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The Kolb Learning Style Inventory 4.0: Guide to Theory, Psychometrics, Research & Applications


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Abstract The Kolb Learning Style Inventory version 4.0 (KLSI 4.0) revised in 2011, is the latest revision of the original Learning Style Inventory developed by David A. Kolb. Like its predecessors, the KLSI 4.0 is based on experiential learning theory (Kolb 1984) and is designed to help individuals identify the way they learn from experience. The Kolb Learning Style Inventory 4.0 is the first major revision of the KLSI since 1999 and the third since the original LSI was published in 1971. Based on many years of research involving scholars around the world and data from many thousands of respondents, the KLSI 4.0 includes four major additions-- A new nine learning style typology, assessment of learning flexibility, an expanded personal report focused on improving learning effectiveness, and improved psychometrics. The technical specifications are designed to adhere to the standards for educational and psychological testing developed by the American Educational Research Association, the American Psychological Association, and the National Council on Measurement in Education (1999). The first chapter describes the conceptual foundations of the LSI 3.1 in the theory of experiential learning (ELT). Chapter 2 provides a description of the inventory that includes its purpose, history, and format. Chapter 3 describes the characteristics of the KLSI 4.0 normative sample. Chapter 4 includes internal reliability and test-retest reliability studies of the inventory. Chapter 5 provides information about research on the internal and external validity for the instrument. Internal validity studies of the structure of the KLSI 4.0.1 using correlation and factor analysis are reported. External validity includes research on demographics, educational specialization, concurrent validity with other experiential learning assessment instruments, aptitude test performance, academic performance and experiential learning in teams. Chapter 6 describes the new Learning Flexibility Index including scoring formulas, normative data and validity evidence. In chapter 7 the current research on educational applications of ELT and the KLSI in many fields is reviewed. ©Experience Based Learning Systems 2013
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Alice Y. Kolb
David A. Kolb
Experience Based
Learning Systems
A Comprehensive Guide to the Theory, Psychometrics,
Research on Validity and Educational Applications
A Comprehensive Guide to the Theory, Psychometrics,
Research on Validity and Educational Applications
Alice Y. Kolb & David A. Kolb
Experience Based Learning Systems, Inc.
The Kolb Learning Style Inventory version 4.0 (KLSI 4.0) revised in 2011, is the
latest revision of the original Learning Style Inventory developed by David A. Kolb. Like its
predecessors, the KLSI 4.0 is based on experiential learning theory (Kolb 1984) and is
designed to help individuals identify the way they learn from experience. The Kolb Learning
Style Inventory 4.0 is the first major revision of the KLSI since 1999 and the third since the
original LSI was published in 1971. Based on many years of research involving scholars
around the world and data from many thousands of respondents, the KLSI 4.0 includes four
major additions-- A new nine learning style typology, assessment of learning flexibility, an
expanded personal report focused on improving learning effectiveness, and improved
psychometrics. The technical specifications are designed to adhere to the standards for
educational and psychological testing developed by the American Educational Research
Association, the American Psychological Association, and the National Council on
Measurement in Education (1999).
The first chapter describes the conceptual foundations of the LSI 3.1 in the
theory of experiential learning (ELT). Chapter 2 provides a description of the inventory that
includes its purpose, history, and format. Chapter 3 describes the characteristics of the KLSI
4.0 normative sample. Chapter 4 includes internal reliability and test-retest reliability studies
of the inventory. Chapter 5 provides information about research on the internal and external
validity for the instrument. Internal validity studies of the structure of the KLSI 4.0.1 using
correlation and factor analysis are reported. External validity includes research on
demographics, educational specialization, concurrent validity with other experiential learning
assessment instruments, aptitude test performance, academic performance and experiential
learning in teams. Chapter 6 describes the new Learning Flexibility Index including scoring
formulas, normative data and validity evidence. In chapter 7 the current research on
educational applications of ELT and the KLSI in many fields is reviewed.
©Experience Based Learning Systems 2013
Table of Contents
LEARNING STYLE…………………………………………………………………9
LEARNING SPACE………………………………………………………………..17
LEARNING FLEXIBILITY……………………………………..………….….....27
3. NORMS FOR THE KLSI VERSION 4.0…………………………………………..….48
4. RELIABILITY OF THE KLSI 4.0………………………………...…………………..51
TEST-RETEST RELIABILITY……………..……………………………………51
5. VALIDITY OF THE KLSI 4.0…………………………………………..……………..53
Correlation of KLSI 4.0 with KLSI 3.1……………………..…………….53
Correlation Studies of the LSI Scales………...….………………………..54
Factor Analysis Studies…………………………………………………….55
Age…………………………………………..…………………………... ….57
Educational Level…………………………………………………………..58
Educational Specialization…………………………………………………59
Other Experiential Learning Assessment Instruments…………………..65
Multiple Intelligences……………………………………………………....68
Epistemological Beliefs Questionnaire…………………………………….68
Aptitude Test Performance………………………………………...……...69
Assessment of Academic Performance……………………………………98
Experiential Learning in Teams……………………….…………………..71
Team member learning style.
Team norms.
6. LEARNING FLEXIBILITY…………………………………………………………...76
Arts Education
Business and Management
Computer and Information Science
Physical Education
Political Science
Social Work
Urban Planning
APPENDIX 1. KLSI 4.0 Raw Score to Percentile Conversion…………………….171
APPENDIX 2. Learning Style and Age………………………………………………178
APPENDIX 3. Learning Style and Gender………………………………….………….179
APPENDIX 4. Learning Style and Educational Level…………………………………180
APPENDIX 5. Learning Style and Educational Specialization…………………… ....182
APPENDIX 6. Learning Style Type and Educational Specialization………………….185
APPENDIX 7. Learning Flexibility Index Percentiles…………………………………187
APPENDIX 8. LFI Item Scores for the Regions of the Learning Space…………...…191
APPENDIX 9. KLSI 4.0 Learning Style Type Descriptions and Case Studies…….…192
APPENDIX 10. Experiential Learning Session Designs………………………………..212
APPENDIX 11. Evaluating Learning: The Personal Application Assignment…...….222
The Kolb Learning Style Inventory differs from other tests of learning style and
personality used in education by being based on a comprehensive theory of learning and
development. Experiential Learning Theory (ELT) draws on the work of prominent 20th
century scholars who gave experience a central role in their theories of human learning and
development—notably John Dewey, Kurt Lewin, Jean Piaget, Lev Vygotsky, William
James, Carl Jung, Paulo Freire, Carl Rogers and Mary Parker Follett—to develop a holistic
model of the experiential learning process and a multi-dimensional model of adult
development (Figure 1.) Figure 1.
The theory, described in detail in Experiential Learning: Experience as the Source of
Learning and Development (Kolb 1984), is built on six propositions that are shared by these
1. Learning is best conceived as a process, not in terms of outcomes. Although
punctuated by knowledge milestones, learning does not end at an outcome, nor is
it always evidenced in performance. Rather, learning occurs through the course of
connected experiences in which knowledge is modified and re-formed. To
improve learning in higher education, the primary focus should be on engaging
students in a process that best enhances their learning – a process that includes
feedback on the effectiveness of their learning efforts. “…education must be
conceived as a continuing reconstruction of experience: … the process and goal
of education are one and the same thing.” (Dewey 1897: 79)
Foundational Scholars of
Experiential Learning
William James
Radical Empiricism
Kurt Lewin
Action Research
The T-Group
Carl Rogers
Self-actualization through
the Process of Experiencing
Carl Jung
Development from
Specialization to Integration
John Dewey
Experiential Education
Jean Piaget
Paulo Freire
Naming Experience in
Lev Vygotsky
Proximal Zone of
Mary Parker Follett
Learning in Relationship
Creative Experience
(C) 2013 EBLSI
2. All learning is re-learning. Learning is best facilitated by a process that draws
out the students’ beliefs and ideas about a topic so that they can be examined,
tested and integrated with new, more refined ideas. Piaget called this proposition
constructivism—individuals construct their knowledge of the world based on
their experience and learn from experiences that lead them to realize how new
information conflicts with their prior experience and belief.
3. Learning requires the resolution of conflicts between dialectically opposed modes
of adaptation to the world. Conflict, differences, and disagreement are what drive
the learning process. These tensions are resolved in iterations of movement back
and forth between opposing modes of reflection and action and feeling and
4. Learning is a holistic process of adaptation to the world. Learning is not just the
result of cognition but involves the integrated functioning of the total person—
thinking, feeling, perceiving and behaving. It encompasses other specialized
models of adaptation from the scientific method to problem solving, decision
making and creativity.
5. Learning results from synergetic transactions between the person and the
environment. In Piaget’s terms, learning occurs through equilibration of the
dialectic processes of assimilating new experiences into existing concepts and
accommodating existing concepts to new experience. Following Lewin’s famous
formula that behavior is a function of the person and the environment, ELT holds
that learning is influenced by characteristics of the learner and the learning space.
6. Learning is the process of creating knowledge. In ELT, knowledge is viewed as
the transaction between two forms of knowledge: social knowledge, which is co-
constructed in a socio-historical context, and personal knowledge, the subjective
experience of the learner. This conceptualization of knowledge stands in contrast
to that of the “transmission” model of education in which pre-existing, fixed ideas
are transmitted to the learner. ELT proposes a constructivist theory of learning
whereby social knowledge is created and recreated in the personal knowledge of
the learner.
ELT is a dynamic view of learning based on a learning cycle driven by the resolution
of the dual dialectics of action/reflection and experience/abstraction. Learning is defined as
“the process whereby knowledge is created through the transformation of experience.
Knowledge results from the combination of grasping and transforming experience.” (Kolb,
1984, p. 41). Grasping experience refers to the process of taking in information, and
transforming experience is how individuals interpret and act on that information. The ELT
model portrays two dialectically related modes of grasping experienceConcrete
Experience (CE) and Abstract Conceptualization (AC)and two dialectically related modes
of transforming experience—Reflective Observation (RO) and Active Experimentation (AE).
Learning arises from the resolution of creative tension among these four learning modes.
This process is portrayed as an idealized learning cycle or spiral where the learner “touches
all the bases”—experiencing (CE), reflecting (RO), thinking (AC), and acting (AE)—in a
recursive process that is sensitive to the learning situation and what is being learned.
Immediate or concrete experiences are the basis for observations and reflections. These
reflections are assimilated and distilled into abstract concepts from which new implications
for action can be drawn. These implications can be actively tested and serve as guides in
creating new experiences (Figure 2).
Figure 2. The Experiential Learning Cycle
In The art of changing the brain: Enriching teaching by exploring the biology of
learning, James Zull a biologist and founding director of CWRU’s University Center for
Innovation in Teaching and Education (UCITE) sees a link between ELT and neuroscience
research, suggesting that this process of experiential learning is related to the process of
brain functioning as shown in Figure 2. “Put into words, the figure illustrates that concrete
experiences come through the sensory cortex, reflective observation involves the integrative
cortex at the back, creating new abstract concepts occurs in the frontal integrative cortex, and
active testing involves the motor brain. In other words, the learning cycle arises from the
structure of the brain.” (Zull 2002: 18-19; 2011)
Figure 3. The Experiential Learning Cycle and Regions of the Cerebral Cortex.
Reprinted with permission of the author (Zull 2002)
Learning style describes the unique ways individuals spiral through the learning cycle
based on their preference for the four different learning modes—CE, RO, AC, & AE.
Because of one’s genetic makeup, particular life experiences, and the demands of the present
environment, a preferred way of choosing among these four learning modes is developed.
The conflict between being concrete or abstract and between being active or reflective is
resolved in patterned, characteristic ways. Much of the research on ELT has focused on the
concept of learning style using the Kolb Learning Style Inventory (KLSI) to assess
individual learning styles (Kolb & Kolb 2005b). In the KLSI a person’s learning style is
defined by their unique combination of preferences for the four learning modes defining a
“kite” shape profile of their relative preference for the four phases of the learning cycle.
Because each person's learning style is unique, everyone's kite shape is a little different.
