning
environment must adapt in order to maximize learning interaction and improve learner
performance. Developing a complete map of the learning topography between human
characteristics and knowledge acquisition is a grand endeavor, one that will be achieved
incrementally with each related study.
Bolliger and Erichsen (2013) investigated the relationship between Myers-Briggs
Type Indicator (MBTI) personality types and student satisfaction with learning
interactions within a broad range of technologically diverse online and blended settings.
Although the authors concluded that personality types correlated with learner satisfaction
levels within differing learning environments, Bolliger and Erichsen identified a gap in
the extant research. Specifically, the authors recommended future research exploring the
relationship between personality characteristics and learner satisfaction with learning
interactions within different settings, with different audiences, or with larger sample sizes
in order to generalize the results. A unique setting is asynchronous video e-learning,
which is an emerging method of instruction that integrates video content with embedded,
online reinforcement activities, such as quizzes, applications, and writing (Stigler, Geller,
& Givvin, 2015), providing a content-rich, entertaining, and efficient environment for
4
increased engagement (Ljubojevic, Vaskovic, Stankovic, & Vaskovic, 2014). The
current study sought to address the gap identified by Bolliger and Erichsen (2013) and
examined the unknown relationship between personality characteristics, using Five-
Factor Model traits, and learner interaction satisfaction as measured by transactional
distance within the previously unexplored setting of asynchronous video e-learning.
The present study examined the correlation and strength of relationships between
Five-Factor Model personality traits, which have been associated with positive
performance in video environments (Barkhi & Brozovsky, 2003; Borup, West, &
Graham, 2013; Tsan & Day, 2007), and transactional distance within the asynchronous
video e-learning environment. Using quantitative methods and a correlational research
design, the study measured the Five-Factor Model personality traits of a sample
population using the Big Five Inventory (BFI; John, 2009), and compared those trait
strengths to the participants’ transactional distance as measured by the Structure
Component Evaluation Test (SCET; Sandoe, 2005) following participant involvement in
a short series of online video course segments. Scores for each trait within the BFI were
measured along a bipolar scale with scores below the midpoint indicating an absence of
the described trait (e.g., introversion) and scores higher than the midpoint indicated a
presence of the described trait (e.g., extroversion). SCET values and transactional
distance are negatively correlated such that higher scores for SCET described a smaller
transactional distance and lower SCET values indicated a larger gap psychological and
communication gap between the learner and the learning environment. As a result, a
positive correlation between a trait and a SCET value describes a negative correlation
between the trait and transactional distance. For example, if trait Extroversion is
5
positively correlated with SCET values, then Extroversion is negatively correlated with
transactional distance. In this example, high Extroversion scores suggests that the learner
experienced a high-quality interaction with the learning environment and low
Extroversion scores indicate the learner experienced a larger TD with a lower-quality
interaction with the learning environment. The present research design is based upon
Kim (2013) which compared personality traits and learner academic outcomes, as well as
Kolb learning styles and learner academic outcomes, following the completion of a
communications course within a blended online and in-class environment.
The results addressed the questions of whether personality traits were correlated
with a learner’s transactional distance within the asynchronous video environment.
Understanding the learner-learning environment interaction in this environment added to
the compendium of knowledge useful for instructional designers in creating an
environment conducive to more satisfying interactions between the learner and the
knowledge source. Additionally, the results of this study extended the scholarly literature
regarding personality trait-learner interaction, particularly as it applied to distance
learning and Transactional Distance Theory, by examining the perceived sense of
improved dialogue due to personality interactions with asynchronous video, resulting in
smaller pedagogical distances.
The remainder of the first chapter is organized to provide the reader an overview
of the research. The discussion begins with a description of the study’s background, the
problem statement that emerges from the literature, the purpose of the study, and the
research questions and hypotheses. Support for the research purpose is summarized in
the sections that follow, which include how the study advances scientific knowledge and
6
the significance of the study. The introductory chapter continues by defining the
proposed methodology for investigating the research questions and by describing the
nature of the research design that will be employed. The chapter concludes by providing
boundaries to the study through the definition of terms and through statements of the
study’s assumptions, limitations, and delimitations.
Background of the Study
A growing body of literature described a variety of theories and approaches that
associated learner characteristics and behaviors with learning outcomes. Theories about
active learning posited that individuals who engaged in learning activities saw increased
performance (Lucas, Testman, Hoyland, Kimble, & Euler, 2013); however, not all
learners engaged equally with the activity, differences that may be explained by self-
efficacy (Hauser et al., 2012), attitudes (Wu & Hwang, 2010), and motivation (Byun,
2014), self-regulatory processes that are positively associated with personality traits
(Caprara, Vecchione, Alessandri, Gerbino, & Barbaranelli, 2011; Donche, De Maeyer,
Coertjens, Van Daal, & Petegem, 2013; Hetland, Saksvik, Albertsen, Berntsen, &
Henriksen, 2012). Attempts to correlate outcomes and learning styles, which were based
upon learner preferences for feeling, watching, thinking, and doing (Chen, Jones, &
Moreland, 2014), have also met with mixed results. Some investigations described
strong correlations between the learning style and performance in traditional classrooms
(Bhatti & Bart, 2013; Black & Kassaye, 2014; Moayyeri, 2015) and in online
environments (Hwang, Sung, Hung, & Huang, 2013; Page & Webb, 2013; Richmond &
Conrad, 2012), while others demonstrated a lack of correlation (Alghasham, 2012;
Breckler, Teoh, & Role, 2011; Hsieh, Mache, & Knudson, 2012). However, correlational
7
differences might be reconciled when learning style is examined as a function of
personality traits, suggesting performance within a learning environment is more closely
related to personality traits than the incumbent learning style (Giannakos,
Chorianopoulos, Ronchetti, Szegedi, & Teasley, 2014; Kim, 2013).
Moore’s (1993) Transactional Distance Theory (TDT) offers that the quality and
intensity of the interaction between the learner and the learning environment influences
performance within distance learning environments. Learners who experience higher
quality interactions as indicated by small transactional distances with the instructional
source performed better than learners that experience a wider psychological or
communication gap with the knowledge source (Hauser et al., 2012). The learner’s
interaction with the learning environment is measured as transactional distance (TD),
which is described as the perceived pedagogical, psychological, and communication
distance between the learner and the learning environment as determined by the learner’s
perceived openness of dialogue, the student’s sense of autonomy within the learning
setting, and the learner’s perception of the learning structure’s flexibility (Chen, 2001;
Moore, 1993; Park, 2011). Active learning, theories on learning style, and Transactional
Distance Theory share common themes. Each theory suggests learning interaction is
influenced by characteristics of the learner and by factors within the learning
environment. Active learning describes variables of behavioral, cognitive, and social
engagement within the learning setting (Drew & Mackie, 2011), and learning style
variables include the learner’s physiological and psychological constructs, and the
learner’s response to the learning environment (Yenice, 2012). TDT’s factors of
dialogue, learner autonomy, and learning structure are defined by the specific learning
8
environment, and each learner’s unique characteristics (Moore, 1993). Each of the three
theories suggests the quality and intensity of the learner-learning environment interaction
is a function of the learner’s individual characteristics and the factors present within each
unique environment (Ustati & Hassan, 2013).
Kickul and Kickul (2006) found that proactive personality traits, which are
defined by Crant, Kim, and Wang (2011) as the characteristics of one who scans for
opportunities and persists to bring about closure, influenced the quality of learning and
satisfaction within computer-assisted instruction (CAI) learning environments. Hauser,
Paul, and Bradley (2012) demonstrated that computer self-efficacy and anxiety
moderated learner performance in a hybrid online and in-seat management information
systems class. Using the MBTI personality inventory, Al-Dujaily, Kim, and Ryu (2013)
showed types Extroversion, Intuitive, and Thinking were predictors of procedural
knowledge performance, while types Intuitive and Feeling were indicative of declarative
knowledge performance within CAI learning environments. Orvis, Brusso, Wasserman,
and Fisher (2011) correlated FFM trait Extroversion and trait Openness to Experience
with learner autonomy as measured by training performance in an undergraduate
management course. In gaming-based learning environments, traits Openness to
Experience and Neuroticism interacted with task difficulty conditions to determine
performance (Bauer et al., 2012).
Both Orvis et al. (2011) and Al-Dujaily et al. (2013) recommended broadening
personality research to other e-learning environments to gain greater understanding of the
relationship between personality and interaction in online learning. Bolliger and Erichsen
(2013) correlated MBTI personality types and learner interaction within a variety of
9
online and blended environments, demonstrating that type Sensor was related to
satisfaction with dialogue tools and independent projects, and that type Intuitive showed
interaction preferences based upon learning environment, favoring online instruction over
blended environments. Bolliger and Erichsen identified a gap in the correlational
research between personality characteristics and learner interaction satisfaction within
emerging technologies and new learning environments, and recommended that such
research should be conducted.
The extant literature examined the relationship between personality traits and
transactional distance within a variety of environments. Although the personality
characteristic measurement scale has varied within the literature, such as Myers Briggs
types (Al-Dujaily et al., 2013; Bolliger & Erichsen, 2013) and Big Five (Orvis, Brusso,
Wasserman, & Fisher, 2011), personality traits remained a central interest of exploration
as a condition within learning research, as traits are a stable facet of human behavior
(Wortman, Lucas, & Donnellan, 2012). Research focusing on learner outcomes also
remained consistent, including study of performance (Lucas et al., 2013; Thomas &
Macias-Moriarity, 2014), attitudes (Killian & Bastas, 2015; Wu & Hwang, 2010),
satisfaction (Bolliger & Erichsen, 2013), and engagement levels (Rodríguez Montequín,
Mesa Fernández, Balsera, & García Nieto, 2013), proving learner outcomes to be an
appropriate variable for comparison. The recent research focused on analysis of learners’
interactions with the learning environment by examining the relationship between
personality traits and transactional distance within a variety of learning circumstances.
The variety of variables examined produced results such that outcomes vary from one
environment type to the next. As a result, it is imperative to examine the relationship
10
between personality traits and transactional distance within each environment so that a
comprehensive theory may be proposed. Thus far, the literature has examined
environments of computer-aided instruction (Kickul & Kickul, 2006), game-based
learning (Bauer et al., 2012), hybrid learning structures (Moffett & Mill, 2014; Velegol,
Zappe, & Mahoney, 2015), blended learning (Bolliger & Erichsen, 2013), face-to-face
learning (Al-Dujaily et al., 2013), and two-way video distance learning (Chen & Willits,
1998; Falloon, 2011).
One environment that was not examined for the relationship between personality
traits and TD was the asynchronous video-based e-learning, a submarket of the $23.8
billion North American e-learning industry (Docebo, 2014), and a niche in which video-
based commercial ventures are growing at a rate of 100% per year (Bersin, 2012). As an
emerging framework of e-learning, asynchronous video integrates video media with
interactive activities to engage learners as a primary form of content delivery (Stigler et
al., 2015). The current study was influenced by the direction of research identified by Al-
Dujaily et al. (2013) and Orvis et al. (2011), and the specific gap identified by Bolliger
and Erichsen (2013). Although the literature explored the relationship between
personality and learner outcomes within a variety of distant learning formats, the question
of if personality traits correlate with transactional distance within asynchronous video-
based e-learning was unknown.
Problem Statement
It was not known if and to what degree personality traits correlate with a learner’s
perceived transactional distance within an asynchronous video-based e-learning
environment. The literature demonstrated that personality traits correlated with TD
11
within asynchronous computer-assisted instruction environments (Kickul & Kickul,
2006), high- and low-autonomy conditions of CAI (Orvis et al., 2011), hybrid CAI and
in-seat environments (Hauser et al., 2012), and gaming-based learning environments
(Bauer et al., 2012), and MBTI personality types correlated with interaction satisfaction
in blended environments (Bolliger & Erichsen, 2013). Because individuals with differing
personality traits demonstrated preferences for diverse learning environments, and
matching learners with engaging learning environments maximized the individual’s
achievement opportunity (Kim, 2013), it is important for instructional designers to design
courses with the appropriate levels of dialogue and structure for the learners in order to
reduce transactional distance based upon learner characteristics (Benson &
Samarawickrema, 2009). This research added to the portfolio of available instructional
design tools for aligning personality traits and learning environments while addressing
the gap in the research as described by Bolliger and Erichsen (2013).
The established research examined the relationship that exists between personality
traits and learner outcomes and behaviors with a focus on the learning environment. As a
result, the variables of personality traits have remained consistent within the research, as
have the variables of learner outcomes, such as interaction (Rodríguez Montequín et al.,
2013), performance (Lucas et al., 2013; Thomas & Macias-Moriarity, 2014), and
attitudes (Killian & Bastas, 2015; Wu & Hwang, 2010). Transactional distance has been
examined using a variety of measures within various learning settings, including
computer-aided instruction (Kickul & Kickul, 2006), game-based learning (Bauer et al.,
2012), hybrid learning structures (Moffett & Mill, 2014; Velegol et al., 2015), face-to-
face (Al-Dujaily et al., 2013), and two-way video distance learning (Chen & Willits,
12
1998; Falloon, 2011). However, Bolliger and Erichsen (2013) recommended that as new
environmental conditions arise, those settings must also be explored. Such was the case
with asynchronous video e-learning. Personality traits had demonstrated associations
with the quality of learner interactions within the video environment, including two-way
video distance education (Barkhi & Brozovsky, 2003; Tsan & Day, 2007) and
asynchronous video discussion boards (Borup et al., 2013), but not within the
asynchronous video e-learning environment.
Having examined the relationship between learner personality traits and
transactional distance within the asynchronous video environment, this research added to
the literature regarding the personality construct-learning interaction relationship with the
goal that future researchers will seek to determine a theory that unifies self-regulatory
processes, learner outcomes, and learning environments. TDT describes the primary
factors for determining transactional distance as dialogue, learner autonomy, and
structure, which are constructs of the learning environment’s design (Park, 2011). The
present research highlighted the role of self-regulatory processes, such as personality
traits, upon transactional distance and emphasized the learner’s role in the two-way
interaction between the learner and the e-learning setting in lieu of focusing on the e-
learning environment exclusively.
Although understanding the relationship between learner personality traits and TD
with the learning environment filled a gap in scholarly research, the real-world
application of the information may be equally significant. As of 2012, the corporate e-
learning market in North America was valued at over $23.8 billion with projections for it
to rise to $27.1 billion by 2016 (Docebo, 2014). Additionally, the Docebo (2014) report
13
identified that video use, both synchronous and asynchronous, is the emerging trend
within the corporate e-learning space. Within the consumer market, demand exists for
distance learning focused on practical skills, with approximately 70% of the market
consisting of women, most of who are affluent and live on the East or West coasts of the
U.S. (LaRosa, 2013). Skills of interest include business-related skills, such as
communication, finance, and computer skills, while interpersonal skills, such as
relationship development, communication, and negotiation, also remain popular.
Although the problem statement applied to both the corporate and consumer markets, as
well as educational markets, the population of interest for the present study was the self-
improvement consumer market. By identifying more effective ways in which learners
can utilize asynchronous video learning, developers for e-learning providers can better
meet market demands of e-learning consumers, providing more satisfying learning
experiences for the customer and a stronger bottom line for the development company.
Purpose of the Study
The purpose of this quantitative method, correlational design study was to
examine the relationship between FFM personality traits and perceived transactional
distance for learners in an asynchronous video-based e-learning environment. The
personality traits were measured using the Big Five Inventory scale, which indicated the
strength of each participant’s personality traits (Benet-Martinez & John, 1998; John,
2009; John, Donahue, & Kentle, 1991; John, Naumann, & Soto, 2008). The second
variable, transactional distance, was measured using the Structure Component Evaluation
Tool (SCET), a transactional distance self-assessment survey instrument (Horzum, 2011;
Sandoe, 2005). The population of interest for this research was individuals within the
14
United States that participate in self-improvement e-learning courses. This population
includes individuals seeking e-learning content designed for personal improvement, skills
development, and individual enjoyment, and does not include formal education, such as
online universities or trade schools, and does not include corporate distance learning.
