personality types and learning styles
TRANSCRIPT
PERSONALITY TYPES AND LEARNING STYLES: AN INVESTIGATION OF
THEIR INFLUENCE ON PERFORMANCE IN A DISTANCE EDUCATION
ENVIRONMENT
by
Stacey Lynn Rimmerman
M.Ed., The University of West Florida, 1997
B.A., The University of West Florida, 1995
A dissertation submitted to the Department of Instructional and Performance Technology College of Professional Studies The University of West Florida
In partial fulfillment of the requirements for the degree of Doctor of Education
2005
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The dissertation of Stacey Lynn Rimmerman is approved: ______________________________________________ __________________ Sandra L. Davis, Committee Member Date ______________________________________________ __________________ Nancy N. Maloy, Committee Member Date ______________________________________________ __________________ Sherri L. Zimmerman, Committee Member Date ______________________________________________ __________________ Karen L. Rasmussen, Committee Chair Date Accepted for the Department/Division: ______________________________________________ __________________ Karen L. Rasmussen, Chair Date Accepted for the College: ______________________________________________ __________________ Don Chu, Dean Date Accepted for the University: ______________________________________________ __________________ Richard S. Podemski, Dean Date Office of Graduate Studies
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ACKNOWLEDGMENTS
Completing this research paper has been both exasperating and exhilarating and I
am thankful for the support and assistance I have received at each stage of the process.
Without the encouraging and loyal attitudes of my family, friends, and academic mentors,
I could not have completed this passage.
My warmest gratitude goes to my immediate family for their patience,
understanding, and encouragement. Never again will you see me read through my
research while sitting through a movie, on a camping trip, or on an outing to the beach.
My children, Erin and Michael, you have been my grandest source of inspiration since
the day you were born. This degree is really for you. Charley, you sacrificed your time to
chauffeur two teens, make dinner any time I was too tired from writing, and rubbed my
back regularly when I had been sitting at my desk for too long. Your constant love and
kindness has been very instrumental in the completion of this project; you’re the best!
I am grateful to my parents, Pat and Don, who gave me the opportunity to build
confidence in my abilities and presented me with an appreciation for knowledge and
education that is unparalleled by anything else in my life.
My dear friend and colleague, Laura Colo—you have gracefully taken my hand
and led me through this journey with wisdom, patience, and honor. I am absolutely
certain that I would not have finished this degree without your unconditional friendship
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and encouragement at each of my small successes. You were my beacon through the
roughest of storms. Thank you.
Sincere appreciation goes to my chairperson, Karen Rasmussen, who pushed me
to do my best and kindly guided me towards accomplishment. I am also grateful to
Morris Marx who, very patiently and thoughtfully, spent many hours helping me analyze
statistics. Additionally, I am appreciative of the rest of my advisory committee for their
support in their areas of expertise: Sandra Davis, Nancy Maloy, and Sherri
Zimmerman— thank you all.
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TABLE OF CONTENTS Page ACKNOWLEDGMENTS .............................................................................................. iii LIST OF TABLES.......................................................................................................... ix LIST OF FIGURES ..........................................................................................................x ABSTRACT.................................................................................................................... xi CHAPTER I. INTRODUCTION ........................................................................1 A. Background of the Study ........................................................3 1. Personality Trait Theory ...................................................3 2. Learning Style Theory ......................................................5 3. Distance Learning .............................................................7 B. Statement of the Problem........................................................8 C. Significance of the Study ........................................................9 D. Purpose and Scope of the Study............................................11 1. Purpose............................................................................12 2. Setting .............................................................................12 3. Participants......................................................................12 E. Variables ...............................................................................13 1. Independent Variables ....................................................13 2. Dependent Variable ........................................................13 F. Research Questions...............................................................13 G. Definitions of Terminology ..................................................14 H. Chapter Summary .................................................................15 CHAPTER II. REVIEW OF THE LITERATURE ............................................16 A. Introduction...........................................................................16 B. Personality.............................................................................17 C. Personality Trait Theories and Models .................................18 1. Psychological Type Theory ............................................18 2. The Big Five ...................................................................20 3. Fulfillment Model ...........................................................23 4. Eysenck’s Theory............................................................24
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D. Instruments for Assessing Personality Type.........................25 1. NEO-PI ...........................................................................25 2. Eysenck Personality Questionnaire (EPQ) .....................25 3. 16 Personality Factor Questionnaire (16PF)...................26 4. Myers-Briggs Type Indicator (MBTI) ............................27 5. Keirsey Temperament Sorter (KTS)...............................29 E. Research on Personality Type...............................................31 1. Personality Type and Performance .................................31 2. Personality Type, Performance, and Distance Education ........................................................................34 F. Learning Styles .....................................................................35 G. Learning Styles Theories and Models...................................37 1. Productivity Environmental Preference (PEP) ...............37 2. Mind Styles Delineator ...................................................39 3. Field Independence Versus Field Dependence ...............41 4. Grasha-Riechmann Learning Styles ...............................41 5. Experiential Learning Model ..........................................42 6. 4Mat System ...................................................................46 H. Instruments for Assessing Learning Styles...........................47 1. Learning Style Inventory (LSI).......................................49 2. Productivity Environmental Preference Survey (PEPS).............................................................................49 3. Mind Style Delineator.....................................................49 4. Group Embedded Figures Test (GEFT)..........................50 I. Research on Learning Styles.................................................50 1. Learning Styles and Performance ...................................50 2. Learning Styles, Performance, and Distance Education ........................................................................52 3. Personality Trait and Learning Style ..............................53 J. Distance Education ...............................................................55 1. Trends in Distance Education .........................................55 2. Characteristics of Distance Learners ..............................56 K. Chapter Summary .................................................................58 CHAPTER III. METHODOLOGY .....................................................................59 A. Introduction...........................................................................59 B. Research Design....................................................................59 1. Setting .............................................................................60 2. Distance Education Delivery ..........................................60 3. Course Information .........................................................61 4. Participants......................................................................62 C. Variables ...............................................................................63 1. Independent Variables ....................................................63 a. Learning Style...........................................................63 b. Personality Trait........................................................64
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2. Dependent Variable ........................................................65 D. Research Questions and Hypotheses ....................................66 E. Instrumentation .....................................................................67 1. Myers-Briggs Type Indicator..........................................68 2. Learning Style Inventory ................................................70 F. Procedure ..............................................................................71 G. Data Analysis ........................................................................73 H. Limitations ............................................................................75 I. Chapter Summary .................................................................75 CHAPTER IV. RESULTS ...................................................................................76 A. Introduction...........................................................................76 B. Participants............................................................................76 C. Summary of Data ..................................................................77 D. Data Analysis ........................................................................79 1. Introduction.....................................................................79 2. Statistical Method ...........................................................80 3. Assumptions....................................................................81 4. Personality Type on Student Performance: Research Question 1 .......................................................82 5. Learning Style on Student Performance: Research Question 2 .......................................................82 6. Personality Type and Learning Style on Student Performance: Research Question 3.................................82 7. Other Data Analysis........................................................83 E. Chapter Summary .................................................................83 CHAPTER V. DISCUSSION.............................................................................85 A. Introduction...........................................................................85 B. Study Summary.....................................................................85 C. Discussion of Results............................................................86 1. Research Question 1 .......................................................86 2. Research Question 2 .......................................................87 3. Research Question 3 .......................................................88 D. Recommendations for Practitioners......................................89 E. Recommendations for Further Research...............................91 F. Limitations of the Study........................................................92 G. Chapter Summary .................................................................94 REFERENCES ...............................................................................................................95 APPENDIXES ..............................................................................................................113 A. E-mail Granting Permission to Use the Learning Style Inventory Version 3 ...................................................114
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B. Letter Granting Permission to Use Pensacola Junior College Course in Study .....................................................116 C. The University of West Florida Institutional Review Board Approval Letter ........................................................118 D. Documents sent to Facilitating Professor to Recruit Participants..........................................................................121
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LIST OF TABLES Table Page 1. Personality Characteristics of Jung’s Psychological Type Theory..................21 2. Type Designations of the Big Five ..................................................................22 3. Comparison Characteristics of Jung’s Psychological Type Theory and the Myers-Briggs Type Indicator ..............................................................28 4. Personality Type Breakdown of the Myers-Briggs Type Indicator.................30 5. Productivity Environmental Preference Classification Model of Learning Styles ................................................................................................38 6. Characteristics of Learning Patterns for the Mind Styles Delineator ..............40 7. Classifications of the Grasha-Riechmann Student Learning Styles ................43 8. 4Mat: Characteristics of the Four Learning Styles ..........................................48 9. Sample Size Breakdown for Each Independent Variable ................................77 10. Descriptive Statistics for Personality Types, Learning Styles, and End- of-Semester Grades..........................................................................................78 11. Correlations Between Personality Type, Learning Style, and Semester Grade (n = 34)..................................................................................................80
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LIST OF FIGURES Figure Page 1. Kolb’s model of learning styles .......................................................................44 2. 4Mat model of learning styles..........................................................................47
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ABSTRACT
PERSONALITY TYPES AND LEARNING STYLES: AN INVESTIGATION OF THEIR INFLUENCE ON PERFORMANCE IN A DISTANCE EDUCATION
ENVIRONMENT
Stacey Lynn Rimmerman
The researcher investigated whether personality type and learning style predicted
performance in distance education. Thirty-four participants from 3 sections of Art
Humanities completed online the Myers-Briggs Type Indicator and the Learning Styles
Inventory. Using regression analysis, it was determined that neither personality type nor
learning style had a statistically significant effect on student performance in this setting.
However, the data did reveal some apparent self-selection of the learning environment.
Sensors outrepresented Intuitives by a large scale, identifying further areas for research.
A binomial test was used to prove these results were not random.
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CHAPTER I
INTRODUCTION
Educators are concerned with methodologies that increase student performance
(Ackerman, Bowen, Beier, & Kanfer, 2001). Recognizing that students are different and
that teachers need to respond to those differences is not a new concept in education
(Peyton, 2003). The introduction of multiple learning environments opens questions
about effective course design based on students’ individual differences. Given the
financial benefits and possibility of enrollment increases, it is not surprising that colleges
and universities are offering more courses utilizing distance education formats (U.S.
Department of Education, 2002). Many facilities and institutions agree that their
campuses are not large enough to accommodate this increasing number of college-age
students (Oblinger, Barone, & Hawkins, 2001). Distance education programs may be one
solution to the capacity pressures that increasing registration may have on higher
education. It seems important in the design of new forms of distance education that
concern be placed on characteristics that may enhance performance. According to Barkhi
and Brozovsky (2004), individual differences can play a role in explaining variances in
performance in face-to-face and distance education settings.
Throughout the research it has become clear to practitioners that there is not a
particular instructional strategy that will benefit all students in all learning situations
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(Dyrud, 1997; Orr, Park, Thompson, & Thompson, 1999; Terry, 2001). With this in
mind, focus needs to be placed on creating learning environments that meet specific
learners’ characteristics creating, in turn, learning situations in which students could
choose an environment based on their individual needs. Practitioners need to find
ways of reaching students with a variety of different learning styles and personality types,
in a variety of different environments, in order to find ways of enabling all learners to
become successful.
Chamberlin (2001) suggests that by taking advantage of the academic strengths of
online teaching environments, faculty can offer students the greatest chance to discover
their strengths and weaknesses as learners and the best opportunity to find their path to
achieving success. Whatever instructional process is selected, individual needs and
learning styles need to be considered when decisions are made about individualizing
instructional methods (Asleitner & Keller, 1995; Diseth, 2003; Jonassen & Grabowski,
1993; Sabry & Baldwin, 2003).
If practitioners are concerned with ability measures in an effort to construct more
meaningful learning environments, then it would make sense to investigate the possible
relationships that learning style and personality may have on performance within specific
learning environments. While there have been quite a few researchers studying the effects
of matching teaching and learning styles (Ahn, 1999; Burger, 1985; Cooper, L. W.,
2001), little research has been conducted to investigate how students with differing styles
or personality traits perform in distance education environments (Barkhi & Brozovsky,
2004). For example, assuming voluntary enrollment in a distance learning course, are
there specific characteristics, learning styles, or both that determine the extent to which
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students will benefit from that environment? If such profiles exist, the conclusions could
help institutions determine the course designs for specific environments. The event of
individualized course design would then provide successful academic climates that serve
the greatest number of students.
Background of the Study
Personality Trait Theory
Psychologists emphasize that one of the important sources of individual
differences nests in personality trait theory (McCaulley, 1990; Myers, 1980; Slaats, Van
der Sanden, & Lodewijks, 1997; Verma & Sheikh, 1996; Wang & Newlin, 2000).
Personality trait is defined as a fairly fixed characteristic of an individual. It determines
how an individual deals with new information and views situations (Jung, 1971; Myers &
McCaulley, 1989). These traits are static and are relatively inbuilt features of the
individual (Verma & Sheikh, 1996).
Swiss psychologist Carl Jung stated that “differences in behavior, which seem so
obvious to the eye, are a result of preferences related to the basic functions our
personalities perform throughout life” (as cited in Kroeger & Thuesen, 1988, p. 11).
Preferences occur early in life, creating the underpinnings of our personalities (Myers,
1980). According to Jung (1971), perception is understood to be the ways people become
aware of their environment, other people, and occurrences, while judgment is considered
the method employed by people to form conclusions about experiences perceived. In
addition to perception and judgment, Jung’s model includes the dominant functions of
extraversion and introversion. “Extraversion and introversion relate to the balance of a
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person’s orientation toward the external world of objects and people or toward the
internal world of conepts and ideas” (McCaulley, 1990, p. 39). Four functions of thought
were also hypothesized: (a) sensing, (b) thinking, (c) feeling, and (d) intuiting (Jung). In
combining the orientations and functions, Jung identified eight personality types.
Messick (1994) indicates that personality trait can help or hinder performance
depending on the “nature and intensity of the personality characteristics” (p. 1). In a
distance education setting, the dominant orientations of extraversion and introversion
may be particularly useful in determining performance. Without face-to-face contact in
distance learning, students with introverted preferences have outperformed students with
extraverted preferences because the environment itself relies on the absence of nonverbal
communication (Bayless, 2001). Similarly, the perceiving and judging orientations might
be indicative of individual performance because of the student’s ability to maintain
deadlines without immediate face-to-face interactions. This being the case, personality
trait theory becomes an important source for the understanding of individual differences
in learning. Personality traits “seem suitable as underlying factors that explain different
typical learning patterns, thus providing valuable additional constructs” (Vermetten,
Lodewijks, & Vermunt, 2001, p. 153).
In summary, the personality make-up of an individual influences the way he or
she views situations and processes information (Lavanya & Karunanidhi, 1997; Myers &
McCaulley, 1989). Practitioners concerned with performance specifically related to
distance education environments may want to take a look at personality trait as applied to
achievement levels in those environments. In addition, there may be individual
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characteristics specifically related to distance education achievements that may also
benefit teachers and facilitators when administering distance instruction.
Learning Style Theory
James and Blank (as cited in Cooper, S. S., 2001) identify the differences between
personality trait and learning style as two separate dimensions of style. Style is often
defined in education as describing individual differences in the context of learning
(Cooper, L. W., 2001). According to James and Blank, learning style is considered to be
cognitive in nature and concerns itself with perceiving, thinking, problem solving, and
memory specific to learning situations. In contrast, personality trait is affective in nature
and concerns itself with attention, emotion, and valuing. Unlike learning style,
personality trait is consistent throughout environments and applies to all areas of ones'
life, not just learning.
Learning styles are unique ways in which a person gathers and processes
information in relation to learning (Davidson, 1990; Kolb & Kolb, 2000). Styles are
relatively stable (Kolb, 1981a, 1984; Miller, 1987) and might affect a variety of learning
behaviors (Goby & Lewis, 2000). Piaget's work describing the ways in which individuals
change through life and continually adapt to their environments has had a direct impact
on learning style theorists (Kolb, 1981a, 1984).
Kolb's (1981b, 1984) theory examined specific features of Piaget's theories of
development, based on the philosophy that learning encompasses two dimensions: (a)
processing—active versus reflective and (b) perception of information during the
experience —abstract versus concrete. He created a learning style instrument that has
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become widely accepted and is used mainly with adult populations, specifically in higher
education (Mainemelis, Boyatzis, & Kolb, 2002; Truluck & Courtenay, 1999).
