Relationships Between Learners’ Personality Traits and Transactional Distance .

The extent of the fit between the learner and learning environment factors (Wu &

Hwang, 2010), such as a video instructor (Kim & Thayne, 2015), on-screen, multimedia

content (Calli, Balcikanli, Calli, Cebeci, & Seymen, 2013), or peer interaction (Wang &

Morgan, 2008), within each learning environment is a critical determinant in student

learning outcomes. The more satisfying, attractive, and useful the learning factors are to

the learner, the more likely the student is to interact with the learning environment, and

ask questions, clarify information, and remain open to new information, and,

subsequently, to perform well (Hauser, Paul, & Bradley, 2012; Wang, Chen, &

Anderson, 2014). Moore’s (1993) Transactional Distance Theory introduced three types

of learner interactions that occur within the distance-learning environment, which are

between learner and instructor, between learners, and between the learner and the

content. Chen (2001) identified the interaction between the learner and the technological

interface as a fourth interaction type. The intensity and quality of the learner’s

interaction experience with the learning environment is measured as transactional

distance (TD), which is the learner’s perceived psychological and communication

distance between the learner and the learning environment (Ustati & Hassan, 2013).

Environments in which the learner perceives easier communication and more comfortable

interactions are characterized by small TD, while environments in which the learner finds

it difficult to ask question or obtain the desired information are marked by large TD

(Moore, 1993). The desired relationship between the learner and the learning

environment is to have as small a TD as possible, a relationship that facilitates the

2

greatest opportunity for a learner to explore and clarify information (Benson &

Samarawickrema, 2009). Transactional distance is influenced by three design factors: the

structure of the environment, the amount and frequency of purposeful and valuable

communication between the learner and learning environment, and the learner’s

autonomy within the environment (Chen, 2001; Park, 2011).

Self-regulatory processes—those psychological characteristics that govern an

individual’s behavior—are also responsible in part for a learner’s interaction experience

and the resulting transactional distance (Moore, 1993). Psychological constructs that

influence self-regulation include personality traits (Legault & Inzlicht, 2013), self-

esteem, self-efficacy, motivation, and attitudes (Fishman, 2014). Individual learner self-

regulatory processes, including self-efficacy (Hauser et al., 2012), attitudes (Wu &

Hwang, 2010), and motivation (Byun, 2014), were correlated to the learner’s personality

traits (Tabak & Nguyen, 2013) and were shown to influence the learner’s propensity to

engage in dialogue and exhibit autonomy within the distance learning environment.

Current studies assessed the relationship between learner personality traits and the

learning environment. Five-Factor Model (FFM) personality traits have been shown to

correlate with learner-learning environment interaction quality and strength in some

distance-learning environments, including two-way video distance learning (Falloon,

2011), hybrid online and in-seat classrooms (Al-Dujaily, Kim, & Ryu, 2013; Murphy &

Rodríguez-Manzanares, 2008), asynchronous computer-assisted instruction (Kickul &

Kickul, 2006), and game-based learning (Bauer, Brusso, & Orvis, 2012). Studies such as

these contributed to a holistic view of the learner-learning environment interaction within

the e-learning environment by providing a map from the most basic of human

3

characteristics—one’s personality—to that person’s interaction preferences within a

learning environment. Additionally, considering the fit between personality traits and

various e-learning settings extended the conclusions of Benson and Samarawickrema

(2009) for instructional designers to determine the environment most preferred by the

learner to reduce communication difficulties and meet the designer’s desired level of

learner autonomy to include learner self-regulatory processes. Because the learner’s

natural tendencies tend not to change (Mōttus, Johnson, & Deary, 2012), the learning

environment must adapt in order to maximize learning interaction and improve learner

performance. Developing a complete map of the learning topography between human

characteristics and knowledge acquisition is a grand endeavor, one that will be achieved

incrementally with each related study.

Bolliger and Erichsen (2013) investigated the relationship between Myers-Briggs

Type Indicator (MBTI) personality types and student satisfaction with learning

interactions within a broad range of technologically diverse online and blended settings.

Although the authors concluded that personality types correlated with learner satisfaction

levels within differing learning environments, Bolliger and Erichsen identified a gap in

the extant research. Specifically, the authors recommended future research exploring the

relationship between personality characteristics and learner satisfaction with learning

interactions within different settings, with different audiences, or with larger sample sizes

in order to generalize the results. A unique setting is asynchronous video e-learning,

which is an emerging method of instruction that integrates video content with embedded,

online reinforcement activities, such as quizzes, applications, and writing (Stigler, Geller,

& Givvin, 2015), providing a content-rich, entertaining, and efficient environment for

4

increased engagement (Ljubojevic, Vaskovic, Stankovic, & Vaskovic, 2014). The

current study sought to address the gap identified by Bolliger and Erichsen (2013) and

examined the unknown relationship between personality characteristics, using Five-

Factor Model traits, and learner interaction satisfaction as measured by transactional

distance within the previously unexplored setting of asynchronous video e-learning.