The research sought to address a gap in the literature identified by Bolliger and
Erichsen (2013) describing the relationship between personality traits and satisfying
interactions within different e-learning environments. A preponderance of research (e.g.,
Killian & Bastas, 2015; Lucas et al., 2013; Wu & Hwang, 2010) investigated the
relationships between various psychological constructs and learner interactions within
differing environments. However, the emerging technology of asynchronous video-based
e-learning had not been investigated with this study’s variables in mind. As a result, the
efforts of this study added to the landscape of research regarding learner interactions
within the online learning environment. Specifically, this research added to literature that
sought to correlate personality traits and transactional distance within specific learning
conditions with the end goal of maximizing positive learning outcomes. The present
research, for example, addressed the suggested research topic of investigating training
outcomes across a variety of learner control conditions based upon personality profiles
(Orvis et al., 2011). This study also extended Al-Dujaily et al. (2013) by examining the
role of personality within the e-learning environment using non-computer science
students. Using non-computer science students was a critical distinction, as computer
experience may mask the moderating effects of some personality traits within the online
environment and experience may contribute to improved learner performance in the
15
online environment beyond the effects of previous knowledge (Simmering, Posey, &
Piccoli, 2009).
The present research also directly addressed the gap in the research as identified
by Bolliger and Erichsen (2013), which recommended that future research investigate the
relationship between personality types and learner interaction satisfaction, which was
measured by transactional distance, within emerging settings. Lastly, the study described
a unique combination of TDT factors dialogue, learner autonomy, and structure,
providing the opportunity to examine the efficacy of TDT within emerging learning
structures (Chen & Willits, 1998). A unique facet of the asynchronous video format is
that perceived dialogue has been noted in non-learning environments between viewers
and on-screen actors, which contributes to viewer-perceived relationships with actors, a
phenomenon that was correlated with personality traits (Maltby, McCutcheon, &
Lowinger, 2011). This perceived dialogue, which correlated with trait Extroversion, is an
internal dialogue within the viewer that assists in creating a cognitive space in which a
relationship can exist. The accumulation of this and related research informs the
instructional design field, enabling the construction of e-learning architectures that adapt
to the learner’s needs based upon individual predispositions (Dominic & Francis, 2015).
More generally, the present research extended the role of self-regulatory processes, such
as personality traits, within Transactional Distance Theory, which focuses on design
elements of structure, designed dialogue paths, and permissible learner autonomy as
primary influencers of transactional distance (Park, 2011).
16
Research Questions and Hypotheses
Scholarly literature regarding the influence of personality traits on video viewing
or learning preferences was limited. Within video conferencing environments, MBTI
type Feeling (Barkhi & Brozovsky, 2003), which most closely correlates to FFM trait
Agreeableness (Furnham, Moutafi, & Crump, 2003), was related to increased individual
communication satisfaction. Higher levels of trait Extroversion were related to improved
trust and more positive attitudes in two-way video counseling (Tsan & Day, 2007). In
contrast, high levels of Extroversion were related to lower student participation patterns
in asynchronous video communications (Borup et al., 2013). Additionally, trait
Extroversion has been positively related to perceived relationship development with on-
screen actors in non-learning environments (Maltby et al., 2011). As a result, this study
focused on the potential relationships between personality traits and interaction
satisfaction, as described by transactional distance theory and measured by the Structure
Component Evaluation Tool (Sandoe, 2005), within the asynchronous video e-learning
environment. Each of the personality traits represented a research variable, the strength
of which was measured for each participant using the Big Five Inventory (John, 2009)
scale before their participation in a 30-minute e-course module on communication in
relationships. Participants then completed the SCET (Sandoe, 2005), which measured
their perception of transactional distance during the e-course. Personality trait data was
analyzed for its relationship to the participant’s perception of TD. A comparison of each
personality trait variable to the transactional distance variable addressed the problem of
determining if there was a relationship between the two variables, and, if so, to what
degree the relationship existed. SCET values are inversely related to transactional
17
distance in which a high SCET value represents a small TD and a low SCET value
represents a wide TD. The following research questions and hypotheses guided this
research study based upon the listed variables:
V1: FFM personality traits as measured by the Big Five Inventory (John, 2009)
• V1O: FFM personality trait Openness as measured by the Big Five Inventory (John, 2009).
• V1C: FFM personality trait Conscientiousness as measured by the Big Five Inventory (John, 2009).
• V1E: FFM personality trait Extroversion as measured by the Big Five Inventory (John, 2009).
• V1A: FFM personality trait Agreeableness as measured by the Big Five Inventory (John, 2009).
• V1N: FFM personality trait Neuroticism as measured by the Big Five Inventory (John, 2009).
V2: Transactional distance as measured by the Structure Component Evaluation
Tool (Sandoe, 2005)
RQ1: Is there a significant correlation between Five-Factor Model personality traits
and transactional distance within the asynchronous video-based e-learning
environment?
H1A-O: Trait Openness correlates significantly with transactional distance in the
asynchronous video-based e-learning environment.
H10-O: Trait Openness does not correlate significantly with transactional distance in
the asynchronous video-based e-learning environment.
H1A-C: Trait Conscientiousness correlates significantly with transactional distance in
the asynchronous video-based e-learning environment.
H10-C: Trait Conscientiousness does not correlate significantly with transactional
distance in the asynchronous video-based e-learning environment.
18
H1A-E: Trait Extroversion correlates significantly with transactional distance in the
asynchronous video-based e-learning environment.
H10-E: Trait Extroversion does not correlate significantly with transactional distance
in the asynchronous video-based e-learning environment.
H1A-A: Trait Agreeableness correlates significantly with transactional distance in the
asynchronous video-based e-learning environment.
H10-A: Trait Agreeableness does not correlate significantly with transactional distance
in the asynchronous video-based e-learning environment.
H1A-N: Trait Neuroticism correlates significantly with transactional distance in the
asynchronous video-based e-learning environment.
H10-N: Trait Neuroticism does not correlate significantly with transactional distance in
the asynchronous video-based e-learning environment.
RQ2: Which personality traits predict transactional distance as explored with
regression analysis within the asynchronous video-based e-learning
environment?
H2A-O: Trait Openness is significantly predictive of transactional distance in the
asynchronous video-based e-learning environment.
H20-O: Trait Openness is not significantly predictive of transactional distance in the
asynchronous video-based e-learning environment.
H2A-C: Trait Conscientiousness is significantly predictive of transactional distance in
the asynchronous video-based e-learning environment.
H20-C: Trait Conscientiousness is not significantly predictive of transactional distance
in the asynchronous video-based e-learning environment.
19
H2A-E: Trait Extroversion is significantly predictive of transactional distance in the
asynchronous video-based e-learning environment.
H20-E: Trait Extroversion is not significantly predictive of transactional distance in the
asynchronous video-based e-learning environment.
H2A-A: Trait Agreeableness is significantly predictive of transactional distance in the
asynchronous video-based e-learning environment.
H20-A: Trait Agreeableness is not significantly predictive of transactional distance in
the asynchronous video-based e-learning environment.
H2A-N: Trait Neuroticism is significantly predictive of transactional distance in the
asynchronous video-based e-learning environment.
H20-N: Trait Neuroticism is not significantly predictive of transactional distance in the
asynchronous video-based e-learning environment.
Within the study, a significant positive or negative correlation between a
personality trait with transactional distance and a statistically significant degree of
prediction supported the associated alternative hypothesis and rejected the null
hypothesis. Additionally, and more meaningfully, such results addressed the gap in the
research as identified by the problem statement by describing the relationship between
the personality trait and learner perceived transactional distance.
Advancing Scientific Knowledge
The existing research was limited in its exploration of the influence of personality
traits on learner behaviors and outcomes within the asynchronous video e-learning
environment. A trend in e-learning research was investigating learner outcomes as it
related to the learner’s psychological constructs. A majority of research in active
20
learning indicated that the greater the amount of learner activity, the better the learner
performs (Lucas et al., 2013). However, not all learners in face-to-face environments
engaged with the activity in the same manner or with the same level of attention,
differences that may be explained by the psychological constructs of self-efficacy
(Hauser et al., 2012), motivation (Byun, 2014), and attitudes (Wu & Hwang, 2010).
Further investigation suggested that learner personality traits might be the underlying
construct (Donche et al., 2013; Kim, 2013).
Research in the online environment experienced a similar path, with research
examining learner outcomes within differing environments. The results indicated that
psychological constructs appeared to correlate with the level of learner satisfaction and
performance based upon the environmental conditions, such as the structure, availability
to communicate, the boundaries set on the learner, and the learner’s behavior (Falloon,
2011). The research examined personality traits as a correlate to learner behavior within
e-learning environments as measured by the self-reported strength of the learner’s
interaction with the instructional source within variety of e-learning environments,
including computer-aided instruction (Kickul & Kickul, 2006), hybrid online and in-class
environments (Al-Dujaily et al., 2013), and game-based learning (Bauer et al., 2012).
However, the developing e-learning environment of asynchronous video instruction had
not yet been explored, thereby creating a gap in the research.
These investigations were supported by personality trait theory, which suggested
that individuals’ personalities are composed of hundreds of facets, which are clustered
into major categories. A widely accepted personality trait model is the Five-Factor
Model, which offers five broad traits of human behavior: Extroversion, Neuroticism,
21
Openness to Experience, Agreeableness, and Conscientiousness (McCrae & Costa,
2003). Individual personality traits are considered stable over time and personality traits
moderate behavior such that individual tendencies within environments are consistent
over time (Wortman et al., 2012).
Within the online environment, the Theory of Transactional Distance assists in
describing the relationships between learner, the instructor, and learner outcomes (Moore,
1993). TDT offers that the interaction between a learner and the instructor is influenced
by three factors: dialogue, the learning structure, and the amount of learner autonomy.
The amount of perceived pedagogical distance between the learner and the instructor is
called transactional distance. The closer the TD, the more able the learner is to ask
questions, clarify information, and engage in learning activities, which, in turn, supports
higher learning performance (Hauser et al., 2012).
Falloon (2011) recommended exploration of the efficacy of the virtual classroom
while considering individual preferences within various environments, a call that has
been answered for a variety of environments, including hybrid online and in-seat
classrooms (Al-Dujaily et al., 2013; Murphy & Rodríguez-Manzanares, 2008),
asynchronous computer-assisted instruction (Kickul & Kickul, 2006), and game-based
learning (Bauer et al., 2012; Mayer, Kortmann, Wenzler, Wetters, & Spaans, 2014).
Bolliger and Erichsen (2013) furthered the call to specifically examine the correlation
between personality types and satisfying interactions within different learning
environments. The present study measured personality traits of the sample population
and compared those measures to the participants’ perceived TD within the asynchronous
video environment. The research determined whether or not a relationship exists
22
between FFM personality traits with learner behavior within the prescribed learning
structure. The immediate results of this study specifically addressed the gap identified by
Bolliger and Erichsen (2013), and advanced scientific knowledge about the relationship
between personality traits and TD within the video e-learning environment, an
environment that had heretofore not been explored.
The present study provided insight into Moore’s (1993) construct of dialogue,
which Moore defines as interaction that is “purposeful, constructive, and valued by each
party” (p. 24). Although dialogue has traditionally been thought of as a series of real
interactions, the asynchronous video environment presents the opportunity for perceived
dialogue between the viewer and the actor, a phenomenon known to occur between fans
and celebrities in which a unidirectional attachment develops, creating a value to the
viewer and sense of interaction between the two as perceived by the viewer (Maltby et
al., 2011). The result of the perceived dialogue is a smaller transactional distance.
Although TDT has transactional distance at the center construct of the theory (Gibson,
2003), Moore also addresses the learner’s characteristics as being salient to the equation.
Moore (1993) emphasized that TD is a relative variable influenced by the learner’s
behaviors and characteristics, amongst other factors. The present study further defined
Moore’s construct of the learner to include self-regulatory processes, such as specific
personality traits, as relevant to individual learning interactions.
The results also provided discussion points regarding personality trait theory.
With a correlation between personality traits and transactional distance, personality
theorists could more fully define the personality trait to include preferences and behaviors
within distant or electronic environments. For example, if Extroversion was correlated
23
with improved interaction within the asynchronous video environment, which was a
measure of the present study, as well as being correlated to procedural knowledge in an
adaptive environment (Al-Dujaily et al., 2013), being positively correlated with high
learner control environments (Orvis et al., 2011), related to increased trust within video
environments (Tsan & Day, 2007), and related to decreased participation on
asynchronous video discussion boards (Borup et al., 2013), personality theorists could
seek commonalities suitable for enhancing the definition of the trait.
Significance of the Study
The literature demonstrated a relationship between personality traits and
transactional distance within a variety of environments, including computer-aided
instruction (Kickul & Kickul, 2006), blended online and face-to-face (Al-Dujaily et al.,
2013), game-based learning (Bauer et al., 2012), and autonomous learning conditions
(Orvis et al., 2011). The compilation of literature allows for the mapping of personality
traits to environments in which the learner produces the most desirable outcomes. The
present research added additional structure to the interaction map for video-based e-
learning. Once developed, the map of relationships between personality traits and
learning environments will inform studies searching to develop theories relating
personality constructs, including FFM personality traits, and learning environments. The
development of such theories will enable researchers and instructional designers the
ability to predict behaviors within future e-learning environments.
For the present time, determining the relationship between personality traits and
transactional distance within the video e-learning environment expanded the scholarly
literature of individual traits and their influence on e-learning. Practical applications of
24
the research results include equipping instructional designers with an extended catalogue
of learning frameworks that includes asynchronous video e-learning and its association
with personality traits for maximizing individual learner outcomes (Benson &
Samarawickrema, 2009; Hwang et al., 2013). Real-world applications included user-
selected learning frameworks based upon learner preferences (Fraihat & Shambour,
2015), and adaptive learning applications (Takeuchi et al., 2009).
Additionally, correlations between learner personality traits and transactional
distance within the video environment provided information beneficial for the design,
development, and implementation of other online video forums, such as social
environments in which trust development is important (Zhao, Ha, & Widdows, 2013),
collaboration within virtual teams (Dullemond, van Gameren, & van Solingen, 2014),
and distant healthcare and social services (Weber, Geigle, & Barkdull, 2014). The
application of trait-interaction information within the video environment extends to any
situation in which video, either synchronous or asynchronous, is practiced. Seemingly
minor applications include understanding the efficacy of video instruction for providing
passenger pre-takeoff instructions for airlines, safety briefings for utility workers, and
organizing large workgroups. Although these purposes may not seem to be related to the
e-learning environment, any social interaction, real or perceived, provides a learning
opportunity (Bandura, 1977; Mintzes, Marcum, Messerschmidt-Yates, & Mark, 2013).
Theoretical insights also emerged from this research. The results helped to
determine whether Agreeableness interacted with the video environment due to a
perceived relationship with the on-screen instructor. Agreeableness is associated with
characteristics of pleasing and accommodating (McCrae & Costa, 2003), which may be
25
related to weak internal motivations based upon others’ expectations (Briki et al., 2015;
Deci & Ryan, 2008). A correlation between Extroversion and learning behavior within
the asynchronous video environment provided additional support for an incentive-based
motivation model for Extroversion. Incentive-based models of motivation state that an
individual becomes motivated by the anticipation of rewarding activity, such as
answering questions correctly and demonstrating knowledge before an audience—in the
case of the present research, the perceived audience of the video instructor (Merrick &
Shafi, 2013). Trait Extroversion also correlated with Entertainment-social scores of
celebrity worship, a phenomenon associated with asynchronous video in which the
viewer develops a perceived attachment and strong interest in the on-screen actor (Maltby
et al., 2011), a construct that might have influenced the characteristic of dialogue within
the asynchronous video e-learning environment and one that might suggest a need to
expand the definition of dialogue to include perceived dialogue as a factor of
transactional distance. Such a construct would be supported by Theory of Mind precepts,
as an internal dialogue exists between the individual and the perceived mind of the other
in order to establish communication and to create a cognitive space for the other persona
(Harbers, Van den Bosch, & Meyer, 2012).
Rationale for Methodology
Research of personality typically follows one of three avenues: the examination of
individual differences, the examination of motivations, or holistic examination of the
individual (McAdams & Pals, 2007). The study of individual differences is based upon
trait study, which is a lexical categorization based upon factor analysis of the words’
applicability to individual tendencies (John & Srivastava, 1999). As a result, it is
26
appropriate to use quantitative methods to study traits, the categorization of which was
born of quantitative methods. Quantitative methods emerge from positivism, the concept
that every problem has a solution and that there is an interrelated cause and effect that can
be measured (Arghode, 2012). The governing epistemology of positivism is one in
which the detached observer seeks out a singular truth through cause and effect, or
through correlation and association, which was of interest to this study. The resulting
methodology analyzes the assumptions, principles, and procedures to seek out the
relationship of interest. Consequently, quantitative methods are appropriate for the
development and testing of hypotheses (Dobrovolny & Fuentes, 2008), for measuring
differences between variables and determining relationships between variables, and for
exploring phenomenon that are repeatable (Arghode, 2012).