Learning styles researchers have produced mixed findings surrounding the idea
that when students are matched with their preferred manner of learning, their
performance improves (Brew, 2002; Dunn, 1984; Goby & Lewis, 2000; Oglesby & Suter,
1995). Lengnick-Hall and Sanders (1997) believe that by matching individual differences
and learning preferences to learning environments, practitioners create more favorable
outcomes. Subsequently, their research has identified relationships between learning
environments and student outcomes in the classroom. On the other hand, findings
provided by Bagui (2000) and Patterson (n.d.) question the validity of learning style
research as it applies to performance criteria. Their research indicates no significant
difference in achievement as it relates to specific instructional environments.
Dunn and Reckinger (1981) identify three key assumptions in learning style
research:
1. People differ in their preferences for ways to learn.
2. It is possible to measure individual differences.
3. Matching or mismatching these preferences with instructional techniques
affects learning.
Distance education may be designed with these assumptions in mind, thereby
identifying characteristics that may predict successful performance. In this manner,
designers and practitioners would be able to design the most effective distance learning
environments for their students.
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Distance Learning
Distance learning has become a popular and practical choice for many students
and institutions. Since the popularity of distance education has accelerated, distance
learning has become a mainstream instructional delivery system (Barkhi & Brozovsky,
2004). A 2-year study of distance learning used in postsecondary institutions released in
2002 by the U.S. Department of Education revealed that more than half of all
postsecondary schools are offering distance learning courses, with another 25% planning
to offer distance education within the next 3 years.
Although various types of technology can be used as the primary mode of
instructional delivery for distance education courses, more institutions use asynchronous
Internet instruction than other forms of technology (Stokes, 2003). According to Barkhi
and Brozovsky (2004), most researchers that question whether or not physical presence
of the instructor and student in the same place influences learning leave out the presence
of individual differences. There is a growing body of evidence on the demographics of
those who choose distance learning and the characteristics of why they prefer this
environment over face-to-face situations (Berge & Mrozowski, 2001). There exists,
however, a lack of research factors related to student characteristics as they relate to
academic performance and achievement within this environment (Stokes).
Individual personality type and learning style have been reported to affect
learning (Wheeler, 2001; Wolk & Nikolai, 1997) and, in turn, performance. Personality
type and learning style may, therefore, affect how a student would respond and learn
under different educational settings. By focusing on two theoretical constructs—
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personality type and learning style—student performance in relation to the design of
distance education environments may be predicted.
Statement of the Problem
The influence of individual cognitive differences may help to explain what type of
student might be more likely to perform well in a distance education setting. Relatively
little is known about the individual differences of students who enroll and succeed in
distance education courses (Stokes, 2003; Wang & Newlin, 2000). This may be an
important factor since the design of any learning environment is hampered without an
understanding of the characteristics and needs of its students (Berge & Mrozowski, 2001;
Smith, 1997). Consequently, Wang and Newlin postulate that the technology that should
serve as a resource in support of student needs may end up driving course design instead.
A major concern of education has been to find ways in which students may learn
most effectively and efficiently (Dunn, 1984; Hunter, 1986; Porter, 1997; Smith, 1997).
Individual differences play an increasingly important role in the design of effective
learning environments (Rasmussen, 1996). Thorough examinations in the areas of trait
theory and learning style might aid instructional designers, educators, and developers of
distance education in improving course design and creating more effective learning
environments.
The shortage of data surrounding the distance education field, specifically related
to individual differences and performance predictors, is unfortunate (Berge &
Mrozowski, 2001; Oswick & Barber, 1998; Ross, Drysdale, & Schulz, 2001; Wang &
Newlin, 2000). It seems unlikely that design of effective learning environments could be
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constructed without a fairly thorough knowledge of the type of student for whom these
environments are being created. Clarification of personality characteristics and learning
styles as they apply to performance will assist educators and developers in designing
more effective curricula that better serve student individual needs and expectations.
Significance of the Study
Recent uses of knowledge relating to personality trait theory and learning style
theory range from improving learner outcomes to the development of alternative
instructional strategies. Perhaps the most important implication from research in these
areas is the relationship between individual differences and performance outcomes.
Designers and instructors may benefit from the use of this research by creating more
effective learning environments specifically designed to accommodate a variety of
individual differences.
The primary purpose of this study is to examine personality trait and learning
style as independent predictors of performance in a distance education environment.
Another significant element of this study is the investigation of both personality trait and
learning style as combined predictors of performance in distance education environments.
In reviewing the literature, numerous studies have been conducted in a variety of
subject areas that attempt to predict performance using either the Myers-Briggs Type
Indicator as a measure of personality or the Learning Style Instrument as a measure of
learning style (Wheeler, 2001). However, despite the high levels of validity and
reliability of both instruments, results have been inconclusive. Oswick and Barber (1998)
conducted two studies that attempted to predict performance in undergraduate accounting
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courses and found no correlation between personality trait and performance; however,
Nourayi and Cherry (1993) found a significant correlation in a similar study. Westerman,
Nowicki, and Plante (2002) also found that personality trait was a significant predictor of
student performance for undergraduates in the management field. However, many of
these studies contain multiple achievement variables making it difficult to correlate
elements of trait or style with specific performance objectives.
Studies have been conducted investigating similar predictive elements of
personality trait and learning style. However, findings have been inconclusive. Moreover,
many of these studies contain multiple achievement variables, making it difficult to
correlate elements of trait or style with specific performance objectives.
By examining individual differences within a distance education setting, results
should assist designers, instructors, trainers, and developers in improving instruction and
better serving the individual needs of their students. By exploring and contributing to the
knowledge base of personality trait theory and learning style theory, Rasmussen (1996)
“acknowledges their existence in the teaching and learning process and, when possible,
permits the tailoring and improvement of instruction” (p. 8).
The significance of individual differences research as it applies to the construct of
distance education is monumental. Oblinger et al. (2001) suggest that college-age
populations are continuing to grow and that campuses are not physically large enough to
accommodate the new numbers of students. Distance education allows campuses the
physical flexibility of being able to accommodate students’ needs without structural
modifications as well as the ability to increase enrollment by reducing the barriers
associated with physical proximity.
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With this growing need for distance education, designers and instructional
practitioners will need to find new ways to administer curriculum and develop effective
course design. Personality trait and learning style research specifically related to distance
education environments is one such area of need. The significance of this research in the
promotion of variant learning environments is critical. With the growing number of
learning environments available to students, linking one’s personality trait or learning
style to a particular environment could have tremendous impact on student performance,
thereby having impact on the institutions providing these choices, from enrollment
increases to increased student satisfaction.
By this study, the current knowledge base is supplemented with regard to
individual differences by examining the influences of personality trait and learning style
as predictors of performance within a distance education environment. Results will assist
instructional designers, trainers, educators, and developers of distance education in
increasing the effectiveness of distance education curriculum and design.
Purpose and Scope of the Study
There has been a tremendous amount of research in the area of distance learning
in the past decade. As the demand for this environment grows, so should the effort to
provide instructional design methods that aid in the achievement of the highest possible
student outcomes. Research on learner characteristics that predict performance success is
one such area.
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Purpose
The intent of this study is to investigate student learning style and personality type
as possible predictors of student performance in distance education. A secondary purpose
of this study is to identify contingent relationships between the two independent
variables: (a) personality type and (b) learning style. This study is intended to assist
instructional designers, educators, and developers of distance education in designing
more effective learning environments.
Setting
Pensacola Junior College (PJC) is located in Pensacola, Florida, and serves a
current student population of 29,590 on three campuses (PJC, 2004). The college offers
associate and applied associate degrees, as well as vocational and technical certificate
programs, an adult high school, dual enrollment opportunities for high school students,
continuing education programs, and remediation classes. Currently, PJC offers 68
distance classes across a variety of disciplines (PJC). All of the distance education classes
use WebCT as the course delivery system.
Participants
The participants are undergraduate students enrolled in a community college Art
Humanities course in the Visual Arts Department at the PJC main campus. According to
the PJC Factbook (2004), the majority of students are part time, approximately 33% of
the students care for dependents, 50% work more than 20 hours per week, and the
average student age is 28.
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Variables
Independent Variables
The two independent variables in this study are the two dimensions of learning
styles, processing and perception, and the four personality type polarities, extraversion-
introversion, sensing-intuition, thinking-feeling, and judging-perceiving. Learning styles
will be determined using Kolb's Learning Style Inventory (Kolb, 1976). Personality type
will be measured using the Myers-Briggs Type Indicator (Myers & McCaulley, 1985).
Dependant Variable
The dependant variable in this study is performance. Performance is measured by
averaging four test grades, given every 4 weeks throughout a 16-week semester. The final
grade average will be used as the dependant variable.
Research Questions
The following three research questions are posed for this study:
1. How does personality type as measured by the Myers-Briggs Type Indicator
(MBTI) predict academic performance in a distance education course
delivered through WebCT?
2. How does learning style as measured by the Learning Style Inventory (LSI)
predict performance in a distance education course delivered through
WebCT?
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3. How does the interaction of personality type as measured by the MBTI and
learning style as measured by the LSI predict performance in a distance
education environment delivered through WebCT?
Definitions of Terminology
To facilitate a better understanding of what is being communicated through this
research, several terms are defined below.
Cognitive learning style. Cognitive learning styles are consistencies in the method
a learner uses to acquire and process information. They are based on a theory that defines
the following four phases in the process of learning from experience: (a) concrete
experience, (b) reflective observation, (c) abstract conceptualization, and (d) active
experimentation (Boyatzis & Kolb, 1995; Kolb, 1981b). Individual learning styles are
defined by a person’s virtual reliance on these four learning modes (Boyatzis & Kolb).
Distance education. Distance education is a way of delivering education and
training through the use of a personal computer with the absence of face-to-face
interaction. Delivery methods can be synchronous or asynchronous and can range from
highly interactive forms of instruction to no interaction between the instructor and the
student.
Learning styles. Learning styles refer to how individuals learn. These are
characteristic ways of gaining, processing, and storing information. These styles are overt
and observable (Kolb, 1981a, 1984) and provide cues about how individuals process or
mediate information.
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Performance. Performance can by defined as a student's display of what has been
learned (Gagne, Briggs, & Wagner, 1988). The learning outcome in this study is retention
of knowledge as measured by four exams that are averaged at the end of the semester.
Personality trait. Personality trait is determined by an individual's preferred way
of dealing with new information as well as how he views situations. These traits are static
and are relatively inbuilt features of the individual (Verma & Sheikh, 1996).
Chapter Summary
The purpose of the proposed study through examination of background research
regarding learning styles, personality traits, and distance education was identified in this
chapter. Along with the purpose and scope of the study, a statement of the problem was
reviewed. Major research questions were outlined. The significance of the problem was
discussed and definitions of terms were listed. A complete review of literature is provided
in chapter 2.
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CHAPTER II
REVIEW OF THE LITERATURE
Introduction
Historically, finding ways students may learn most effectively and efficiently has
been a major concern of education. In the past, learning styles and personality differences
have both been theorized to affect student performance (Aragon, Johnson, & Shaik, 2002;
Ross et al., 2001). According to the U.S. Department of Education (2002), determinations
regarding these differences have helped educators develop materials and teaching
methods compatible with student inclinations for many years.
However, Aragon et al. (2002) note that researchers have tended to confine their
studies to specific classes of attributes: (a) human abilities, (b) interests, (c) personality,
or (d) biological and environmental attainments. Few research programs have examined
these attributes concurrently for their role in explaining and predicting performance
(Ackerman & Heggestad, 1997; Lounsbury, Sundstrom, Loveland, & Gibson, 2003). The
majority of cognitive research in education has been limited to face-to-face learning
environments (Berge & Mrozowski, 2001; Crossman, 1995; Joughin, 1992). Since
distance education settings offer a relatively new form of presentation, it seems logical
that we begin to examine human attributes that may be specifically related to
performance within those environments.
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The literature relevant to this study, specifically personality trait theory, learning
styles theory, and student characteristics related to distance learning environments, is
reviewed in this chapter. These domains are explained in terms of their relationship to
performance in a distance education setting. The research findings involving the
individual differences of personality type and learning style, respectively, are reviewed in
the first two sections. In the third section, learner characteristics related to students in
distance education environments are examined. Lastly, implications of the literature
review for the study are presented along with summary information.
Personality
Personality is defined as all the relatively stable and distinctive forms of thought,
behavior, and emotional responses that represent a person's ability to adjust to
surrounding conditions (Gordon & Yocke, 1999; Jung, 1971; Myers, 1980). For the
purpose of this study, an adaptation of Carl Jung's definition of personality type will be
used. Jung defined personality as dispositions and preferences that make seemingly
random behaviors not random at all (as cited in Dewar & Whittington, 2000; Myers,
1962). Jung also hypothesized that these seemingly random behaviors were in fact quite
orderly and consistent and are a function of different ways in which people prefer to use
their perception and judgment (Myers, 1962).
Personality traits are important variables in the learning process. Awareness of
personality type in the formulation of teaching and learning strategies is essential to
learners, educators, and designers (Dewar & Whittington, 2000). Kretovics and
McCambridge (2002) also support the importance of the variables of personality type,
18
specifically in distance education environments. Messick (1993) states that personality
attributes can enhance the content aspects of performance and may also distort and
interfere with functioning, depending on the nature and intensity of the personality
characteristics.
Personality Trait Theories and Models
The question regarding personality classification was raised first over 2000 years
ago by Theophrastus in his book Characters: “Why is it that while all Greece lies under
the same sky and all the Greeks are educated alike, yet we all have characters differently
constituted” (as cited in Jonassen & Grabowski, 1993, p. 303). Eysenck (1981) maintains
that individuality and variability are so common among people that many psychologists
have become disappointed trying to find a scientific basis for constructing a model of
personality. Yet the ancient Greeks suggested an answer that has lasted longer than any
other psychological theory (Eysenck; Guilford, 1967; Jung, 1971)—the theory of the four
temperaments, embodying the concepts of traits and types in a classification system that
has acted as a catalyst for much further research on the matter.
Psychological Type Theory
Early conceptions of personality trait theory were proposed by Swiss psychologist
Carl Jung. His theory of psychological types was based on the idea that apparently
random behavior is not really random at all, but rather has a pattern to it (Dewar &
Whittington, 2000; Jung, 1971; Myers, 1962). Jung postulated that this pattern mirrors
the person's propensity for collecting information (perception) and making decisions
19
(judgment). These two processes were considered to be auxiliary functions, described in
more detail below.
Working toward a psychological symmetry, dominant functions or processes are
paired with auxiliary processes to manifest a balanced personality (Jung, 1933, 1971;
Myers, 1980). Dominant functions explain how individuals reflect the world in which
they feel most comfortable (Myers): the outer world of action (extraversion) or the inner
world of ideas (introversion). “This behavior, Jung suggests, is inborn, just like being
right or left-handed” (Dewar & Whittington, 2000, p. 386).
The two general attitudes characterized above, introversion and extraversion, are
described by Jung (1933) as complementary attitudes or orientations toward life.
According to Jung, everyone is capable of being both introverted and extraverted, even
though these actually are opposite tendencies. This assumption contains the premise that
“as people grow to adulthood, one of the attitudes comes to be dominant so that
observationally, the person is either introverted or extraverted” (Maddi, 1989, pp. 310).
Jung (1933) believed that everyone uses four basic mental processes, which he
called (a) sensing, (b) intuition, (c) thinking, and (d) feeling. His theory assumes that any
cognizant mental action can be classed as one of these four functions (Jung). Moreover,
the personality of an individual is characterized by the dominance of one of these
functions over the others. He also added that each person uses his dominant function in
either an extraverted way or an introverted way. Jung's personality theory had eight
different typological groups.
According to Jung's (1933) theory, these eight typological groups were (a)
introverted sensors, (b) introverted intuitors, (c) introverted thinkers, (d) introverted
20
feelers, (e) extraverted sensors, (f) extraverted intuitors, (g) extraverted thinkers, and (h)
extraverted feelers (see Table 1).