ELT posits that learning style is not a fixed psychological trait but a dynamic state
resulting from synergistic transactions between the person and the environment. This
dynamic state arises from an individual’s preferential resolution of the dual dialectics of
experiencing/conceptualizing and acting/reflecting. “The stability and endurance of these
states in individuals comes not solely from fixed genetic qualities or characteristics of human
beings: nor, for that matter, does it come from the stable fixed demands of environmental
circumstances. Rather, stable and enduring patterns of human individuality arise from
consistent patterns of transaction between the individual and his or her environment…The
way we process the possibilities of each new emerging event determines the range of choices
and decisions we see. The choices and decisions we make to some extent determine the
events we live through, and these events influence our future choices. Thus, people create
themselves through the choice of the actual occasions that they live through” (Kolb, 1984, p.
Previous research with KLSI versions 1-3.1 has identified four learning style
groupings of similar kite shapes that are associated with different approaches to learning
Diverging, Assimilating, Converging, and Accommodating. This research has shown that
learning styles are influenced by culture, personality type, educational specialization, career
choice, and current job role and tasks (Kolb & Kolb, 2013; Kolb, 1984). These patterns of
behavior associated with the four basic learning styles are shaped by transactions between
persons and their environment at five different levels—personality, educational
specialization, professional career, current job role, and adaptive competencies. While some
have interpreted learning style as a personality variable (Garner 2000, Furnam, Jackson &
Miller 1999), ELT defines learning style as a social psychological concept that is only
partially determined by personality. Personality exerts a small but pervasive influence in
nearly all situations; but at the other levels learning style is influenced by increasingly
specific environmental demands of educational specialization, career, job, and tasks skills.
Table 1 summarizes previous research that has identified how learning styles are determined
at these various levels.
Table 1
Relationship Between Learning Styles and Five Levels of Behavior.
Behavior level
Arts, English
Social service
Social service
Current jobs
Personality Types.
Although the learning styles of and learning modes proposed by ELT are derived
from the works of Dewey, Lewin and Piaget many have noted the similarity of these
concepts to Carl Jung’s descriptions of individuals’ preferred ways for adapting in the
world. Several research studies relating the LSI with the Myers-Briggs Type Indicator
(MBTI) indicate that Jung’s Extraversion/Introversion dialectical dimension correlates
with the Active/Reflective dialectic of ELT and the MBTI Feeling/Thinking dimension
correlates with the LSI Concrete Experience/ Abstract Conceptualization dimension. The
MBTI Sensing type is associated with the LSI Accommodating learning style and the
MBTI Intuitive type with the LSI Assimilating style. MBTI Feeling types correspond to
LSI Diverging learning styles and Thinking types to Converging styles. The above
discussion implies that the Accommodating learning style is the Extraverted Sensing
type, and the Converging style the Extraverted Thinking type. The Assimilating learning
style corresponds to the Introverted Intuitive personality type and the Diverging style to
the Introverted Feeling type. Myers (1962) descriptions of these MBTI types are very
similar to the corresponding LSI learning styles as described by ELT (Kolb, 1984, pp:
Educational Specialization.
Early educational experiences shape people’s individual learning styles by instilling
positive attitudes toward specific sets of learning skills and by teaching students how to
learn. Although elementary education is generalized, there is an increasing process of
specialization that begins in high school and becomes sharper during the college years.
This specialization in the realms of social knowledge influences individuals’ orientations
toward learning, resulting in particular relations between learning styles and early
training in an educational specialty or discipline. For example, people specializing in the
arts, history, political science, English, and psychology tend to have Diverging learning
styles, while those majoring in more abstract and applied areas like medicine and
engineering have Converging learning styles. Individuals with Accommodating styles
often have educational backgrounds in education, communication and nursing, and those
with Assimilating styles in mathematics and physical sciences.
Professional Career.
A third set of factors that shape learning styles stems from professional careers.
One’s professional career choice not only exposes one to a specialized learning
environment, but it also involves a commitment to a generic professional problem, such
as social service, that requires a specialized adaptive orientation. In addition, one
becomes a member of a reference group of peers who share a professional mentality, and
a common set of values and beliefs about how one should behave professionally. This
professional orientation shapes learning style through habits acquired in professional
training and through the more immediate normative pressures involved in being a
competent professional. Research over the years has shown that social service and arts
careers attract people with a Diverging learning style. Professions in the sciences and
information or research have people with an Assimilating learning style. The
Converging learning styles tends to be dominant among professionals in technology
intensive fields like medicine and engineering. Finally, the Accommodating learning
style characterizes people with careers in fields such as sales, social service and
Current Job Role.
The fourth level of factors influencing learning style is the person’s current job role.
The task demands and pressures of a job shape a person’s adaptive orientation. Executive
jobs, such as general management, that require a strong orientation to task
accomplishment and decision making in uncertain emergent circumstances require an
Accommodating learning style. Personal jobs, such as counseling and personnel
administration, that require the establishment of personal relationships and effective
communication with other people demand a Diverging learning style. Information jobs,
such as planning and research, that require data gathering and analysis, as well as
conceptual modeling, require an Assimilating learning style. Technical jobs, such as
bench engineering and production that require technical and problem-solving skills
require a convergent learning orientation.
Adaptive competencies.
The fifth and most immediate level of forces that shapes learning style is the specific
task or problem the person is currently working on. Each task we face requires a
corresponding set of skills for effective performance. The effective matching of task
demands and personal skills results in an adaptive competence. The Accommodative
learning style encompasses a set of competencies that can best be termed Acting skills:
Leadership, Initiative, and Action. The Diverging learning style is associated with
Valuing skills: Relationship, Helping others, and Sense-making. The Assimilating
learning style is related to Thinking skills: Information-gathering, Information-analysis,
and Theory building. Finally, the Converging learning style is associated with Decision
skills like Quantitative Analysis, Use of Technology, and Goal-setting (Kolb, 1984).
The following summary of the four basic learning styles is based on both research
and clinical observation of these patterns of KLSI scores (Kolb, 1984, Kolb & Kolb 2013).
An individual with diverging style has CE and RO as dominant learning abilities.
People with this learning style are best at viewing concrete situations from many different
points of view. It is labeled “Diverging” because a person with it performs better in
situations that call for generation of ideas, such as a “brainstorming” session. People with a
Diverging learning style have broad cultural interests and like to gather information. They
are interested in people, tend to be imaginative and emotional, have broad cultural interests,
and tend to specialize in the arts. In formal learning situations, people with the Diverging
style prefer to work in groups, listening with an open mind and receiving personalized
An individual with an assimilating style has AC and RO as dominant learning
abilities. People with this learning style are best at understanding a wide range of
information and putting into concise, logical form. Individuals with an Assimilating style are
less focused on people and more interested in ideas and abstract concepts. Generally, people
with this style find it more important that a theory have logical soundness than practical
value. The Assimilating learning style is important for effectiveness in information and
science careers. In formal learning situations, people with this style prefer readings, lectures,
exploring analytical models, and having time to think things through.
An individual with a converging style has AC and AE as dominant learning abilities.
People with this learning style are best at finding practical uses for ideas and theories. They
have the ability to solve problems and make decisions based on finding solutions to questions
or problems. Individuals with a Converging learning style prefer to deal with technical tasks
and problems rather than with social issues and interpersonal issues. These learning skills
are important for effectiveness in specialist and technology careers. In formal learning
situations, people with this style prefer to experiment with new ideas, simulations, laboratory
assignments, and practical applications.
An individual with an accommodating style has CE and AE as dominant learning
abilities. People with this learning style have the ability to learn from primarily “hands-on”
experience. They enjoy carrying out plans and involving themselves in new and challenging
experiences. Their tendency may be to act on “gut” feelings rather than on logical analysis.
In solving problems, individuals with an Accommodating learning style rely more heavily on
people for information than on their own technical analysis. This learning style is important
for effectiveness in action-oriented careers such as marketing or sales. In formal learning
situations, people with the Accommodating learning style prefer to work with others to get
assignments done, to set goals, to do field work, and to test out different approaches to
completing a project.
The nine learning styles of the KLSI 4.0
Data from empirical and clinical studies over the years has shown that these original
four learning style typesAccommodating, Assimilating , Converging and Divergingcan
be refined further into a nine style typology that better defines the unique patterns of
individual learning styles and reduces the confusions introduced by borderline cases in the
old 4 style typology (Eickmann, Kolb, & Kolb, 2004; Kolb & Kolb, 2005a&b; Boyatzis &
Mainemelis, 2000). With feedback from users we first began noticing a fifth “balancing”
style describing users who scored at the center of the Learning Style grid. Later we
discovered that individuals who scored near the grid boundary lines also had distinctive
styles. For example an “Experiencing” style was identified between the Accommodating and
Diverging styles Four of these style types emphasize one of the four learning modes—
Experiencing (CE), Reflecting (RO), Thinking (AC) and Acting (AE) (Abbey, Hunt &
Weiser, 1985; Hunt, 1987). Four others represent style types that emphasize two learning
modes, one from the grasping dimension and one from the transforming dimension of the
ELT model—Imagining (CE & RO), Analyzing (AC & RO), Deciding (AC &AE) and
Initiating (CE &AE). The final style type balances all four modes of the learning cycle
Balancing (CE, RO, AC &AE; Mainemelis, Boyatzis, & Kolb, 2002).
The new KLSI 4.0 introduces these nine style types by moving from a 4 pixel to 9
pixel resolution of learning style types as described below. The learning style types can be
systematically arranged on a two-dimensional learning space defined by Abstract
Conceptualization-Concrete Experience and Active Experimentation-Reflective Observation.
This space, including a description of the distinguishing kite shape of each style, is depicted
in Figure 4. See Appendix 9 for detailed descriptions and case studies of the nine types.
Figure 4. The Nine Learning Styles in the KLSI 4.0
The Initiating style - initiating action to deal with experiences and situations.
The Initiating style is characterized by the ability to initiate action in order to deal with
experiences and situations. It involves active experimentation (AE) and concrete experience
The Experiencing style - finding meaning from deep involvement in experience. The
Experiencing style is characterized by the ability to find meaning from deep involvement in
experience. It draws on concrete experience (CE) while balancing active experimentation
(AE) and reflective observation (RO).
The Imagining style - imagining possibilities by observing and reflecting on
experiences. The Imagining style is characterized by the ability to imagine possibilities by
observing and reflecting on experiences. It combines the learning steps of concrete
experience (CE) and reflective observation (RO).
The Reflecting style - connecting experience and ideas through sustained reflection.
The Reflecting style is characterized by the ability to connect experience and ideas through
sustained reflection. It draws on reflective observation (RO) while balancing concrete
experience (CE) and abstract conceptualization (AC).
The Analyzing style - integrating ideas into concise models and systems through
reflection. The Analyzing style is characterized by the ability to integrate and systematize
ideas through reflection. It combines reflective observation (RO) and abstract
conceptualization (AC).
The Thinking style - disciplined involvement in abstract reasoning and logical
reasoning. The Thinking style is characterized by the capacity for disciplined involvement in
abstract and logical reasoning. It draws on abstract conceptualization (AC) while balancing
active experimentation (AE) and reflective observation (RO).
The Deciding style - using theories and models to decide on problem solutions and
courses of action. The Deciding style is characterized by the ability to use theories and
models to decide on problem solutions and courses of action. it combines abstract
conceptualization (AC) and active experimentation (AE).
The Acting style - a strong motivation for goal directed action that integrates people
and tasks. The Acting style is characterized by a strong motivation for goal directed action
that integrates people and tasks. It draws on active experimentation (AE) while balancing
concrete experience (CE) and abstract conceptualization (AC).
The Balancing style - adapting by weighing the pros and cons of acting versus
reflecting and experiencing versus thinking. The Balancing style is characterized by the
ability to adapt; weighing the pros and cons of acting versus reflecting and experiencing
versus thinking. It balances concrete experience, abstract conceptualization, active
experimentation and reflective observation.