Quantitative methods also provide a fixed standard against which the theory,
research question, hypotheses, and variables are measured and compared, providing a
series of theoretical and procedural benchmarks against which all similar research is
contrasted (Wallis, 2015). The nature of quantitative methods offers structure within
which the data is assembled for examination in an objective manner that is acceptable to
the research community. Such methodology contrasts with qualitative methodology,
which seeks to develop theory based upon an interpretation by an involved observer of
the phenomenon (Arghode, 2012).
The current study’s purpose was to measure the strength of the relationship
between each personality trait’s effect and transactional distance within the learning
environment, which suggested that the research utilize quantitative methodology. Several
characteristics of personality traits influenced methodology selection: Individual trait
27
dispositions were testable, the measurement of personality traits produced a value along a
continuous scale, and, although personality traits cannot be manipulated, sufficient
samples were taken to create a quasi-experimental approach. Instruments, such as the
Big Five Inventory (John, 2009), Myers Briggs Type Indicator (Furnham et al., 2003),
Trait Descriptive Adjectives (John & Srivastava, 1999), Saucier’s Mini-Markers (Dwight,
Cummings, & Glenar, 1998), and the revised NEO personality inventory (NEO-PI-R)
(Costa & McCrae, 1995) have been developed to measure the strength of personality
traits along each instrument’s respective axes. Previous research has shown that
transactional distance, which is measured using quantitative surveys (Chen, 2001;
Horzum, 2011; Huang, 2002; Sandoe, 2005), changes based upon differences in the
personality variable following experience within a specific learning environment (Al-
Dujaily et al., 2013; Bauer et al., 2012; Kickul & Kickul, 2006; Orvis et al., 2011). Each
of these characteristics fit the definition of a variable.
Quantitative research investigates psychological constructs through statistical
means. The design most suited to address the research questions and hypotheses for the
selected environment was correlational design (Jamison & Schuttler, 2015; Rumrill,
2004). Quantitative methodology and correlational design afforded the research the
opportunity to maintain an objective view and minimize observer bias (Trofimova, 2014),
while enumerating the strength of the relationship between the two variables.
Quantitative methods also afforded future researchers the opportunity to verify, enhance,
and expand the current research. Quantitative methods do not discover new variables as
qualitative methods would discover factors, nor do quantitative methods describe a
situation globally or holistically. Quantitative methods are limited to answering the
28
specific question around which the research was designed, which is demonstrated through
similar research including Kim (2013) and Bolliger and Erichsen (2013).
Nature of the Research Design for the Study
This study used a correlational design. The correlational design offered the
benefit of identifying associative relationships between variables and allowed the
researcher to measure relationship strength (Rumrill, 2004). Data collected from a
correlational study must meet the criteria that measurements of the variables must be
continuous in nature, which is true of FFM traits (John et al., 2008); and TD
measurements from the Structured Component Evaluation Tool (Sandoe, 2005).
Correlational design is also useful for non-experimental or quasi-experimental
environments in which the variables cannot be manipulated or controlled (Jamison &
Schuttler, 2015; Rumrill, 2004), which was the case with personality traits in this study.
It is also important to note that correlational designs do not attempt to identify causal
relationships; however, covariation is a necessary condition for causality.
The personality variables were FFM personality traits Openness,
Conscientiousness, Extroversion, Agreeableness, and Neuroticism, each of which was
investigated independently in relation to the learning outcome variable. These traits were
selected for examination based upon previous associations of personality traits with
learner interaction within the video environment, including two-way video distance
education (Barkhi & Brozovsky, 2003; Tsan & Day, 2007) and asynchronous video
discussion boards (Borup et al., 2013). Personality traits were measured using the Big
Five Inventory, which assigned a score for each trait, which was normalized to a range
from 0 to 100, with 50 representing the midpoint (John, 2009). Scores higher than the
29
midpoint represent the high dimension of the trait (e.g., extroversion), while scores lower
than the midpoint represent the lower dimensional trait (e.g., introversion). The bipolar
nature of each dimension puts forth that the further the score is from the midpoint, the
stronger the expression of that dimension. The present research design was based upon
Kim (2013) in which the researcher examined the relationship between personality traits
and academic outcomes, as well as the relationship between Kolb learning styles and
academic outcomes, following the learner’s completion of a blended e-learning and in-
class communications course.
The learning outcome variable was transactional distance, which represented the
perceived strength of the interaction between the learner and the learning environment.
TD is measured using the Structured Component Evaluation Tool (SCET) (Sandoe,
2005). SCET was developed to measure TD within e-learning environments that exhibit
high levels of structure, which was the case with an asynchronous e-learning
environment. SCET scores range from 0, which represents no perceived learner-
instructor pedagogical relationship, to 24, which represents a very strong learner-learning
environment relationship.
The design facilitated Pearson correlation analysis to determine whether any
personality variable exhibited a significant relationship with TD. Pearson correlation
analysis was the most suitable method as it was reliable for bivariate correlation of
continuous variables in linear relationships. Studies similar to the present research (e.g.,
Caprara et al., 2011; Kamaluddin, Shariff, Othman, Ismail, & Saat, 2014) successfully
used a Pearson correlation. Results from the Pearson correlational analysis addressed the
hypotheses, with significant results affirming the alternative hypotheses (Kim, 2013).
30
Additionally, correlational design offered the benefit of comparing variables over which
the experimenter had no control (Rumrill, 2004), which was the case with personality and
learning outcome variables. Because the variables were unable to be experimentally
manipulated, experimental designs were inappropriate. In the unlikely event that one of
the variables was determined to be non-continuous, or if significant outliers were present,
Spearman correlation analysis would have been used, as it is a method suitable for
continuous and ordinal data sets, and an analysis better suited to address outlier data sets
(Gravetter & Wallnau, 2013).
The design also employed an analysis of regression, which measured the ability of
the personality traits to predict learners’ ratings of transactional distance. Data for
analysis of regression assumes the data is linear, normally distributed, homoscedastic, the
variables are not auto-correlated, and the data is not collinear (Meyers, Gamst, &
Guarino, 2013). Personality trait measures, as determined by BFI results, were compared
to transactional distance measures, as described by SCET results. Each trait was
independently compared to determine the extent of the variance of TD as explained by
the personality trait. Significant results (p < .05) rejected the null hypothesis, and non-
significant results fail to reject the null hypothesis. A positive correlation between a
personality trait and SCET values represent a negative correlation between the
personality trait and TD, since high SCET values represent small transactional distances
and low SCET values represent large transactional distances. Personality trait-based
research utilized analysis of regression to determine the degree to which personality traits
explained the outcome variable across the literature, to include Jong (2013), Saricaoglu
and Arslan (2013), and Kim (2013).
31
The target population was a subset of the commercial e-learning market. The
$23.8 billion e-learning market in North America is projected to rise to $27.1 billion by
2016 (Docebo, 2014), and video use, both synchronous and asynchronous, is anticipated
to be the emerging trend within the e-learning space. The consumer e-learning market, of
which 70% are women, the majority of who live on the East or West U.S. coasts, and
who are affluent, is focused on practical skills (LaRosa, 2013). This market typically
accesses learning from home and is interested in self-improvement through courses
focused on business-related skills, such as communication, finance, and computer skills,
and interpersonal skills, such as relationship development, communication, and
negotiation. Thus, the sample for the present study was participants in self-improvement
e-learning courses. Using a bivariate normal model approach for correlation, the
G*Power 3.1 software program calculated that a minimum of 84 data sets were necessary
for this study to achieve a power of .80 and a maximum error probability of .05 based
upon an anticipated moderate correlation (r2 = .3) and a two-tailed test based upon a
general population of greater than 10,000 (N < 10,000) (Orvis et al., 2011; Peng, Long, &
Abaci, 2012).
Participants were recruited via advertising methods. Direct mail postcards and
Internet advertising were employed seeking individuals 18-years of age or older
interested in taking a free online course on the topic of communication skills for
relationships for this convenience sample. In order to maximize the advertising
opportunities, marketing targeted individuals in a relationship so that the e-learning
course, communication within relationships, was relevant to the participant. Direct mail
mailing lists targeted suburban single-family home communities in the Phoenix
32
metropolitan area, where 73% of single-family homes are purchased by either married or
unmarried couples (Snowden, 2015). Internet advertising utilized keywords marriage,
relationship, marriage courses, free online communication courses, and marriage courses
online, within major search engines (Google, n.d.). Advertising for participants was
ongoing and continued for the time necessary to collect the required minimum sample
size of completed data sets. This approach addressed the need to ensure a qualified
sample population, as well as to address attrition. Individuals interested in participating
were provided a web link via the advertising material to the research study website at
which point the participant was presented with a video introduction to the study. A video
then described the Informed Consent Form (see Appendix B), which was presented for
review and electronic signature. The next video segment asked participants to complete a
brief demographic survey and an online version of the Big Five Inventory (John, 2009).
Upon completion of the pre-course form and instrument, participants in the
proposed research study experienced an independent e-learning course delivered via
asynchronous video instruction. The course featured three modules, each of which began
with a slide showing the module objectives, followed by a five- to seven-minute video
discussing a facet of interpersonal communication. Within each module, the video
instructor directly addressed the camera as if speaking directly to the individual learner,
and did so using casual conversation and personal anecdotes, which has been shown to
develop a stronger rapport with online video learners (Kim & Thayne, 2015). Each
module provided interactivity through two multiple-choice questions based upon the
learning objectives. As a non-credit course, response accuracy bore no influence on the
participant’s completion of the course, although participants received prompts for
33
incorrect answers and were offered the opportunity to reattempt answering the question.
Each participant experienced the same three-module course and the course provides no
opportunities for learner interaction with the instructor or other learners. The
transactional distance factors that describe this asynchronous video course were high
structure due to the rigidity of the course flow (Park, 2011), low learner autonomy with
learners having little freedom to explore information outside of the course, which is a
function of the high structure (Benson & Samarawickrema, 2009), and low dialogue with
learners having no opportunity to ask questions or clarify concepts with an instructor or
peers (Moore, 1989; Park, 2011).
Following the third module, participants viewed video instructions for completing
the Structure Component Evaluation Tool (Sandoe, 2005) and then were presented with
the SCET instrument. Upon completion of the SCET, a short video played thanking the
participant for his or her involvement with the research and a brief summary of the study.
The video provided the participant with contact information in the event he or she would
like follow-up communication with the researcher.
The design included inherent risks. The distribution of personality traits may not
have been normal, producing a restricted range of data, and validity may have been
questioned due to potential covariance between the personality variables (Levy & Ellis,
2011). Such covariance would have been examined via analysis of covariance
(ANCOVA) provided the data meets the assumptions of linearity of regression and
homogeneity of regression (Meyers et al., 2013). These risks were mitigated through an
appropriate sample size calculated to match the design, including number of variables,
34
effect size, statistical analysis method (Gravetter & Wallnau, 2013), and by selecting a
diverse sample population (Al-Dujaily et al., 2013).
Definition of Terms
Using clear and unequivocal definitions is important for unambiguous
understanding of terms and constructs used within a study (Howards, Schisterman, Poole,
Kaufman, & Weinberg, 2012). The following terms are defined to afford a common and
clear understanding for the purposes of this study. The order in which the terms are
presented is intended to allow the reader to understand and define terms beginning with
broad concepts and then to focus upon specific constructs within each significant area of
study.
For the purpose of this study, the following terms are defined as follows:
Personality trait. The grouped collection of behavioral descriptors that is
taxonomically interrelated (McCrae & Costa, 2003). There are five such groupings per
the Five-Factor Model, which include Extroversion, Agreeableness, Conscientiousness,
Openness to Experience, and Neuroticism.
Big Five. The Big Five is a reference to the five personality traits clusters
evolving from the work of Tupes and Christal (1992). The Big Five traits are
Extroversion, Agreeableness, Conscientiousness, Openness to Experience, and
Neuroticism.
Five-Factor Model. An integrated taxonomy of the Big Five personality traits
developed by McCrae and Costa (2003) to provide a unified model of personality. Five-
Factor Model (FFM) suggests that personality traits do not change significantly over the
35
course of an individual’s life and they are useful for predicting individual tendencies in
known circumstances (Wortman et al., 2012)
Openness to Experience. Also known as Openness. Behavioral characteristics
and descriptors related to an individual’s tendencies for valuing individual expression and
for exhibiting intellectual curiosity. Facet descriptors include idealism, intellectualism,
and adventurousness (Soto & John, 2012). Individuals high in Openness are interested in
others’ opinions, even if they initially disagree, and are willing to change their mind
based upon the evidence presented.
Conscientiousness. Behavioral characteristics and descriptors related to an
individual’s tendencies to organize and stay focused on tasks. Descriptive facets include
industriousness, orderliness, self-discipline, moral seriousness, work ethic, and focus on
long-term goals (Soto & John, 2012). Individuals high in Conscientiousness are
organized, with neat desks, files in order, and goals set for their day.
Extroversion. Also known as Extraversion. Behavioral characteristics and
descriptors related to an individual’s tendencies within social interactions and to their
sense of agency (Klimstra, Luyckx, Goossens, Teppers, & De Fruyt, 2013). Extroversion
describes the level of individual assertiveness, social confidence, and gregariousness
(Soto & John, 2012). An example of an individual with Extroversion is one who is
comfortable socializing with everybody in attendance at a party, while someone who is
low in Extroversion would be more comfortable talking with the same, familiar person all
evening, or retreating to a quiet location with no one around.
Agreeableness. Behavioral characteristics and descriptors related to an
individual’s tendencies for straightforwardness and modesty (Klimstra et al., 2013).
36
Descriptive facets include trustfulness, compassion, and humility (Soto & John, 2012).
Characteristics of an individual with high Agreeableness tendencies is one who attempts
to please those around, such as not sending back an undercooked steak at a restaurant. A
person low in Agreeableness would, on the other hand, send the steak back and ask for a
free appetizer. Agreeableness includes a sense of caring how others consider the
individual.
Neuroticism. Behavioral characteristics and descriptors related to an individual’s
tendencies to feel negative affect, such as to feel nervousness, fear, or sadness. High
neuroticism is susceptible to intrusive thoughts and behaviors, and is described with
descriptors such as anxiety, depression, rumination, and irritability (Soto & John, 2012).
Individuals high in Neuroticism tend to display nervous or stressful behaviors, even if the
situation does not merit higher levels of affective arousal.
Bipolar. Representing two ends of the same personality trait scale. Each
personality characteristic (e.g., Extroversion) may exhibit one tendency of a trait to some
extent, such as gregariousness, or it may exhibit an opposite tendency of the trait to some
extent, such as shyness (McCrae & Costa, 2003). This use of this term should not be
confused with bipolar disorder, describing manic episodes of mood disturbances
(American Psychiatric, 2013).
Transactional distance. Transactional distance, or TD, is the perceived
pedagogical distance between a learner and the learning environment (Park, 2011). TD is
a result of the psychological and communication closeness that the learner experiences
with the instructional source. A high TD refers to a lack of communication or
understanding between the learner and instructor, and a low TD refers to an intellectual
37
and affective closeness between the learner and instructor. Low TD is associated with
improved learner performance (Hauser et al., 2012). TD is determined by factors of
dialogue, structure, and learner autonomy.
Interaction. Interaction is the interplay of and satisfaction with knowledge,
affect, and behaviors between the learner and the learning environment (Mason, 2013).
The quality and intensity of an interaction within the distance-learning environment is
measured as transactional distance (Ustati & Hassan, 2013).
Dialogue. Dialogue describes the broad spectrum of purposeful, positive, and
synergistic interaction between the learner and the instructor (Moore, 1993). Dialogue
connotes the idea of multi-directional communication for the purpose of clarifying,
understanding, and furthering the learning of the student. Dialogue does not include the
act of programmed content delivery.
Structure. Structure refers to instructional design by which the curriculum is
delivered to the learner via the prescribed communication medium (Moore, 1993).
Concepts, such as the flexibility of the instructional design to adjust to the learner’s needs
and the ability for the technology to accommodate the instructional design, are included
within the structural taxonomy, as are pedagogical considerations of educational
objectives, learning content, assessment activities, and addressing student motivation
(Benson & Samarawickrema, 2009).