In addition to these dominant functions, Jung (1933) proposed auxiliary functions.
The auxiliary for a psychologically healthy individual was a perceiving function if the
dominant was a judging function and a judging function if the dominant was a perceiving
function. Extraverts would rely on the auxiliary for introverting and introverts would rely
on it for extraverting (Maddi, 1989; Myers & McCaulley, 1989).
Jung believed that the attitudes and functions combine to affect how individuals
relate to the world and the people around them (Maddi, 1989; Myers, 1980). These
cognitive styles are typically bipolar and value free (Myers & McCaulley, 1989). Each
style dimension has different implications, none of which are any more or less optimal
than the other (Myers, McCaulley, Quenk, & Hammer, 1998).
The Big Five
The Big Five is a five dimensional model of personality based on experience as
opposed to theory. “The model was identified by searching for the smallest number of
synonym clusters that could account for the largest variation in individual differences in
personality” (Center for Applied Cognitive Studies, 2004, ¶ 2). These factors are
dimensions, not types, so their measurement is changed regularly. They are also partly
genetic and universal (McCrae & Costa, 1997). The five factors as defined by The Center
for Applied Cognitive Studies are listed in Table 2.
Table 1 Personality Characteristics of Jung’s Psychological Type Theory
Dominant temperaments Descriptions
Extraverted sensor Realists, sensualists, people who are attracted by the physical characteristics of objects and people. Not reflective, strive for intensity of experience, consciousness is directed outward.
Introverted sensor Perception is very subjective, may seem indifferent to objective reality. Perceives the world as amusing and reacts subjectively to events in a way that is unrelated to objective criteria.
Extraverted intuitive Attempts to see all of the possibilities in a situation. Constantly needs new experiences in order to maintain interest. Highly enthusiastic and inspiring to others.
Introverted intuitive Inwardly directed with visionary ideals. Aloof, with little interest in explaining their vision. Life becomes a mission, often misunderstood.
Extraverted thinker Links ideas together in rational and logical ways. Conclusions drawn are directed outward. Thinking is a private, subjective experience. Expect others to recognize and obey a universal moral code.
Introverted thinker Contemplative and directed inward to subjective ideas. Elaborates all the ramifications and implications of an idea. Complex thinking, impractical and indifferent to objective concerns.
Extraverted feeler Conforming, adjusting response to objective circumstances. Strive for harmony, convictions of the heart take precedence over the head.
Introverted feeler Strives for inner intensity that is unrelated to external objects. Seemingly negative or indifferent, the focus is on inner processes. Inconspicuous nature can be seen as neutral, cold or dismissive.
Note. Table adapted from Jungian Psychology: Jung’s Theory of Psychological Types (p. 28), by M. Daniels, 2003.
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Table 2 Type Designations of the Big Five
Designation Definition Associated facets
N Need for stability, negative emotionality or neuroticism
Sensitiveness, intensity, interpretation, and rebound time.
E Extraversion or surgency Enthusiasm, sociability, energy mode, taking charge, trust of others, and tact.
O Openness, culture, originality or intellect
Imagination, complexity, change, and scope.
A Agreeableness or accommodation
Service, agreement, deference, reserve, and reticence.
C Conscientiousness, consolidation or will to achieve
Perfectionism, organization, drive, concentration, and methodicalness.
Note. From What are the Big Five? (¶ 1), by Center for Applied Cognitive Studies, 2004. Links exists between The Big Five, Carl Jung's theory of psychological types, and
Myers-Briggs additions to Jung's original theory. Although The Big Five is based on
experience and not theory, developers were “closely attuned to human experience when
defining their four dimensional model” (Center for Applied Cognitive Studies, 2004, p.
8). For example, two of the five factors are precisely related to social contexts,
specifically extraversion. The judgment dimension is linked to accommodation (A) and
the dimension of introversion can be correlated to originality and openness (O) in The
Big Five.
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Fulfillment Model
Alfred Adler's fulfillment model represents “interrelated peripheral characteristics
that find regular expression in people and that determine individuality” (Maddi, 1989, p.
320). In other words, Adler's theory was predicated on peripheral characteristics, rather
than dominant ones, making up an individual type. The basis of the theory stems largely
in part from a person's
Sense of inferiority and their means of circumventing or transcending it;
expressive of the core tendency of striving for superiority. Additionally, an
individual's style of life will evolve from the content of real and imagined
inferiorities and from the manner in which they are transcended or circumvented.
(Maddi, 1989, p. 321)
Adler postulated that the individuals' style of life is established by the age of 5 and that
there is no change thereafter (Adler, 1964).
Adler (1956) clearly designates the relationship between peripheral and core
personality traits. Additionally, family constellations, defined as a person's status with
regard to their siblings, is another important part the model (Adler). Basically, in
overcoming inferiority and striving to reach superiority within a family grouping, two
distinctions have come to formulate Adlerian typology: (a) constructiveness-
destructiveness and (b) activeness-passiveness. According to Adler (1964), the
constructive-destructive classification refers to individual social interest, while the active-
passive classification concerns itself more with the individualistic implications of striving
for perfection.
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Drawn from the above distinctions, four types of peripheral personality styles are
suggested: (a) active-constructive, (b) passive-constructive, (c) active-destructive, and (d)
passive-destructive (Adler, 1956). These styles are established in childhood and are
believed to remain static. Unlike Jung’s theory of typology, “Adler considers the
constructive-deconstructive dimension somewhat more important than activeness-
passiveness in determining what is ideal” (Maddi, 1989, p. 323).
Eysenck's Theory
“Hans Eysenck believed that heredity played a large role in determining
personality. His theory is based on physiology and genetics, although he was a
behaviorist who considered learned habits of great importance” (Brand, 1997, p. 80).
Eysenck (1981) believed that personality differences were hereditary. His theory was
heavily influenced by Carl Jung and he favored the temperament side of typology
(Ackerman et al., 2001; Maddi, 1989).
Eysenck's (1981) original research discovered two dominant dimensions of
temperament: (a) neuroticism-stability and (b) extraversion-introversion. “Neuroticism-
stability described a range from calm and collected to people that have a tendency to be
nervous” (Brand, 1997, p. 80). He believed this was a “genetically-based,
physiologically-supported dimension of personality” (Guilford, 1967, p. 102).
Extraversion-introversion is described in Jungian terms as an internal versus external
reflection of the world in which we feel most comfortable (Eysenck & Eysenck, 1985).
The two dimensions of neuroticism-stability and introversion-extraversion are not
interrelated and form two quadratic axes. The neurotic extravert, neurotic introvert, stable
25
extravert, and stable introvert generally combine in most people (Guilford, 1967). The
majority of people are closer to the center of the model and are called ambiverts (Eysenck
& Eysenck, 1985).
Instruments for Assessing Personality Type
Personality trait theory is determined by the instruments intended to measure the
presented dimensions of personality. In other words, personality type instruments
measure matching personality type theories. There are numerous instruments that
measure personality type, including the (a) NEO-PI, Eysenck Personality Questionnaire
(EPQ), (b) the 16 Personality Factor Questionnaire (16PF), (c) the Myers-Briggs Type
Indicator (MBTI), and (d) the Keirsey Temperament Sorter (KTS).
NEO-PI
The NEO-PI is derived from The Big Five and contains 23 personality scores in
five dimensions: (a) neuroticism, (b) extroversion, (c) openness, (d) agreeableness, and
(e) conscientiousness (Grabowski & Jonassen, 1993). The dimension of extraversion also
contains the subscales of (a) warmth, (b) gregariousness, (c) assertiveness, (d) activity,
(e) excitement seeking, and (f) positive emotions. The NEO-PI is a self-report
questionnaire that can be taken in paper form or online.
Eysenck Personality Questionnaire (EPQ)
Eysenck (1981) originally developed the Maudsley Medical Questionnaire, which
measured neuroticism (N) solely. He subsequently published the Personality Inventory
26
(MPI), which added an extraversion scale to his earlier version. Further research showed
Eysenck that the inventory needed improvement. This led to the development of The
Eysenck Personality Inventory (EPI), which added a lie scale to the form (Eysenck &
Eysenck, 1985). The Eysenck Personality Questionnaire (EPQ) added the fourth
dimension of psychoticism (P). The EPQ is a personality questionnaire devised to
measure not only introversion-extroversion but also neuroticism. The EPQ divides the
dimensions of extraversion-introversion and neuroticism-stability into four defined
quadrants:
1. Stable extraverts—sanguine qualities such as outgoing, talkative, responsive,
easygoing, lively, carefree, and leadership.
2. Unstable extraverts—choleric qualities such as touchy, restless, excitable,
changeable, impulsive, and irresponsible.
3. Stable introverts—phlegmatic qualities such as calm, even-tempered, reliable,
controlled, peaceful, thoughtful, careful, and passive.
4. Unstable introverts—melancholic qualities such as quiet, reserved,
pessimistic, sober, rigid, anxious, moody. (Shepard, 1985, ¶ 5)
16 Personality Factor Questionnaire (16PF)
Raymond Cattell developed an instrument that measured fundamental dimensions
of normal personality (Murphy & Davidshofer, 1998). “Cattell found evidence for a first-
order factor of introversion-extroversion and a second-order factor that he thought came
even closer to what Jung had in mind” (Maddi, 1989, p. 455). A relationship also exists
between the introversion-extroversion scales offered by Cattell and by Eysenck. Crookes
27
and Pearson (1970) compared scores on the two procedures and found significant
correlations between the measures of introversion-extroversion.
Relying on a list of personality descriptors developed by his peers (Fehriinger,
2004), Cattell grouped these descriptors into categories. Through the analysis of these
categories he identified surface and source traits that he believed were representative of
the structure of personality (Murphy & Davidshofer, 1998). Although these 16 factors are
considered independent of one another, there are associations among them.
Unlike Eysenck’s EPQ, the 16PF rates each item response with a point value. The
values are used to produce 16 raw scores. When the point values are compared with one
of the norm tables provided in the manual, they translate into standard scores known as
stens (area transformation scores on a standard ten base). “Each sten is then profiled on a
graph that shows where the individual stands in reference to the norm group used for
comparison” (Murphy & Davidshofer, 1998, p. 379).
Myers-Briggs Type Indicator (MBTI)
Born from Carl Jung's discovery that all people have both an extraverted and
introverted nature, Catherine Briggs and her daughter Isabell Myers devised an
instrument to measure eight bipolar functions (Myers, 1962). These functions were based
upon the introverted and extraverted expressions of the four Jungian mental functions of
sensing, intuition, thinking, and feeling (Myers, 1980). “The shorthand designation of
these functions is as follows: sensing extravert (Se), sensing introvert (Si), intuitive
extravert (Ne), intuitive introvert (Ni), thinking extravert (Te), thinking introvert (Ti),
28
feeling extravert (Fe), feeling introvert (Fi)” (Myers, 1962, p. 5). Correlation data exist
between the MBTI and Jung's psychological types (see Table 3).
Table 3 Comparison Characteristics of Jung’s Psychological Type Theory and the Myers-Briggs Type Indicator
Introverts Extraverts
MBTI type Jung type MBTI type Jung type
ISTJ IS(T) ESTP ES(T)
ISTP IT(S) ESTJ ET(S)
ISFJ IS(F) ESFP ES(F)
ISFP IF(S) ESFJ EF(S)
INFJ IN(F) ENFP EN(F)
INFP IF(N) ENFJ EF(N)
INTJ IN(T) ENTP EN(T)
INTP IT(N) ENTJ ET(N)
Note. Table adapted from Jungian Psychology: Jung’s Theory of Psychological Types (p. 6), by M. Daniels, 2003. In addition to Jung's 16 preference types, Myers and Briggs added action and
reflection (judging and perceiving) functions to the theory (Myers, 1962). The following
describes each of the Myers-Briggs polarities:
1. Extraversion versus introversion (how a person becomes energized).
2. Sensing versus intuition (how a person perceives information).
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3. Thinking versus feeling (how a person makes decisions).
4. Judging versus perceiving (what type of lifestyle a person adopts).
A preference for one function on each polarity results in 16 different types as listed in
Table 4 (Myers et al., 1998).
The MBTI is a “forced-choice, self-report instrument, designed for administration
by qualified professionals and intended for use with normal subjects” (Thompson &
Borrello, 1986, p. 748). It is currently the most frequently used psychometric instrument
in the study of education, training, and management (Capraro & Capraro, 2002; Sabatier
& Oppenheim, 2001).
Keirsey Temperament Sorter (KTS)
Using Jungian theory, Keirsey and Bates (1984) developed an indicator that
describes the four temperaments based on combinations of two of the four Myers-Briggs
dimensions. The KTS identifies how each of the initial temperaments is either concrete or
abstract in thought and speech, and either cooperative or pragmatic in getting what they
want (Keirsey, 1998). He then divides each temperament into two distinct subtypes,
depending on their inclination to be directive or informative in dealing with others.
Like Jung, and Myers and Briggs, Keirsey (1998) theorizes that the four
temperaments are unique consistent patterns of personality which are fundamentally
different from one another. Using the bipolar scales from the MBTI, Keirsey organizes
the temperaments accordingly. The same 16 temperaments exist in the KTS as in the
MBTI; however, the definitions vary slightly and the terminology of the temperaments
has been altered.
Table 4 Personality Type Breakdown of the Myers-Briggs Type Indicator
ISTJ Serious, quiet, logical, dependable, well organized.
ISFJ Quiet, friendly, responsible, conscientious, accurate with figures, patient with detail.
INFJ Gifted and original, desire to please, quiet, conscientious, considerate of others.
INTJ Original, large amount of drive, organized, skeptical, critical and independent.
ISTP Quiet, reserved, analytical, exerts himself only as much as he considers necessary.
ISFP Retiring, quietly friendly, sensitive, hates arguments, modest, loyal, lives in the present moment.
INFP Enthusiastic, interested and responsive. Friendly, warm but not sociable for the sake of sociability.
INTP Quiet, reserved, good at theoretical or scientific subjects. Logical, has no capacity for small talk.
ESTP Matter of fact, doesn't worry or hurry, always has a good time. Blunt and sometimes insensitive.
ESFP Outgoing, easygoing, uncritical, fond of a good time, joins in helpfully, literal minded, tries to remember rather than to reason.
ENFP Warmly enthusiastic, high-spirited, ingenious, imaginative, can do almost anything that interests him, often relies on spur of the moment ability.
ENTP Quick, ingenious, gifted in many lines, lively and stimulating. Alert and outspoken, argues for fun on either side of any question. Resourceful in solving problems.
ESTJ Practical, realistic, matter of fact, with a natural head for business. Not interested in subjects he sees no actual use for. Good at organizing.
ESFJ Warm-hearted, talkative, popular, conscientious, interested in everyone. Cooperative, no capacity for abstract thinking, always doing something nice for everyone.
ENFJ Responsive, responsible, feels a real concern for what others think and want, tries to handle things with due regard for other people's feelings and desires. Sociable and popular.
ENTJ Hearty, frank, good at things that require reasoning and intelligent talk, like debate or public speaking. Well-informed and self-confident.
Note. Adapted from The Myers-Briggs Type Indicator Manual (p. 64) by I. B. Myers, 1962, Princeton, NJ: Educational Testing Service.
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The Keirsey and Bates (1984) instrument defines the temperaments as (a)
Dionysian (sensing and perceiving preferences), (b) Epimethean (sensing and judging
preferences), (c) Promethean (intuitive and thinking preferences), and (d) Apollonian
(intuitive and feeling preferences (Borg & Shapiro, 1996). These four dimensions are
further classified in terms of two subtypes, depending on their inclination to be directive
or informative in dealing with others.
Research on Personality Type
The establishment of personality type theory has appeared in psychological and
educational literature for approximately 40 years (Lounsbury et al., 2003). However,
there is little agreement as to the effects personality traits may have on instructional
practices and learning.
Personality Type and Performance
Research studies show mixed results of the significance of personality type in
relation to individual performance. Lengnick-Hall and Sanders (1997) conducted
numerous studies matching personality type to performance with significant correlations.