The present study examined the correlation and strength of relationships between

Five-Factor Model personality traits, which have been associated with positive

performance in video environments (Barkhi & Brozovsky, 2003; Borup, West, &

Graham, 2013; Tsan & Day, 2007), and transactional distance within the asynchronous

video e-learning environment. Using quantitative methods and a correlational research

design, the study measured the Five-Factor Model personality traits of a sample

population using the Big Five Inventory (BFI; John, 2009), and compared those trait

strengths to the participants’ transactional distance as measured by the Structure

Component Evaluation Test (SCET; Sandoe, 2005) following participant involvement in

a short series of online video course segments. Scores for each trait within the BFI were

measured along a bipolar scale with scores below the midpoint indicating an absence of

the described trait (e.g., introversion) and scores higher than the midpoint indicated a

presence of the described trait (e.g., extroversion). SCET values and transactional

distance are negatively correlated such that higher scores for SCET described a smaller

transactional distance and lower SCET values indicated a larger gap psychological and

communication gap between the learner and the learning environment. As a result, a

positive correlation between a trait and a SCET value describes a negative correlation

between the trait and transactional distance. For example, if trait Extroversion is

5

positively correlated with SCET values, then Extroversion is negatively correlated with

transactional distance. In this example, high Extroversion scores suggests that the learner

experienced a high-quality interaction with the learning environment and low

Extroversion scores indicate the learner experienced a larger TD with a lower-quality

interaction with the learning environment. The present research design is based upon

Kim (2013) which compared personality traits and learner academic outcomes, as well as

Kolb learning styles and learner academic outcomes, following the completion of a

communications course within a blended online and in-class environment.

The results addressed the questions of whether personality traits were correlated

with a learner’s transactional distance within the asynchronous video environment.

Understanding the learner-learning environment interaction in this environment added to

the compendium of knowledge useful for instructional designers in creating an

environment conducive to more satisfying interactions between the learner and the

knowledge source. Additionally, the results of this study extended the scholarly literature

regarding personality trait-learner interaction, particularly as it applied to distance

learning and Transactional Distance Theory, by examining the perceived sense of

improved dialogue due to personality interactions with asynchronous video, resulting in

smaller pedagogical distances.

The remainder of the first chapter is organized to provide the reader an overview

of the research. The discussion begins with a description of the study’s background, the

problem statement that emerges from the literature, the purpose of the study, and the

research questions and hypotheses. Support for the research purpose is summarized in

the sections that follow, which include how the study advances scientific knowledge and

6

the significance of the study. The introductory chapter continues by defining the

proposed methodology for investigating the research questions and by describing the

nature of the research design that will be employed. The chapter concludes by providing

boundaries to the study through the definition of terms and through statements of the

study’s assumptions, limitations, and delimitations.

Background of the Study

A growing body of literature described a variety of theories and approaches that

associated learner characteristics and behaviors with learning outcomes. Theories about

active learning posited that individuals who engaged in learning activities saw increased

performance (Lucas, Testman, Hoyland, Kimble, & Euler, 2013); however, not all

learners engaged equally with the activity, differences that may be explained by self-

efficacy (Hauser et al., 2012), attitudes (Wu & Hwang, 2010), and motivation (Byun,

2014), self-regulatory processes that are positively associated with personality traits

(Caprara, Vecchione, Alessandri, Gerbino, & Barbaranelli, 2011; Donche, De Maeyer,

Coertjens, Van Daal, & Petegem, 2013; Hetland, Saksvik, Albertsen, Berntsen, &

Henriksen, 2012). Attempts to correlate outcomes and learning styles, which were based

upon learner preferences for feeling, watching, thinking, and doing (Chen, Jones, &

Moreland, 2014), have also met with mixed results. Some investigations described

strong correlations between the learning style and performance in traditional classrooms

(Bhatti & Bart, 2013; Black & Kassaye, 2014; Moayyeri, 2015) and in online

environments (Hwang, Sung, Hung, & Huang, 2013; Page & Webb, 2013; Richmond &

Conrad, 2012), while others demonstrated a lack of correlation (Alghasham, 2012;

Breckler, Teoh, & Role, 2011; Hsieh, Mache, & Knudson, 2012). However, correlational

7

differences might be reconciled when learning style is examined as a function of

personality traits, suggesting performance within a learning environment is more closely

related to personality traits than the incumbent learning style (Giannakos,

Chorianopoulos, Ronchetti, Szegedi, & Teasley, 2014; Kim, 2013).