Learner autonomy. Learner autonomy addresses two principle concepts within
the learning environment. The first is the amount of flexibility a learner is provided by
the learning structure to determine learning objectives, create knowledge, and achieve
38
goals (Moore, 1993). The second concept of learner autonomy includes the
psychological view of a learner’s willingness or ability to be self-directed (Park, 2011).
Learning environment. Learners may engage in up to four different types of
interactions within the distance-learning environment in order to acquire knowledge.
Engagement may occur between a learner and an instructor, between learning peers,
between a learner and the content, such as the text or video providing information
(Moore, 1993), and between a learner and the interface through which the learner
accesses the instruction (Chen, 2001). The learning environment encompasses all four
types of engagement. Most TDT concepts apply consistently to all learner-learning
environment interactions. For those cases in which a broad application does not apply,
the specific interaction type (e.g., learner-content) is identified.
Asynchronous video-based e-learning. Asynchronous video-based e-learning
refers to the learning environment in which video content is presented to the learner at the
learner’s convenience, including the factors of time scheduling and Internet-connected
device, such as laptop or mobile device. This learning environment is delivered via the
Internet and typically includes interactive activities, such as assessments, unstructured
research, and related discussion boards (Stigler et al., 2015). This learning environment
compares to computer-aided instruction, except that the primary media for content
delivery is video instead of text, for a richer form of media presentation (Ljubojevic et al.,
2014).
Assumptions, Limitations, Delimitations
Assumptions, limitations, and delimitations of the research provided
epistemological boundaries in order to support the internal and external validity of
39
research (Ellis & Levy, 2009). By stating the restrictions a priori, readers are better able
to understand the viewpoint of the researcher and limit the challenges to the research
methodology. Assumptions represented the values and epistemological positions of the
researcher and affected how the research was conducted (Kirkwood & Price, 2013).
Limitations were potential problems or weaknesses as identified a priori by the
researcher, and represented an uncontained threat to the to the internal validity of the
study (Ellis & Levy, 2009). On the other hand, delimitations represented actions, factors,
or variables left out of the research, resulting in a narrower investigation of the research
question (Ellis & Levy, 2009; Gallarza, Gil-Saura, & Holbrook, 2011).
This study relied upon several assumptions. These assumptions were:
1. The sample population represented the general population. By using direct mail and online advertisements to attract the sample population, it was possible that the sample might display psychological characteristics, such as motivation, that were slightly different than the general population. However, it was assumed that any individual that was seeking a course on communication skill for relationships was motivated by the content and not by the opportunity to participate in a research study.
2. It was assumed that participants connected to the research website using a high- quality Internet connection in order to receive the video content as it was intended to be delivered. While the study instructions recommended a high-speed connection, it was impossible to ensure this was the case.
3. It was assumed that study participants answered the survey questions honestly and that participants were not deceptive in their responses. Peter and Valkenburg (2011) found that given the appropriate introduction, survey participants provide honest answers instead of socially acceptable answers. For the purposes of this research, a video narrator asked participants to complete the instruments according to their experiences. In reference to SCET, the video stated that some learners felt that the video environment provided a high level of instruction or interaction while others felt the video environment provided a low level of instruction or interaction. Providing this information informed participants that there was not a socially correct answer.
4. The Five-Factor Model included descriptors for all normal human behavior. There is some disagreement that FFM includes all personality constructs. It has been argued that facets of honesty, humility, integrity, and greed are not included
40
within FFM (Thalmayer, Saucier, & Eigenhuis, 2011), while others suggested these elements are included within Agreeableness and Conscientiousness (McCrae & Costa, 2003). It was assumed within this research that those elements that may influence learner behaviors within the asynchronous environment were included within the FFM traits, and that any facets excluded by FFM did not have any bearing on the results (e.g., greed did not influence a learner’s interaction with the content). If any relevant facets of personality were excluded by FFM, those exclusions limit this study.
The study faced several limitations and delimitations.
1. A limitation of the study was that because the advertisement reached a national audience, it was not anticipated that a geographically-oriented population represented a majority of participants; however, it was not possible to predict the demographics of participants.
2. A limitation of the study was that there was no way to ensure that a normal distribution of personality traits was represented within the survey. In the event of a non-normal distribution based upon national surveys of personality distribution, such as described by Soto and John (2012), analysis would have included non-parametric statistical analysis.
3. A delimitation of the study was that the Structure Component Evaluation Tool was selected due to the structured nature of the video environment. Although the SCET is a validated and reliable instrument (Sandoe, 2005), it is possible that other tests for transactional distance may have returned different results based upon each test’s unique focus. This difference may affect generalizability of the results.
4. A delimitation of the study was that the study was examining FFM personality traits. It is possible that other psychological constructs, including motivation, attitudes, and self-efficacy, have a correlational relationship with the learner- learning environment interaction; however, these traits and constructs were not tested within this study, thus limiting the ability to generalize the results for all self-regulatory constructs.
Summary and Organization of the Remainder of the Study
A review of the extant literature found that potential correlations between
personality traits and transactional distance had not been investigated within the
asynchronous video e-learning environment; Bolliger and Erichsen (2013) identified a
gap in the research and recommended investigation of personality traits and learner
interactions within technologically diverse online and blended environments. Some
41
studies investigated the relationship between personality traits and transactional distance
in environments such as computer-aided instruction (Kickul & Kickul, 2006), hybrid
online and in-seat classes (Al-Dujaily et al., 2013), high and low learner autonomy online
environments (Orvis et al., 2011), and game-based learning environments (Bauer et al.,
2012). The studies found that personality traits correlated with transactional distance;
however, different traits influenced transactional distance dependent upon the unique
learning environment, differences that may be explained by the differing levels of
dialogue, structure, and learner autonomy available to learners within each environment.
Other studies investigated elements of video-based communication, including the
face-to-face classroom (Ljubojevic et al., 2014), two-way videoconferencing classrooms
(Chen & Willits, 1998), and blended environments, such as flipped classrooms (Moffett
& Mill, 2014; Velegol et al., 2015), to determine the influence of video upon
performance. Similar to the results found for the online learning environment studies, the
unique characteristics of the video environment appeared to influence outcomes, such as
satisfaction and academic performance. Only recently had asynchronous video-based e-
learning begun to receive attention. Vural (2013) investigated an asynchronous learning
environment to determine if active learning correlated with academic performance. In
the few studies relating personality traits and video environments, trait Agreeableness
was associated with individual communication satisfaction within two-way
videoconferencing environments (Barkhi & Brozovsky, 2003; Furnham et al., 2003), and
trait Extroversion was related to student participation patterns in asynchronous video
communications (Borup et al., 2013), and was related to trust and smaller psychological
distances in two-way counseling (Tsan & Day, 2007).
42
In order to add to the scientific literature, this study investigated the correlation of
personality traits with transactional distance within the asynchronous video e-learning
environment, and the extent to which the relationships predicted transactional distance.
Respondents to direct mail and online advertisements for an online course covering
communication skills for relationships were asked to participate in the online study, with
a minimum of 84 necessary to complete the study. The participants were asked to
provide demographic information (e.g., age, gender, average time each week spent using
a computer and the Internet), complete the Big Five Inventory, complete the
communications course, and complete the SCET. The data was screened and validated,
and imputation methods and pairwise deletion was used for missing data. Pearson
correlational analysis checked for significant relationships between the variables and
analysis of regression explained the degree of variance. Significant relationships
supported the alternative hypotheses and rejected the null hypotheses, and non-significant
relationships rejected the alternative hypotheses and accepted the null hypotheses.
The following chapter provides a development of personality trait theory and
transactional distance theory, and a thorough review of the extant literature on the topics
of constructivist learning, online learning, psychological construct correlations with
learning performance, personality trait correlations with learning performance, and the
evolution of video’s use for instruction. Next, the methodology chapter presents the
research design and describes the population, data collection, and data analysis process.
Chapter 4 presents the full implementation of the research, including the data screening,
testing of assumptions for statistical analysis, descriptive and inferential statistics, and the
results of the correlational analysis and analysis of regression. Chapter 5 discusses the
43
results through the lens of the research questions, relating the results to the previous
research and theories upon which the research was based, and discussing the implications
for future research and practice.
44
Chapter 2: Literature Review
Introduction to the Chapter and Background to the Problem
In order to describe the foundational factors that supported this investigation of
personality traits and their relationship with transactional distance in a video e-learning
environment, this chapter examines current and historical research on several important
concepts. The review of the literature surveyed peer reviewed journal articles and
dissertations found in the EBSCOhost search engine focusing on keywords of
personality, personality traits, Big Five, Five-Factor Model, Openness,
Conscientiousness, Extroversion, Agreeableness, Neuroticism, active learning, learning
style, e-learning, online education, distance learning, transactional distance, transaction,
video (not including games), self-esteem, self-efficacy, motivation, and satisfaction, as
well as books focused on these key areas. Literature searches focused on research
published in 2011 or later to ensure the inclusion of contemporary findings within the key
concept areas. The requirement for historical perspectives and concept development
supported using materials dated before 2011, particularly in the instances of theoretical
development, which leveraged the original research contained within seminal works. The
research attempted to demonstrate a combination of historical and contemporary research
to establish a path of related research and to expose areas requiring further investigation.
Overall, the chapter describes the background of the study, reviews the theoretical
foundations, and describes the extant literature to reveal the history, related theories, and
research accomplished on the topics relevant to the current study. The summary
concludes the chapter, exploring the gaps in the literature that led to the purpose of the
current study.
45
The chapter begins by discussing the theoretical foundations upon which the
balance of the research was conducted. Personality trait theory was developed so as to
provide understanding as to how traits describe an individual’s psychological construct
and why these constructs are useful for empirical research. Transactional Distance
Theory (Moore, 1993) was introduced to identify the e-learning components that
influence a learner’s transaction with the instructional source, which subsequently
influences learner outcomes. The first concept of constructivism was introduced in order
to lay a practical foundation for measuring learner interactions within the learning
environment (Ustati & Hassan, 2013). Within this section, research based upon active
learning (Lucas et al., 2013; Thomas & Macias-Moriarity, 2014) and Kolb’s Learning
Styles (Bhatti & Bart, 2013; Black & Kassaye, 2014; Chen et al., 2014) examined the
relationship between learners and their learning interactions as they influence
performance outcomes.
The next section explores the online learning environment in research that closely
mirrors the path of study taken by researchers of constructivism. Based upon
Transactional Distance Theory (Giossos, Koutsouba, Lionarakis, & Skavantzos, 2009;
Park, 2011), initial examinations of distance learning explored the types of relationships
and interactions that developed between learners and their instructors and peers (Chen,
2001; Moore, 1993). Early qualitative examinations of the distance-learning environment
identified learner psychological constructs and personality traits as influencers of the
learning process (Falloon, 2011; Murphy & Rodríguez-Manzanares, 2008). Subsequent
quantitative investigations explored individual factors of TDT. Specifically, the research
addressed the three components that make up transactional distance (TD), dialogue,
46
structure, and learner autonomy, and their influence on learner satisfaction and learner
academic performance (Benson & Samarawickrema, 2009; Hsia, Chang, & Tseng, 2014;
Islam, 2012; Papadopoulos & Dagdilelis, 2007; Wang & Morgan, 2008; Zhou, 2014).
The research broadened to include the effect of psychological constructs on each of the
transactional distance factors (Caprara et al., 2011; Hertel, Schroer, Batinic, & Naumann,
2008; Hetland et al., 2012; Wu & Hwang, 2010). With each individual transactional
distance factor thoroughly investigated, the research explored learner outcomes within
integrated learning settings, recognizing that dialogue, structure, and learner autonomy
vary dependent upon the unique learning environment (Hauser et al., 2012; Kim &
Thayne, 2015; Ljubojevic et al., 2014; Vural, 2013).
The research continued through the examination of psychological constructs and
personality traits as related to learner interaction within classroom environments and the
effects of those interactions on learning outcomes (Byun, 2014; Gosling, Augustine,
Vazire, Holtzman, & Gaddis, 2011; Killian & Bastas, 2015; Rodríguez Montequín et al.,
2013). Similar to the manner of the previous research, the literature review naturally
extended into the influence of psychological constructs and personality traits on learner
interactions within the e-learning environment by examining personality trait
relationships within a variety of learning environments, creating correlational ties
between traits and transactional distance as expressed within each unique learning
environment (Al-Dujaily et al., 2013; Bauer et al., 2012; Chang & Chang, 2012; Kickul
& Kickul, 2006; Orvis et al., 2011). As more data accumulated through the literature
describing the relationships between personality traits and learner interactions with the
different learning environments, it was expected that a pattern would emerge that allows
47
the development of theory to explain the relationships. In order to do so, additional
examination was needed of current and emerging learning settings (Benson &
Samarawickrema, 2009; Bolliger & Erichsen, 2013).
Throughout the preceding sections, a variety of learning environments were
explored, such as computer-aided instruction (Murphy & Rodríguez-Manzanares, 2008),
game-based learning (Bauer et al., 2012), and hybrid learning environments (Velegol et
al., 2015). However, research on the reemerging use of video within the online
environment was limited. The available research demonstrated the learning-applied uses
of video, including video as a support media within the face-to-face classroom
environment (Barkhi & Brozovsky, 2003; Ljubojevic et al., 2014), as a two-way
communication tool, such as videoconferencing (Falloon, 2011), and as a tool for hybrid
learning environments in which video provides the content to learners at home and then
the learners attend class to work on related activities (Moffett & Mill, 2014; Velegol et
al., 2015). The existing research of asynchronous video e-learning explored its
effectiveness through the lens of active learning (Vural, 2013). The following section
summarizes the key concepts of the literature review, identifying the gap that will be
investigated by the proposed study.
The literature was then used to examine the appropriate methodology for use in
the study. Quantitative methods were compared and contrasted with qualitative methods
to identify the benefits and shortcomings of each methodology in answering the research
questions. Qualitative methods reveal life stories of individuals and identify themes
associated with psychological constructs (Ma & Zi, 2015), while quantitative methods
provide methods for determining the strength of relationships between variables with the
48
literature that examines the relationships between personality traits and outcomes relying
primarily upon correlational design (Rumrill, 2004). The use of psychological constructs
such as personality traits as variables supported the use of correlational design, including
exploring relationships between Big Five traits and customer service job performance
(Blignaut & Ungerer, 2014), personality type and quality of life for cancer patients (Shun
et al., 2011), and psychological constructs of emotional intelligence, anxiety, stress, and
attitudes with learning outcomes (Opateye, 2014).
The chapter concludes with an examination of the instrumentation useful for
addressing the research questions. Personality traits may be examined using a variety of
measures, including revised NEO personality inventory (NEO PI-R) (Costa & McCrae,
1995), the Big Five Inventory (BFI) (Feldt, Lee, & Dew, 2014), and Saucier’s Mini-
Markers (Dwight et al., 1998). The literature examined each of the instruments by
identifying the instrument’s strengths and weaknesses, and comparing those attributes
against the study’s requirements in order to determine the most appropriate instrument for
this study, which is the BFI (Dwight et al., 1998; John & Srivastava, 1999).
Transactional distance measures were evaluated in a similar manner with the attributes of
Chen (2001), Huang (2002), Horzum (2011), and Sandoe (2005) identified and graded
against the proposed study’s needs. As a result, the Structure Component Evaluation
Tool (Sandoe, 2005) emerged as the favored instrument.
Theoretical Foundations and Conceptual Framework
Five-Factor Model of Personality. In a first of its kind review, Allport and
Odbert (1936) noted over 18,000 unique words extracted from the dictionary that are
useful for describing an individual. In order to manage this large list, the scientists
49
categorized the words into four general groups: personality traits, temporary states,
judgments of personal conduct and reputation, and physical characteristics (John &
Srivastava, 1999). Cattell (1956) addressed Allport and Odbert’s list of over 4,500
personality descriptors and began to organize the characteristics by broad, but unique,
categories, developing 20 primary clusters of personality descriptors, and then eventually
landed upon 16 personality factors, or the 16PF model. Cattell (1956) derived the 16PF
model through factor analysis of the descriptive traits, which described the trait
categories by lexical similarities. Words that tended to mean the same or that exhibited a
similar characteristic were grouped together. Using Cattell’s 16 factors, Tupes and
Christal (1992) tested eight large sample populations across the personality traits, and
then conducted factor analysis of the results. Through this testing, Tupes and Christal
identified five primary personality traits, which became known as the Big Five: surgency,
also known as extroversion, agreeableness, dependability, emotional stability, and
culture. It is also significant that the traits they identified are global descriptions that are
bipolar in nature along a continuous spectrum, suggesting that each trait describes a
characteristic that has two extremes and a continuum of values in between. For example,
surgency included one extreme of extroversion and the other as introversion.