Westerman et al. (2002) expanded Lengnick-Hall and Sanders’ research by examining
relationships between personality type, learning environment, and performance. They
found that personality remained a significant predictor of student performance,
specifically related to the dimension of introversion (Westerman et al.). However, neither
of these studies measured personality using the MBTI nor were the performance
outcomes specifically described.
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Other studies endorse the belief that personality type influences performance.
Wheeler (2001) conducted a literature review of 16 articles specifically related to
accounting courses. All of the studies examined used the MBTI to measure personality
and seven of the studies assessed performance with course grade. There was a significant
interrelation between personality type and performance in all seven studies. The most
significant correlations were on the sensing-intuitive scale. These results seem to indicate
that there are dimensions of personality type that may be important in the
individualization of course design.
According to Felder, Felder, and Dietz (2002),
Studies of type effects in engineering education have been carried out by a
consortium of eight universities and the Center for Applications of Psychological
Type. In all of these studies, introverts, intuitors, thinkers, and judgers generally
outperformed their extraverted, sensing, feeling and perceiving counterparts.
(p. 3)
It is unclear how measures of performance were determined; however, these results
certainly support the idea that personality type may influence performance.
Performance variables such as cumulative grade point averages over the course of
years have been studied in relation to personality type. Rosati (1999) observed type
differences for students at the lower end of the academic range with no distinction by
type for the higher level students (as cited in Felder et al., 2002). Felder et al. showed
similar findings studying admissions indices and grade point averages of freshman
engineering students. Among the stronger students, introverts had higher grade point
33
averages and a higher admissions index; however, the differences were not statistically
significant.
Studies examining personality types as predictors of performance have also been
conducted on the general college population. Kahn, Nauta, Gailbreath, Tipps, and
Chartrand (2002) conducted a study using 677 college freshman enrolled in orientation
courses. Using the MBTI and several other personality assessment instruments, their
findings uniquely predicted grade point average and freshman-to-sophomore persistence.
Reviewing studies of type effects in education, McCaulley (1990) reports the
sensing-intuition difference to be the most important preference. Myers et al. (1998)
report that preference for intuition, which involves perceiving patterns and connections in
information, is related to higher scores on standardized tests than the preference for
sensing, which implies a focus on details. Rosati (1999) and Felder et al. (2002) support
Myers and McCaulley in their findings that intuitors consistently outperformed sensors in
college engineering courses, thus confirming the possibility that personality type and
performance may be of particular interest in course design and instruction.
Results of these studies suggest that personality type may influence performance.
Most of these studies examined performance through matching preferences with varying
assessment criteria. In many cases, correlations were made; however, specific outcomes
were not discussed. For this study, performance will be measured by averaging four
exams throughout a 16-week semester. The idea of personality type influencing
performance further implies that individuals displaying specific personality types may be
better able to learn effectively through different learning environments, such as distance
education.
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Personality Type, Performance, and Distance Education
The ability to organize thoughts and manage time contributes to each individual's
measure of success in educational settings (Atman, 1988). The lack of face-to-face
interaction in distance education implies that certain characteristics be present in the
individuals who choose this environment. The following explanation from Myers and
McCaulley (1985) provides further interpretation for the consideration of the application
of psychological type elements in academic settings.
Academic achievement requires the capacity to deal intensively with concepts and
ideas, which are mainly the province of introversion. It also requires the capacity
to work with abstraction, symbols, and theory, which are the province of intuition.
. . . Type theory predicts . . . that types with introversion and intuition (IN types)
will have a relative advantage, since their interests match academic tasks.
Academic tasks requiring logical analysis favor thinking types, and academic
tasks requiring understanding of human motivations favor feeling types. The
perceptive attitude (open, spontaneous, and curious) favors a wide acquaintance
with many subjects, which may lead to increased scores on aptitude measures.
The judging attitude (planful, focused and organized) is related to application and
is often associated with higher grades. (p. 96)
Distance education environments require a tremendous amount of planning,
organizing, and time management on the part of the learner. Eyong and Schniederjans
(2004) found personality type to be a significant predictor of grade achievement in a
totally Web-based education course. Their findings indicated that because of the
35
independent nature of distance education, introverts and individuals with preferences for
paying attention to detail outperformed their counterparts.
In a similar study, Montgomery (n.d.) found that students who performed best in a
variety of multimedia-based distance education settings were active learners, sensors, and
judgers, or those who prefer global thinking. Her findings also correlated visual learners
in this environment as outperforming their verbal classmates.
When faculty move their classes online, the assumption (Patterson, n.d.) is that
the students are the same as face-to-face students. What works in a face-to-face
environment does not necessarily work in a distance education setting (Diaz & Cartnal,
1999). According to Diaz and Cartnal, online students differ considerably in cognitive
styles from their face-to-face counterparts.
With increasingly refined technology available, the implications for uniquely
designed distance education programs continually grow. It would be possible to develop
individualized curricula and course design focused on information management, self-
monitoring techniques, and time-use control skills that address the specific needs of
distance learners.
Learning Styles
Quite a few definitions of learning styles have emerged from the review of
literature. Terry (2001) reviewed a variety of interpretations and revealed learning styles
definitions based on “self-views, needs, personalities, individual strategies, differences,
processes, temperaments, autonomies, modalities, aptitudes, values, ideal environments,
personal touches, motivations, behavior sets, characteristics, preferences, patterns and
36
nature and make-up” (p. 68). Although an exhaustive definition has not evolved, there are
commonalities in the suggested definitions that can be used as a foundation in the
examination of learning styles.
Learning styles refer to how individuals process and organize information. Kolb
(1984) refers to learning styles as the characteristic ways each individual collects,
organizes, and transforms information into useful knowledge. Messick (1993) agrees with
Kolb and states that the focus is on the schematization and management of approaches to
learning and the addition of knowledge. The types of things students want to learn about,
how they will approach learning situations, and the settings in which they prefer to learn
are all influenced by individual learning style preferences (Conti & Welborn, 1986;
Messick; Soliday & Sanders, 1993). Learning styles are a student’s consistent way of
responding to and using stimuli in the context of learning (Davidson, 1990; Dunn &
Brunner, 1997). Learning styles tend to be fixed characteristics (Kolb, 1976, 1981b;
Miller, 1987) affecting a variety of learning behaviors. These observable behaviors
(Gregorc, 1985) provide clues to the ways individuals process and perceive information
(Davidson; Kolb, 1981a, 1984). Hunt (1982) adds dimensionality to the topic by adding
the role of structure to the definition, asserting that individuals require a certain amount
of structure, which may be high or low, in order to meet their individual learning needs.
These definitions suggest that learning style is a preference an individual has for
processing and perceiving information in a distinct manner specifically related to
learning. Most learning styles theorists agree that these traits are observable, fixed
characteristics that are consistent across a variety of learning situations. “The dimensions
37
of processing and perception form the basis of understanding how learning styles
influence the learning process” (Rasmussen, 1996, p. 13).
Learning Styles Theories and Models
“Learning styles are related to patterns of individual thoughts, beliefs, attitudes,
and behaviors” (Terry, 2001, p. 124). Although certain agreement exists regarding the
general definition of learning styles, the ways in which those styles are classified depend
largely upon individual theorists. Classifications are based on varying perceptions of the
learning process (Rasmussen, 1996). Terry asserts that theorists typically focus on the
affective, cognitive, and behavioral components of the learning process. Affective
behaviors are defined as those resulting from attitudes, opinions, or beliefs. Cognitive
behaviors refer to the ways in which individuals process information, and behavioral
components consist of environmental or biological factors that influence learning (Dunn
& Griggs, 2000). The following learning style classifications emphasize one or more of
these behaviors.
Productivity Environmental Preference (PEP)
Rita and Ken Dunn developed a model of learning styles that reflects their belief
that “learning style is a biologically and developmentally determined set of personal
characteristics that make the identical instruction effective for some students and
ineffective for others” (Dunn & Griggs, 2000, p. 9). The Productivity Environmental
Preference (PEP) model is comprised of five categories that explain individual student
performance variances. Originally, the model contained affective and physiological
38
attributes; however, newer versions include a cognitive component (Rasmussen, 1996).
The roots of this model can be traced to cognitive-style theory. “Cognitive-style theory
suggests that individuals process information differently on the basis of learned or
inherent traits” (Dunn & Griggs, p. 9).
The PEP includes five separate categories associated with learning behaviors: (a)
environmental, (b) emotional, (c) sociological, (d) physical, and (e) psychological. The
elements associated with each category are defined in Table 5.
Table 5 Productivity Environmental Preference Classification Model of Learning Styles
Category Elements
Environmental Sound, light, temperature, design
Emotional Motivation, persistence, responsibility, structure
Sociological Colleagues, self, pair, team, authority, varied
Physical Perceptual, intake, time, mobility
Psychological Analytic and global, cerebral preference, reflective and impulsive Note. Table adapted from Dunn, 1986 and Dunn, Dunn, & Price, 1979 (as cited in Learning Styles and Adult Intellectual Development: An Investigation of Their Influence on Learning in a Hypertext Environment, by K. Rasmussen, 1996, Unpublished doctoral dissertation, University of South Alabama, Mobile).
Environmental aspects are defined as “reactions to the immediate instructional
environment” (Dunn & Griggs, 2000, p. 9). This category identifies reactions to sound,
lighting, temperature, and seating arrangement. Emotional aspects are defined as an
individual's own emotionality and include areas of (a) motivation, (b) persistence, (c)
responsibility, and (d) preferences for the amount of structure required. Sociological
39
aspects are defined as social attitudes of the learning environment and include
preferences for (a) working alone or with peers, (b) group or individualized instruction,
and (c) routines in the methods of learning or variety. Physical attributes of learning
include (a) perceptual strengths, (b) time-of-day energy levels, (c) intake (snacking while
concentrating), and (d) mobility needs. Psychological elements of learning include global
versus analytic processing determined by correlations among (a) sound, (b) light, (c)
design, (d) persistence, (e) sociological preference, and (f) intake (Dunn, Bruno, Sklar, &
Beaudry, 1990; Dunn, Cavanaugh, Eberle, & Zenhausern, 1982; Rasmussen, 1996).
According to Dunn (1996), in order for students to be successful in a variety of
educational situations, individual learning styles should be considered. Many
practitioners have studied the impact of learning styles on achievement and have found
that matching individual styles to environment or instruction significantly contributes
to performance (Dunn et al., 1990; Dunn & Griggs, 2000; McCaulley, 1990; Terry,
2001).
Mind Styles Delineator
Gregorc (1985) proposed a four-quadrant model of learning styles, the Mind
Styles Delineator, which describes learning within the polarities of perception and order
(DePorter, 2000). Using the dimension of perception, Gregorc postulated that individuals
process information in either an abstract or concrete manner. Similarly, the dimension of
order refers to the ways in which individuals prioritize or use incoming information either
sequentially or randomly (DePorter; Gregorc). The four scales are (a) abstract thinking,
(b) concrete thinking, (c) sequential thinking, and (d) random thinking.
40
Abstract thinking describes individuals who prefer to work with concepts and
ideas. Concrete thinking refers to more detail-oriented thought processes. Sequential
thinking describes orderly, step-by-step thinking, and random thinking refers to the
process of skipping from one idea to another without order (DePorter, 2000).
Combinations of the two scales generate four possible types. Although individuals tend to
be dominant in one or two dimensions, most people use all of the styles in different times
and contexts (Rasmussen, 1996). The elements associated with each learning pattern are
described in Table 6.
Table 6 Characteristics of Learning Patterns for the Mind Styles Delineator Style Characteristics Concrete sequential (CS)
Order and logical sequencing of information, process information step-by-step, prefer following directions, physical concrete interaction with the world.
Abstract sequential (AS) Translate and interpret what they learn with what they know, good at research, inquisitive and curious, want to understand theories, prefer learning information that is logical and sequential.
Abstract random (AR) Global thinkers, need time to reflect before making decisions, prefer big picture, people oriented, creative.
Concrete random (CR) Divergent thinkers, enjoy experimentation, creative, lose track of time and deadlines, look for options and possibilities, intuitive and insightful.
Note. Table adapted from Discovering Your Personal Learning Style, by B. DePorter, 2000, Oceanside, CA: Learning Forum and from Learning Styles and Adult Intellectual Development: An Investigation of Their Influence on Learning in a Hypertext Environment, by K. Rasmussen, 1996, Mobile: University of South Alabama.
41
Field Independence Versus Field Dependence
The dimension of field independence versus field dependence measures whether
the learner uses an “analytical as opposed to a global way of experiencing the [subject
matter] environment” (Keefe, 1979, p. 9). Both field independent and dependent
dimensions rely on an individual’s method of perceiving his learning field or
environment. Field dependent modes of perceiving refer to an individual’s “perception
being dominated by the overall organization of the surrounding field, and parts of the
field are experienced as fused. In a field independent mode of perceiving, parts of the
field are experienced as discrete from the organized ground” (Sims & Sims, 1995, p. 51).
In other words, field dependent individuals rely on their environment for structure
and they rely heavily on external stimuli. They are social learners with short attention
spans who like informal learning situations (Sims & Sims, 1995). Field independent
learners are analytical and do not rely on their learning environment for stimuli. These
learners are self-motivated, task-oriented and internally structured (Grabowski &
Jonassen, 1993).
Grasha-Riechmann Learning Styles
Riechmann and Grasha examined the learning styles of college students through a
social, affective perspective (Solihull Secondary SCITT, 2002). The theory refers to the
different ways individuals approach the learning environment as opposed to an
individual's perception of learning itself (Keefe, 1979). “This measure can be classified
as a social interaction scale because it deals with patterns of preferred styles for
42
interacting with teachers and fellow students in a learning environment rather than how
information is perceived or organized” (Grabowski & Jonassen, 1993, p. 281).
Riechmann and Grasha (1974) identified the following bipolar scales: (a)
avoidant-participant, (b) competitive-collaborative, and (c) dependent-independent. The
avoidant-participant scale measures how much learners want to be involved in the
learning environment. This includes attitudes toward learning and reactions to the
classroom environment. The competitive-collaborative scale measures learner
motivations in relationships with other students, including the nature of the interaction.
The independent-dependent scale measures how much structure the learner desires and
his attitude toward teachers. The individual dimensions are described in Table 7.
The Grasha-Riechmann learning styles are closely linked to other cognitive styles
and controls such as locus of control and Kolb’s learning styles (Grabowski & Jonassen,
1993). Locus of control is a measure of one’s feelings regarding individual internal
versus external responsibility for events. Kolb’s learning styles are a measure of a
person’s preferred style of perceiving and processing information and are defined
thoroughly later in this chapter. Although the model indicates bipolar dimensions,
Riechmann and Grasha (as cited in Solihull Secondary SCITT, 2002) found that most
learners indicate some degree of preference in each of the categories.
Experiential Learning Model
Kolb's (1981a) experiential learning styles model has roots stemming from
multiple theories. Among them, Kolb has drawn conclusions from John Dewey's
emphasis on the need for learning to be grounded in experience, Kurt Lewin's emphasis
43
Table 7 Classifications of the Grasha-Riechmann Student Learning Styles
Learning style Definition Participant
Desire to learn course content, responsible for own learning, participates with others, is independent and collaborative.
Avoidant No desire to learn course content, assumes no responsibility, does not participate with others, is dependent and is competitive.
Collaborative Work well with other students, enjoy group or team activities.
Competitive See the classroom as a win-lose situation in which they need to win. Do not work well with other students.
Independent Confident and curious learners. Prefer to work alone. Enjoy self-paced work and independent study.
Dependent Need to be told what to do. The teacher is the source of all information. Will learn only what is required. Need quite a bit of guidance.
Note. Table adapted from Handbook of Individual Differences, Learning, and Instruction (p. 250) by B. L. Grabowski and D. H. Jonassen, 1993, Hillsdale, NJ: Lawrence Erlbaum.
on the importance of a person being active in learning, and Jean Piaget's theory on
intelligence as the result of the interaction of the person and the environment (Grabowski
& Jonassen, 1993). Kolb's cognitive theory is based on four classifications that illustrate
competencies learners need in order to learn effectively.