These nine KLSI 4.0 learning styles further define the experiential learning cycle by
emphasizing four dialectic tensions in the learning process. In addition to the primary
dialectics of Abstract Conceptualization/Concrete Experience and Active
Experimentation/Reflective Observation, The combination dialectics of
Assimilation/Accommodation and Converging/Diverging are also represented in an eight
stage learning cycle with Balancing in the center. Thus The Initiating style has a strong
preference for active learning in context (Accommodation) while the Analyzing style has a
strong preference for reflective conceptual learning (Assimilation). The Imagining style has
a strong preference for opening alternatives and perspectives on experience (Diverging)
while the Deciding style has a strong preference for closing on the single best option for
action (Converging). The formulas for calculating the continuous scores on these
combination dialectics are reported on page 41. Figure 5 depicts this expanded learning
cycle and illustrates how an individual's particular style represents their preferred space in
the cycle.
Figure 5
If learning is to occur, it requires a space for it to take place. While, for most, the
concept of learning space first conjures up the image of the physical classroom environment,
it is much broader and multi-dimensional. Dimensions of learning space include physical,
cultural, institutional, social and psychological aspects (See Figure 6).
Figure 6
In ELT these dimensions all come together in the experience of the learner. This
concept of learning space builds on Kurt Lewin’s field theory and his concept of life space
(1951). For Lewin, person and environment are interdependent variables where behavior is a
function of person and environment and the life space is the total psychological environment,
which the person experiences subjectively. To take time as an example, in many
organizations today employees are so busy doing their work that they feel that there is no
time to learn how to do things better. This feeling is shaped by the objective conditions of a
hectic work schedule along with the expectation that time spent reflecting will not be
Three other theoretical frameworks inform the ELT concept of learning space. Urie
Bronfrenbrenner’s (1977, 1979) work on the ecology of human development has made
significant sociological contributions to Lewin’s life space concept. He defines the ecology
of learning/development spaces as a topologically nested arrangement of structures each
contained within the next. The learner’s immediate setting such as a course or classroom is
called the microsystem, while other concurrent settings in the person’s life such as other
courses, the dorm or family are referred to as the mesosystem. The exosystem encompasses
the formal and informal social structures that influence the person’s immediate environment,
such as institutional policies and procedures and campus culture. Finally, the macrosystem
refers to the overarching institutional patterns and values of the wider culture, such as
cultural values favoring abstract knowledge over practical knowledge, that influence actors
in the person’s immediate microsystem and mesosystem. This theory provides a framework
for analysis of the social system factors that influence learners’ experience of their learning
spaces. Another important contribution to the learning space concept is situated
learning theory (Lave and Wenger 1991). Like ELT situated learning theory draws on
Vygotsky’s (1978) activity theory of social cognition for a conception of social knowledge
that conceives of learning as a transaction between the person and the social environment.
Situations in situated learning theory like life space and learning space are not necessarily
physical places but constructs of the person’s experience in the social environment. These
situations are embedded in communities of practice that have a history, norms, tools, and
traditions of practice. Knowledge resides, not in the individual’s head, but in communities of
practice. Learning is thus a process of becoming a member of a community of practice
through legitimate peripheral participation (e.g. apprenticeship). Situated learning theory
enriches the learning space concept by reminding us that learning spaces extend beyond the
teacher and the classroom. They include socialization into a wider community of practice
that involves membership, identity formation, transitioning from novice to expert through
mentorship and experience in the activities of the practice, as well as the reproduction and
development of the community of practice itself as newcomers replace old-timers.
Finally, in their theory of knowledge creation, Nonaka and Konno ( 1998) introduce
the Japanese concept of “ba”, a “context that harbors meaning”, which is a shared space that
is the foundation for knowledge creation. “Knowledge is embedded in ba, where it is then
acquired through one’s own experience or reflections on the experiences of others.” (Nonaka
and Konno 1998:40) Knowledge embedded in ba is tacit and can only be made explicit
through sharing of feelings, thoughts and experiences of persons in the space. For this to
happen, the ba space requires that individuals remove barriers between one another in a
climate that emphasizes “care, love, trust, and commitment”. Learning spaces similarly
require norms of psychological safety, serious purpose, and respect to promote learning.
Since a learning space is in the end what the learner experiences it to be, it is the
psychological and social dimensions of learning spaces that have the most influence on
learning. From this perspective learning spaces can be viewed as aggregates of human
characteristics. “Environments are transmitted through people and the dominant features of a
particular environment are partially a function of the individuals who inhabit it” (Strange &
Banning, 2001). Using the “human aggregate” approach, the experiential learning space is
defined by the attracting and repelling forces (positive and negative valences) of the poles of
the dual dialectics of action/reflection and experiencing/conceptualizing, creating a two
dimensional map of the regions of the learning space like that shown in Figure 4. An
individual’s learning style positions him/her in one of these regions depending on the
equilibrium of forces among action, reflection, experiencing and conceptualizing. As with
the concept of life space, this position is determined by a combination of individual
disposition and characteristics of the learning environment.
The KLSI measures an individual’s preference for a particular region of the learning
space, their home region so to speak. The regions of the ELT learning space offer a typology
of the different types of learning based on the extent to which they require action vs.
reflection and experiencing vs. thinking, thereby emphasizing some stages of the learning
cycle over others. A number of studies of learning spaces in higher education have been
conducted using the human aggregate approach by showing the percentage of students whose
learning style places them in the different learning space regions (Kolb & Kolb, 2005a;
Eickmann, Kolb & Kolb, 2004). Figure 7, for example, shows the ELT learning space of the
MBA program in a major management school. In this particular case, students are
predominately concentrated in the abstract and active regions of the learning space, as are the
faculty. This creates a learning space that tends to emphasize the quantitative and technical
aspects of management over the human and relationship factors.
Figure 7. The Learning Space of an MBA Program Defined by the
Learning Styles of MBA Students (n = 1286; Kolb & Kolb 2005a)
The ELT learning space concept emphasizes that learning is not one universal process
but a map of learning territories, a frame of reference within which many different ways of
learning can flourish and interrelate. It is a holistic framework that orients the many different
ways of learning to one another. The process of experiential learning can be viewed as a
process of locomotion through the learning regions that is influenced by a person’s position
in the learning space. One’s position in the learning space defines their experience and thus
defines their “reality.” Teachers objectively create learning spaces by the information and
activities they offer in their course; but this space is interpreted in the students’ subjective
experience through the lens of their learning style.
Creating learning spaces for experiential learning
In our recent research we have focused on the characteristics of learning spaces that
maximize learning and development and have developed principles for creating them (Kolb
& Kolb, 2005a). For a learner to engage fully in the learning cycle, a space must be provided
to engage in the four modes of the cycle—feeling, reflection, thinking, and action. It needs to
be a hospitable, welcoming space that is characterized by respect for all. It needs to be safe
and supportive, but also challenging. It must allow learners to be in charge of their own
learning and allow time for the repetitive practice that develops expertise.
The enhancement of experiential learning can be achieved through the creation
of learning spaces that promote growth producing experiences for learners. A central
concept in Dewey’s educational philosophy is the continuum of experience that arrays
experiences that promote or inhibit learning. “The belief that all genuine education comes
about through experience does not mean that all experiences are genuinely educative…For
some experiences are mis-educative. Any experience is mis-educative that has the effect of
arresting or distorting the growth of further experience…Hence the central problem of an
education based on experience is to select the kind of present experiences that live fruitfully
and creatively in subsequent experiences” (Dewey 1938, p. 25-28). There are a number of
educational principles that flow from this philosophy.
Respect for Learners and their Experience. A growth producing experience in the
philosophy of experiential learning refers not just to a direct experience related to a subject
matter under study but to the total experiential life space of the learner. This includes the
physical and social environment and the quality of relationships. We refer to this as the
Cheers/Jeers experiential continuum. At one end learners feel that they are members of a
learning community who are known and respected by faculty and colleagues and whose
experience is taken seriously, a space “where everybody knows your name”. At the other
extreme are “mis-educative” learning environments where learners feel alienated, alone,
unrecognized and devalued. Learning and growth in the Jeers environment “where nobody
knows your name” can be difficult if not impossible. This principle an be problematic for
even the finest educational institutions. President Lawrence Summers of Harvard dedicated
his 2003 commencement address to the introduction of a comprehensive examination of the
undergraduate program, motivated in part by a letter he received from a top science student
which contained the statement, “I am in the eighth semester of college and there is not a
single science professor here who could identify me by name.” Summers concludes “The
only true measure of a successful educational model is our students’ experience of it.”
(Summers 2003:64)
Begin Learning with the Learner’s Experience of the Subject Matter. To learn
experientially one must first of all own and value their experience. Students will often say,
“But I don’t have any experience.” meaning that they don’t believe that their experience is of
any value to the teacher or for learning the subject matter at hand. The new science of
learning (Bransford, Brown and Cocking 2000) is based on the cognitive constructivist
theories of Piaget and Vygotsky that emphasize that people construct new knowledge and
understanding from what they already know and believe based on their previous experience.
Zull (2002) suggests that this prior knowledge exists in the brain as neuronal networks which
cannot be erased by a teacher’s cogent explanation. Instead the effective teacher activates
prior knowledge, building on exploration of what students already know and believe, on the
sense they have made of their previous concrete experiences. Beginning with these or
related concrete experiences allows the learner to re-examine and modify their previous
sense-making in the light of new ideas.
Creating and Holding a Hospitable Space for Learning. To learn requires facing
and embracing differences; be they differences between skilled expert performance and one’s
novice status, differences between deeply held ideas and beliefs and new ideas or differences
in the life experience and values of others that can lead to understanding them. These
differences can be challenging and threatening, requiring a learning space that encourages
the expression of differences and the psychological safety to support the learner in facing
these challenges (Sanford 1966). As Robert Kegan says, “…people grow best where they
continuously experience an ingenious blend of challenge and support” (1994: 42). As
Kegan implies by his use of the term “ingenious blend”, creating and holding this learning
space is not easy. He notes that while educational institutions have been quite successful in
challenging students, they have been much less successful in providing support. One reason
for this may be that challenges tend to be specific and immediate while support must go
beyond an immediate “You can do it.” statement. It requires a climate or culture of support
that the learner can trust to “hold” them over time. In Conversational Learning (Baker,
Jensen and Kolb 2002) we draw on the work of Henri Nouwen (1975) and Parker Palmer
(1983, 1990, 1998) to describe this challenging and supportive learning space as one that
welcomes the stranger in a spirit of hospitality where “students and teachers can enter into a
fearless communication with each other and allow their respective life experiences to be their
primary and most valuable source of growth and maturation” (Nouwen: 60).
Making Space for Conversational Learning. Human beings naturally make
meaning from their experiences through conversation. Yet genuine conversation in the
traditional lecture classroom can be extremely restricted or nonexistent. At the break or end
of the class the sometimes painfully silent classroom will suddenly come alive with
spontaneous conversation among students. Significant learning can occur in these
conversations, although it may not always be the learning the teacher intended. Making
space for good conversation as part of the educational process provides the opportunity for
reflection on and meaning making about experiences that improves the effectiveness of
experiential learning (Keeton, Sheckley, and Griggs 2002, Bunker 1999). For example the
creation of learning teams as part of a course promote effective learning when
psychologically safe conditions are present (Wyss-Flamm 2002). Conversational Learning
describes the dimensions of spaces that allow for good conversation. Good conversation is
more likely to occur in spaces that integrate thinking and feeling, talking and listening,
leadership and solidarity, recognition of individuality and relatedness and discursive and
recursive processes. When the conversational space is dominated by one extreme of these
dimensions, e.g. talking without listening, conversational learning is diminished.
Making Space for Development of Expertise. With vast knowledge bases that are
ever changing and growing in every field, many higher education curricula consist of course
after course “covering” a series of topics in a relatively superficial factual way. Yet as the
National Research Council in it’s report on the new science of learning recommends on the
basis of research on expert learners; effective learning requires not only factual knowledge,
but the organization of these facts and ideas in a conceptual framework and the ability to
retrieve knowledge for application and transfer to different contexts (Bransford, Brown, and
Cocking 2002). Such deep learning is facilitated by deliberate, recursive practice on areas
that are related to the learner’s goals (Keeton, Sheckley, and Griggs 2002). The process of
learning depicted in the experiential learning cycle describes this recursive spiral of
knowledge development. Space needs to be created in curricula for students to pursue such
deep experiential learning in order to develop expertise related to their life purpose.