Contemporary Big Five models were born from the work of Tupes and Christal
(1992). A current model is the Five-Factor Model (FFM), which describes that
personality descriptors can be grouped into one of five traits: Openness to Experience,
Conscientiousness, Extroversion, Agreeableness, and Neuroticism (McCrae & Costa,
2003). FFM was developed as a result of examining the covariance of descriptors, such
as those identified by Allport and Cattell, and determining the natural clustering of the
50
adjectives which describe an individual’s preferences and tendencies (Soto & John,
2012). McCrae and Costa (2003) describe each of the FFM traits in the following
manner. Openness to Experience describes the degree to which an individual is willing
to experience something new, whether it is an idea, a new food, an imaginative thought,
new art, or an activity. Conscientiousness describes the group of facets that explore an
individual’s competence, organization, dutifulness, deliberation, and planning for the
future. Extroversion clusters those facets of personality that describe an individual’s
social interactions, with interpersonal and temperamental traits including warmth,
gregariousness, assertiveness, activity, excitement seeking, and positive emotions.
Agreeableness embodies the characteristics of trust, compliance, and tender-mindedness.
The last trait, Neuroticism, addresses emotional states and expression, with key facets of
anxiety, angry hostility, depression, self-consciousness, impulsiveness, and vulnerability
to stress. FFM is widely accepted to account for natural personality variations between
people (McCrae & Costa, 2003; Thalmayer et al., 2011).
FFM is a lexical approach to provide a common language within the scientific
community and does not attempt to describe how an individual develops a personality.
However, FFM offers three criteria to support its validity (McCrae & Costa, 2003). First,
FFM suggests that an individual’s personality dimensions are summarized by a taxonomy
of five traits, and that any descriptor within the English language, or the many other
languages that have been tested, complies with one of the five categories (Soto & John,
2012). Second, FFM traits are measureable, and that using any of a variety of validated
and reliable instruments, an individual’s personality can be enumerated for comparative
purposes (Thalmayer et al., 2011). Lastly, FFM traits are stable across an individual’s
51
lifetime (Wortman et al., 2012). Although it is arguable whether personality is a
biological function (McAdams, Gregory, & Eley, 2013), an environmentally caused
attribute (Beijersbergen, Juffer, Bakermans-Kranenburg, & van IJzendoorn, 2012), or a
result of the interaction between genetics and environment (Winham & Biernacka, 2013),
the literature demonstrates that an individual’s personality remains stable across their
lifespan, with exceptions for individuals who experience neurological damage or disease
(Briley & Tucker-Drob, 2014; McCrae & Costa, 2003), and within temporary state
changes based upon situational circumstances (Yeager et al., 2014).
Each of these three criteria was significant to this study. In order to compare
personality traits, which were variables in this study, to the learner outcome variable, all
recognized personality traits had to be accounted for in order to determine whether or not
personality traits shared a relationship with the learning interaction. It was equally
important that each trait was measurable in order to quantitatively assess the relationship
between the trait and the learner outcome variable. Stability of personality traits is
important for individual learners in order to provide consistency of learning environment
preferences, which is a condition that affords the individual the opportunity to maximize
learning success (Hsieh, Lee, & Su, 2013).
Transactional Distance Theory. In the early 20th century, as formalized
education began to take root, educator and philosopher John Dewey opined that humans
are social by nature and derive a sense of self from interactions with others and
environment (Mason, 2013). Dewey went on to describe an individual’s interaction with
others and their surroundings as transaction, an interplay resulting in the individual
experiencing either a sense of connection or distance based upon situational factors. The
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concept of transaction continued to develop within education, spawning theories of
experiential learning (Ord & Leather, 2011), and social constructivism (Willey & Burke,
2011), each of which described that learning occurs as a measure of the quality of the
interaction between the individual and the learning environment.
The distance-learning environment presented a new set of circumstances for
consideration within the context of Dewey’s transaction. Whereas Dewey’s
circumstances were considered within the context of individuals being in the presence of
others, distance learning created a new format of presence, and, subsequently, a new form
of distance. Michael Graham Moore addressed this new phenomenon within the Theory
of Transactional Distance. Because the learner and instructor are physically separated
within the distance-learning environment, each must cross a psychological and
communication space in order to create the interplay necessary for learning (Moore,
1993). This psychological and communication space leaves the potential for
misunderstandings and lack of engagement, necessitating special patterns of behavior in
order to bridge the divide. Moore (1993) described this psychological and
communication space as transactional distance.
Transactional distance (TD) is a measure of the relative relationship strength
between the learner and the instructor, and is dependent upon the elements within the
learning situation; namely, the behaviors of the learner and the teacher, and those factors
within their mutual environment (Moore, 1993). While TD is able to measure the
relationship strength in a face-to-face environment, it was developed with geographical
separation in mind (Reyes, 2013). Moore’s (1993) Transactional Distance Theory (TDT)
53
identifies three interrelated clusters of behaviors and factors that demonstrate influence
within the relationship and govern TD: dialogue, structure, and learner autonomy.
Dialogue. Dialogue describes the broad spectrum of purposeful, positive, and
synergistic interactions between the learner and the instructor (Moore, 1993). Dialogue
connotes the idea of multi-directional communication for the purpose of clarifying,
understanding, and furthering the learning of the student, and does not include the act of
programmed content delivery (Giossos et al., 2009). The learning interaction depends
significantly upon the ability of the learner to communicate with the instructor, and, as a
result, is dependent upon the structure of the curriculum, a relationship addressed in the
discussion of structure (Chen, 2001). Additionally, dialogue is influenced by
environmental factors, such as the number of students to whom a teacher must tend, the
frequency of opportunity for communication, the emotional environment provided by the
instructor, and the psychological disposition of the learner (Moore, 1993). Specifically,
Moore (1993) addressed that dialogue was influenced by the personality of the teacher
and the learner, a concept relevant to this study.
Structure. Structure refers to instructional design by which the curriculum is
delivered to the learner via the prescribed communication medium (Ustati & Hassan,
2013). Concepts, such as the flexibility of the instructional design to adjust to the
learner’s needs and the ability for the technology to accommodate the instructional
design, are included within the structural taxonomy, as are pedagogical considerations of
educational objectives, learning content, assessment activities, and addressing student
motivation (Horzum, 2015). Structure and dialogue demonstrate a consistent inverse
relationship in distance learning in which high structure environments produce low
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dialogue opportunities, while low structure designs encourage dialogue (Larkin &
Jamieson-Proctor, 2015). Highly structured environments, such as traditional video
delivery of content, account for each element of content and time with little opportunity
for deviation from the curriculum (Benson & Samarawickrema, 2009). High structure
environments are associated with large TD due to the inability to shift instruction based
upon learner needs (Park, 2011). On the other hand, low structure designs allow for
broad flexibility within the course, including varied frequency and size of content
delivery, altering syllabus direction to expand upon topical concepts, and adjusting
content based upon learner inputs. Due to the capacity for improved understanding and
clarification based upon learner feedback, low structure environments are associated with
small TD (Benson & Samarawickrema, 2009; Park, 2011).
Learner autonomy. Learner autonomy addresses two principle concepts within
the learning environment. The first is the amount of flexibility a learner is provided by
the learning structure to determine learning objectives, create knowledge, and achieve
goals (Moore, 1993). This first concept demonstrates the strong relationship between
structure and learner autonomy in which a highly structured environment imparts low
learner autonomy, whereas a low structure environment allows for learners to choose
syllabus make-up, learning activities, and resources, demonstrating high learner
autonomy. The second concept of learner autonomy includes the psychological view of a
learner’s willingness or ability to be self-directed (Liu, 2015). Learner autonomy
requires that the learner possess the skills and experience to engage in independent study
as well as to be suitably motivated, organized, and open to self-study. Both concepts—a
facilitating structure and a psychologically prepared learner—are essential for high
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learner autonomy. High learner autonomy is associated with low structure environments
and low dialogue environments, resulting in high transactional distances (Park, 2011), as
there is less interaction between the learner and the instructor.
Although Dewey utilized the term transaction to describe the interplay between
the learner and the classroom learning environment (Mason, 2013), Moore’s (1989)
Transactional Distance Theory utilized the term interaction to describe the same
phenomenon within the distance learning environment. Moore identified three learning
interaction types that may exist within the distance learning and e-learning environments:
learner-instructor, learner-learner, and learner-content (Anderson, 2003; Moore, 1989).
The learner-instructor interaction describes a relationship between two people in
hierarchical roles in which the instructor provides feedback, dialogue, and motivation,
which is most commonly associated with the traditional teacher-student roles. The
learner-learner interaction describes the exchange of information between peers, which is
typical within social learning environments and online discussion groups. The learner-
content interaction describes the exchange of intellectual information between a learner
and the material, such as a computer application, online materials, or video source.
Chen (2001) introduced a fourth interaction, the learner-interface relationship, to
account for the influence of communication devices and software interfaces that regulate
the learner’s interaction with the instructor, content, and peers. A learner may engage in
multiple interaction types within a single learning environment based upon the dialogue,
structure, and learner autonomy afforded the learner by the instructional design (Moore,
1993). As a result, the phrase learning environment is consistently used throughout the
literature to describe the setting in which the four types of possible learner interactions
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transpire (Chen, 2001; Ustati & Hassan, 2013). When a specific relationship type is
salient to the discussion, it is uniquely identified.
Understanding TDT assists in identifying the characteristics of each learning
environment, including the asynchronous video e-learning structure, which subsequently
offers the opportunity to relate these characteristics with learner personality traits.
Defining a learning environment’s factors of dialogue, structure, and learner autonomy
provides a standard by which each learning environment’s characteristics may be
compared, providing greater insight into the relationship between learning environment
characteristics and learner personality traits. The ultimate goal is to develop a
compendium of environmental circumstances that best match with each combination of
learner personality traits in order for instructional designers to develop courses intended
to maximize a learner’s outcomes. This study examined the relationship of personality
traits with transactional distance within the asynchronous video e-learning environment.
TDT provided the opportunity to categorize the dialogue, structure, and learner autonomy
elements of the video environment so that this and future research may compare the
relationship between personality traits and TD with specific levels of dialogue, structure,
and learner autonomy, offering predictive capabilities as future pedagogical and
technological methods emerge that exhibit similar characteristics.
Review of the Literature
This literature review examines individual preferences for interacting within a
learning environment with the purpose of understanding individual characteristics that
influence a learner’s interaction within online environments, and for the purpose of
informing curriculum design in the online environment. The review begins with an
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exploration of current learning themes, which includes learner interaction and learning
environment constructs through active learning (Lucas et al., 2013; Thomas & Macias-
Moriarity, 2014) and learning styles (Bhatti & Bart, 2013; Black & Kassaye, 2014). It is
through this initial discussion that the concept of the learner interaction as a salient
variable evolved.
Various learning environments are then explored with a focus on the learner
interaction, yielding evidence that the learner’s satisfaction with the learning environment
varies with factors specific to each learning setting (Islam, 2012; Secreto &
Pamulaklakin, 2015). The learning interaction discussion begins with a thorough
exploration of the online environment through the lens of TDT, including the interaction
types and TD factors that explain learning outcomes (Ali, Ghani, & Latiff, 2015; Hsia et
al., 2014; Papadopoulos & Dagdilelis, 2007). TD is defined as the measure of the online
interaction quality and intensity (Ustati & Hassan, 2013) and satisfaction (Horzum, 2011)
and, consequently, is a variable of interest within the literature. Various online delivery
settings are explored, concluding with the various uses of video technology within the
online environment (Barkhi & Brozovsky, 2003; Falloon, 2011; Ljubojevic et al., 2014).
With the second variable of TD and the characteristics of learning environments defined,
the discussion transitions to an exploration of psychological constructs that may be
related to a learner’s interaction choices.
The discussion develops the relationship of FFM personality traits with other
psychological constructs (Batey, Booth, Furnham, & Lipman, 2011; Caprara et al., 2011;
Hetland et al., 2012; Hertel et al., 2008). The review then explores the confluence of
personality traits with active learning environments, learning styles, and various online
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learning environments, demonstrating personality as a variable that correlates with the
learner interaction within each learning environment (Bolliger & Erichsen, 2013).
Asynchronous video e-learning is identified as an environment in which the learner
interaction as a function of personality traits has not been explored. However, two
personality traits, Extroversion and Agreeableness, are shown as being related to
behavior within other video environments (Barkhi & Brozovsky, 2003; Borup et al.,
2013; Maltby et al., 2011; Tsan & Day, 2007), suggesting that this research examine their
effects as variables within the asynchronous video setting.
The chapter continues by clearly stating the gap in the research of the relationship
between personality traits with transactional distance within the asynchronous video e-
learning environment was unknown, and that exploration into this missing evidence was
warranted (Bolliger & Erichsen, 2013). With the research gap identified, the review
transitions to examining methodologies and research designs used to examine
relationships between personality traits and individual behaviors (Blignaut & Ungerer,
2014; Pretz & Folse, 2011; Reyes et al., 2015; Rumrill, 2004). Research instruments are
then discussed in order to identify the appropriate tools for addressing the gap in the
research (Chen, 2001; Costa & McCrae, 1995; Feldt et al., 2014; Horzum, 2011; Huang,
2002; John, 2009; John & Srivastava, 1999; Dwight et al., 1998; Sandoe, 2005). The
chapter concludes by introducing the need to detail the selected methodology and
research design necessary to explore the issue.
Characteristics of learning. Much of the current literature exploring learning
theory centers around constructivist themes with a focus on interaction between the
learner and the learning environment (Ustati & Hassan, 2013). The quality of the
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experience is thought to influence learner performance (Mason, 2013). One
constructivist approach is active learning, which describes individuals who engage in
learning activities demonstrating increased performance and skills (Ito & Kawazoe,
2015).
Active learning. Lucas, Testman, Hoyland, Kimble, and Euler (2013) sought to
determine the effectiveness of active learning strategies in a series of courses.
Participants included 70 fourth-year students in a doctoral of pharmacology program who
participated in three pharmacotherapy courses. The first course was a lecture-based
course, and the second and third courses used active learning strategies. A
comprehensive exam was given that included questions specific to knowledge from each
course. The results indicated that performance in the lecture-based course was not as
strong as performance of knowledge based on the active strategy courses. The results
suggest that learners that actively engage with the content demonstrate higher levels of
knowledge performance than those that are only consumers of the content. A limitation
of the test includes the temporal distance between the first class and subsequent classes,
resulting in decayed performance on specific knowledge.
However, not all active learning environments produce superior results. Thomas
and Macias-Moriarity (2014) examined the effectiveness of active learning in a clinical
toxicology course used to satisfy requirements for a doctor of pharmacology degree. The
graduate students (N = 45) participated in the quantitative method, quasi-experimental
design study in which both the instructor and students presented course topics. In
addition to participating in peer-to-peer presentations, learners were required to engage in
classroom activities of developing classroom quizzes, rating the presenters, and asking
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questions of presenters. In a comparison of posttest scores, student-presented topic
scores and instructor presented material scores were nearly identical, indicating similar
results regardless of whether or not the learners were actively participating in the learning
activity. Learner-oriented factors, such as motivation and intelligence, amongst others,
may have influenced other behaviors leading to test performance.
Learning styles. Another constructivist approach to improving learning outcomes
and describing learner interaction is matching the individual’s learning style with the
instructional environment. Learning style is based upon Kolb’s four approaches to
learning, which describe the learner’s preferences in assimilating knowledge (Chen et al.,
2014). Although learners may exhibit characteristics of any learning style, they tend to
demonstrate a preference for one of four styles: Diverger, Assimilator, Converger, or
Accomodator. Divergers tend to watch and feel, or sense, the instruction and reflect upon
the information shared. Assimilators watch and think, showing an ability to
conceptualize abstract thoughts. Convergers share thinking and doing traits, formulating
an idea of the new knowledge, and then put it into practice. Accommodators integrate
doing and feeling, preferring hands-on experience to determine a comfort level with the
material.