The classifications are (a) concrete experience (CE), (b) reflective observation
(RO), (c) abstract conceptualization (AC), and (d) active experimentation (AE). These
modes are situated at the ends of two intersecting continua according to learners'
corresponding preferences for feeling versus thinking (CE versus AC) and watching
44
versus doing (RO versus AE). This intersection forms a matrix with four quadrants into
which individual preferences fall. The four learning styles are (a) diverger (CE and RO),
(b) assimilator (RO and AC), (c) converger (AC and AE), and (d) accommodator and are
displayed in Figure 1 (AE and CE).
Figure 1. Kolb’s model of learning styles. From Learning Styles and Disciplinary Differences (pp. 31-57) by D. A. Kolb, 1981b, London: Wiley & Sons.
General descriptions of learner characteristics are from Kolb (1976, 1981b, 1984),
Rasmussen (1996), and Terry (2001). These style delineations are provided below:
45
1. Accommodators learn best through hands-on experience. They like to carry
out plans and take risks. Accommodators enjoy solving problems through trial
and error. They are adaptable, concrete, and active.
2. Divergers enjoy brainstorming, imagination, and emotionality. They are
interested in cultural activities and are multiperspective when problem
solving. Divergers have strengths in concrete and reflective thinking.
3. Assimilators are more concerned with theories and less with people. They are
thinkers and watchers and like to put things in concise, logical formats.
Assimilators rely on inductive reasoning and have dominant preferences in
abstract conceptualization and reflective observation.
4. Convergers choose to deal with things rather than people and prefer technical
tasks and practical solutions. They are thinkers and doers and are best at
finding practical uses for ideas and theories. Convergers use deductive
reasoning and learn best abstractly and actively.
Henson and Hwang (2002) note that the theory is cyclical in nature and that “an
effective learner typically participates in new experiences (CE) and then reflects on these
experiences (RO) to develop informal theories (AC). The learner then uses these theories
to make decisions or solve problems (AE)” (p. 713). Further delineation of the model is
depicted in the competition amongst the abilities of process and perception. According to
Kolb, Boyatzis, and Mainemelis (2001), the abilities within the dimensions of process
and perception represent polarized aptitudes that lie on different ends of the continuum.
Although effective learners utilize all four abilities, the average learner favors one ability
on each dimension. It is the combination of learners’ abilities on abstractness over
46
concreteness (AC-CE) and action over reflection (AE-RO) that constitutes ones learning
style preference.
4Mat System
Based on Kolb's learning types and Jung's concepts of psychological type,
McCarthy (1981) added recommended teaching methods based on sequential processes
and developed the 4Mat system. Beginning with the diverger and successively continuing
through the assimilator, converger, and accommodator, she found a way to include all
learners in their natural preferences while encouraging them to develop skills in the other
three styles. Figure 2 summarizes these sequential processes.
The model requires each lesson or content chunk to be directed around the circle
answering questions relevant to each of Kolb's quadrants: “Why? (relevance), what?
(facts and descriptive material), how? (methods and procedures) and what if?
(exceptions, applications, creative combination with other material” (Cooper, L. W.,
2001, p. 17). She also included brain dominance from other researchers (McCarthy,
1981). The 4Mat model specifically “reflects brain research indicating that the focus of
traditional teaching is too narrow and may put students at risk for not working up to their
potential” (Kise, 2004, p. 67).
Unlike other cognitive learning models, McCarthy (1981) places her focus on
creating learning environments that utilize all of the learning styles. In an effort to
support the dominant and nondominant styles of all students, McCarthy has created
strategies to assist educators and designers through the use of methods that promote the
47
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use of specific learning styles. In Table 8, McCarthy summarizes characteristics of
learners and designers and teachers.
Instruments for Assessing Learning Styles
Learning styles instruments measure corresponding learning styles theories.
Additionally, styles are often defined by the instruments designed to measure them
48
(Rasmussen, 1996). There are several instruments currently available to measure learning
styles. These instruments include the (a) Learning Style Inventory (LST), (b) Productivity
Environmental Preference Survey (PERS), (c) Mind Style Delineator and (d) Group
Embedded Figures Test (GEFT).
Table 8 4Mat: Characteristics of the Four Learning Styles Learning styles
Learner characteristics
Instructional design and teacher characteristics
Diverger Concrete perception of information, process reflectively, interest in people and culture, commitment, meaning and clarity are high priorities.
Aid in self-awareness, encourage discussions, team work, explicit meaningful goals.
Assimilator Abstract perception of information, process reflectively, detail oriented, seek continuity, expert opinions are of great importance.
Transmit knowledge, facts and details, organized sequential thinking, demonstrations of knowledge.
Converger Abstract perception of information, process actively, pragmatic, strategic thinkers, skill oriented, like to experiment.
Encourage productivity and competence, skills for adult life are important, increase independence with knowledge level.
Accommodator Concrete perception of information, process actively, learn by doing, interested in self-discovery, enthusiastic, flexible, risk takers, people are important.
Enable self-discovery, gear curricula to leaner interests, encourage experiential learning, help students act on their own visions.
Note. Table adapted from The 4Mat System: Teaching to Learning Styles With Right/Left Mode Techniques (pp. 37-43), by B. McCarthy, 1981, Barrington, IL: Excel.
49
Learning Style Inventory (LSI)
The LSI is a self-reporting instrument consisting of 12 sets of sentence
completions. Each sentence has four possible endings that individuals rank in order of
how they learn best. Once the dimensional scores are calculated, results are shown on the
active-refIective and abstract-concrete continua. In addition to the paper format, the LSI
has been placed online where it can be computer scored for more accurate results. The
instrument was specifically designed for adults to help them understand their strengths
and weaknesses in terms of learning (Rasmussen, 1996).
Productivity Environmental Preference Survey (PEPS)
The PEPS is a self-report inventory consisting of 100 true or false questions.
Individuals answer the questions based on how they would act in certain situations (Dunn
& Griggs, 2000). Once the survey is calculated, the respondent is provided with a list of
elements important to him or her. Since the initial list of questions is so large, learning
styles are unique for each individual (Rasmussen, 1996).
Mind Style Delineator
The Mind Style Delineator is a self-reporting instrument consisting of 40 words
arranged in 10 columns of four items each. Individuals rank the word clusters relative to
who they really are (Gregorc, 1985). Scores are graphed for a visual representation of an
individual's learning style. The graph has four axes representing (a) concrete sequential,
(b) abstract sequential, (c) abstract random, and (d) concrete random. Scores range from 4
50
to 40. High scores show dominant styles, median scores show intermediate styles, and
low scores show mediating styles (Gregorc).
Group Embedded Figures Test (GEFT)
The GEFT measures field dependence-independence. The GEFT is a self-
reporting 25-item assessment. The test requires participants to locate geometric shapes
embedded in larger, more complex designs. Low scores on the test indicate that one is
unaffected by environmental distractions while learning. High scores indicate the
opposite. The test “was initially developed for research into cognitive functioning, but
has become a recognized tool for exploring analytical ability, social behavior, body
concept, preferred defense mechanism and problem solving style as well as other areas”
(Mind Garden, Inc., 2004, ¶ 3).
Research on Learning Styles
The concept of learning styles has appeared in educational literature for close to
30 years (Rasmussen, 1996). There seems to be a consensus as to the importance of
learning styles in the creation of individualized learning environments (Myers &
McCaulley, 1989; Ross et al., 2001). However, the significance of learning styles as they
relate to performance has been reported with mixed results (Ross et al.).
Learning Styles and Performance
Many researchers support the idea that learning styles influence performance
(Biner et al., 1997; Ross et al., 2001; Sabry & Baldwin, 2003). It is estimated that three
51
fourths of the population do not learn using their preferred learning style in conventional
academic settings (Davidson, Savenye, & Orr, 1992). Ross et al. examined the effects of
learning style on performance in university students. Using the mind style delineator,
research was gathered over a 4-year period. Their findings revealed that sequential
learners performed better than random learners in both courses investigated.
Wey and Waugh (as cited in Hsiao, n.d.) investigated undergraduate students in
Western Civilization courses who completed the GEFT. One treatment group was given
text-only lessons, while the other used a combination of text and graphics. Their results
showed that field-independent students out performed the field-dependent students in the
text-only group. There was no significant difference between the two groups in the text
and graphics format.
Keirsey and Bates (1984) found significant relationships between certain types of
learning and personality types. They noted that intuitive types prefer environments that
allow for symbol recognition and comprehension, like reading. In the same study they
found that individuals with extroversion and perception spent more time trying to develop
better academic skills than their introverted and judging counterparts.
Other studies also support the idea that learning styles affect performance. In a
study using learning style workshops to predict higher grade point averages in college
students, Nelson et al. (1993) found that students involved in the workshops had higher
grade point averages than students who were not involved. Davidson et al. (1992) found
significant differences in performance among students with high abstract sequential
scores as opposed to high abstract random scores. Students with high abstract sequential
52
scores received considerably higher point totals in undergraduate computer applications
courses than the students with high abstract random scores.
Learning Styles, Performance, and Distance Education
When implementing technology into education and training, distance education
allows for vast possibilities. However, certain considerations should be taken toward the
observance of the type of individual that may benefit from computer instruction (Lyons-
Lawrence, 1994; Patterson, n.d.; Roblyer, 1999).
According to McCarthy (1981), some styles may be more effective than others in
certain situations. Wang and Newlin (2002) examined learning styles in a hypertext
environment using the field-dependence and field-independence scales. These results
indicated that field-independent students spent significantly more time on screen and
covered more of the program than the field-dependent students. As a result, field-
independent students generally outperformed their field-dependent counterparts.
Lyons-Lawrence (1994) investigated the relationships of learning style, computer
usage, and performance. Her findings showed notable differences in visually perceptive
and nonvisually perceptive student achievement. The study also showed a correlation
between posttest scores and visual perception, “which helps to support Dunn's theory that
students' learning styles are related to their performance in instructional settings” (Lyons-
Lawrence, p. 173).
Studies have been published that note the use of multimedia technology as a
performance neutralizer may greatly impact students with different learning styles
(Grasha & Yangarber-Hicks, 2000). Karakaya, Ainscough, and Chopoorian (2001)
53
reported similar findings in their study of class size, learning style, and performance.
They found that by matching certain types of technologies with specific learning styles
they were able to neutralize performance differences related to differences in learning
style.
The role of learning style has been explored as a potential predictor of student
achievement in myriad technological environments. Evidence exists linking specific
learning styles to performance in computer-assisted instruction (Biner et al., 1997;
Bostrom, Olfman, & Sein, 1990; Carlson, 1991; Davidson et al., 1992). Bostrom et al.
discovered that learners with a converger style performed better than others when
learning to use a computer. Similarly, Barkhi and Brozovsky (2004) implied that by using
specific learning style preferences educators and designers may be able to create more
effective learning environments, such as that offered through computer-mediated course
delivery systems.
These studies suggest that learning style, performance, and distance education
may be related as they apply to effective learning environments. In many of these studies,
specific outcomes are not discussed in terms of their application to design. Implications
for further research in the area of learning styles and distance education might need to
include a variety of different curricula and technologies that support specific styles
related to current performance data.
Personality Trait and Learning Style
According to Messick (1994), the most essential relationship between type and
learning style can be seen in the nature of the dominant mental processes in personality.
54
Relationships exist between dominant thinking types and logical, analytical, well-
organized learning styles. Similarly, individuals with dominant feeling types prefer
learning environments in which relationships are formed and attachments to the subject
matter are made (Myers, 1980).
Margerison and Lewis (1979) correlated MBTI with Kolb's LSI general
characteristics and learning styles. They found the following relationships of the general
characteristics: (a) concrete related to feeling, (b) abstract related to thinking, (c) active
related to extroverts, (d) reflective related to introverts, (e) abstract conceptualization
related to judgment, and (f) concrete experience related to perception. Myers (1962)
relates that of all the learning styles, (a) accommodators were associated with extroverted
sensing, and assimilative with introversion and intuitive; (b) divergers were associated
with introversion and feeling; and (c) convergers were associated with extroversion and
thinking.
Further studies have been carried out using a variety of learning styles and
personality type instruments. Kulkarni (1996) found that extroverts had high scores in the
social and people subset of the decision preference analysis (DPA) and sensors scored
high in practical and manual subsets. In the same study he also revealed similarities
between thinkers and the scientific and analytical subset.
A more recent study performed by Husch (2001) determined that significant
relationships exist between personality trait as defined through the MBTI and learning
style as measured by the Felder-Silverman Index of Learning Styles. In addition to this
relationship, Husch also found that reflective learners scored higher on exams in first
55
semester college calculus courses than those categorized as sensing perceptors on the
MBTI.
Distance Education
Historically, the term distance education has been used to refer to everything
from telecourses to interactive video to correspondence courses to computer-assisted and
computer-mediated instruction (Wang & Newlin, 2000). Bayless (2001) defines distance
education as “taking place when a student and instructional source are separated by
physical or temporal distance, and a combination or voice, video, data, and/or computer
technology are used to facilitate the instructional process” (p. 10). Most of the literature
supports definitions similar to the one above. For the purpose of this study, distance
education refers to education delivered to a remote location via computer technology in a
synchronous and asynchronous instructional format (on-campus testing is required and
some instruction may be conducted via e-mail).
Trends in Distance Education
From recent studies, we know that more students are choosing distance learning
formats than ever before, at least at the postsecondary level, and that the demographics of
distance learners are changing to reflect that of the typical college student (Roblyer,
1999). According to the Pew Learning and Technology Program (as cited in AFT Higher
Education, n.d.), “94 percent of all colleges and universities are currently (63 percent) or
planning (31 percent) to offer distance and distributed learning” (¶ 1).
56
Growing enrollments in higher education are forcing the demand for increases in
distance delivery methods (Howell, Williams, & Lindsay, 2003). The National Center for
Education Statistics (NCES, 2003) estimates approximately 5,000 postsecondary 2-year
and 4-year institutions in the U.S. enrolling nearly 14.4 million students. At a recent
University Continuing Education Association conference, Callahan (2003) noted that the
largest high school class in history will occur in 2009. With the increases in college-age
populations, many facilities and institutions agree that their campuses are not large
enough to accommodate this growing number of students (Oblinger et al., 2001).
Distance education programs may be one solution to the capacity pressures that
increasing registration may have on higher education.
Rapid developments in technology have made it increasingly easy for colleges
and universities to take advantage of distance delivery methodologies. Institutions are
able to offer instructional programs to students who need scheduling flexibility such as
individuals living in remote areas, holding fulltime jobs, or those with family needs. With
these issues in mind, students are beginning to look for courses that can meet their
individual needs and learning styles. As more distance education opportunities become
available, the need for quality competitive programs will grow. Environments that target
specific learner traits and styles may be one way to maintain quality instruction and
provide the most effective global learning situations.
Characteristics of Distance Learners
According to Howell et al. (2003), distance learners are “practical problem
solvers” (p. 3) who are motivated to take courses for a practical purpose such as
57
professional advancement or interest in the subject matter. Berge and Mrozowski (2001)
believe that distance learners have certain common lifestyle characteristics. They work
full or part time and balance a variety of family roles. Often these students are restricted
by region or circumstance. Bayless (2001) adds that some dropouts, including students
who decide to take a break, and those beginning a second career often choose distance
education as an alternative to face-to-face traditional delivery.
Howell et al. (2003) state that learner needs are changing and that their demands
include “time, scheduling, money, and long-term commitment constraints. They also tend
to feel insecure about their ability to succeed in distance learning, find instruction that
matches their learning style, and have sufficient instructor contact, support services, and
technology training” (p. 3).
In terms of learning style and personality type, the lack of face-to-face interaction
in distance education implies certain characteristics be present in the individuals who
choose this environment. Myers and McCaulley (1985) postulate that achievement in
academic settings requires the ability to deal with concepts and ideas, which are mainly
the zone of introversion. It also requires the capacity to work with abstraction, symbols,
and theory, which are the zone of intuition. Without the aid of face-to-face interaction,
these traits could play significant roles in the determination of performance within
distance education environments. Bostrom et al. (1990) discovered that learners with a
converger style performed better than others when learning to use a computer. Similarly,
Barkhi and Brozovski (2004) implied that by using specific learning style preferences
educators and designers may be able to create more effective learning environments, such
as that offered through computer-mediated course delivery systems.