Making Spaces for Acting and Reflecting . Learning is like breathing; it involves a
taking in and processing of experience and a putting out or expression of what is learned. As
Dewey noted, “…nothing takes root in mind when there is no balance between doing and
receiving. Some decisive action is needed in order to establish contact with the realities of
the world and in order that impressions may be so related to facts that their value is tested
and organized.” (1934: 45) Yet many programs in higher education are much more focused
on impressing information on the mind of the learner than on opportunities for the learners to
express and test in action what they have learned. Many courses will spend 15 weeks
requiring students to take in volumes of information and only a couple of hours expressing
and testing their learning, often on a multiple choice exam. This is in contrast to arts
education built on the demonstration-practice-critique process where active expression and
testing are continuously involved in the learning process. Zull (2002) suggests that action
may be the most important part of the learning cycle because it closes the learning cycle by
bringing the inside world of reflection and thought into contact with the outside world of
experiences created by action. (cf. Dewey 1897) Keeton, Sheckley and Gross (2002)
propose another level of action/reflection integration, emphasizing the importance of active
reflection in deepening learning from experience.
Making Spaces for Feeling and Thinking. We have seen a polarization between
feeling and thinking in the contrast between the feeling oriented learning space of CIA arts
education and the thinking oriented learning spaces of the Case undergraduate and MBA
programs (Kolb & Kolb 2005a). It seems that educational institutions tend to develop a
learning culture that emphasizes the learning mode most related to their educational
objectives and devalues the opposite learning mode. Yet, Damasio (1994, 2003), LeDoux
(1997), Zull (2002) and others offer convincing research evidence that reason and emotion
are inextricably related in their influence on learning and memory. Indeed it appears that
feelings and emotions have primacy in determining whether and what we learn. Negative
emotions such as fear and anxiety can block learning, while positive feelings of attraction
and interest may be essential for learning. To learn something that one is not interested in is
extremely difficult.
Making Space for Inside-out Learning. David Hunt (1987, 1991) describes
inside-out learning as a process of beginning with oneself in learning by focusing on one’s
experienced knowledge; the implicit theories, metaphors, interests, desires and goals that
guide experience. Making space for inside-out learning by linking educational experiences to
the learner’s interests kindles intrinsic motivation and increases learning effectiveness. Under
the proper educational conditions, a spark of intrinsic interest can be nurtured into a flame of
committed life purpose. (Dewey 1897) Yet learning spaces that emphasize extrinsic reward
can drive out intrinsically motivated learning (Kohn 1993, Deci and Ryan 1985, Ryan and
Deci 2000). Long ago Dewey described the trend toward emphasis on extrinsic reward in
education and the consequences for the teacher who wields the carrot and stick: “Thus in
education we have that systematic depreciation of interest which has been noted…Thus we
have the spectacle of professional educators decrying appeal to interest while they uphold
with great dignity the need of reliance upon examinations, marks, promotions and emotions,
prizes and the time honored paraphernalia of rewards and punishments. The effect of this
situation in crippling the teacher’s sense of humor has not received the attention which it
deserves. (1916: 336)
Making Space for Learners to Take Charge of their own Learning . Many
students enter higher education conditioned by their previous educational experiences to be
passive recipients of what they are taught. Making space for students to take control of and
responsibility for their learning can greatly enhance their ability to learn from experience.
Some use the term self-authorship to describe this process of constructing one’s own
knowledge vs. passively receiving knowledge from others, considering self-authorship to be
a major aim of education (Kegan 1994, King 2003, Baxter-Magolda 1999). Others describe
this goal as increasing students’ capacity for self direction (Boyatzis 1994, Robertson 1988).
The Management Development and Assessment course in the Case MBA program aims to
develop student self direction through assessment and feedback on learning skills and
competencies and the development of a learning plan to achieve their career/life goals
(Boyatzis 1994). Bransford, Brown, and Cocking (2002) argue for the development of meta-
cognitive skills to promote active learning. By developing their effectiveness as learners
(Keeton, Sheckley and Griggs 2002), students can be empowered to take responsibility for
their own learning by understanding how they learn best and the skills necessary to learn in
regions that are uncomfortable for them. Workshops on experiential learning and learning
styles can help students to develop meta-cognitive learning skills. At CIA and the Case
undergraduate programs student workshops help students interpret their LSI scores and
understand how to use this information to improve their learning effectiveness. John Reese
at the University of Denver Law School conducts “Connecting with the Professor”
workshops in which students select one of four teaching styles based on the four predominant
learning styles that they have difficulty connecting with. The workshop gives multiple
examples of remedial actions that the learner may take to correct the misconnection created
by differences in teaching/learning styles. Peer group discussions among law students give
an opportunity to create new ideas about how to get the most from professors with different
learning/teaching styles (Reese 1998).
In ELT, adult development occurs through learning from experience. This is based
on the idea that the experiential learning cycle is actually a learning spiral. When a concrete
experience is enriched by reflection, given meaning by thinking and transformed by action,
the new experience created becomes richer, broader and deeper. Further iterations of the
cycle continue the exploration and transfer to experiences in other contexts. In this process
learning is integrated with other knowledge and generalized to other contexts leading to
higher levels of adult development.
Zull (2002) explained a link between ELT and neuroscience research, suggesting that
the spiraling process of experiential learning is related to the process of brain functioning.
Humberto Maturana (1970) also arrived at the concept of a spiral when he searched for the
pattern of organization that characterizes all living systems. He concluded that all living
systems are organized in a closed circular process that allows for evolutionary change in a
way that circularity is maintained. He called this process autopoeisis, which means “self-
making,” emphasizing the self-referential and self-organizing nature of life. Applying
autopoeisis to cognition, he argued that the process of knowing was identical to autopoeisis,
the spiraling process of life (Maturana & Varela, 1980). As these researchers suggest, the
organization of the mind can be viewed as networks of autopoeitic learning spirals which are
embodied in the neuronal networks that cover the surface layer of the neo-cortex. These
neuronal networks are strengthened and enlarged by spirals of learning recursively cycling
through these major regions of the neo-cortex.
Progress toward development is seen as increases in the complexity and
sophistication of the dimensions associated with the four modes of the learning cycle
affective, perceptual, symbolic and behavioral complexity—and the integration of these
modes in a flexible full cycle of learning.
The ELT developmental model (Kolb, 1984) follows Jung's theory that adult
development moves from a specialized way of adapting toward a holistic integrated stage
that he calls individuation. The model defines three stages: (1) acquisition, from birth to
adolescence where basic abilities and cognitive structures develop; (2) specialization, from
formal schooling through the early work and personal experiences of adulthood where social,
educational, and organizational socialization forces shape the development of a particular,
specialized learning style; and (3) integration in mid-career and later life where non-
dominant modes of learning are expressed in work and personal life. Development through
these stages is characterized by increased integration of the dialectic conflicts between the
four primary learning modes (AC-CE and AE-RO) and by increasing complexity and
relativism in adapting to the world. Each of the learning modes is associated with a form of
complexity that is used in conscious experience to transform sensory data into knowledge
such that development of CE increases affective complexity, of RO increases perceptual
complexity, of AC increases symbolic complexity, and of AE increases behavioral
complexity (Figure 8). These learning modes and complexities create a multi-dimensional
developmental process that is guided by an individual’s particular learning style and life
Figure 8
The concept of deep learning describes the developmental process of learning that
fully integrates the four modes of the experiential learning cycle—experiencing, reflecting,
thinking and acting (Jensen & Kolb, 1994; Border, 2007). Deep learning refers to the kind of
learning that leads to development in the ELT model. Development toward deep learning is
divided into three levels. In the first level learning is registrative and performance-oriented,
emphasizing the two learning modes of the specialized learning styles. The second level is
interpretative and learning-oriented involving three learning modes, and the third level is
integrative and development-oriented involving all four learning modes in a holistic learning
process. In his foundational work, Learning from Experience toward Consciousness, William
Torbert (1972) described these levels of learning as a three-tiered system of feedback loops;
work that has been extended by Chris Argyris, Donald Schön, Peter Senge and others in the
concepts of single and double loop learning. The traditional lecture course, for example,
emphasizes first level, registrative learning emphasizing the learning modes of reflection and
abstraction involving little action (often multiple choice tests that assess registration of
concepts in memory) and little relation to personal experience. Adding more extensive
learning assessments that involve practical application of concepts covered can create second
level learning involving the three learning modes where reflection supplemented by action
serve to further deepen conceptual understanding. Further addition of learning experiences
that involve personal experience such as internships or field projects create the potential for
third level integrative learning (cf. Kolb `1984, Chapter 6). As a counter example, an
internship emphasizes registrative learning via the modes of action and experience. Deeper
interpretative learning can be enhanced by the addition of activities to stimulate reflection
such as team conversation about the internship experience and/or student journals. Linking
these to the conceptual material related to the experience adds the fourth learning mode,
abstraction and integration though completion of the learning spiral.
A study by Clarke (1977) of the accounting and marketing professions illustrates the
ELT developmental model. The study compared the learning styles of cross-sectional
samples of accounting and marketing students and professionals in school and at lower,
middle and senior level career stages. The learning styles of marketing and accounting
students were similar, being fairly balanced among the four learning modes. Lower level
accountants had convergent, abstract and active learning styles, and this convergent emphasis
was even more pronounced in middle-level accountants, reflecting a highly technical
specialization. The senior level accountants, however, became more accommodative in
learning style integrating their non-dominant concrete learning orientation. Clark found a
similar pattern of development in the marketing profession. Gypen (1981) found the same
move from specialization to integration in his study of the learning styles of a cross-sectional
sample of social work and engineering university alumni from early to late career. “As
engineers move up from the bench to management positions, they complement their initial
strengths in abstraction and action with the previously non-dominant orientations of
experience and reflection. As social workers move from direct service into administrative
positions they move in the opposite direction of the engineers.” (1981: ii)
Notice that in both studies the transitions to non-dominant learning modes in later life
stages are associated with changes in the work environment. Development appears not to be
solely a function of individual factors alone, but of the transaction between the person and
his or her environment. For example, engineers who move from the “bench” into
management may become more integrated because of the demands of the interpersonal and
unstructured management role. However, choosing to move into the management position
required individual development in interest and talent to do so. It is also important to note
that these cross-sectional studies do not offer proof of the sequential development through
stages predicted in Jung’s model. This would require longitudinal studies of individuals
showing that they must first be in a specialized developmental stage before proceeding to the
integrative stage. In fact, in spite of their theoretical similarity, elegance and plausibility, we
are aware of no empirical evidence for stage-related development in any of the theories of
adult development. This evidence is lacking in both the psychoanalytic models of Erikson
and Loevinger and the Piaget inspired theories of King and Kitchner, Kegan, or Perry.
For both of these reasons, in our recent work we have considered development in a
way that is more context specific, less age related and non-hierarchical. ELT describes
registrative, interpretative and integrative levels of consciousness and three modes of
adaptation -performance, learning and development (Boyatzis & Kolb, 2000) - which
individuals will enter into at different times and situations depending on their life
circumstances (Table 1). While these modes may be typical of the acquisition, specialization
and development ELT developmental stages, there may be many exceptions in individual
cases. Thus, a young person who has been primarily in a performance mode may transition
into a period in the development mode “to figure out what to do with his life” or an older
person in the development mode may return to the performance mode to work on a project of
Another important aspect of learning style is learning flexibility, the extent to which
an individual adapts his or her learning style to the demands of the learning situation. As we
have seen above, learning style is not a fixed personality trait but more like a habit of
learning shaped by experience and choices—it can be an automatic, unconscious mode of
adapting or it can be consciously modified and changed. The stability of learning style arises
from consistent patterns of transaction between individuals and learning situations in their
life. This process is called accentuation—the way we learn about a new situation determines
the range of choices and decisions we see, the choices and decisions we make influence the
next situation we live through and this situation further influences future choices. Learning
styles are thus specialized modes of adaptation that are reinforced by the continuing choice
of situations where a style is successful (Kolb 1984).