Attempts to correlate learning styles with performance have met with mixed
results. Bhatti and Bart (2013) used the traditional university classroom to examine
whether learning style was predictive of academic achievement. Participants (N = 193)
completed the Kolb learning styles inventory and granted access to school records to
obtain GPA information. GPA reflected course grades across a broad spectrum of
classes, mitigating student preference and self-efficacy within a particular subject. The
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results indicated that learning style was statistically significant in determining GPA, with
Convergers garnering the highest scores, followed in order by Assimilator, Diverger, and
Accommodator learning styles.
Black and Kassaye (2014) explored the influence of course design on student
performance in order to determine if learning styles are related to course design.
Students (N = 563) at a large university were enrolled in business classes with three
different instructional styles. The traditional course used typical classroom pedagogy of
lecture and quiz to present information and assess uptake, a format representing limited
interaction. The experiential design engaged learners in practical experiences related to
occupations covered by course content. Experiences included exercises, writing
assignments, and case study of related topics, representative of high interaction. The
participative design allowed learners a great deal of autonomy in selecting the conduct of
the class, including syllabus design, grading options, learning objectives, and classroom
participation models. Learning styles of the students were measured in accordance with
Kolb’s learning stages: concrete experience (CE) learners, reflective observation (RO)
learners, abstract conceptualization (AC) learners, and active experimentation (AE)
learners. It is noteworthy that although Black and Kassaye elected to describe learners by
the learning stage, Kolb learning styles are typically referred by the processes that occur
between the learning stages, such as the Assimilator, which describes the process of
moving from observation and reflecting to abstract conceptualization. Results showed
learner performance in experiential design courses was better than in traditional design
courses. Additionally, in these courses, learners in the experiential design courses held
more positive perceptions of course conduct than did learners within the traditional
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courses. Finally, the participative design resulted in more positive student perceptions of
course conduct and higher learner performance than the experiential design. These
results suggest that the learning environment—traditional, experiential, or participative—
exhibits a significant influence on learner performance and attitudinal outcomes. The
conclusions of the study are two-fold. First, active course designs, such as experiential
and participative course designs, are either equivalent or more effective for student
outcomes than traditional designs. The second conclusion is that learning styles
influence outcomes based upon the learning environment. CE learners, for instance,
favored environments that offered engagement and interactivity, which is expected.
However, traditional designs offer enough interaction such that differences in learner
outcomes based upon learning styles are not significant. It is noteworthy that differences
in learner performance were not statistically significant for any of the learning style
conditions within any learning design.
Moayyeri (2015) examined the influence of undergraduate students learning
preferences on language achievement. Participants (N = 360) were undergraduate
students from different academic disciplines at four Iranian universities. A correlational
design was used to examine the relationship between learning style and language
achievement. The VARK questionnaire was used to determine students’ learning style,
using visual, aural, read/write, and kinesthetic as the modalities of interest. A
standardized language proficiency test was used to evaluate learning performance.
Results showed that learning style differences were significant in determining learning
outcomes. Study conclusions suggest that learning style for Iranian university language
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learners influenced learning performance, and, more broadly, that learning styles
influence overall performance within certain environments.
Conclusions from Moayyeri (2015) are supported by Hwang, Sung, Hung, and
Huang (2013), which correlated learning styles with academic performance within the
online learning environment. With the goal of showing the importance of adaptive
learning systems based upon learning styles, the researchers presented 288 Taiwanese
elementary students with a choice of online games based upon natural science content.
The two versions of the online game represented the same content, but were presented
with either an autonomous learner condition or a high level of structure. Students were
tested for learning style preference and given a pretest on the material. End of unit
performance was measured with a unit test. The results showed that students whose
learning style matched the style of game they selected experienced greater improvement
of performance scores compared to students whose learning styles did not match the
game style they selected. These results suggest that a characteristic of learning styles in
combination with learning environment conditions influence learner outcomes.
Richmond and Conrad (2012) investigated the relationship between online student
thinking styles and academic performance. Participants (N = 187) were undergraduate
psychology students from seven different classes across three universities. The
correlational design measured 13 independent variables of learning style using the
Thinking Style Inventory (TSI), of which four were significant in determining GPA. The
results show that learning styles positively predicted GPA based upon style type within
the online psychology class environment. Instructional design applications are drawn
from the results, suggesting that course developers consider student learning styles when
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analyzing course requirements and designing the curriculum. A recommendation for
future research included investigating the relationship of learning style and learning style
factors in comparison to other performance indicators besides GPA. Learning style
factors, as this chapter later develops, include learner personality traits.
Not all studies demonstrate a relationship between learning style and academic
performance. Hsieh, Mache, and Knudson (2012) investigated the effect of learning style
preferences on performance on multiple-choice examinations. Participants (N = 90) were
students enrolled in a biomechanics class at a state university, who responded to the
VARK Learning Style Inventory to determine learning style preferences. Multiple-
choice exams were given, each reflecting a specific learning style (e.g., kinesthetic
diagrams or text-based descriptors). The results indicated no significant differences in
test results within differing learning style preferences for text-only and kinesthetic
diagrams. Hsieh et al. suggested that learning style might be more accurately called
learner preference, referring to the format the learner enjoys the most rather than the
approach most suited for knowledge acquisition. As developed in personality trait
theory, learner preference is a construct of personality trait taxonomy. The results of the
learning style literature suggest that learning style as a determinant of performance is
inconclusive, suggesting that learning style influences learner satisfaction, but does not
influence performance (Kim, 2013). As such, it is important to investigate a more
fundamental psychological construct that may correlate with the learning environment to
encourage learning.
Learning environments. Within the exploration of active learning and learning
styles were a number of different learning modalities. Lucas et al. (2013), and Thomas
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and Macias-Moriarity (2014) explored active learning within traditional classroom
settings, and Hwang et al. (2013) examined learning styles within an online environment.
Moayyeri (2015) and Hwang et al. showed that learning modality influenced learner
outcomes, suggesting that conditions within each learning environment exhibit
characteristics that uniquely influence the learner and that these characteristics should be
further explored. The following section explores three learning environments: face-to-
face, online, and hybrid, revealing the influences of these settings while developing the
measure of perceived learner interaction within the online environment as a variable.
Face-to-face. Hauser et al. (2012) examined the relationship between
transactional distance, computer self-efficacy, and computer anxiety on performance of
computer related-tasks within the face-to-face environment with some participants
sampled from the online environment. Using a quantitative method, correlational design,
the authors measured anxiety, computer self-efficacy (CSE), and transactional distance.
The sample population (N = 240) was from a junior level management information
systems university class and was biased towards the face-to-face environment with 205
participants, with an additional 35 online learners participating. The authors determined
correlational factors for the anxiety-CSE-performance relationship within each learning
environment. Within the face-to-face environment, significant relationships occurred
between each of the variables, which were TD, anxiety, general CSE, and specific CSE.
Additionally, general CSE and specific CSE were related to performance. Within the
online environment, similar relationships were shown, except that no relationship existed
between anxiety and specific CSE. The results described that the strength of the learner’s
interaction with the learning environment influences psychological constructs, which, in
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turn, is related to learning performance. Additionally, the results indicated that
psychological constructs, such as anxiety, changed based upon the learning environment.
Online learning. Constructivist approaches within the e-learning and distance
learning environments are addressed by Transactional Distance Theory (Moore, 1989,
1993; Ustati & Hassan, 2013). Supporting the approach that the quality of the interaction
between learner and the learning environment is determinant of learning outcomes, TDT
states there are three learning characteristics for examination, including the learner, the
instructor, and the interaction between the two (Chen, 2001; Moore, 1989). Additionally,
Moore (1993) suggests within each distant learning environment that there are three
factors that influence the interaction strength: dialogue, structure, and learner autonomy.
TDT also states that these interactions may take on any combination of four forms:
learner-instructor, learner-learner, learner-content (Moore, 1993), and learner-interface
(Chen, 2001). The following section discusses learner interactions within differing
learning environments, while emphasizing the environmental learning factors of
dialogue, structure, and learner autonomy.
Dialogue. Zhou (2014) examined dialogue through the effectiveness of instructor
interaction with learners in a global business project. Students (N = 112) were
international graduate students from a variety of countries and who spoke different
languages. The course promoted discussion and reflection upon real-world business
problems. The research question involved determining whether students’ language and
cultural learning outcomes improved with the aid of a faculty language mentor as
compared to an environment having no mentor and greater autonomy. As measured by
the course survey using a five point Likert-type scale, there was a significant difference
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in student language learning effectiveness when learners had the assistance of a language
advisor to facilitate dialogue when compared to an autonomous learner. The research
indicated that the ability to communicate with the instructor and with peers leads to
greater interactive effectiveness and stronger performance, results supported by TDT
(Chen, 2001; Moore, 1989, 1993; Ustati & Hassan, 2013).
Dialogue between peers is another condition conducive to increased interaction
and decreased pedagogical distance (Moore, 1989, 1993). Wang and Morgan (2008)
examined student perceptions of the learning environment when instant messaging
software afforded peer-to-peer communication within an online graduate school
environment. Online learners were responsible for preparing a chapter of the course
content and discussing the themes via instant messaging. A repeated-measures design
was conducted to compare student perception of the study conditions between a non-
instant messaging environment and an instant messaging environment. Results indicated
significant differences between conditions for student cooperation, active learning,
contact with instructor, and prompt feedback, demonstrating that learners feel a closer
communication distance when using messaging technology within peer-to-peer and
learner-to-instructor environments as compared to non-instant messaging environments,
demonstrating a preference for instant messaging-enabled environments.
Ali, Ghani, and Latiff (2015) explored the learner-content relationship through the
study of effectiveness within a personal learning environment (PLE), in which e-learning
content is served to the learner based upon learner preferences. The problem Ali et al.
addressed is the issue of cold start, in which the content delivery system knows nothing
about the learner. Ali et al. described a proposed system in which the learner selects
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metatags that describe the learner’s interests when registering for the course. These tags
were then compared to historical learner preferences, which allowed the system to
recommend content based upon others’ experiences. Ali et al. used an online dataset of
movie viewers (N = 71,567) to test their system. Participants interacted with the content,
which measured learner interest and provided more or less of the same style of content
based upon participant feedback. The results showed that as viewers interacted with the
content, the precision of the content served, which is the presentation of relevant content,
increased, while content recall, which is the presentation of irrelevant material,
decreased. Increased learner-content communication shortened the pedagogical distance,
as the content was able to deliver information relevant to the learner.
Secreto and Pamulaklakin (2015) assessed learner satisfaction with an e-learning
interface. Feedback was solicited from undergraduate and graduate students (N = 147),
who were involved in online education at the University of the Philippines Open
University. The user interface served as a gateway to the learner’s online education, both
as the content delivery mechanism and as the administrative portal. The mixed-method
design used an online survey to measure learner satisfaction with the portal’s usefulness,
appearance, efficiency, functionality, ease of use, security, and completeness.
Approximately 90% of total participants reported that the online portal was more cost-
effective, time-efficient, and convenient than using in-person transactions for university
administrative functions. Learner satisfaction levels were high in the areas of response to
inquiries, administrative support areas, availability of contact information, simplicity and
clarity of instructions, reliability of networks, and asynchronous access. Other functional
areas received similar high satisfaction ratings of either satisfied or very satisfied,
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including usefulness, functionality, efficiency, appearance, ease of use, and
completeness. Based upon written feedback, the learners valued the system as it
provided communication channels to administration, provided timely academic grade
results, and distributed relevant news. According to the authors, the learner portal
delivered a beneficial learner-interface relationship that narrowed the pedagogical
distance between the learner and the university, and the portal served as a gateway
between the learner and the content, instructor, and peers.
Structure. The learning structure defines the pedagogical and technological
boundaries of the e-learning environment, and, as a result, is interrelated with dialogue
and learner autonomy. Papadopoulos and Dagdilelis (2007) studied the formation of
transactional distance within an elementary geometry class based upon structural
restrictions of computer-assisted instruction. Using qualitative methodology, the
researchers provided 5th and 6th grade students a geometry problem and assigned each
student a software program designed to assist in learning the mathematical concept. The
researchers then observed students’ interactions with the software to assess the perceived
transactional distance. Papadopoulos and Dagilelis noted that five barrier types within
this environment inhibited interaction. Each structural obstacle created a wider distance
in the interaction, contributing to a larger transactional distance. The results also
reinforced the definition of structure in which the learning environment, whether
autonomous or restricted, defines the boundaries the learner must maintain and delineates
the allowable level of interaction.
A psychological boundary of a system’s structure is the perceived quality of the
environment. Islam (2012) investigated the role of perceived system quality in users’
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choices to continue using an e-learning system. The results indicated that perceived
system quality and perceived usefulness account for a majority of the variance of
satisfaction, which, in turn, significantly influenced continuance intention. A notable
non-significant result was that perceived system quality was not directly related to
continuance intention. In general terms, the study showed that the learning
environment’s structure influenced learning behaviors and attitudes, including a
willingness to continue interacting with the system.
Learner autonomy. The last of the three individual factors that influence
transactional distance is learner autonomy, which describes the flexibility a learner has in
selecting learning objectives, content, and activities. Benson and Samarawickrema
(2009) investigated the influence of learning supports, which are the concepts that govern
the level of learner autonomy within a learning environment. Using a qualitative method,
case study design, the authors examined six cases with widely varying distance-learning
environments to determine the level of dialogue, structure, and autonomy, with the
ultimate purpose of using this information to inform instructional design. A conclusion
Benson and Samarawickrema reached is that certain circumstances, such as low dialogue
and low structure and high dialogue and high structure dictated the level of transactional
distance regardless of the learner autonomy. Learner autonomy was more influential in
determining transactional distance in mixed environments, such as low dialogue and high
structure, and high dialogue and low structure.
Learner autonomy also refers to the willingness of the learner to be self-directed
within the e-learning environment, concepts highly correlated to self-efficacy (Bullock-
Yowell, Peterson, Wright, Reardon, & Mohn, 2011) and locus of control (Duman & Sen,
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2012). Hsia, Chang, and Tseng (2014) examined this construct as they explored the
feasibility of the technology acceptance model (TAM) to explain employee acceptance of
e-learning systems. The results determined that internal locus of control, which is the
perception that events are under the control of the individual, had a positive effect on
perceived usefulness of the e-learning system, and that internal locus of control had a
positive effect on perceived ease of use. Self-efficacy was positively related to perceived
ease of use and intention to use. The results suggest that there is a strong relationship
between some psychological constructs and a learner’s willingness to interact with the
learning environment.
Although a number of studies have been conducted to investigate the individual
factors of transactional distance, other studies examined complete systems, which is the
construction developed by the integration of dialogue, structure, and learner autonomy.
The overall purpose of these studies is to determine the characteristics of the selected
environments and to establish the effectiveness of the chosen interaction level to
encourage learning. A concept that emerges from the study of learning environments is
that psychological factors appear to be related to transactional distance, and that each
learning environment consists of a unique combination of TD factors.
Computer-aided instruction. Murphy and Rodríguez-Manzanares (2008)
researched the effectiveness of high school distance education (DE) as measured by
transactional distance theory using case study methodology. Results indicated successful
academic performance and learner satisfaction in DE requires building rapport and
community in the e-classroom, to which there are many obstacles both within the system
and by way of student and instructor personality. Students reported mixed perceptions as
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to whether they were more successful in an asynchronous environment compared to a
synchronous environment, with relationship skills as a proposed explanation for the
difference.
Kizilcec and Schneider (2015) examined the effects of motivation types on
learning behavior outcomes within the online learning environment. The researchers
conducted a quantitative method correlational design with 71,475 participants from 14
Stanford University massive open online courses (MOOC), which are free, non-credit
courses available to the public. Results indicate that motivational intentions were
predictive of student behavior within the online classes. Individuals that expressed
scholastic or professional motivations (e.g., relevant to current studies, professional
advancement, and professional certificates) completed high percentages of the optional
assignments, but participated in few discussion posts. Participants whose motivations
were ego or socially-oriented (e.g., prestigious university, participate with others, and
meet new people) completed few assignments, but responded to at least 50% of
discussion posts. The authors reflected that motivational intentions influenced learner
behavior within the autonomous MOOC environment, with learners selecting the
activities they thought would most benefit their goals.