58
There are some obvious implications of personality trait and learning styles
research as they relate to distance education. Snow, Corno, and Jackson (1996) use the
term macroadaptation to suggest the importance of individualized instruction through the
design of alternate environments that engage students through different forms of
information processing. Instructors and designers may find that understanding the
application of student personality type and learning style may provide guidelines and
solutions to questions currently being asked regarding the quality of distance education
programs and how to improve them. Results of personality type and learning style
research could have serious implications for course design and the implementation of
current curricula in distance education formats.
Chapter Summary
Personality type and learning style literature related to theories and models and
instruments for and research on performance outcomes were examined in this chapter.
Areas related to distance education and its future impact on course design and instruction
have been included.
Implications for further research in the areas of course design and specific
curricula that accounts for learning style and personality trait are noted in the literature
review. In the growing area of distance delivery systems, practitioners will need to
concern themselves with the new and competitive distance education market. Learning
styles and personality types are ways to create effective learning systems that target
individual differences.
59
CHAPTER III
METHODOLOGY
Introduction
The research design, variables, and instrumentation used to conduct this study are
outlined in this chapter. Also included are the procedures and statistical method for data
analysis. The study was designed to investigate student learning style and personality
type as possible predictors of student performance in distance education. A secondary
purpose of this study was to identify contingent relationships between the two
independent variables: (a) personality type and (b) learning style. Finally, a chapter
summary will be presented.
Research Design
This was a predication study, intended to identify variables that forecast student
performance in distance education and to maximize “the correlation between the
predictor variables and the criterion” (Borg & Gall, 1989, p. 584). The purpose of a
predictive study is to determine the ability of an independent variable(s) to predict the
values of one, dependent or criterion variable. For the purpose of this study, the
relationship between several independent variables, (a) personality type and (b) learning
style on the criterion variable, performance in distance learning, was studied. Because
60
the independent variables were being examined both in isolation and jointly, a simple
linear regression was used as well as a multiple linear regression.
Both independent variables are related to the predictor variable but are
uncorrelated with one another (Bagui, 2000). The assumption for a normal distribution
and a potential sample size of 75 students should minimize threats to validity, reliability,
and generalizability (Lomax, 2001).
Setting
Pensacola Junior College (PJC) is located in Pensacola, Florida, and serves a
diverse student population on three campuses. The college offers associate and applied
associate degrees, as well as vocational and technical certificate programs, an adult high
school, dual enrollment opportunities for high school students, continuing education
programs, and remediation classes. PJC is considered a large community college with an
average of 10,000 students per semester (PJC, 2004). The institution currently offers 68
distance learning classes across a variety of disciplines using WebCT as the course
delivery system (PJC).
Distance Education Delivery
WebCT is a Web-based instructional delivery medium used for computer-assisted
and computer-managed instruction. The product features (a) online testing and grading
services, (b) student tracking databases, (c) student grading databases, (d) threaded
discussion groups, (e) chat groups, (f) e-mail services, (g) hotlinks to external sites, (h)
the ability to upload files from a personal computer, and (i) a variety of calendar options
61
for deadlines and due dates. The interface design is partially customizable and the
instructor inputs and uploads course materials independently. The courses are password
protected and can be entered from any computer with Internet access.
Course Information
Art Humanities (ARH 2000W) designed to serve nonart majors was used. Several
sections of this course are offered each semester using distance education. All sections of
the course are taught by the same instructor with the same WebCT format and access for
all sections, minimizing the risk of instructional variances being examined as a variable.
Course content is available solely through WebCT delivery and personal e-mail contact
with the professor. Communication in the course is asynchronous, extracting time of day
as a possible extraneous variable. Students are required to use the testing center on
campus to take four exams. These exams are given on the computers at the testing center
where they are proctored and graded electronically; immediate feedback is sent to both
the student and the instructor. Final course grade is an average of the four exams and
there is no other graded assessment for the course.
Enrollment in each section is capped at 25 students. In Spring 2005, the semester
during which data was collected, three sections of the course are offered. Total
enrollment in all three sections is currently 75 students; however, enrollment may
fluctuate slightly during the semester due to students withdrawing from the course.
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Participants
The population for this study consists of 34 Art Humanities students at PJC who
self-selected to enroll in one of three sections of ARH 2000W. The course was offered in
the spring and fall semesters and was taught by the same professor each semester. The
course was also offered in a face-to-face environment during the same semester, giving
students the opportunity to choose the desired course format.
According to the PJC (2004) Factbook, the majority of PJC’s students are part
time, approximately 33% of the students care for dependents, 50% work more than 20
hours per week, and the average student age is 28. Recent demographic estimates found
in the 2004 Community College Survey of Student Engagement (Community College
Leadership Program, n.d.) identify the average age of community college students at 26
years with annual fluctuations as high as 28 to 30 years. These statistics are consistent
with the PJC student body with 50% of the student population being over the age of 25
(Pensacola Junior College, 2004).
In addition to age factors, PJC is also congruent with other community colleges in
terms of gender and ethnic comparisons. Typical community college demographics show
larger numbers of female students enrolled than their male counterparts (Community
College Leadership Program, n.d.). In this respect, PJC is typical of community college
environments with a ratio of 56% female to 44% male. Furthermore, PJC students are
77% Caucasian, 16% African-American, with 7% of the population made up of other
ethnic origins (Pensacola Junior College, 2004). This, too, is indicative of other
community colleges in the State of Florida. Demographic data for the Art Humanities
course was not collected for this study in an effort to isolate the specific variables.
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Variables
Independent Variables
There are two independent variables for this study: (a) personality type and (b)
learning style. Both of these variables have a basis in Jungian psychology and have been
widely researched and measured across various disciplines (Mainemelis et al., 2002).
Personality trait theory and experiential learning theory reflect the influence of
Piaget with references to developmental studies, Dewey regarding experiential
learning, Lewin in terms of dialectical tension between analytical thinking and
concrete experience, and Jung as applied ideas of types and nonpreferred modes of
learning. (Kolb, 1976, p. 12)
Learning style. Learning style, based on Kolb's (1984) theory of experiential
learning, defines four learning modes that correspond to the following four processing
dimensions: (a) affective (sensing, feeling), (b) symbolic (cognitive, thinking skills), (c)
behavioral (doing), and (d) perceptual (skills of observation). The learning modes are
conceptualized as learning abilities and identified as follows: (a) concrete experience
(feeling), (b) reflective observation (reflection, watching), (c) abstract conceptualization
(abstractness, thinking), and (d) active experimentation (action, doing). These learning
abilities resolve a tension between immediate concrete experience and analytical
detachment (Kolb & Kolb, 2000). In Kolb's model, there are two learning continuums.
Learners must choose a location between abstract conceptualization to concrete
experience on one continuum and active experimentation to reflective observation on the
other. These two learning continuums or dimensions are polar opposites. The
combination of choices one makes between abilities indicates both a preference for one
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ability over another and a preference for a specific construct or combination of abilities,
namely, a learning style (Kolb, 1976, 1984).
For the purpose of this study, discrete data (whole numbers) were retrieved from
the two continuums. Ordinal continuous measurement was employed in order to rank
number values corresponding to the learning styles. Using this data, specific learning
abilities were determined. These scores were then correlated and used in conjunction with
the raw performance scores to determine predictability.
Personality trait. Personality trait, according to Carl Jung (1933), is a person's
preferred way of attending to the world and making decisions based on psychological
types. Identification from self-reporting reactions, including basic preferences with
regard to perception and judgment along with the ability to establish the effect of each
preference, constitutes personality trait variances as described by Myers and McCaulley
(1989).
Personality traits as described by Myers et al. (1998) portray preferences along
four dichotomies: (a) extraversion-introversion (E-I), (b) sensing-intuition (S-N), (c)
thinking-feeling (T-F), and (d) judging-perceiving (J-P). The E-I dichotomy describes
whether the respondent prefers to direct energy toward the outer world of people and
objects (E) or toward the inner world of experiences and ideas (I). The S-N dichotomy
describes preferred processes of perceiving information, either through the five sense (S)
or by perceiving patterns and interrelationships among information (N). The T-F
dichotomy describes the preferred process of drawing conclusions from perceived
information, either by using objective and logical analysis (T) or by using personal and
social values (F). Finally, the J-P dichotomy describes preferred attitudes toward dealing
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with the outside world, either preferring decisiveness and closure (J) or preferring
flexibility and spontaneity (P). These four bipolar dimensions combine into 16
personality types. Each type has a distinctive way of attending to the world and making
decisions.
For the purpose of this study, discrete continuous data were retrieved from each of
the four dichotomous scales. Raw scores were employed in order to rank number values
corresponding to the specific preference within each dichotomy. Using this data, specific
personality types were identified. Using a regression analysis, the scores within each
dichotomy were then correlated and used in conjunction with the performance scores to
determine predictability.
Dependent Variable
The criterion variable for this study was student performance. Performance was
measured by four semester exams, administered in the student testing center at PJC’s
main campus. These exams were offered in 4-week intervals over the course of the 16-
week semester. All of the exams were averaged by the professor at the end of the term,
resulting in a final course grade. The researcher measured student performance using the
professor’s reported final course grades in online ARH 2000W. Student performance
ranged from A to F on the following grading scale: (a) 90-100, (b) 80-89, (c) 70-79, (d)
60-69, and (e) 59 and below.
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Research Questions and Hypotheses
The researcher investigated the predictive nature of personality type and learning
style as they applied to performance in distance education courses. To accomplish this,
the following questions and hypotheses were posed:
1. How does personality type as measured by the Myers-Briggs Type Indicator
(MBTI) predict academic performance in a distance education course
delivered through WebCT?
Ho: There is no significant relationship between personality type and student
performance in a distance education course delivered through WebCT.
H1: Specific personality types lend themselves to improved student
performance in a distance education course delivered through WebCT.
2. How does learning style as measured by the Learning Style Inventory (LSI)
predict performance in a distance education course delivered through
WebCT?
Ho: There is no significant relationship between learning style and student
performance in a distance education course delivered through WebCT.
H1: Specific learning styles lend themselves to improved student performance
in a distance education course delivered through WebCT.
3. How does the interaction of personality type as measured by the MBTI and
learning style as measured by the LSI predict performance in a distance
education environment delivered through WebCT?
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Ho: There is no significant interaction between personality type coupled with
learning style on academic performance in a distance education environment
delivered through WebCT.
H1: Personality type coupled with learning style has a significant effect on
academic performance in a distance education environment delivered through
WebCT.
Instrumentation
Data pertaining to student personality type and learning style were collected using
the Myers Briggs Type Indicator (MBTI) Form M, and the Learning Style Inventory
version 3 (LSI3) online. Both of these instruments were designed to help individuals
identify the ways in which they learn and process information. Both instruments were
founded on the “Jungian concept of styles or types, which states that fulfillment in adult
development is accomplished by higher level integration and expression of nondominant
modes of dealing with the world” (Hay Resources Direct, 2004, ¶ 3). However, the
instruments are not correlated and remain independent of one another. Both instruments
are self-report inventories that draw on item response theory. Self-report instruments are
used widely in the arenas of education and psychology (Harrington & O’Shea, 1993).
These instruments are thought by many researchers to be important indicators of behavior
(Schwarz, 1999). According to Harrington and O’Shea, “an open scoring system [self-
scorable] does not lead to a greater degree of subject response bias than a closed system”
(p. 67).
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The researcher did not need to obtain permission to administer the MBTI because
she is a certified MBTI administrator. Passing the certification exam entitles the
practitioner to purchase and administer restricted MBTI materials. Permission to use the
LSI3 was sought and received from the Hay Group in Boston, Massachusetts (Appendix
A). Hay Group screens and qualifies all research requests. A research application,
curriculum vita, and conditional use agreement were submitted to David Kolb for
approval.
Myers-Briggs Type Indicator
The MBTI is based on the work of Carl Jung and reports a person's preferred
ways of attending to the world and making decisions based on psychological types (Jung,
1933). The purpose of the MBTI is to
identify, from self-report of easily recognized reactions, the basic preferences of
people in regard to perception and judgment, so that the effects of each
preference, singly and in combination, can be established by research and put to
practical use. (Myers & McCaulley, 1989, p. 1)
The MBTI Form M online (Myers et al., 1998) is a self-reporting questionnaire
containing 93 items. From responses to 47 word pairs and 46 phrases, the respondent's
preferences can be described along four bipolar scales. Each scale is composed of a set of
forced-choice items, with the four scales being (a) extraversion-introversion (E-I), (b)
sensing-intuition (S-N), (c) thinking-feeling (T-F), and (d) judging-perceiving (J-P). The
E-I dichotomy (21 items) describes whether the respondent prefers to direct energy
toward the outer world of people and object (E) or toward the inner world of experiences
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and ideas (I). The S-N dichotomy (26 items) describes preferred processes of perceiving
information, either through the five senses (S) or by perceiving patterns and
interrelationships among information (N). The T-F dichotomy (24 items) describes the
preferred process of drawing conclusions from perceived information, either by using
objective and logical analysis (T) or by using personal and social values (F). The J-P
dichotomy (22 items) describes preferred attitudes toward dealing with the outside world,
either preferring decisiveness and closure (J) or preferring flexibility and spontaneity (P).
Test-retest reliability measurements on dichotomies are .84 to .96 and .83 to .97
on continuous scales. The reliability of preferences is generally .90 or higher, making the
instrument a very robust measurement (Myers et al., 1998). Item weights for the MBTI
Form M online are based on a standardization sample of 3,200 adults in a random
national sample (Myers et al., 1998). MBTI Form M online scoring has been improved
by using item response theory (IRT).
This method allows the selection of items that provide better information about
the respondent's preferences, and more accurate scoring. IRT nearly eliminates the
possibility of tied preference scores, and improves the accuracy of preference
identification at the midpoint by including items that better distinguish between
preferences. (Myers et al., 1998, p. 164)
“A wealth of validity data exists for the MBTI-M, including confirmatory factor
analysis supporting the four-factor structure and expected relationships between MBTI-M
scores and other self-report personality inventories” (Myers et al., 1998, p. 4). The online
Form M is slightly more reliable than its paper counterpart since the paper version is self-
scored by hand, whereas the electronic version is scored by computer.
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Learning Style Inventory
The LSI3 was revised in 1999 from David Kolb's Learning Style Inventory and is
designed to help individuals identify the way they learn from experience. “The revised
LSI includes improvements designed to enhance psychometric specifications and the
inventory's practical uses in a wide range of occupations and educational settings” (Hay
Resources Direct, 2004, ¶ 1). The inventory measures the degree to which individuals
exhibit the different learning styles attained from experiential learning theory.
The LSI3 is designed with three objectives in mind. First, the inventory is concise
and direct, making it easy to understand for both research and feedback purposes. Next,
the instrument design requires that individuals respond to it like they would respond to an
actual learning situation. Lastly, the measures of learning styles are supposed to predict
behavior consistent with experiential learning theory.
The LSI3 is a 12-item questionnaire in which respondents answer each question
by ranking four sentence endings that correspond to the four learning modes: (a) concrete
experience, (b) reflective observation, (c) abstract conceptualization, and (d) active
experimentation. Two combination scores measure preferences for abstractness over
concreteness (AC-CE) and action over reflection (AE-RO). This combination of scores
on the two dimensions classifies individuals into one of four orthogonal learning styles:
(a) accommodators (CE and AE), (b) divergers (CE and RO), (c) convergers (AC and
AE), and (d) assimilators (AC and RO). According to Brew (2002), “Kolb emphasized
the fluidity of individuals; a particular learning style reflects a predominant rather than
absolute orientation” (p. 374). Therefore, although an individual may have strong
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preferences for one style over another, most individuals use characteristics from all four
learning styles when necessary.
The LSI3 has very good internal consistency as measured by coefficient alpha and
test-retest results for the randomized scoring format (Koob & Funk, 2002). Hay
Resources Direct (2004), current suppliers and administers of the LSI, report construct
validity with test-retest validity of 0.30 to 0.71 overall, and reliabilities ranging from 0.66
to 0.86 in the difference scales with an overall average of 0.78.