Since a specialized learning style represents an individual preference for only one or
two of the four modes of the learning cycle, its effectiveness is limited to those learning
situations that require these strengths. Learning flexibility indicates the development of a
more holistic and sophisticated learning process The learning style types described above
portray how one prefers to learn in general. Many individuals feel that their learning style
type accurately describes how they learn most of the time. They are consistent in their
approach to learning. Others, however, report that they tend to change their learning
approach depending on what they are learning or the situation they are in. They may say, for
example, that they use one style in the classroom and another at home with their friends and
family. These are flexible learners.
Learning flexibility indicates the development of a more holistic and sophisticated
learning process. Following Jung's theory that adult development moves from a specialized
way of adapting toward a holistic integrated way, development in learning flexibility is seen
as a move from specialization to integration. Integrated learning is a process involving a
creative tension among the four learning modes that is responsive to contextual demands.
Learning flexibility is the ability to use each of the four learning modes to move freely
around the learning cycle and to modify one’s approach to learning based on the learning
situation. Experiencing, reflecting, thinking and acting each provide valuable perspectives
on the learning task in a way that deepens and enriches knowledge.
This can be seen as traveling through each of the regions of the learning space in the
process of learning. The flexibility to move from one learning mode to another in the
learning cycle is important for effective learning. Learning flexibility can help us move in
and out of the learning space regions, capitalizing on the strengths of each learning style.
Learning flexibility broadens the learning comfort zone and allows us to operate comfortably
and effectively in more regions of the learning space, promoting deep learning and
development. In addition to providing a measure of how flexible one is in their approach to
learning, the KLSI 4.0 also provides an indication of which learning space they move to in
different learning contextstheir back-up learning styles. Figure 9 shows the backup styles
of Initiating and Balancing for an Experiencing type with a low flexibility score and the
backup styles of Experiencing, Imagining, Balancing, Reflecting and Thinking for an
Initiating learning style with a high flexibility score. High flexibility individuals tend to
show more backup styles and hence a greater ability to move around the learning cycle (See
Chapter 6).
Figure 9
Backup Styles for High and Low Learning Flexibility Learners
A primary purpose of the KLSI 4.0 is empower learners to understand and intentionally
improve their learning capability. This ability to deliberately learn from experience is
perhaps the most powerful source of adult learning. In leadership development for example,
Ashford and DeRue point out, “…consider the fact that leadership development programs
customarily teach leadership concepts and skills, but rarely do development programs teach
individuals how to learn leadership — which is ironic considering that over 70% of
leadership development occurs as people go through the ups and downs of challenging,
developmental experiences on the job. We contend that the return on investment in
leadership development would be much greater if organizations invested in developing
individuals’ skills related to the learning of leadership from lived experiences, as opposed to
simply teaching leadership concepts, frameworks, and skills.(2012 p147). Deliberate
experiential learning draws on theories in three areas; meta-cognition (Kolb & Kolb 2009),
mindfulness (Yeganeh 2006; Yeganeh & Kolb 2009),) and studies of expert learning called
deliberate practice (Ericsson, Krampe & Tesch-Römer 1993).
Meta-cognition--Understanding yourself as a learner
Deliberate experiential learning refers to individuals’ conscious meta-cognitive
control of their learning process that enables them to monitor and select learning approaches
that work best for them in different learning situations. In the late 1970’s Flavell (1979)
introduced the concept of meta-cognition. He divided meta-cognitive knowledge into three
sub-categories: 1) Knowledge of person variables refers to general knowledge about how
human beings learn and process information, as well as individual knowledge of one's own
learning processes. 2) Task variables include knowledge about the nature of the task and
what it will require of the individual. 3) knowledge about strategy variables include
knowledge about ways to improve learning as well as conditional knowledge about when and
where it is appropriate to use such strategies.
By using the experiential learning model, learners can better understand the learning
process, themselves as learners and the appropriate use of learning strategies based on the
learning task and environment. When individuals engaged in the process of learning by
reflective monitoring of the learning process they are going through, they can begin to
understand important aspects of learning: how they move through each stage of the learning
cycle, the way their unique learning style fits with how they are being taught, and the
learning demands of what is being taught. This comparison results in strategies for action
that can be applied in their ongoing learning process.
Develop a learning identity. A key aspect of meta-cognitive learning is a person’s
beliefs about themselves, particularly their views about their ability to learn. At the extreme,
if a person does not believe that they can learn they won’t. Learning requires conscious
attention, effort and “time on task”. These activities are a waste of time to someone who
does not believe that they have the ability to learn. On the other hand there are many
successful individuals who attribute their achievements to a learning attitude. Oprah
Winfrey for example has said, “I am a woman in process. I’m just trying like everybody
else. I try to take every conflict, every experience, and learn from it. Life is never dull.”
One’s self-identity is deeply held. One is likely to defend against experiences that
contradict this identity. For the vast majority of us our self-identity is a mix of fixed and
learning beliefs. We may feel that we are good at learning some things like sports and not
good at others like mathematics. Every success or failure can trigger a reassessment of one’s
learning ability. Figure 10 depicts one’s self-identity as balancing characteristics that
reinforce a fixed self and a learning self. Fixed self characteristics shift the balance to the
fixed self. Factors associated with the learning self tip the balance toward becoming a
Figure 10
From the above figure we suggest several practical steps for developing a positive
meta-cognitive learning identity.
Trust your experience. Place experience at the center of your learning process,
making it the focal point of your choices and decisions. This does not mean that you
shouldn’t learn from experts or the experience of others since this advice is also part of your
experience. The key is to own your choices and validate them in your experience. When you
do this you take charge of your learning and your life.
Trust the learning process. Avoid an excessive focus on the outcomes of immediate
performance and focus instead on the longer term recursive process of learning by tracking
your performance progress over time. Rarely is a single performance test a matter of life and
death, and to treat it as such only reinforces a fixed identity. Every performance is an
occasion for learning and improvement in future performances.
Redefine your relationship to failure. No one likes to fail but failure is an inevitable
part of doing something new. Thomas Edison provided a role model for the learning
response to failure when he said “Failure is the most important ingredient for success.”
James Dyson, the inventor of the Dyson vacuum cleaner and founder of Dyson, Inc, sees
Edison as a role model saying he, “achieved great success through repeated failure. His
10000 failures pale in comparison to his 1093 US patents. Each one of Edison’s inventions,
from the Dictaphone to the light bulb came from his inability to give up” (Yang 2008:28).
Failures can also help focus your priorities and life path on your talents and strengths.
In her commencement address to the 2008 graduates of Harvard University, J. K. Rowling
described the low period in her life after graduation, which was marked by failure on every
front, and talked about its benefits; “…failure meant a stripping away of the inessential. I
stopped pretending to myself that I was anything other than what I was, and began to direct
my energy into finishing the only work that mattered to me. Had I succeeded at anything
else, I might never have found the determination to succeed in the one arena where I believed
I truly belonged. I was set free because my greatest fear had been realized and I was still
alive, and I still had a daughter whom I adored, and I had an old typewriter and a big idea.”
(Rowling 2008:56)
Let go of strong emotional responses in order to learn from failure. Failures, losses
and mistakes provoke inevitable emotional responses. Yet it is important to learn to regulate
emotional reactions that block learning and feed into a fixed identity. Golfers who slam their
club and curse themselves and the game after a bad shot lose the opportunity to coolly
analyze their mistake and plan for corrections on the next hole. An effective way to deal with
the emotions that follow judging oneself a failure is to breath calmly and intentionally while
accepting the current moment as it is. This enables a clearer mind with which to move
forward. Risk losing. Joel Waitzkin in The art of learning provides a handbook of his meta-
cognitive learning based on his process of becoming first a chess master and then a martial
arts champion. He emphasizes the importance of losing in order to learn how to win. “If a
big strong guy comes into a martial arts studio and someone pushes him, he wants to resist
and push the guy back to prove that he is a big strong guy. The problem is that he isn’t
learning anything by doing this. In order to grow, he needs to give up his current mindset.
(Waitzkin 2007: 107).
Reassess your beliefs about how you learn and what you are good at. It is important
to consciously reflect on and choose how you define yourself as a learner. Often people are
unaware of the way in which they characterize themselves and their abilities.
Monitor the messages you send yourself. Pay attention to your self-talk. Saying to
yourself, “I am stupid.” or, “I am no good at …” matters and reinforces a negative fixed
identity; just as saying, “I can do this” reinforces a positive learning identity. Beware of
internalized oppression. Some of these messages are introjections from others that you have
swallowed without careful examination.
Balance your success/failure accounts. Most of us remember our failures more
vividly than our successes. For example, in our experience as teachers we both tend to focus
on the one or two negative remarks in our course ratings and ignore the praise and positive
reactions. The danger of this type of focus is adjusting one’s teaching style to suit one or two
negative comments and risking losing the majority of positive experiences in the room. A
deeper danger is that such a focus will negatively shape longer term thoughts and behaviors
about oneself (Blackwell, Trzesniewski, & Dweck 2007:259-260). Sometimes it is useful to
make an inventory of learning strengths and successes to balance your accounts.
Learning style. In addition to believing in ourselves as learners, it is also important
to understand how it is that we learn best, our learning style. An understanding of one’s
unique learning preferences and capabilities, and the match between these and the demands
of learning tasks, can increase learning effectiveness. It can suggest why performance is not
optimal and suggest strategies for improvement, as well as help explain why some topics and
courses are interesting and others are painful. It can also help explain why some develop a
non-learning self-identity. Our most gratifying experiences in teaching individuals about
their learning style have been when they come up and say, “My whole life I thought I was
stupid because I didn’t do well in school. Now I realize that it is just because I learn in a
different way than schools teach.”
Those who use the KLSI to assess their learning style often decide that they wish
to develop their capacity to engage in one or more of the four learning modes, experiencing
(CE), reflecting (RO), thinking (AC), and acting (AE). In some cases this is
based on a desire to develop a weak mode in their learning style. In others it may be
to increase capability in a mode that is particularly important for their learning tasks.
Because of the dialectic relationships among the learning modes, containing the
inhibiting effects of opposing learning modes can be as effective in getting into a
mode as actively trying to express it. Overall learning effectiveness is improved
when individuals are highly skilled in engaging all four modes of the learning cycle.
One way to develop in the learning modes is to develop the skills associated with
them. The Learning Skills Profile (Boyatzis & Kolb, 1991, 1992, 1995) was created
to help learners assess the learning skills associated with the four modes of the learning
cycle—interpersonal skills for CE, information skills for RO, analytic skills for
AC, and action skills for AE.
Developing the capacity for experiencing. Experiencing requires fully opening
oneself to direct experience. Direct experience exists only in the here and now, a
present moment of endless depth and extension that can never be fully comprehended.
In fact, the thinking mode, being too much “in your head,” can inhibit the
ability to directly sense and feel the immediate moment. Engagement in concrete
experience can be enhanced by being present in the moment and attending to direct
sensations and feelings. This presence and attention are particularly important
for interpersonal relationships. Interpersonal skills of leadership, relationship, and
giving and receiving help in the development and expression of the experiencing
mode of learning.
Developing the capacity for reflecting. Reflection requires space and time for it
to take place. It can be inhibited by impulsive desires and/or pressures to take action.
It can be enhanced by the practices of deliberately viewing things from different perspective
and empathy. Stillness and quieting the mind foster deep reflection.
Information skills of sense making, information gathering, and information analysis
can aid in the development and expression of the reflecting mode of learning.
Developing the capacity for thinking. Thinking requires the ability to represent
and manipulate ideas in your head. It can be distracted by intense direct emotion
and sensations as well as pressure to act quickly. Engagement in thinking can be
enhanced by practicing theoretical model building and the creation of scenarios for
action. Analytical skills of theory building, quantitative data analysis, and technology
management can aid in the development and expression of the thinking mode of
Developing the capacity for action. Acting requires commitment and involvement
in the practical world of real consequences. In a sense it is the “bottom line” of the
learning cycle, the place where internal experiencing, reflecting, and thinking are
tested in reality. Acting can be inhibited by too much internal processing in any of
these three modes. Acting can be enhanced by courageous initiative taking and the
creation of cycles of goal setting and feedback to monitor performance. Action skills
of initiative, goal setting, and action taking can aid in the development and expression
of the acting mode of learning.