Video learning. The previously discussed studies examined emerging pedagogies
and technologies of their era. An old technology that continues to be technologically
improved for e-learning is video. This section explores the traditional use of video within
the classroom as a supplementary material (Ljubojevic et al., 2014), video’s evolution as
a two-way communication format (Falloon, 2011), video’s use as a primary instructional
source (Kim & Thayne, 2015; Simonds & Brock, 2014), and then its transition to hybrid
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environments (Moffett & Mill, 2014; Velegol et al., 2015). The section concludes by
exploring the literature on the emerging technology of asynchronous video e-learning
(Vural, 2013).
Ljubojevic, Vaskovic, Stankovic, and Vaskovic (2014) explored the efficiency of
use of supplementary video content in multimedia teaching within the face-to-face
classroom. The experimental design used one of seven experimental conditions: class
lecture with no video, class lecture with related educational content video at the
beginning, middle, or end of the class, and class lecture with entertainment
supplementary video positioned at the beginning, middle, or end of the class. The results
indicated that video enhanced learning within the classroom, regardless of the type of
video, but that video related to the content and that was played in the middle of the
instruction resulted in the highest level of learner performance. The authors suggest that
the video medium enhances the learning experience.
Falloon (2011) addressed students’ perceptions of the virtual classroom’s effect
on relationship formation and communication with instructors and peers using qualitative
methods, and investigated which aspects of the classroom most affected students’
engagement in the virtual classroom. The virtual classroom was a synchronous online
communication system that allowed students to see and hear the material, instructors, and
peers within the class in real time. The interpretive case study method found that many
learners built trust and rapport between peers and with the instructor within the
synchronous video environment due to the high quality of the interaction that comes with
real-time video conversation. Communication was effective because students could see
facial expressions and hear tone of voice within conversations. Improved relationship
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strength, as identified by trust and rapport, led to shorter transactional distance.
However, some participants expressed reluctance in the virtual environment, citing
concerns over “looking silly” (p. 205) or needing time to reflect prior to responding to
classroom discussion. Falloon identified the lack of understanding about the interaction
effect of personality within a synchronous learning structure upon transactional distance
as a limitation of the study. Falloon suggested student preferences and personality may
be considered an influencing factor of dialogue and learner autonomy.
Kim and Thayne (2015) examined relationship-building strategies for
asynchronous video-based instruction. Using experimental design, the researchers were
interested in whether the learner-instructor relationship could be developed through the
asynchronous video medium. The investigators use a two-group repeated measures
design to compare the treatment conditions and time upon learner attitudes, learner self-
efficacy, and learning performance. The results showed that video instructors that
intentionally exuded warmth and caring, and that used personal, relatable examples
engendered more favorable attitudes from learners than straight-forward, unemotional
instructors. The inclusion of affective traits by the instructor maintained a preferable
attitudinal state in the learners, illustrating the moderating influence of the learning
environment and a factor for instructional design consideration. Learner attitudes
correlate with personality traits, suggesting that personality may influence learner
satisfaction and continuance within the course. It is noteworthy that no significant effects
were seen within the two video conditions for learner self-efficacy, learner-instructor
relationship, module completion, or learning gains.
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Individual attitudes and perceptions are also influenced by humanlike
characteristics exhibited by non-human entities. Using a repeated-measures design
ANOVA, Broadbent et al. (2013) examined the differences in perceptions of robot faces
by patients during medical procedures. Medical robots designed to perform basic
functions, such as taking blood pressure, were configured with one of three video screen
faces to look like a human face, silver face, or no face. Patients rated the robot’s
personality, mind, and eeriness in each condition. Robots with the human face on the
screen were rated has being almost humanlike, alive, sociable, and amiable. The results
support theory of mind principles that individuals assign human characteristics, feelings,
and associated attributes to non-human objects when a human characteristic, such as a
face, is displayed. Patient perceptions of the humanlike robot led to greater trust, higher
perceived capabilities of the machine, a sense of agency on behalf of the robot, and a
higher sense of relationship between the patient and the care-giving robot.
Simonds and Brock (2014) explored age-based learning preferences in online
video courses. The mixed-methods design surveyed learners about their learning
preferences within various e-learning environments. The results were statistically
significant for differences in e-learning preferences based upon learner age, with older
learners preferring to watch archived lectures asynchronously and preferences for
watching prerecorded video lectures. A salient learner comment was, “Instructor
comments and videos help one to feel more connected when the face-to-face aspect is not
present with this type of learning” (p. 10). Within interviews, the young learners
expressed a greater interest for learning activities, such as discussion group comments,
interactions, and synchronous interactions, over asynchronous video lecture. The results
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of this study inform future design considerations when creating curricula for broad
audiences. The research also informs the present study of the potential for age to be a
confounding variable.
Vural (2013) investigated the effect of activity-based video e-learning on student
achievement. The quasi-experimental design compared learner performance following
learners watching an online instructional video with standard playback controls and no
required interaction, and learners watching online videos in which interactive questions
were embedded into the video, requiring the student to accurately respond to content-
related questions in order to continue viewing. The results showed statistically
significant differences in learner performance with learners who engaged with interactive
learning performing better on the end of course quiz than learners experiencing only the
lecture. The results are in alignment with transactional distance theory (Moore, 1993), in
which environments that support greater learner-learning environment interaction, thus
reducing transactional distance, lead to higher performance.
The examination of the video environment revealed that a variety of individual
learner factors, such as personality, level of control, and age, play a role in the
development of a relationship between the learner and learning environment.
Hybrid environments. Video technology played a role in developing a specific
type of blended learning environment known as the flipped classroom (McCallum,
Schultz, Sellke, & Spartz, 2015). According to Gross, Marinari, Hoffman, DeSimone,
and Burke (2015), the flipped concept emerges from an inversion of traditional classroom
models where content is delivered in the classroom and the learner independently
accomplishes the learning activities (e.g., homework). Within the flipped classroom
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environment, content is delivered in an independent, asynchronous manner to the learner,
and then learning activities, such as assessments, projects, and writing, are accomplished
in the classroom environment with the benefit of instructor and peer scaffolding.
Velegol, Zappe, and Mahoney (2015) examined the flipped classroom through
evaluation of students’ interactions, preferences, and performance. Using case study
design, the researchers examined two versions of the flipped classroom. The first version
of flipped classroom used recordings of in-class lectures to create 40 videos of 50-
minutes time each. The second version used professional production techniques to create
11 self-contained modules, each with seven to 18 short video segments, with a maximum
length of 20 minutes time. The results indicated that learner engagement with the content
was strong regardless of flipped classroom version. Learners regularly re-watched videos
when the content was unclear. When attendance in class was optional, students tended to
attend classes to participate in activities, indicating a preference for using the in-class
time for problem solving rather than listening to lectures. Learners also preferred shorter
video lengths—10 minutes or less—even though they were required to watch more
videos. Learning performance as measured by final exam grades across semesters
showed no significant difference between traditional and flipped classroom methods.
When given a choice between taking a traditional class or flipped class in the future, over
three-quarters of students stated they would prefer the flipped class. Students expressed
three reasons for preferring the flipped class: flexibility in learning, the ability to re-watch
lectures, and instructor and peer interaction for homework problem solving. Student
responses highlight the influence of learner autonomy and the presence of dialogue in
determining the level of learner interaction, functions determined by the learning
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structure and availability of dialogue. The authors recommended measuring additional
psychological constructs in flipped environments and using quantitative measures to
triangulate their research.
Moffett and Mill (2014) evaluated the use of the flipped classroom approach on
the effectiveness of training. In this experimental design, 197 postgraduate veterinary
students participated in both a traditional classroom course and a flipped classroom
course with video-delivered content teaching separate topics. The results indicated
statistically significant differences with traditional classroom learners showing better
performance than the flipped classroom learners. There were statistically significant
differences between student preferences between the two environments, with learners
favoring the flipped classroom. Despite preferences for a flipped classroom, learner
performance was better in the traditional format, a difference that may be explained by
the disparity between the two course topics.
Psychological constructs in the e-learning environment. Evidence of
psychological constructs is threaded throughout the reviewed literature. Murphy and
Rodríguez-Manzanares (2008) cited relationship skills as a potential factor in
strengthening transactional distance, Falloon (2011) noted the potential relationship
between personality and the willingness to interact within a two-way video environment,
and Velegol et al. (2015) and Moffett and Mill (2014) showed that learner preferences
swayed attitudes towards learning environments. This section reviews the literature
related to principle psychological constructs represented in the learning-based literature.
The review examines the literature associating personality traits with interactions (Kickul
& Kickul, 2006; Kim, 2013; Orvis et al., 2011), which leads to the establishment of
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personality traits as primary variables for use in examining learner engagement in various
learning environments. Other psychological constructs, such as attitudes (Broadbent et
al., 2013; Kim & Thayne, 2015), self-efficacy (Caprara et al., 2011; Hsia et al., 2014),
and motivation (Batey et al., 2011) are shown to co-vary with personality traits,
confirming the use of personality traits as a variable.
Personality traits. Personality traits have been shown to predict learner
interaction and behavior within a variety of environments. Hertel, Schroer, Batinic, and
Naumann (2008) examined the role of personality traits Extroversion and Neuroticism on
media preference for communication. Media that is rich has the ability to communicate
in a timely manner and the availability to interpret communication cues surrounding the
message. Formats with low media richness include email and messaging, and high media
richness includes face-to-face and telephone. The results indicated that extroverted
participants preferred rich media compared to introverted participants, and trait
Neuroticism was negatively correlated with rich media, suggesting that individuals with
social anxiety prefer asynchronous communications, such as text or email.
Gosling, Augustine, Vazire, Holtzman, and Gaddis (2011) examined the role of
personality traits in online social network participation. Online social network
participation was measured by activity on Facebook, including number of posts, number
of groups, and number of total friends in network. Results showed significant
correlations between Extroversion and normal social media activities, such as posting
photos, joining groups, and making comments. Trait Openness to Experience was
positively related to the number of friends. Gosling et al. demonstrated that personality
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traits directly relate to online interactions within social environments, which may offer
insight into peer-peer learning interactions.
Attitudes, self-efficacy, and motivation. A psychological construct associated
with learner interaction within active learning is learner attitudes. As part of a broader
study examining the socio-technical systems theory, Wu and Hwang (2010) explored
whether learning attitudes positively influence students’ use of e-learning. 1,227 students
from National Taipei University participated in the quasi-experimental design with
results indicating that attitudes exhibit a direct positive relationship with the use of e-
learning. Wu and Hwang concluded that a student’s learning attitude amplifies the
positive effects of a good e-learning system.
Attitudes are associated with personality traits throughout the literature. Hetland,
Saksvik, Albertsen, Berntsen, and Henriksen (2012) explored the relationship between
personality traits and attitudes through the specific attitude of over commitment. The
results indicate that four of the five FFM personality traits are significantly related to the
attitude of over commitment, with positive correlations in Conscientiousness,
Neuroticism, and Openness, and a negative correlation with Agreeableness. It is
noteworthy that each of the FFM traits, except Extroversion, is related to attitude,
indicating that personality traits influence a factor related to interaction in learning.
When examining the relationship between self-efficacy and personality traits,
Caprara, Vecchione, Alessandri, Gerbino, and Barbaranelli (2011) found that FFM
personality traits Openness and Conscientiousness moderated self-efficacy. Caprara et al.
used a sample of 412 Italian high school students within a quantitative, longitudinal
design. Neither Openness nor Conscientiousness was significant in its direct contribution
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to academic performance, indicating the role of personality trait may be related to other
functions within the learning environment.
Just as Caprara et al. (2011) and Hetland et al. (2012) examined the relationship
of psychological constructs with personality, Batey, Booth, Furnham, and Lipman (2011)
also investigated the interrelatedness of personality with contextual factors, in this case,
motivation. The results showed significant relationships between personality traits and
facets of motivation, including Extraversion and status, Agreeableness and communion,
and Conscientiousness and accomplishment. Batey et al. suggested that because
personality is a stable characteristic with a strong biological origin (see McAdams et al.,
2013), it is probable that personality is causal in the relationship with motivation. This
logic would also apply in relationships between personality and other psychological
constructs, as well.
Personality and learning. The preceding review of the literature developed the
case for variables worthy of examination; namely, personality traits as a variable,
transactional distance, or interaction strength, as a second variable, and each learning
environment as consisting of a unique combination of TD factors. The following review
examines the literature in which these variables were explored, providing guidance for
the exact applications of such variables and setting precedence for how such studies
should be undertaken. The review begins with a look at personality traits and active
learning environments.
Previously reviewed studies showed the potential for active learning strategies to
produce equivalent results to traditional methods, and the potential for psychological
constructs to influence performance based upon the environment. It has also been shown
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that not all learners engage equally with classroom activity. Rodríguez Montequín, Mesa
Fernández, Balsera, and García Nieto (2013) studied how differing combinations of
student personality profiles would explain group interaction and project success. Groups
were assigned an engineering project to complete, and students were asked to rate peers
within the group based upon participation, leadership, and contribution to the overall
project. The study compared personality types of the leaders with project success, but the
authors were unable to draw a correlation between a particular MBTI type and the
group’s success. However, participation within the groups was dependent upon the
personality type of the leader and the personality types of the group members. Some
group members did not participate or did so with low motivation and low creativity,
while other groups experienced high participation rates with activity by individuals
appearing to be a function of the environment and the learner.
Killian and Bastas (2015) found that students engaged with active learning
achieved equal performance outcomes when compared to those engaged in lecture-based
learning. Using a sample of 74 college students from two separate classroom sections
engaged in a sociology class, the researchers applied lecture-based instruction to one
section, the control, while utilizing team-based learning, in which learners were
responsible for teaching concepts, with the second section. Differences in post-course
exams caused the researchers to reject the hypothesis that activity-based learning resulted
in improved results when compared to static-based learning. It is noteworthy that attitude
indices were significantly higher for activities involving greater levels of interaction.
This study was limited based upon using a single active learning strategy. The
researchers recommended continuing to examine the relationship between learner
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attitudes and other psychological constructs in relationship to learner performance in
active learning strategy. Killian and Bastas recognized that specific psychological
constructs may correlate with specific learning environments in a positive manner, while
others will have no or negative effect.
A third psychological construct associated with active learning environment
outcomes is motivation. Using the backdrop of the economic principle of the Prisoner’s
Dilemma in which rewards are presented based upon the combination of choices between
two participants, Byun (2014) examined the connection between active learning and
performance as moderated by motivation. Participants were 71 students enrolled in a
university economics course. Following instruction on the Prisoner’s Dilemma model,
students were placed in their own dilemma with their grades at stake. Motivation was
measured as a function of the choice each student made between being cooperative,
which is the safer, but a guaranteed punitive position, or non-cooperative, which is a
riskier, but potentially more rewarding position. Following the activity, the results
indicated that the non-cooperative and more motivated students demonstrated better
performance throughout the course. There was a moderately negative correlation
between cooperation, which is lower motivation, and classroom performance, suggesting
that motivation is moderately correlated with classroom performance and that
cooperation with others is dependent upon the learning circumstances, risks, and rewards.
Personality and learning styles. Following the path of examination of learner
performance as influenced by active learning approaches and personality traits, other
constructivist styles took the same approach. Furnham (2012) examined the relationship
between learning style, intelligence, and personality, and these characteristics’ ability to
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predict exam success one year later. Personality traits Conscientiousness and
Agreeableness were each positively related to exam performance. Learning styles were
also related to performance, with deep learning styles—those that seek to achieve full
understanding of the material—negatively correlated with performance, while achieving
styles—those that do the amount of preparation necessary to achieve a high score—
showed positive relationships with performance. The results imply that learners that
attempt a full understanding of the material do not score as well on exams as those more
focused on the extrinsic motivator of the exam grade. Personality traits were related to
exam performance with limited variance due to learning style.
Because of the increasing interest in the potential relationship between personality
and learning style, Threeton, Walter, and Evanoski (2013) investigated the relationship
between personality types and learning styles within the trade and industry sector of
career and technical education. Within active learning strategy and learning style
approaches to performance, numerous factors appear to influence learning performance.
However, the relationships between the specific construct and performance have proved
elusive. When evaluated for common psychological constructs that might explain learner
interaction and performance, one contributory factor that is consistent is personality
traits. The results showed that one personality type, vocational personality type Realistic,
represented 84% of technicians, suggesting that each environment attracts certain
personality types. Learning styles tended to be more equally distributed. Although the
study did not correlate vocational personality type with learning style, it did demonstrate
a self-selecting tendency between the personality types within the automotive repair
industry.