Studies regarding the LSI's predictive validity have been substantiated throughout
the years (Kolb & Kolb, 2000). Hudak and Anderson (1990) supported the LSI's
predictive validity in a study with 94 undergraduate students in an Introduction to
Statistics class. The researchers concluded that the LSI effectively differentiated the
successful students from the unsuccessful ones. The authors also noted a high correlation
between the results of the LSI and the Instrument for Formal Operational Thought
(FORT), which assesses individual abilities for formal operational thought. Many of
these studies have provided validation for the constructs of experiential learning theory
using the Learning Style Inventory (Kolb et al., 2001).
Procedure
After seeking approval from PJC to use ARH 2000W and the institution's name in
the study (see Appendix B for a copy of the letter granting permission), approval for the
research study was requested from The University of West Florida Institutional Review
Board for Human Subjects (IRB) during Spring Term 2005. A copy of IRB approval may
be found in Appendix C.
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Following IRB approval, the facilitating professor was sent an e-mail with the
following documentation: (a) a letter requesting student participation, (b) an informed
consent form and assurance of confidentiality, (c) an explanation of the study and routine
procedures for completing both the LSI3 and the MBTI, (d) location, and (e) username
and password information for both instruments (see Appendix D for documents). The
professor was asked to forward the researcher's notices to the students' e-mail accounts
and post the information on the WebCT course page. To aid in obtaining the largest
sample size, the professor offered the students extra credit for their participation. For the
purposes of data collection, extra credit points obtained because of the study were not
used in the final raw performance score. However, the additional points were added to the
students' final grades at the end of the semester. The memo outlining the extra credit
agreement was posted on WebCT by the professor.
Prediction studies have a higher dependability if the predictor variable is
measured “before the criterion behavior pattern occurs” (Borg & Gall, 1989, p. 586). To
meet this requirement, participants had the opportunity to complete both inventories prior
to their first exam. By this date in the semester, drop/add had ended and students were
settled into their study routines and had a thorough understanding of the course design.
Invitations to participate were sent three times throughout the semester (every 6 weeks,
just prior to the exam dates). Although it might not have been necessary to invite
participation this frequently, this researcher felt it would be helpful in increasing the
sample size for the study. It was reasonable to allow participation for the entire 16-week
semester since the criterion variable in this study was an average of all four exam grades,
the last of which was not administered until the last week of the semester.
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At the end of the semester, a list of participants was sent to the professor so she
could add in extra credit points for participation in the study. After the data had been
collected from the MBTI and the LSI3 at the end of the Spring Semester 2005, final
grade rosters were collected from the professor. Only the last four digits of the students’
social security numbers were used for identification purposes on all of the
instrumentation, including grade rosters in order to preserve anonymity. Social security
numbers were then destroyed after data collection to ensure student confidentiality.
Performance was determined by an average of four exams throughout the semester.
Raw scores were used for data collection and did not include extra credit points. Upon
receipt of all data, Microsoft Excel was used to analyze the data.
Data Analysis
For the purpose of this study a regression analysis was used. In the social and
natural sciences, regression procedures are widely used in research (Tabachnick & Fidell,
2001). The general purpose of regression (Miles & Shevlin, 2001) is to learn more about
the relationship between several independent or predictor variables and a dependent or
criterion variable. This study contained two continuous predictor variables (personality
trait and learning style) and one dependent criterion variable (student performance).
In order to predict the independent influence personality trait and learning style
had on performance, one single predictor variable was considered at a time; therefore, a
linear regression analysis was used. This analysis is a “bivariate situation where only two
variables are being considered, one predictor variable and one dependant variable”
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(Lomax, 2001, p. 192). Multiple regression analysis was also used to determine the
combined significance that personality trait and learning style had on performance.
Regression shares all the assumptions of correlation: linearity of relationships, the
same level of relationship throughout the range of independent variable, interval
or near interval data, and data whose range is not truncated. In addition, it is
important that the model being tested is correctly specified. (Glass & Hopkins,
1996, p. 180)
With this in mind, certain assumptions of regression beyond predictor and
criterion variables needed to be met. First, linear regression supposes that the criterion
variable scores are independent of one another and are normally distributed. In this study,
student performance measured by final course grades are independent of one another and
are normally distributed. Another assumption is that the independent variables are
independent and uncorrelated. Personality trait and learning style are related, but they are
not correlated. Independent variables should also be measured without error (Glass &
Hopkins). Next, a linear relationship between the predicted scores and the raw scores of
the dependent variable was maintained, allowing the residuals to maintain a mean of
zero. Moreover, the assumption of homoscedasticity assures that the residuals are
dispersed randomly throughout the range of the estimated dependent (Berry, 1993).
Furthermore, it is customary for the predictor variables to be controlled by the researcher.
However, in this study, the predictor variables were self-reported by the students,
violating this assumption. This is a common violation in regression analysis and indicates
that any inferences made are said to be conditional on the self-reported behavior (Gunst
& Mason, 1980).
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Limitations
One of the limitations of the study was the sample size. One class was used with
the same instructor and multiple sections in an effort to lessen the possibility of
instructional variance as an extraneous variable. This decision might have restricted the
number of possible students that participated in the study. A secondary limitation may be
that extra credit was offered as an incentive to participate, offering the possibility that this
might automatically appeal to the above-average students and not the students who
choose not to do anything extra. The contrary may also be true. If the above-average
students were satisfied with their grades, they may have decided not to take advantage of
the extra credit and the sample may be weighted with the below-average students. Both
of these possibilities could affect the personality trait profile and the learning style
profile.
Chapter Summary
The purpose of this research study, the research questions, and the researcher's
hypothesis are outlined in this chapter, including the (a) research design, (b) setting, (c)
participants, and (d) variables. Furthermore, descriptions of the MBTI and the LSI have
been included for data collection. Lastly, procedures for research along with linear
regression methodologies have been included.
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CHAPTER IV
RESULTS
Introduction
Personality type and learning style were investigated in this study in order to
determine whether they could be identified as predictors of performance in a distance
education environment. The dependant variable was performance in a community college
Art Humanities course offered online. The independent variables were personality type
and learning style. In this chapter, the findings of the research will be described. To
achieve this, a description of the participants, an explanation of the methodology related
to regression analysis, the research questions, and a breakdown of the findings associated
with each question have been included.
Participants
Seventy-five students in three sections of an online Art Humanities course at
Pensacola Junior College (PJC) were invited to participate in this study. Of these 75
students, 39 chose to complete either the LSI3 or the MBTI online. Since the secondary
concern of this study was to measure the interaction of both independent variables on the
dependant variable, only 34 of the 39 students could be used—the number of students
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who completed both instruments. A summary of the sample size breakdown for each
independent variable is listed in Table 9.
Table 9 Sample Size Breakdown for Each Independent Variable Variable Sample size
Extraverts 18
Introverts 16
Sensors 29
Intuitives 5
Feelers 22
Thinkers 12
Judgers 18
Perceivers 16
Accommodators 11
Convergers 7
Assimilators 8
Divergers 7
No LSI preference 1 Note. N = 34 students per instrument.
Summary of Data
Data were collected on each of the MBTI categories: (a) extraversion, (b)
introversion, (c) sensing, (d) intuition, (e) feeling, (f) thinking, (g) judging, and (h)
perceiving. Data collection of the LS13 came from the following categories: (a) concrete
experience, (b) reflective observation, (c) abstract experimentation, and (d) abstract
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conceptualization. All of the dichotomous data for personality type and learning style has
been treated independently.
Descriptive statistics are used to summarize data or illustrate critical
characteristics of a population or sample (Leech, Barrett, & Morgan, 2005). In this study,
descriptive statistics are offered for the independent variables personality type and
learning style and for the dependant variable end of semester grade. See Table 10 for a
summary of descriptive statistics. The similarities of the performance data for all of the
independent variables and the commonalities between the standard deviations are
outlined in this table.
Table 10
Descriptive Statistics for Personality Types, Learning Styles, and End-of-Semester Grades Performance Variable Minimum Maximum M SD Extroverts 60 100 84.44 11.46 Introverts 47 99 80.68 14.75 Sensors 47 100 82.65 13.78 Intuitives 72 91 82.80 8.64 Feelers 47 100 81.27 13.77 Thinkers 60 100 85.25 11.73 Judgers 57 100 84.61 12.53 Perceivers 47 100 80.50 13.68 Accommodators 47 95 82.54 15.98 Assimilators 70 100 86.12 12.11 Convergers 60 99 81.57 14.22 Divergers 63 90 78.28 8.51 Semester grade 47 100 82.67 13.05
Note. N = 34 students.
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Regrettably, the sample size of 34 does not contain the statistical power needed to
predict performance in other situations. Furthermore, performance for the 34 student
participants was representative of the 75 students originally invited to participate in the
study. According to the professor, performance for the participants followed a normal
distribution for this particular course: 14 students earned As, 8 students earned Bs, 8
students earned Cs, 2 students earned Ds, and 2 students earned Fs.
Data Analysis
Introduction
Three research questions were addressed in this study related to the predictive
effects of personality type and learning style, in isolation and collectively, on student
performance in a distance education environment. Correlation coefficients were analyzed
and regression analysis was used to prove or disprove the related hypotheses. The results
that follow include data reported by individual research questions and corresponding
hypotheses.
Correlations were computed among the independent and dependent variables in
order to assess the autonomy of the variables and the commonality of the sample. No
significant relationships were found, indicating that these variables do not prove success
in the course. The correlations between personality type, learning style, and semester
grade are listed in Table 11.
Simple linear regression was run between each of the independent variables
(personality type and learning style) and the dependent variable (semester grade), using a
sample of 34 students. A multiple regression analysis was used to determine the
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Table 11 Correlations Between Personality Type, Learning Style, and Semester Grade (n = 34)
Variable CE RO AE AC E/I S/N F/T J/P Semester
grade CE - .355 .013 .289 .030 .008 .019 .039 .054
RO - .272 .011 .285 .084 .009 .001 .226
AE - .293 .272 .128 .071 .001 .445
AC - .027 .015 .244 .010 .180
E/I - .086 .0001 .011 .810
S/N - .040 .103 .245
F/T - .021 .603
J/P - .435 Semester grade -
*p < .05. interaction of the independent variables (personality type and learning style) on student
performance. Data collection on personality type was completed using the Myers-Briggs
Type Indicator (MBTI), online version. The Learning Styles Inventory (LS13) version 3
online was used to measure learning style. The dependent variable (performance) was an
average of four exams taken over the 16-week term.
Statistical Method
Regression analysis was used in this study because it is often used in the social
sciences to determine the effects of one or more independent variables on a single
dependant variable. Single linear regression was used to analyze the independent
predictive value of personality type and learning style on performance in a distance
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education setting. Multiple linear regression was used to determine the interactive value
that personality type and learning style had on performance. Certain assumptions underlie
the predictive value of regression analysis. Serious violations of these assumptions may
call into question any conclusions drawn from this type of statistical analysis (Leech et
al., 2005).
Assumptions
Certain assumptions must be met in regression analysis in order to make
significant statistical predictions. The sample size for each of the independent variables in
this study was not large enough to guarantee statistical power for the instruments used.
(Refer to Table 9 for sample sizes for each independent variable.) Nonetheless,
performance for the participants in the sample was typical for the Art Humanities online
courses offered at PJC. Each of the variables in this study was normally distributed and
had similar variances, except for the Intuitive personality type. Therefore,
homoscedasticity was attained for all of the independent variables except for the Intuitive
personality type. Students in this category made up only 6.8% of the participants in the
study. However, the mean semester scores for this group were still representative of the
normal grade distribution (M = 82.8). Consequently, it is difficult to ascertain whether
homoscedasticity has been violated for this variable with such a small representative
sample. Other MBTI and LSI categories showed equal variances on the dependant
variable, performance. Another important assumption in regression is the linear
relationship between variables. Using a bivariate scatterplot of the variables, it was
determined that the predicted scores and the raw scores of all of the variables except
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extraversion and introversion were linear. According to Leech et al. (2005), small
deviations from this assumption may occur without greatly affecting the procedure.
Personality Type on Student Performance: Research Question 1
How does personality type as measured by the Myers-Briggs Type Indicator
(MBTI) predict academic performance in a distance education course delivered through
WebCT? The present data indicate that there is no statistically significant predictive
effect between the independent and dependant variables. Thus, the null hypothesis is
accepted, indicating that personality type did not predict student performance in distance
education in this study.
Learning Style on Student Performance: Research Question 2
How does learning style as measured by the Learning Style Inventory (LSI)
predict performance in a distance education course delivered through WebCT? The
present data indicate that there is no statistically significant predictive effect between the
independent and dependant variables. Thus, the null hypothesis is accepted, indicating
that learning style did not predict student performance in distance education in this study.
Personality Type and Learning Style on Student Performance: Research Question 3
How does the interaction of personality type as measured by the MBTI and
learning style as measured by the LSI predict performance in a distance education
environment delivered through WebCT? The present data indicate that there is no
statistically significant predictive effect between the independent variables and the
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dependant variable. Consequently, the null hypothesis is accepted, indicating that the
interaction between personality type and learning style did not predict student
performance in distance education in this study.
Other Data Analysis
“A correlation exists between two variables when one of them is related to the
other in some way” (Plesa, 2003, p. 38). To further explore the meaning of the data, an
additional set of analyses was conducted. Correlations between personality type, learning
style, and semester grades in Art Humanities were examined. Correlations were
examined for each dichotomous characteristic of personality type and learning style. No
significant correlations were found.
Chapter Summary
The results of this study have been presented with associated explanations. First,
the participants’ scores were discussed and data were presented. Descriptive statistics and
an outline of performance data followed. Next, the assumptions of regression analysis
were addressed. Lastly, the results of the data analysis and a description of further
correlation analysis were presented. Although there was no statistically significant
predictive value of personality type or learning style on student performance, other
interesting questions have emerged from the data. Understanding that these particular
variables do not present a problem in distance education settings may be of great value to
practitioners. Additionally, the underrepresentation of the Intuitive personality type may
be worthy of further exploration in terms of self-selection. This possibility will be
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discussed in chapter 5. Further, the results of this study indicate that further research with
larger sample sizes may be necessary in order to verify predictive validity.
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CHAPTER V
DISCUSSION
Introduction
The research study and results are summarized in this chapter. Additionally, an
examination of the questions and discussion regarding the data analysis are provided.
Recommendations for further research and limitations of the study conclude the chapter.
Study Summary
Personality type and learning style were investigated in this study as predictors of
performance in a distance education setting. Personality type was measured by the
Myers-Briggs Type Inventory (MBTI) and learning style was calculated by Kolb’s
Learning Styles Indicator (LSI). Both variables were also investigated in terms of their
possible interactive effect on performance.
Students in three online sections of Art Humanities (ARH 2000W) at Pensacola
Junior College (PJC) in Florida were invited to participate in the study. Participation
included completing the online versions of the MBTI and the LSI. To increase the sample
size, extra credit points were offered by the professor for participation—34 out of 75
students participated in the study.
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Since this was a predictive study, regression analysis was used in the statistical
design to investigate the extrapolative capabilities of personality type and learning style
on student performance in a distance education setting. Although the data did not confirm
statistical significance, valuable information for further research and insights for
practitioners was provided by this study.
Discussion of Results
Research Question 1
How does personality type as measured by the Myers-Briggs Type Indicator
(MBTI) predict academic performance in a distance education course delivered through
WebCT?
Data analysis in this study showed no statistically significant predictive effect
between personality type and student performance in distance education. However, other
studies performed in distance education settings have established correlations between
personality type and student performance (Diaz & Cartnal, 1999; Eyong &
Schniederjans, 2004; Montgomery, n.d.). The variance in findings makes it a topic
worthy of further exploration.
The most plausible explanation for lack of significance in this study is the lack of
sample size per dichotomous characteristic on the MBTI. Although the participant sample
size was 34 students, the largest sample for an individual characteristic was 29 students;
the smallest sample was 5 students. With these numbers in mind, a true predictive
regression analysis could not occur. Another possible explanation is the potential of self-
selection occurring in distance education environments. This is possibly the most
87
revealing factor that warrants further attention. The balance of personality types for the
participants in this study was normally distributed except for the sensing/intuitive scale.
The results of the MBTI revealed 29 sensors (S) and 5 intuitives (N) indicating the
possibility that some type of self-selection for distance education may have occurred.