Mindful Experiential Learning
Mindfulness is one special form of meta-cognition that is especially effective for
enhancing learning from experience. Mindfulness is an age old set of practices used to
overcome the tendency to “sleep walk” automatically through our lives. In recent times
these practices have been accepted into mainstream psychology, social psychology, and
medicine. Empirical studies are now finding statistical support for what many have known
for two millennia: that practicing mindfulness enhances mental and physical health,
creativity, and contextual learning.
William James (1890), the originator of the theory of experience on which ELT is
based, stated, “no state once gone can recur and be identical with what it was before”
(p.155). The mind often neglects the rich context available for observation. Instead it
automatically labels stimuli based on limited exposure and moves on to the next stimulus to
under-observe. Labeling experiences as fun, boring, sad, happy, urgent, relaxed, and so on
are also often based in automatically categorizing experience, rather than being fully present
in the unique context of every moment. For James, everything begins and ends in the
continuous flux and flow of experience. This emphasis on immediate direct sensual
experience is exactly the focus on here and now experience that characterizes mindfulness.
James emphasized the importance of attention, as he noted“My experience is what I agree
to attend to.” (1890, p. 403). This also is a central element of mindfulness.
The practices of mindfulness are aimed at helping the individual: 1) focus on present
and direct experience, 2) be intentionally aware and attentive and accept life as an emergent
process of change. Our research on mindfulness and experiential learning (Yeganeh 2006,
Yeganeh & Kolb 2009) suggests that the practice of mindfulness can help individuals learn
from experience by enhancing presence and intentional attention.
To be present and engaged in direct experience, one must anchor in present-centered
awareness by attending to the 5 senses. One of the strongest ways to attend to the present
moment is through calm and aware breathing (Good & Yeganeh 2006, Yeganeh, 2006,
Yeganeh & Kolb, 2009). Attending to the present moment serves to quiet the mind; reducing
automatic, habitual patterns of thinking and responding. Presence enhances Concrete
Experience and allows the learning cycle to begin. In a sense, we cannot learn from
experience if we do not first have an experience, and often, automatic routines make it
difficult for direct experiencing in the moment to occur.
Intentional attentionthe process of being aware and choiceful about what we are
attending tois, as James says, the process that creates our experience. Mindfulness
becomes important when we consider how we choose to process and learn from the events in
our lives. By intentionally guiding the learning process and paying attention to how we are
going through the phases of the learning cycle, we make ourselves through learning. How
and what we learn determines the way we process the possibilities of each new emerging
experience, which in turn determines the range of choices and decisions we see. The choices
and decisions we make to some extent determine the events we live through, and these events
influence our future choices. Thus, we create ourselves through the choices of the actual
occasions they live through. For many, this learning style choice is relatively unconscious,
an auto-pilot program for learning. Mindfulness can put the control of our learning and our
life back in our hands.
Deliberate PracticeBecoming an Expert Learner
We all know that learning involves repeated practice. However time spent practicing
does not necessarily lead to learning and improved performance. Going to the golf practice
range and hitting bucket after bucket of balls doesn’t necessarily improve your game and in
fact may make it worse by ingraining bad habits. Expert performance research initiated in
the early 1990’s by K. Anders Ericsson (Ericsson, Krampe & Tesch-Römer 1993; Ericsson
& Charness 1994; Ericsson 2006; Baron & Henry 2010) teaches a great deal about learning
from practice. The good news from this work is that greatness, for the most part, is not a
function of innate talent; it is learned from experience. The not-so-good news is that it
involves long term commitment (ten years or 10,000 hours for many top experts) and a
particular kind of practice that is hard work, called deliberate practice.
The basic techniques of deliberate practice are useful for improving our ability to
learn from experience. Essentially deliberate practice involves intense concentrated,
repeated performance that is compared against an ideal or “correct” model of the
performance. It requires feedback that compares the actual performance against the ideal to
identify “errors” that are corrected in subsequent performance attempts. In this sense
deliberate practice can be seen as mindful experiential learning—focused reflection on a
concrete performance experience that is analyzed against a meta-cognitive ideal model to
improve future action in a recurring cycle of learning. Learning relationships can be of great
help in deliberate practice by providing expert models, feedback and support for the focused
effort required.
The major implication of ELT for education is to design educational programs in a
way that teaches around the learning cycle so that learners can use and develop all learning
styles in a way that completes the learning cycle for them and promotes deep learning.
Chapter seven includes numerous examples of programs that have been created in this way in
many fields of study. Appendix 10 gives sample experiential learning designs that teach to
all learning styles and Appendix 11 describes the Personal Application Assignment which
was created as a way to holistically assess learning in a way that equally evaluates all
learning modes.
In our interviews and observations of experienced, successful educators we find that
they tend to “teach around the learning cycle” in this manner. They organize their
educational activities in such a manner that they address all four learning modes—
experiencing, reflecting, thinking, and acting. As they do this, they lead learners around the
cycle; shifting the role they play depending on which stage of the cycle they are addressing.
In effect the role they adopt helps to create a learning space designed to facilitate the
transition from one learning style to the other as shown in Figure 11. Often they do this in a
recursive fashion, repeating the cycle many times in a learning program. In effect the cycle
becomes a spiral with each passage through the cycle deepening and extending learners’
understanding of the subject.
Figure 11
When a concrete experience is enriched by reflection, given meaning by thinking and
transformed by action the new experience created becomes richer, broader and deeper.
Further iterations of the cycle continue the exploration and transfer to experiences in other
contexts. The New Zealand Ministry of Education (2004) has used this spiraling learning
process as the framework for the design of middle school curricula. Figure 12 describes how
teachers use the learning spiral to promote higher level learning and to transfer knowledge to
other contexts.
Figure 12. Teaching and the Learning Spiral
Educator Roles
Teaching around the learning cycle and to different learning styles introduces the
need for adjustments in the role one takes with learners. The Educator Role Profile (Kolb &
Kolb, 2011) was created to help educators understand their preferred teaching role and plan
for how they can adapt to teaching around the learning cycle. The self-report instrument is
based on the assumption that preferences for teaching roles emerge from a combination of
beliefs about teaching and learning, goals for the educational process, preferred teaching
style, and instructional practices. Educator roles are not limited to individuals in formal
classroom teaching situations. The framework can be extended to individuals in all walks of
life who “teach” as leaders, coaches, parents, friends, etc.
A teaching role is a patterned set of behaviors that emerge in response to the learning
environment, including students and the learning task demands. Each teaching role engages
students to learn in a unique manner, using one mode of grasping experience and one mode
of transforming experience. In the facilitator role, educators draw on the modes of concrete
experience and reflective observation to help learners get in touch with their own experience
and reflect on it. Subject matter experts, using the modes of reflective observation and
abstract conceptualization, help learners organize and connect their reflection to the
knowledge base of the subject matter. They may provide models or theories for learners to
use in subsequent analysis. The standard setting and evaluating role uses abstract
conceptualization and active experimentation to help students apply knowledge toward
performance goals. In this role, educators closely monitor the quality of student performance
toward the standards they set, and provide consistent feedback. Finally, those in the
coaching role draw on concrete experience and active experimentation to help learners take
action on personally meaningful goals. These roles can also be organized by their relative
focus on the student versus the subject and action versus knowledge as illustrated in Figure
Figure 13
The Educator Role Profile (ERP) describes four role positions—Facilitator, Expert,
Evaluator and Coach. Educators play these roles as they help learners maximize learning by
moving through the four stages of the experiential learning cycle.
The Facilitator Role. When facilitating, educators help learners get in touch with their
personal experience and reflect on it. They adopt a warm affirming style to draw out
learners’ interests, intrinsic motivation and self-knowledge. They often do this by
facilitating conversation in small groups. They create personal relationships with
The Expert Role. In their role as subject expert, educators help learners organize and
connect their reflections to the knowledge base of the subject matter. They adopt an
authoritative, reflective style. They often teach by example, modeling and encouraging
critical thinking as they systematically organize and analyze the subject matter knowledge.
This knowledge is often communicated through lectures and texts.
The Evaluator Role. As a standard setter and evaluator, educators help learners master the
application of knowledge and skill in order to meet performance requirements. They adopt
an objective results-oriented style as they set the knowledge requirements needed for quality
performance. They create performance activities for learners to evaluate their learning.
The Coaching Role In the coaching role, educators help learners apply knowledge to
achieve their goals. They adopt a collaborative, encouraging style, often working one-on-one
with individuals to help them learn from experiences in their life context. They assist in the
creation of personal development plans and provide ways of getting feedback on
Most of us adopt each of these roles to some extent in our educational and teaching activities.
This is in part because these roles are determined by the way we resolve fundamental dilemmas
of teaching. Do we focus on the learner’s experience and interest or subject matter
requirements? Do we focus on effective performance and action or on a deep understanding of
the meaning of ideas? All are required for maximally effective learning. Individuals, however,
tend to have a definite preference for one or two roles over the others; because of their
educational philosophy, their personal teaching style, and the requirements of their particular
educational setting including administrative mandates and learner needs. The ERP is designed to
help you sharpen your awareness of these preferences and to make deliberate choices about what
works best for you in your specific situation.
The Learning Style inventory (LSI) was created to fulfill two purposes:
1. To serve as an educational tool to increase individuals’ understanding
of the process of learning from experience and their unique individual approach
to learning. By increasing awareness of how they learn, the aim is to increase
learners’ capacity for meta-cognitive control of their learning process; enabling them
to monitor and select learning approaches that work best for them in different
learning situations. By providing a language for talking about learning styles and the
learning process the inventory can foster conversation among learners and educators
about how to create the most effective learning environment for those involved. For
this purpose the inventory is best presented, not as a test, but as an experience in
understanding how you learn. Scores on the inventory should not be interpreted as
definitive, but as a starting point for exploration of how one learns best. To facilitate
this purpose a self-scoring and interpretation book that explains the experiential
learning cycle and the characteristics of the different learning styles along with
scoring and profiling instructions is included with the inventory.
2. To provide a research tool for investigating experiential learning theory
(ELT) and the characteristics of individual learning styles. This research can
contribute to the broad advancement of experiential learning and specifically to the
validity of interpretations of individual learning style scores. A research version of
the instrument including only the inventory to be scored by the researcher is available
for this purpose.
The LSI is not a criterion-referenced test and is not intended for use to predict
behavior for purposes of selection, placement, job assignment, or selective treatment.
This includes not using the instrument to assign learners to different educational
treatments, a process sometimes referred to as “tracking”. Such categorizations based on
a single test score amounts to stereotyping that runs counter to the philosophy of
experiential learning that emphasizes individual uniqueness. “When it is used in the
simple, straightforward, and open way intended, the LSI usually provides a valuable self-
examination and discussion that recognizes the uniqueness, complexity and variability in
individual approaches to learning. The danger lies in the reification of learning styles into
fixed traits, such that learning styles become stereotypes used to pigeonhole individuals
and their behavior.” (Kolb, 1981: 290-291)
The LSI is constructed as a self-assessment exercise and tool for construct
validation of ELT. Tests designed for predictive validity typically begin with a criterion
like academic achievement and work backward to identify items or tests with high
criterion correlations. Even so, even the most sophisticated of these tests rarely rises
above a .5 correlation with the criterion. For example, while Graduate Record
Examination Subject Test scores are better predictors of first-year graduate school grades
than either the General Test score or undergraduate GPA, the combination of these three
measures only produces multiple correlations with grades ranging from .4 to .6 in various
fields (Anastasi & Urbina, 1997).
Construct validation is not focused on an outcome criterion, but on the theory or
construct the test measures. Here the emphasis is on the pattern of convergent and
discriminant theoretical predictions made by the theory. Failure to confirm predictions
calls into question the test and the theory. "However, even if each of the correlations
proved to be quite low, their cumulative effect would be to support the validity of the test
and the underlying theory." (Selltiz, Jahoda, Deutsch, & Cook, 1960, p. 160) Judged by
the standards of construct validity ELT has been widely accepted as a useful framework
for learning centered educational innovation, including instructional design, curriculum
development, and life-long learning. Field and job classification studies viewed as a
whole also show a pattern of results consistent with the ELT structure of knowledge
There have been six versions of the Learning Style Inventory published over the last
40 years. Through this time attempts have been made to openly share information about
the inventory, its scoring, and technical characteristics with other interested researchers.