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In order to address conflicting results from previous studies correlating learning
style and performance, Kim (2013) explored the effects of Big Five personality traits and
Kolb’s learning styles to identify any relationships with performance. Students (N =
200) from a blended communications university-level course participated in this
correlational design. The results indicated correlations with course grades and traits
Conscientiousness and Extroversion. There were no significant correlations between the
learning styles and course grades, but there were relationships between personality traits
and learning styles. Kim provided data to support the conclusion that correlational
differences between learning style and performance might be reconciled when learning
style is examined as a function of personality traits, suggesting engagement and
performance within a learning environment is more closely related to personality than to
the incumbent learning style.
Personality within online environments. In addition to personality being linked
with overall performance within the active learning environments, personality has been
linked specifically to the interaction between the learner and the learning environment.
Orvis et al. (2011) studied the relationship between personality and learner preference for
control, a quality of learner autonomy, in an e-learning environment featuring interactive
video instruction. The study explored whether trainees were better suited for e-learning
with high learner control compared to low learner control based upon certain personality
characteristics. Results indicated that Openness to Experience and Extroversion
correlated with learner control preferences. The authors recommended similar research
with other e-learning formats.
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Kickul and Kickul (2006) investigated the relationships between student
characteristics, such as learning goal orientation and proactive personality, which are
defined by Crant et al. (2011) as the characteristics of one who scans for opportunities
and persists to bring about closure, influenced the quality of learning and satisfaction
within computer-assisted instruction (CAI) learning environments, and learning
outcomes. Graduate and undergraduate students (N = 241) who were enrolled in an
online course participated in the study. The study compared independent variables of
personality types and goal orientation with perceived quality of learning and satisfaction
as dependent variables in order to determine the relationships. The results indicated that
proactive personality characteristics and learning goal orientation were correlated with
perceived quality of learning and overall satisfaction. Student comments, such as the
following, suggest a higher level of interactivity from learners with proactive
personalities:
I particularly like the discussion portion of the classroom or online setting,
as it is a very meaningful part of how I learn. The online forum actually
has allowed me to participate in discussions all week versus one night a
week. (p. 369)
Although proactive personality does not directly correlate with a Big Five trait, it does
suggest an inherent individual tendency for learning.
Al-Dujaily et al. (2013) examined the relationship between personality and
outcomes of learners using computer-based learning systems. The findings showed that
MBTI personality types are related to online interaction choices by learners. Individuals
high in type Extroversion preferred environments offering learners greater control over
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the system, while learners low in Extroversion showed greater activity within systems of
high structure and low learner control. Additionally, learners with high Thinking types
were more successful with procedural tasks, and Feeling types were more successful with
declarative knowledge tasks. Additionally, the technology-familiar participants exhibited
self-efficacy with the learning system, which may have masked facets of personality for a
sample population with less technical skill, a consideration when selecting participants
for future research.
Providing additional quantitative investigation into student personality effect in e-
learning, Chang and Chang (2012) investigated the relationship between learning
performance, e-learning, and personality traits within the computer-assisted instruction
environment. The correlational study of 226 Taiwanese participants addressed the
question of whether or not personality traits are related to activity and performance
within an online learning structure. The personality scale used by Chang and Chang
included Extroversion, Neuroticism, and Impulse Control, which were derived from
Singh (1988, as cited in Chang & Chang, 2012). The results showed personality traits
Extroversion, Neuroticism, and Impulse account for some of the variance in learning
interaction and performance, leading to the conclusion that a composite of personality
traits is statistically significant in determining the success of e-learning students. The
personality axis of Impulse Control is not widely used, limiting its comparative value.
However, the overarching results demonstrated the relationship between personality traits
and learner activity and performance conforms to similar studies.
Bauer, Brusso, and Orvis (2012) examined the relationship of personality traits
Openness to Experience, Neuroticism, and Conscientiousness with task difficulty
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changes within a military first-person shooter video game-based training environment.
Task difficulty is defined as the degree to which a task represents a personally demanding
environment requiring a large amount of cognitive effort in order to improve the learner’s
knowledge and skills. Participants higher in Openness to Experience performed better in
conditions in which the task difficulty increased or decreased based upon participant
performance, and participants lower in the trait performed better in conditions that did not
experience changes of task difficulty. Results demonstrated that participants higher in
Neuroticism performed better in adaptive difficulty environments compared to static
difficulty conditions. Similar to previous research, personality traits are correlated with
learner behavior within a learning environment.
Bolliger and Erichsen (2013) investigated the differences in perceived student
satisfaction due to personality types in online and blended learning environments.
Student satisfaction was highest amongst learners with MBTI type Extrovert.
Additionally, type Sensor learners preferred online dialogue and independent work
compared to type Intuitive learners. Learner behavior was influenced by personality
type, a factor significant to the present study. Bollinger and Erichsen identified a need
for continued research in this area, specifically the gap in the research of understanding
the relationship between learner personality types and traits, and interaction within
emerging instructional technologies.
Personality and video. Borup, West, and Graham (2013) examined how learner
characteristics engaged with others in an asynchronous video e-learning environment.
Using case study methodology, the researchers examined students’ behaviors within an
asynchronous video e-learning discussion board, which requires learners to record a
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video of themselves responding to the discussion board prompt or to other participants’
video posts. The significant findings of the case study analysis showed that an
extroverted learner engaged with the video discussion board in order to earn participation
credit, and was comfortable expressing her thoughts through the medium. She valued
making comments, but did not value the comments of peers. The introverted learner,
who was typically uncomfortable engaging in live classroom discussions, valued the time
available to formulate her thoughts and commit them to video. The introverted learner’s
experience within the asynchronous video environment is in contrast to the learners of
Falloon (2011), who felt they looked silly within the synchronous two-way video
classroom. A difference between the asynchronous and synchronous conditions is the
individual’s ability to process her thoughts prior to committing them to the class. The
cases point toward individual psychological characteristics and motivations as regulating
the level of engagement within the video environment. Additionally, the research points
towards trait Extroversion as having an influence on learner interaction within the video
environment.
Barkhi and Brozovsky (2003) investigated the perception and performance of
individuals with differing MBTI types in traditional face-to-face classrooms and
individuals enrolled in distance classes facilitated by two-way video. The researchers
examined individual preferences of media richness based upon those MBTI types to find
that MBTI type Feeling perceived the rich, two-way video communication to be an
appropriate manner by which to communicate within the course. On the other hand,
MBTI type Intuitive preferred lean communication types, such as email and messaging.
The study informs future studies, including this study, that MBTI type Feeling is known
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to influence behavior within the video environment. MBTI type Feeling correlates with
FFM trait Agreeableness (Furnham et al., 2003).
Maltby, McCutcheon, and Lowinger (2011) examined the relationship between
FFM personality traits and celebrity worship, which is a strong psychological absorption
with an on-screen persona in an attempt to establish a sense of identity and fulfillment.
Characteristics of celebrity worship include fantasized conversations with the actor and
increased attentiveness to the on-screen persona’s words and actions. The basic level of
celebrity worship is Entertainment-social, which states that individuals learn about the
on-screen actor to fulfill social needs and provide opportunities for conversation, and is
not considered to be unhealthy behavior. Other levels of celebrity worship are Intense-
personal and Borderline-pathological, which include increasing intensity of personal
feelings and perceived sense of relationship towards the celebrity, and are considered
unhealthy behaviors. The researchers examined correlational tendencies between the
FFM traits and the three levels of celebrity worship, finding that trait Extroversion
exhibited a significant positive correlation with Entertainment-social levels of celebrity
worship. The results suggested that individuals exhibiting a higher level of trait
Extroversion perceived a higher level of relationship with the on-screen persona and
tended to be more attentive to the actor’s words and actions. The viewer’s perceived
dialogue and subsequent attentiveness is postulated as being due to the viewer creating a
cognitive space in which to create a dialogue and a schema in which the celebrity can
exist, resulting in greater attentiveness and less distraction due to cognitive dissonance.
This review examined the literature surrounding the topic of learning interaction,
interaction within the online environment, and psychological constructs that have been
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reported to influence the learning interaction. Consistently throughout the literature, the
primary unit of measure was the individual and the behavior that results, which in the
learning environment was the learning interaction and learning outcomes. As a result, the
literature consistently used individual learner characteristics, such as age (Simonds &
Brock, 2014), learning style (Furnham, 2012; Kim, 2013; Threeton, Walter, & Evanoski,
2013), and personality type (Bauer et al., 2012; Orvis et al., 2011), as a variable. The
variables for comparison in the respective studies were learner outcomes, such as
interaction preferences (Huang, 2002) and performance (Barkhi & Brozovsky, 2003;
Bauer et al., 2012; Chang & Chang, 2012). Although the literature thoroughly examined
personality traits as a variable and interaction measurements as a variable for comparison,
the literature was incomplete with regard to the various factors within emerging
modalities, which Bolliger and Erichsen (2013) identified as a gap in the research.
Environments of computer-aided instruction (Kickul & Kickul, 2006), game-based
learning (Bauer et al., 2012), two-way video (Barkhi & Brozovsky, 2003), hybrid (Al-
Dujaily et al., 2013), and face-to-face (Furnham, 2012) have been explored with the
defined variables; however, the related literature is devoid of asynchronous video e-
learning research, a gap this study addressed. Borup et al. (2013) and Barkhi and
Brozovsky (2003) identified a relationship between personality traits and video
environments. In summary, the relationship of personality traits with learner interaction
as measured by TD within the asynchronous video e-learning environment was explored.
Methodology. The nature of a research study’s design is influenced by the
research questions to be answered, the hypotheses that result from the research questions,
and the variables that are measured (Ingham-Broomfield, 2014). Each study’s variables
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exhibit unique characteristics that inform the design of the study. In the present study,
the use of personality traits as variables establishes parameters for which research design
consideration was given. Such considerations included the inability to manipulate the
variables and the ability to measure the variables within the non-experimental
environment. It is within these boundaries that the methodology and research design
suitable for the present study was explored.
Quantitative versus qualitative methods for personality research. Two research
methodologies are available for examining gaps in the literature: qualitative and
quantitative. Each method presents strengths and weaknesses for answering certain gaps
within the literature. Qualitative research, for example, offers the ability to identify
psychological characteristics within specific environments. One example is Ma and Zi
(2015), which explored and delineated common characteristics of college students with
perfectionism. The researchers utilized a narrative qualitative research method to
examine the life stories of students who exhibited strong tendencies of perfectionism. Ma
and Zi conducted semi-structured interviews with nine college students. Following the
interviews, the text was examined and coded for themes, which were then compared
across the interviews in an iterative manner until dominant themes emerged. Results
were compared to perfectionism surveys that were administered at the beginning of the
research. The results identified that perfectionists focus upon self-control, status and
success, and love and friendship, with learners that display negative affect personality
traits showing a desire for powerful energy, a sense of control, and status. Although Ma
and Zi provided valuable insight into personality research by identifying perfectionism
themes useful for future research, these types of results were not appropriate for
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addressing the question of determining relationship strength between the personality traits
of interest and transactional distance.
A review of the extant literature indicated that the appropriate approach for
addressing measurable relationships is quantitative methodology, the results of which
provide an enumeration of the relationship useful for addressing the primary research
questions. The specific design for investigating relationships involving variables is
correlational design. Correlational design is appropriate for examining relationships
between variables, particularly those of in situ or self-reported medical and psychological
environments. Within correlational design research, variables are compared to determine
the nature and magnitude of the relationship shared between the two (Rumrill, 2004). It
is important to note that correlational designs demonstrate the strength of relationship
between the variables, but do not establish cause. Analysis of regression measures the
strength of the relationship by determining the degree of shared variance, which
expresses how predictive one variable is of another (Meyers et al., 2013). The literature
demonstrated the appropriateness of correlational design for examining the relationship
between personality traits and other criterion variables, such as transactional distance,
and the suitability for analysis of regression for examining the predictive nature of
variables upon outcomes.
Pretz and Folse (2011) examined the relationship between both nursing
experience and intuition in decision-making within the clinical environment. Student and
practicing nurses (N = 175) participated in this correlational design. In addition to
general nursing experience, the study focused on the participants’ use of intuition, which
is exhibited as the responses that are reached with little or no cognitive effort, or
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conscious awareness or deliberation. Although it is not defined within FFM, intuitive
behavior is similar to personality trait responses as a natural behavior within a specific
situation or environment, and is measured within MBTI as a bipolar type Intuitive-
Sensate. Intuition was measured using several instruments, including Miller Intuitiveness
Instrument, MBTI, Types of Intuition Scale, and the Smith Intuition Instrument. Factor
analysis was used on individual scales to assess clusters of similar factors, identifying
five primary factors within the Miller instrument and six within the Smith instrument.
Correlational analysis demonstrated that the intuition factors for the Miller and Smith
instruments were positively related to decision-making within the nursing environment,
but not necessarily within the construct of general decision-making. When nursing
experience is included as an independent variable, factor analysis indicated that
experience and intuition were positively related: the greater the experience, the greater
the intuition within the nursing environment. The study’s design utilized factor analysis
when comparing variables with multiple factors, which provided understanding of the
relationship between factors in order to create useful clusters of traits. Valuable
information necessary to answer the research questions was provided by comparisons of
the factor groupings to decision-making, which was a result of correlational analysis.
Reyes et al. (2015) examined the relationship between two dimensions of
perfectionism and depression. The correlational design study examined 173 gifted
Filipino adolescent students (38% males) using a depression inventory and a
perfectionism scale designed for children and adolescents. Perfectionism is defined as
having two facets. The first factor, socially prescribed perfectionism (SPP), is an
introjected phenomenon in which the individual strives to meet a level of excellence due
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to the perceived desires or expectations of others, such as parents. The second facet, self-
oriented perfectionism (SOP), is an intrinsic motivation in which the individual sets his or
her own standard of achievement. In either case, the achievement of perfectionism is
unattainable, which creates an environment of perceived failure. Reyes et al. used
correlational analysis to examine the relationship between depression measures and each
of the perfectionism factors, finding that SPP and depression were moderately correlated,
while SOP and depression were not related. The correlational design was effective at
determining the relationship between perfectionism and depression. Reyes et al.
paralleled the requirements of the present study in independently evaluating two non-
manipulated variables against a single, self-reported dependent variable.
Shun et al. (2011) examined the relationship between personality type and quality
of life measures for patients with colorectal cancer. The researchers examined Type D
personality facets, which are associated with personality traits of negative affectivity and
social inhibition, and are measured using the Type D Scale-14 (DS-14). Quality of life
was measured using four different surveys that addressed various aspects of quality of
life, such as fatigue, anxiety, and depression. Patients (N = 124) completed the surveys
at the conclusion of their primary treatment. Shun et al. utilized correlational design to
determine that both facets of Type D personality were significantly related to all of the
quality of life outcomes measured by the four surveys, reaching the conclusion that
negatively oriented personality types—those that are prone to a negative disposition or
those who anticipate a negative outcome—experience a lower quality of life during
treatment. Analysis of regression indicated that certain facets of personality, such as
social inhibition, were predictive of quality of life.
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Opateye (2014) examined the relationship between emotional intelligence, test
anxiety, stress, academic success, and attitudes of high school students within the subject
of electrochemistry. Participants in the study were 600 high school students in Lagos,
Nigeria. Opateye utilized correlational design to investigate emotional intelligence’s
relationships with academic success and attitudes towards the subject. A separate
analysis was conducted to examine stress level’s relationships with academic success and
attitudes towards the subject. A third analysis compared test anxiety to academic success
and attitudes towards the subject. The results indicated a significant negative relationship
between stress and academic success, and a significant positive relationship between
emotional intelligence and attitudes towards electrochemistry. Similar to previous
research, correlational design was the appropriate approach for addressing the
relationship between a non-manipulated variable and an outcome.
Blignaut and Ungerer (2014) explored the relationship between Big Five
personality traits and customer service center job performance within the banking
industry. Sampling 89 agents from within a banking group, the researchers utilized a
correlational design to assess the relationships. Personality was measured using the
Occupational Personality Questionnaire 32r (OPQ32r) instrument and job performance
measures were based upon biannual performance assessments. Factor analysis of the
OPQ32r validated the instrument’s use as a FFM measure. Correlational analysis found
that a significant relationship existed between trait Openness and the performance
criterion adhere to and live values, and between trait Agreeableness and performance
criterion emails or calls versus cases ratio. Analysis of regression determined that a
small, but significant amount of the total variation was due to either Agreeableness or