There is no current literature available on the possibility of self-selection for specific
educational environments based upon personality type. Observations from a binomial test
proved these results were not random. Consequently, the disproportionate number of
students on the S/N scale merits further discussion and will be addressed in the
recommendations section of this chapter.
Research Question 2
How does learning style as measured by the Learning Style Inventory (LSI)
predict performance in a distance education course delivered through WebCT?
The present data indicate that there is no statistically significant predictive effect
between learning style and student performance in distance education, although previous
research has proven the opposite. Evidence exists linking learning style to student
performance in distance education throughout the literature (Barkhi & Brozovsky, 2004;
Grasha & Yangarber-Hicks, 2000; Karakaya et al., 2001; Lyons-Lawrence, 1994).
However, the strength of the relationship that exists between learning style and
performance is still in question (Husch, 2001; Sabry & Baldwin, 2003). This research
contributes to that debate.
Again, the lack of a large representative sample is the most reasonable
explanation for the inconsistency of results between this study and others. Another
88
possible explanation is the lack of demographic information in conjunction with the
isolation of learning styles from other variables. Technical skills, course set-up and
implementation, previous experience with distance education, motivation, and number of
hours a student can devote to class are all possible predictors of performance in distance
education (Berge & Mrozowski, 2001). These factors may also contribute to a student’s
preferred learning style. Survey data and follow-up interviews indicating demographic
information were collected by Barkhi and Brozovsky (2004). This information was then
correlated with learning style preferences and some indications linking gender with style
preferences were found.
Another possible explanation for the research outcomes was given by Grasha and
Yangarber-Hicks (2000) in their study of multimedia technology and learning styles.
Grasha and Yangarber-Hicks found that certain technologies matched with specific
learning styles neutralized performance differences commonly related to differences in
learning style. In a similar study, Bayless (2001) states that the nonacademic needs of
distance learners are largely underrepresented in the research and may have the most to
contribute to performance. Finally, it might be that students who choose to take distance
education classes have a predisposition to certain learning styles, again moving towards
the idea of self-selection for certain learning environments.
Research Question 3
How does the interaction of personality type as measured by the MBTI and
learning style as measured by the LSI predict performance in a distance education
environment delivered through WebCT?
89
The present data indicate that there is no statistically significant predictive effect
between personality type and learning style on student performance in distance education.
Since personality type and learning style are heavily correlated (Husch, 2001; Kolb et al.,
2001), as discussed in chapter 2, these results were not surprising. Considering the fact
that these variables had no effect on performance when in isolation, it is reasonable to
assume that their interaction would not be statistically significant either.
Although the findings from this study show no effect on performance based upon
the interaction of personality type and learning style, many other reported studies have
shown high correlation between individual differences (Kulkarni, 1996; Margerison &
Lewis, 1979; Messick, 1994; Myers, 1980). For example, significant relationships have
been determined between personality type and learning style using a variety of
instrumentation. The most common correlation as it applies to performance has been
between the Extraversion-Introversion scale on the MBTI and the Active-Reflective scale
on the LSI (Husch, 2001; Margerison & Lewis; Myers & McCaulley, 1985). Thus, the
findings of this study contradict the findings from larger scale research efforts, increasing
the necessity for further exploration.
Recommendations for Practitioners
Since distance learning environments continue to increase and impact institutional
enrollment, it seems reasonable that educators, administrators, and researchers would
interest themselves in the methodologies pertaining to these new systems of learning.
One of these areas that may be of increasing importance is students’ individual
differences. Although this study did not confirm predictive value of specific student
90
characteristics, practitioners’ understanding of the students in their classrooms could be
very helpful for both instruction and design. Specifically, understanding these
characteristic differences could aid in the creative application of instruction, assessment,
and evaluation that included as many personality types and learning styles as possible.
Further, it is important for students in any environment to have a clear understanding of
their strengths and weaknesses in personality and learning style in order to improve their
confidence and success in the classroom. It may be that students do not have a clear
understanding of the strategies they may need to explore in order to learn with a variety
of learning styles or personality traits that may not be as comfortable for them. This may
be particularly crucial in distance education, especially when the students may have no
history taking distance education courses.
Practitioners should encourage the use of self-reporting instruments upon
registering for distance education courses. Students with a clear understanding of
themselves and the learning environment they are entering into may be better suited for
success than students who register without the same level of knowledge and
understanding (Peyton, 2003). For example, preferences such as the judging personality
type or the active learning style are more structured in nature and may require more
controlled learning environments than their counterparts with preferences for
unstructured learning.
This is not to say that curriculum should necessarily be changed to accommodate
individual differences; rather student and teacher sensitivity toward individual disparities
may increase success in unfamiliar environments, such as distance education.
91
Instructors, designers, and developers of distance education courses may be able to
reduce student failures and withdraw rates in distance environments if they have a better
understanding of the personality types and learning styles present in their classrooms.
Finally, it may not seem probable but it is certainly possible that curriculum and
course design could be created with all personality types and learning styles in mind. In
so doing, designers and practitioners would be giving students more extensive and
diverse options for exercises and assignments. Finally, if personality assessments and
learning styles inventories are given at the beginning of the semester, students can choose
the assignments that best fit their individual differences, giving them greater chances for
success.
Recommendations for Further Research
Personality type and learning style have been shown to affect performance in a
variety of settings (Boyatzis & Kolb, 1995; Husch, 2001; Kolb et al., 2001). Although
this study did not support previous findings shown in similar studies, attention may need
to be given to similar studies that focus on the following recommendations:
1. The small sample size in this study raises issues of validity, reliability and
generalizability. Additional research for replication with a larger population in
order to ascertain significant findings would be appropriate.
2. Research studies should be performed with the same research design and
instrumentation as the current study using many different types of institutions
and courses. This would greatly increase generalizability to other institutions
and populations.
92
3. Similar studies should be conducted using volunteer and nonvolunteer
participants. This would remove the extraneous variable that volunteer
participants may have similar personality traits or learning styles thereby
reducing the potential of skewing the sample population.
4. Based on the findings for self-selection, studies should be conducted using the
MBTI in distance education with questions regarding the population
demographic, specifically as it relates to the sensing and intuitive scale.
5. In addition to using the MBTI and the LSI, future studies should add other
quantifiable measures such as survey instruments and demographic
information in order to determine the characteristics of individuals who
choose distance education.
6. It would be prudent to compare face-to-face and distance learning
environments in terms of learner characteristics and performance. Considering
that this study did not confirm the findings revealed in other similar studies,
comparisons may be needed in order to delineate important personality
characteristics and learning styles in a variety of learning environments.
Limitations of the Study
Although every effort was made in the current study to decrease any extraneous
variables, certain limitations to the current research exist. With these limitations in mind,
explanations regarding the significance of the study may be inferred.
1. Small sample size for each independent characteristic on both the MBTI and
the LSI makes it difficult to generalize the data to other populations.
93
Furthermore, the sample did not provide significant correlations with which to
obtain a true regression fit.
2. Extra credit points were offered to participants in the study. It could be
inferred that certain personality types or students with specific learning styles
may be more likely to take advantage of extra credit situations, skewing the
sample population.
3. Students who participated in the study were volunteers. Variances may occur
with participants who are not volunteers. Again, this makes generalizing to
other populations difficult, unless the other populations are also made up of
volunteers.
4. Both the MBTI and the LSI are self-report instruments. Although self-report
instruments are widely accepted in the social sciences, other quantifiable data
collection measures should be used for support.
5. Participants were enrolled in three sections of Art Humanities at PJC.
Although the demographic of the Art Humanities course was similar to the
demographic of the college, this same subset may not be representative of
other college or university populations. Again, this raises issues of
generalizability.
6. Student performance, based on four exams during the semester, was used as
the dependant variable. The interval scale that the professor used in order to
rate student performance from A to F might not be the same scale other
professors or institutions choose. This may decrease the validity of the results.
94
Chapter Summary
This study was conducted to determine whether or not there was predictive value
in personality type or learning style in distance education. Although the current study did
not confirm the findings found in similar studies, some interesting questions surfaced
about the possibility of self-selection in distance education. The limitations of the study,
implications for practitioners, and possibilities for further research were outlined in this
chapter.
Considering that distance education is growing rapidly in many areas, researchers
will need to focus their efforts on creating the best possible options for learning in this
new medium that include the largest number of students. Students’ personality type and
learning styles are two important areas of interest in determining curriculum design,
teaching style, matriculation, and enrollment issues for today’s administrators and
practitioners.
95
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APPENDIXES
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Appendix A
E-mail Granting Permission to Use the Learning Style Inventory Version 3
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Appendix B
Letter Granting Permission to Use Pensacola Junior College Course in Study
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Appendix C
The University of West Florida Institutional Review Board Approval Letter
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Appendix D
Documents Sent to Facilitating Professor to Recruit Participants
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Introductory Letter and Invitation to Participate Hello everyone, My name is Stacey Rimmerman and I am an assistant professor here at Pensacola Junior College and a doctoral student at the University of West Florida. This spring I will be conducting research in your Humanities Art class for my dissertation. This research will be very helpful for educators and designers of distance education environments, similar to the one you are in right now. In my study, I am trying to find out if learning styles and learning preferences have anything to do with performance in online classes. For this research I am inviting you to participate by taking two inventories online during the duration of the course. The Myers-Briggs Type Indicator (MBTI) and the Learning Style Inventory (LSI) have been used widely and are both very beneficial to educators when planning curriculum and designing courses. Both of these instruments will help you to identify how you prefer to learn and how you prefer to process information. Neither of these instruments are used for psychiatric evaluations. These tests are used strictly to help individuals find out more about themselves and the people around them. Both instruments will be available online and each will take approximately 15-20 minutes. In addition, if you are interested in your results they will be available immediately online upon completion of the inventories. Your results will only be known to you and me, the researcher, and your name will not be used in the research. Instead you will be identified by the last four digits of your social security number. Additionally, Ms. Horigan has graciously offered extra credit to anyone who chooses to participate in the study. If you are interested in participating in this study, please read and sign the informed consent form that is attached to this letter. Ms. Horigan will place the website addresses on your WebCT course page later in January and instructions will be provided at that time. Thank you so much for your participation, I really appreciate it. Sincerely, Stacey Rimmerman
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Informed Consent Form Title of Research: Personality Types and Learning Styles: An Investigation of their
Influence on Performance in a Distance Education Environment.
I. Federal and university regulations require us to obtain signed consent for participation in research involving human participants. After reading the attached letter and statements in section II and IV below, please indicate your consent by signing and dating this form.
II. Statement of Procedure: Thank you for your interest in this research project
being conducted by Stacey Rimmerman, an assistant professor at Pensacola Junior College and doctoral student at the University of West Florida. Hopefully, the introductory letter, enclosed with this consent form, explained the research project. This stage of the research involves my administering the Myers-Briggs Type Indicator (MBTI) and the Learning Style Inventory (LSI) to you. This will be done online, through two websites that will be provided to you by the participating professor. The major aspects of the study are described in the statements below, including the risks and benefits of participating. Your information will be kept in the strict confidence with only you and the researcher having access to the results of the MBTI and the LSI instruments. Carefully read the information provided below. If you wish to participate in this study, type your name and the date and e-mail this form back to [email protected]. If you have questions or concerns regarding this project, please contact Stacey Rimmerman in the Visual Arts Department at Pensacola Junior College at (850) 484-1462 or by e-mail at [email protected].
I understand that:
1) I will be administered the commercially produced Myers-Briggs Type Indicator (MBTI) online. Depending on the type of computer you are using the length of the inventory will be approximately 30 minutes. I will also be administered the commercially produced Learning Style Inventory (LSI) online. The length of inventory will be approximately 15 minutes, depending on your computer.
2) My end of the semester grade for Humanities Art (ARH 2000W) will be given to the researcher and compared to my MBTI and LSI results.
3) I will be given immediate results online from both the MBTI and the LSI as soon as I have completed the inventories. Explanations of the results will also be available online.
4) While data is being gathered, my name will be replaced by the last four numbers of my social security number. At no time will my name be referenced in the study results and/or reports.
5) My professor will be adding extra credit to my end of the term grade, for my participation in this study.
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6) I may discontinue participation in this study at any time without penalties or repercussions.
III. Potential Risks of the Study:
1) There are no foreseeable risks involved with the study. IV. Potential Benefits of the Study:
1) Data obtained from this study may provide educational professionals information that would allow them to better facilitate learning experiences in future classes.
2) Information obtained from this study may provide the participants with valuable information about his/her learning style and preferred methods of processing information.
3) Participants may gain a greater respect for the personal learning styles and information processing preferences of their fellow students.
4) Comparison of data should give educators, designers and researcher additional information about the learning styles and type preferences as they relate to distance education courses.
Statement of Consent: I certify that I have read and fully understand the Statement of Procedure given above and agree to participate in the research project described therein. Permission is given voluntarily and without coercion or undo influence. It is understood that I may discontinue participation at any time without penalty or loss of any benefits to which I may otherwise by entitled. I may print a copy of this consent form for my records. If you have any questions or concerns please call Stacey Rimmerman, the researcher, at (850) 484-1462. Please e-mail the signed consent form to: [email protected]. ___________________________________________ Participant’s Name (Please Print) ___________________________________________ __________________ Participant’s Signature Date
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Introductory Letter and Invitation to Participate Hello everyone, My name is Stacey Rimmerman and I am an assistant professor here at Pensacola Junior College and a doctoral student at the University of West Florida. This spring I will be conducting research in your Humanities Art class for my dissertation. This research will be very helpful for educators and designers of distance education environments, similar to the one you are in right now. In my study, I am trying to find out if learning styles and learning preferences have anything to do with performance in online classes. For this research I am inviting you to participate by taking two inventories online during the duration of the course. The Myers-Briggs Type Indicator (MBTI) and the Learning Style Inventory (LSI) have been used widely and are both very beneficial to educators when planning curriculum and designing courses. Both of these instruments will help you to identify how you prefer to learn and how you prefer to process information. Neither of these instruments are used for psychiatric evaluations. These tests are used strictly to help individuals find out more about themselves and the people around them. Both instruments will be available online and each will take approximately 15-20 minutes. In addition, if you are interested in your results they will be e-mailed directly to you upon completion of the inventories. Your results will only be known to you and me, the researcher, and your name will not be used in the research. Instead you will be identified by the last four digits of your social security number. Additionally, Ms. Horigan has graciously offered extra credit to anyone who chooses to participate in the study. In order to participate you must:
1. Read the instructions for accessing BOTH instruments. 2. Take both inventories before May 1st. The websites will be locked after that
date. 3. Take both instruments. (the study is not valid if only 1 instrument is taken). 4. Your results will be e-mailed directly to you by the researcher.
Thank you so much for your participation, I really appreciate it. Sincerely, Stacey Rimmerman
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Taking the LSI3 online
1. From your Internet browser (Netscape Navigator or Internet Explorer versions 4.0 or higher) go to HTTP://www.hayresourcesdirect.haygroup.com/lsi/default-new.asp?oz=476. This will bring you to the survey login page.
2. Enter a username – This must be your first name underscore last
name e.g. Joe_Sample 3. Enter a password - this is a personal password of your choice but it must
be 6 characters only (no more, no less!)
4. Enter the organizational password 0305PA
You can then access the test
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Taking the MBTI® Instrument Online Thank you for participating in this research project. Instructions for taking the Myers-Briggs Type Indicator® instrument online are provided below. It is essential for you to select the batch name Horigan to denote that you are participating in this particular study.
• Go to the web address http://online.cpp.com
• Login: (case sensitive) Enter capt for Account Login
• Password: (case sensitive) Enter takethembti for Account Password
• User ID: This is configured for you upon completion of your first instrument. No need to enter data here (unless you are returning to resume).
Choose the MBTI® Step I (Form M) instrument from the assessment column by clicking on the "Take It" button.
Note: DO NOT take the MBTI® Step II (Form Q). It is not the instrument being used for this research study.
• Select the batch for your program in the Assessment Information field as shown below.
• Fill out the personal information form (note that all demographic information is optional except for First Name, Last Name and Gender) and finally click on "Submit" when finished. Complete the instrument and click on the "Done" button.