Trait Conscientiousness correlates significantly with transactional distance.
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.
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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.
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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
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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
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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.
