Current studies assessed the relationship between learner personality traits and the learning environment

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

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

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

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

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

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

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

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

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

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

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,

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

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

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

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).

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

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.

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.

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

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,

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

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

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

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

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

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

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

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

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).

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).

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

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

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,

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

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).

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

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

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

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

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

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).

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

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.

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Current studies assessed the relationship between learner personality traits and the learning environment

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