The results of their research have been instrumental in the continuous improvement of the
Learning Style Inventory—Version 1 (Kolb 1971, Kolb 1976).
The original Learning Style Inventory (LSI 1) was created in 1969 as part of a MIT
curriculum development project that resulted in the first management textbook based on
experiential learning (Kolb, Rubin and McIntyre 1971). It was originally developed as an
experiential educational exercise designed to help learners understand the process of
experiential learning and their unique individual style of learning from experience. The
term learning style” was coined to describe these individual differences in how people
Items for the inventory were selected from a longer list of words and phrases
developed for each learning mode by a panel of four behavioral scientists familiar with
experiential learning theory. This list was given to a group of 20 graduate students asking
them to rate each word or phrase for social desirability. Attempting to select words that
were of equal social desirability, a final set of 12 items including a word or phrase for
each learning mode was selected for pre-testing. Analysis showed that 3 of these sets
produced nearly random responses and were thus eliminated resulting in a final version of
the LSI with 9 items. These items were further refined through item-whole correlation
analysis to include six scored items for each learning mode.
Research with the inventory was stimulated by classroom discussions with students
who found the LSI to be helpful to them in understanding the process of experiential
learning and how they learn. From 1971 until it was revised in 1985 there were over 350
published research studies using the LSI. Validity for the LSI 1 was established in a
number of fields including education, management, psychology, computer science,
medicine, and nursing (Hickcox 1990, Iliff 1994). The results of this research with LSI 1
provided provided empirical support for the most complete and systematic statement of
ELT, Experiential Learning: Experience as the Source of Learning and Development
(Kolb 1984). There were several studies of the LSI 1 that identified psychometric
weaknesses of the instrument, particularly low internal consistency reliability and test-
retest reliability.
Learning Style Inventory—Version 2 (Kolb 1985)
Low reliability coefficients and other concerns about the LSI 1 led to a revision of the
inventory in 1985 (LSI 2). Six new items chosen to increase internal reliability (alpha)
were added to each scale making 12 scored items on each scale. These changes increased
scale alphas to an average of .81 ranging from .73 to .88. Wording of all items was
simplified to a 7th grade reading level and the format was changed to include sentence
stems (e.g. “When I learn”). Correlations between the LSI 1 and LSI 2 scales averaged
.91 and ranged from .87 to .93. A new more diverse normative reference group of 1446
men and women was created.
Research with the LSI 2 continued to establish validity for the instrument. From
1985 until the publication of the LSI 3 1999 over 630 studies were published most using
the LSI 2. While internal reliability estimates for the LSI 2 remained high in independent
studies, test-retest reliability remained low.
Learning Style Inventory—Version 2a (Kolb 1993).
In 1991 Veres, Sims and Locklear published a reliability study of a randomized
version of the LSI 2 that showed a small decrease in internal reliability but a dramatic
increase in test-retest reliability with the random scoring format. To study this format a
research version of the random format inventory (LSI 2a) was published in 1993.
Kolb Learning Style Inventory—Version 3 (Kolb 1999).
In 1999 the randomized format was adopted in a revised self scoring and
interpretation booklet (KLSI 3) that included a color-coded scoring sheet to simplify
scoring. The new booklet was organized to follow the learning cycle emphasizing the LSI
as an “experience in learning how you learn”. New application information on teamwork,
managing conflict, personal and professional communication and career choice and
development were added. The KLSI 3 continued to use the LSI 2 normative reference
group until norms for the randomized version could be created.
Kolb Learning Style Inventory—Version 3.1 (Kolb 2005)
The KLSI 3.1 modified the LSI 3 to include a new normative data sample of 6977
LSI users. The format, items, scoring and interpretative booklet remain identical with
KLSI 3. The only change in the KLSI 3.1 is in the norm charts used to convert raw LSI
Kolb Learning Style Inventory—Version 3.2 (Kolb and Kolb 2013)
The KLSI 3.2 was created in 2013 to incorporate the new nine learning style typology
of the KLSI 4.0 in a paper version. The instrument and normative sample are identical to
the KLSI 3.1. The self-scoring and Interpretation booklet was changed to explain the nine
learning styles and their application to problem solving, relationships, etc..
Kolb Learning Style Inventory—Version 4.0 (Kolb and Kolb 2011)
The Kolb Learning Style Inventory 4.0 is the first major revision of the KLSI since
1999 and the third since the original LSI was published in 1971. Based on many years of
research involving scholars around the world and data from many thousands of
respondents, the KLSI 4.0 includes four major additions:
A new 9 Learning Style Typology. Data from empirical and clinical studies
over the years has shown that the original 4 learning style types—Accommodating,
Assimilating , Converging and Diverging— can be refined further into a 9 style typology
that better defines the unique patterns of individual learning styles and reduces the
confusions introduced by borderline cases in the old 4 style typology. The new nine styles
are Initiating, Experiencing, Imagining, Reflecting, Analyzing, Thinking, Deciding,
Acting and Balancing.
Assessment of Learning Flexibility. The experiential learning styles are not
fixed traits but dynamic states that can “flex” to meet the demands of different learning
situations. For the first time the KLSI 4.0 includes a personal assessment of the degree to
which a person changes their style in different learning contexts. The flexibility score also
shows which learning style types the individual uses in addition to their dominant learning
style type. This information can help individuals improve their ability to move freely
around the learning cycle and improve their learning effectiveness.
An Expanded Personal Report Focused on Improving Learning Effectiveness.
The new personal interpretative report has been redesigned to focus on improving
personal learning effectiveness based on a detailed profile of how the person prefers to
learn and their learning strength and weaknesses. It helps learners take charge of their
learningwith a planning guide for learning and tips for application in work and personal
Improved Psychometrics. This revision includes new norms that are based on
a larger, more diverse and representative sample of 10423 LSI users. The KLSI 4.0
maintains the high scale reliability of the KLSI 3.1 while offering higher internal validity.
Score on the KLSI 4.0 are highly correlated with scores on the previous KLSI 3.1 thus
maintaining the external validity that the instrument has shown over the years.
The Learning Style Inventory is designed to measure the degree to which individuals
display the different learning styles derived from experiential learning theory. The form of
the inventory is determined by three design parameters. First, the test is brief and
straightforward, making it useful both for research and for discussing the learning process
with individuals and providing feedback. Second, the test is constructed in such a way that
individuals respond to it as they would respond to a learning situation: it requires them to
resolve the tensions between the abstract-concrete an active-reflective orientations. For this
reason, the LSI format requires them to rank order their preferences for the abstract,
concrete, active and reflective orientations. Third, and most obviously, it was hoped that the
measures of learning styles would predict behavior in a way consistent with the theory of
experiential learning.
All previous versions of the LSI have had the same format—a short questionnaire (9
items for LSI 1 and 12 items for subsequent versions) that asks respondents to rank four
sentence endings that correspond to the four learning modes – Concrete Experience (e.g.,
experiencing), Reflective Observation (reflecting), Abstract Conceptualization (thinking),
and Active Experimentation (doing). The KLSI 4.0 has 20 items in this format—12 that are
similar to the items in the 3.1and 8 additional items that are about learning in different
contexts. These 8 items are used to assess learning flexibility. The KLSI 4.0 is only
available online due to the complex scoring formula for learning flexibility.
Items in the LSI are geared to a 7th grade reading level. The inventory is intended for use by
teens and adults. It is not intended for use by younger children. The LSI has been translated
into many languages, including, Arabic, Chinese, French, Japanese, Italian, Portuguese,
Spanish, Swedish and Thai; and there have been many cross cultural studies using it
(Yamazaki 2002).
The Forced-choice Format of the LSI
The format of the LSI is a forced choice format that ranks an individual’s relative
choice preferences among the four modes of the learning cycle. This is in contrast the more
common normative or free choice format, such as the widely used Likert scale, that rates
absolute preferences on independent dimensions. The forced choice format of the LSI was
dictated by the theory of experiential learning and by the primary purpose of the instrument.
ELT is a holistic, dynamic and dialectic theory of learning. Because it is holistic the
four modes that comprise the experiential learning cycle, CE, RO, AC, and AE are conceived
as interdependent. Learning involves resolving the creative tension among these learning
modes in response to the specific learning situation. Since the two learning dimensions, AC-
CE and AE-RO are related dialectically, the choice of one pole involves not choosing the
opposite pole. Therefore, because ELT postulates that learning in life situations requires the
resolution of conflicts among interdependent learning modes; to be ecologically valid the
learning style assessment process should require a similar process of conflict resolution in
the choice of ones preferred learning approach.
The primary purpose of the LSI is to provide learners with information about
their preferred approach to learning. The most relevant information for the learner is about
intra-individual differences, his or her relative preference for the four learning modes, not
inter-individual comparisons. Ranking relative preferences among the four modes in a forced
choice format is the most direct way to provide this information. While individuals who take
the inventory sometimes report difficulty in making these ranking choices, they report that
the feedback they get from the LSI gives them more insight than has been the case when we
use a normative Likert rating scale version. This is because the social desirability response
bias in the rating scales fails to define a clear learning style, i.e. they say they prefer all
learning modes. This is supported by Harland’s (2002) finding that feedback from a forced
choice test format was perceived as more accurate, valuable and useful than feedback from a
normative version.
The adoption of the forced choice method for the LSI has at times placed it in the
center of an ongoing debate in the research literature about the merits of forced choice
instruments between what might be called “rigorous statisticians” and “pragmatic
empiricists”. Statisticians have questioned the use of the forced choice format because of
statistical limitations, called ipsativity, that are the result of the ranking procedure. Since
ipsative scores represent the relative strength of a variable compared to others in the ranked
set the resulting dependence among scores produces method induced negative correlations
among variables and violates a fundamental assumption of classical test theory required for
use of techniques such as analysis of variance and factor analysisindependence of error
variance. Cornwell and Dunlap (1994) stated that ipsative scores cannot be factored and that
correlation-based analysis of ipsative data produced uninterpretable and invalid results (c.f.
Hicks 1970, Johnson et al. 1988). Other criticisms include the point that ipsative scores are
technically ordinal, not the interval scales required for parametric statistical analysis; that
they produce lower internal reliability estimates and lower validity coefficients (Barron
1996). While critics of forced choice instruments acknowledge that these criticisms do not
take away from the validity of intra-individual comparisons (LSI purpose one), they argue
that ipsative scores are not appropriate for inter-individual comparisons since inter-individual
comparisons on a ranked variable are not independent absolute preferences but preferences
that are relative to the other ranked variables in the set (Barron 1996, Karpatschof and
Elkjaer 2000). However, since ELT argues that a given learning mode preference is relative
to the other three modes, it is the comparison of relative not absolute preferences that the
theory seeks to assess.
The “pragmatic empiricists” argue that in spite of theoretical statistical arguments,
normative and forced choice variations of the same instrument can produce empirically
comparable results. Karpatschof and Elkjaer (2000) advance this case in their
metaphorically titled paper “Yet the Bumblebee Flies”. With theory, simulation and
empirical data they present evidence for the comparability of ipsative and normative data.
Saville and Wilson (1991) found a high correspondence between ipsative and normative
scores when forced choice involved a large number of alternative dimensions.
Normative tests also have serious limitations which the forced choice format was
originally created to deal with (Sisson 1948). Normative scales are subject to numerous
response biases—central tendency bias where respondents avoid extreme responses,
acquiescence response, and social desirability responding—and are easy to fake. Forced
choice instruments are designed to avoid these biases by forcing choice among alternatives in
a way that reflects real live choice making (Hicks 1970, Barron 1996). Matthews and Oddy
found large bias in the extremeness of positive and negative responses in normative tests and
conclude that when sources of artifact are controlled “individual differences in ipsative
scores can be used to rank individuals meaningfully” (1997: 179). Pickworth and Shoeman
(2000) found significant response bias in two normative LSI formats developed by Marshall
and Merritt (1986) and Geiger et al. (1993). Conversely, Beutell and Kressel (1984) found
that social desirability contributed less that 4% of the variance in LSI scores in spite of the
fact that individual LSI items all had very high social desirability.