Moore’s (1993) Transactional Distance Theory (TDT) offers that the quality and

intensity of the interaction between the learner and the learning environment influences

performance within distance learning environments. Learners who experience higher

quality interactions as indicated by small transactional distances with the instructional

source performed better than learners that experience a wider psychological or

communication gap with the knowledge source (Hauser et al., 2012). The learner’s

interaction with the learning environment is measured as transactional distance (TD),

which is described as the perceived pedagogical, psychological, and communication

distance between the learner and the learning environment as determined by the learner’s

perceived openness of dialogue, the student’s sense of autonomy within the learning

setting, and the learner’s perception of the learning structure’s flexibility (Chen, 2001;

Moore, 1993; Park, 2011). Active learning, theories on learning style, and Transactional

Distance Theory share common themes. Each theory suggests learning interaction is

influenced by characteristics of the learner and by factors within the learning

environment. Active learning describes variables of behavioral, cognitive, and social

engagement within the learning setting (Drew & Mackie, 2011), and learning style

variables include the learner’s physiological and psychological constructs, and the

learner’s response to the learning environment (Yenice, 2012). TDT’s factors of

dialogue, learner autonomy, and learning structure are defined by the specific learning

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environment, and each learner’s unique characteristics (Moore, 1993). Each of the three

theories suggests the quality and intensity of the learner-learning environment interaction

is a function of the learner’s individual characteristics and the factors present within each

unique environment (Ustati & Hassan, 2013).

Kickul and Kickul (2006) found that proactive personality traits, which are

defined by Crant, Kim, and Wang (2011) as the characteristics of one who scans for

opportunities and persists to bring about closure, influenced the quality of learning and

satisfaction within computer-assisted instruction (CAI) learning environments. Hauser,

Paul, and Bradley (2012) demonstrated that computer self-efficacy and anxiety

moderated learner performance in a hybrid online and in-seat management information

systems class. Using the MBTI personality inventory, Al-Dujaily, Kim, and Ryu (2013)

showed types Extroversion, Intuitive, and Thinking were predictors of procedural

knowledge performance, while types Intuitive and Feeling were indicative of declarative

knowledge performance within CAI learning environments. Orvis, Brusso, Wasserman,

and Fisher (2011) correlated FFM trait Extroversion and trait Openness to Experience

with learner autonomy as measured by training performance in an undergraduate

management course. In gaming-based learning environments, traits Openness to

Experience and Neuroticism interacted with task difficulty conditions to determine

performance (Bauer et al., 2012).

Both Orvis et al. (2011) and Al-Dujaily et al. (2013) recommended broadening

personality research to other e-learning environments to gain greater understanding of the

relationship between personality and interaction in online learning. Bolliger and Erichsen

(2013) correlated MBTI personality types and learner interaction within a variety of

9

online and blended environments, demonstrating that type Sensor was related to

satisfaction with dialogue tools and independent projects, and that type Intuitive showed

interaction preferences based upon learning environment, favoring online instruction over

blended environments. Bolliger and Erichsen identified a gap in the correlational

research between personality characteristics and learner interaction satisfaction within

emerging technologies and new learning environments, and recommended that such

research should be conducted.

The extant literature examined the relationship between personality traits and

transactional distance within a variety of environments. Although the personality

characteristic measurement scale has varied within the literature, such as Myers Briggs

types (Al-Dujaily et al., 2013; Bolliger & Erichsen, 2013) and Big Five (Orvis, Brusso,

Wasserman, & Fisher, 2011), personality traits remained a central interest of exploration

as a condition within learning research, as traits are a stable facet of human behavior

(Wortman, Lucas, & Donnellan, 2012). Research focusing on learner outcomes also

remained consistent, including study of performance (Lucas et al., 2013; Thomas &

Macias-Moriarity, 2014), attitudes (Killian & Bastas, 2015; Wu & Hwang, 2010),

satisfaction (Bolliger & Erichsen, 2013), and engagement levels (Rodríguez Montequín,

Mesa Fernández, Balsera, & García Nieto, 2013), proving learner outcomes to be an

appropriate variable for comparison. The recent research focused on analysis of learners’

interactions with the learning environment by examining the relationship between

personality traits and transactional distance within a variety of learning circumstances.

The variety of variables examined produced results such that outcomes vary from one

environment type to the next. As a result, it is imperative to examine the relationship

10

between personality traits and transactional distance within each environment so that a

comprehensive theory may be proposed. Thus far, the literature has examined

environments of computer-aided instruction (Kickul & Kickul, 2006), game-based

learning (Bauer et al., 2012), hybrid learning structures (Moffett & Mill, 2014; Velegol,

Zappe, & Mahoney, 2015), blended learning (Bolliger & Erichsen, 2013), face-to-face

learning (Al-Dujaily et al., 2013), and two-way video distance learning (Chen & Willits,

1998; Falloon, 2011).

One environment that was not examined for the relationship between personality

traits and TD was the asynchronous video-based e-learning, a submarket of the $23.8

billion North American e-learning industry (Docebo, 2014), and a niche in which video-

based commercial ventures are growing at a rate of 100% per year (Bersin, 2012). As an

emerging framework of e-learning, asynchronous video integrates video media with

interactive activities to engage learners as a primary form of content delivery (Stigler et

al., 2015). The current study was influenced by the direction of research identified by Al-

Dujaily et al. (2013) and Orvis et al. (2011), and the specific gap identified by Bolliger

and Erichsen (2013). Although the literature explored the relationship between

personality and learner outcomes within a variety of distant learning formats, the question

of if personality traits correlate with transactional distance within asynchronous video-

based e-learning was unknown.

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