In addition, ipsative tests can provide external validity evidence comparable to
normative data (Barron 1996) or in some cases even better (Hicks 1970). For example,
attempts to use normative rating versions of the LSI report reliability and internal validity
data but little or no external validity (Pickworth and Shoeman 2000, Geiger et al. 1993,
Romero et al. 1992, Marshall and Merrit 1986, Merrit and Marshall 1984). Jamieson2010
also found no external validity in her study comparing the LSI 3.1 with semantic differential
and Likert scale versions of the instrument. Her results suggest caution in comparing
research results from the LSI and these other formats since she found only a 47% match
between style classifications with the three instruments and learning mode correlations “only
explained 13% to 16% of the variance and the bi-polar dimensions explained 24% to 41% of the
variance” between instruments (p 73).
Characteristics of the LSI Scales.
The LSI assesses six variables, four primary scores that measure an individual’s
relative emphasis on the four learning orientations –Concrete Experience (CE), Reflective
Observation (RO), Abstract Conceptualization (AC), and Active Experimentation (AE)
and two combination scores measure an individual’s preference for abstractness over
concreteness (AC-CE) and action over reflection (AE-RO). The four primary scales of
the LSI are ipsative because of the forced choice format of the instrument. This results in
negative correlations among the four scales the mean magnitude of which can be estimated
(assuming no underlying correlations among them) by the formula -1/(m 1) where m is the
number of variables (Johnson et al. 1988). This results in a predicted average method
induced correlation of -.33 among the four primary LSI scales.
The combination scores AC-CE and AE-RO, however, are not ipsative. Forced
choice instruments can produce scales which are not ipsative (Hicks 1970, Pathi, Manning
and Kolb 1989). To demonstrate the independence of the combination scores and
interdependence of the primary scores, Pathi, Manning and Kolb (1989) had SPSS-X
randomly fill out and analyze 1000 LSI’s according to the ranking instructions. While the
mean inter-correlation among the primary scales was -.33 as predicted; the correlation
between AC-CE and AE-RO was +.038.
In addition, if AC-CE and AE-RO were ipsative scales the correlation between the
two scales would be -1.0 according to the above formula. Observed empirical relationships
are always much smaller, e.g. +.13 for a sample of 1591 graduate students (Freedman and
Stumpf 1978), -.09 for the LSI 2 normative sample of 1446 respondents (Kolb 1999b), -.19
for a sample of 1296 MBA students (Boyatzis and Mainemelis 2000) and -.21 for the
normative sample of 6977 LSI for the KLSI 3.1 described below.
The independence of the two combination scores can be seen by examining some
example scoring results. For example, when AC-CE or AE-RO on a given item takes a value
of +2 (from, say, AC = 4 and CE = 2 or AC = 3 and CE = 1) the other score can take a value
of +2 or -2. Similarly when either score takes a value of +1 (from 4 -3, 3-2 or 2-1) the other
can take the values of +3, +1, -1, or -3. In other words, when AC-CE takes a particular
value, AE-RO can take two to four different values, and the score on one dimension does not
determine the score on the other.
In the new KLSI 4.0 we introduce two new non-ipsative continuous combination
scores in addition to the primary learning cycle dialectics of AC-CE and AE-RO. These
scores assess the combination dialectics of Assimilation – Accommodation and Converging
– Diverging assessed by the four learning style types in previous LSI versions:
Assimilation - Accommodation = (AC+RO) - (AE+CE)
A high score on this dimension indicates a learning preference for assimilation or
generalized, conceptual learning, while a low score indicates a learning preference for
accommodation or active contextual learning. The concepts of assimilation and
accommodation are central to Piaget’s (1952) definition of intelligence as the balance of
adapting concepts to fit the external world (accommodation) and the process of fitting
observations of the external world into existing concepts (assimilation). This measure was
used in the validation of the Learning Flexibility Index (Sharma & Kolb 2010—see chapter
6) and has been used by other researchers in previous studies (Wiersta, and de Jong 2002,
Allison and Hayes 1996).
Converging – Diverging = (AC+AE) (CE+RO)
A high score on this dimension indicates a learning preference for converging or
evaluative decision making that closes down on the best solution to a problem versus
diverging to open up new imaginative possibilities and alternatives. The concepts of
converging and diverging originated in Guilford’s (1988) structure of intellect model as the
central dialectic of the creative process. This dialectic concept has been used in research on
ELT by Gemmell (2012) and Kolb (1983).
Continuous Balance Scores
Some studies have used continuous balance scores for ACCE and AERO to assess
balanced learning style scores (Mainemelis, Boyatzis and Kolb 2002, Sharma and Kolb
2010). These variables compute the absolute values of the ACCE and AERO scores adjusted
to center on the 50th percentile of the normative comparison group, in this case the KLSI 4.0.
New norms for the KLSI 4.0 were created from responses by several groups of users
who completed the instrument online. These norms are used to convert LSI raw scale scores
to percentile scores (See Appendix 1). The purpose of percentile conversions is to achieve
scale comparability among an individual’s LSI scores (Barron 1996) and to define cut-points
for defining the learning style types. Table 1 shows the means and standard deviations for
KLSI scale scores for the normative groups.
Table 2. KLSI 4.0 Scores for Normative Sample and Sub-samples
Adult HE
Normative percentile scores for the LSI 4.0 are based on a total sample of 10423
valid LSI scores from users who took the instrument online. The norm group is composed of
53% women and 47% men. Their ages range as follows--<19 = 1.3%, 19-24 = 19.9%, 25-34
= 29.6%, 35-44 = 26.5%, 45-54 = 17.9%, 55-64 = 5.3 %, >64 = .5%. Their educational level
is as followsprimary school graduate = 2.3%, secondary school degree= 16.8%, university
degree= 49.9%, master’s degree = 20.5% and doctoral degree = 10.5%. The sample includes
college students and working adults in a wide variety of fields. It is made up primarily of US
residents (65%) with the remaining users residing in 121different countries.
Seven more homogenous sub-groups that were selected from the norm group are described
Medical students. This sample includes 670 medical students and residents from several US
medical schools. 51.5% of the sample are men and 48.5% are women.
Nursing students. This sample is composed of 38 entering freshmen at a top research
university. 7.9% are men and 92.1% are women. All are between the ages of 25 and 55.
Law students. This group consists of 166 law students from an eastern US law school. Half
are men and half are women. Ages range from 25-60.
University undergraduates. This sample is composed of 500 undergraduate students from
several US colleges and universities. 73.6% are women and 26.4% are men. 84% are below
age 25.
University graduate students. This group includes 1478 graduate students in business,
education, psychology, nursing, engineering and other fields. They are 59% female and 41%
male. 80% are between the ages of 25 & 54.
Adult higher education e-learning students. This sample is composed of 663 adult
learners enrolled in e-learning programs at a large eastern US university. 37% were women
and 63% men. 92% are between the ages of 25 & 54.
Managers. This is a diverse group of 1724 managers from many organizations in the US
and around the world. 45.6% are female and 54.4% are male. 85% are between the ages of
25 and 54.
Recent theoretical and empirical work is showing that the original four learning
styles can be refined to show nine distinct styles (Eickmann, Kolb & Kolb 2004, Kolb &
Kolb 2005a, Boyatzis & Mainemelis 2000). David Hunt and his associates (Abby, Hunt
and Weiser 1985, Hunt 1987) identified four additional learning styles which they
identified as Northerner, Easterner, Southerner, and Westerner. In addition a Balancing
learning style has been identified by Mainemelis, Boyatzis and Kolb (2002) that
integrates AC and CE and AE and RO. These nine learning styles can be defined by
placing them on the Learning Style Type Grid (See Figure 4, p 13). Instead of dividing
the grid at the 50th percentiles of the 3.1 LSI normative distributions for AC-CE and AE-
RO, the nine styles are defined by dividing the two normative distributions into thirds.
On the AE-RO dimension the active regions are defined by percentiles greater than
66.67% (raw scores > 11) while the reflective regions are defined by percentiles less than
33.33% (< +1). On the AC-CE dimension the concrete regions are defined by < 6 and the
abstract regions by > 14. For example the Initiating region would be defined by AC-CE
raw scores < 6 and AE-RO scores > 11. The resulting 9 styles are thus defined as follows:
InitiatingACCE <6, AERO >11
Experiencing—ACCE <6, AERO > 0 & < 12
Imagining—ACCE <6, AERO <1
Reflecting—ACCE > 5 & < 15, AERO <1
Analyzing—ACCE >14, AERO <1
Thinking—ACCE >14, AERO > 0 & < 12
DecidingACCE >14, AERO >11
Acting—ACCE > 5 & < 15, AERO >11
Balancing—ACCE > 5 & < 15, AERO > 0 & < 12
This section reports internal consistency reliability studies using Cronbach’s alpha
and test-retest reliability studies for the randomized KLSI 3.1.
The KLSI 4.0 maintains the high scale reliability of the KLSI 3.1 with an average
scale reliability (Cronbach Alpha) = .81 (4.0) vs .80 (3.1). Table 3 shows the alpha
coefficients for the normative grop and sub-groups.
Table 3. Internal Consistency Alphas for the Scale Scores of the KLSI 4.0
Adult HE
There have been no studies to date of test-retest reliability of the KLSI 4.0. Two test-
retest reliability studies of the randomized format KLSI 3.1 have been published. Veres et al.
(1991) administered the LSI three times at 8 week intervals to initial (N = 711) and
replication (N =1042) groups of business employees and students and found test-retest
correlations well above .9 in all cases. Kappa coefficients indicated that very few students
changed their learning style type from administration to administration (See Table 4). Ruble
and Stout (1991) administered the LSI twice to 253 undergraduate and graduate business
students and found test-retest reliabilities that averaged .54 for the six LSI scales. A Kappa
coefficient of .36 indicated that 47% of students changed their learning style classification on
re-test. In these studies test-retest correlation coefficients range from moderate to excellent.
Table 4. Test-Retest Reliability for the KLSI 3.1 (Veres 1991)
LSI Scales
Concrete Reflective Abstract Active
Time 1 2 3 1 2 3 1 2 3 1 2 3
Initial Samples (N=711)
1 - .95 .92 - .96 .93 - .97 .94 - .95 .91
2 - .96 - .97 - .97 - .96
3- Replication Sample (N=1042)
1 - .98 .97 - .98 .97 - .99 .97 - .98 .96
2 - .99 - .98 - .99 - .99
3 Data source: Veres et al. (1991). Reproduced with permission. Time between tests was 8 weeks
Note: Kappa coefficients for the initial sample were .81 for Time 1-Time2, .71 for time 1-Time 3 and .86 for Time 2-Time
3. These results indicate that very few subjects changed their learning style classification from one administration to
Table 5. Test-retest Reliability for KLSI 3.1 (Ruble and Stout 1991)
LSI was randomized but in different order than KLSI 3.1. Time between tests was 5 weeks. Kappa coefficient was .36
placing 53% of respondents in the same category on retest.
The discrepancy between the studies is difficult to explain and there has been a long-
standing debate about the meaningfulness of test-retest reliability for the LSI since ELT
hypothesizes that learning style is situational, varying in response to environmental demands.
Changes in style may be the result of discontinuous intervening experiences between test
and retest (Kolb 1981) or individuals’ ability to adapt their style to changing environmental
demands (Mainemelis, Boyatzis and Kolb 2002, Jones, Reichard, and Mokhtari 2003).
This chapter reports studies on the validity of the KLSI 4.0. It begins with analysis of
the relationship between scores on the KLSI 4.0 and the previous KLSI 3.1 followed by other
internal validity evidence for the KLSI 4.0 normative group including correlation and factor
analysis studies of the LSI scales. The final part is focused on external validity evidence for
the KLSI 4.0 and other LSI versions. It begins with demographic relationships of learning
style with age, gender and educational level in the KLSI 4.0. This is followed by evidence
for the relationship between learning style and educational specialization. Concurrent
validity studies of relationships between learning style and other experiential learning
assessment inventories are then presented followed by studies relating learning style to
performance on aptitude tests and academic performance. Finally research on ELT and
learning style in teams is presented.
Correlation of KLSI 4.0 with KLSI 3.1
Table 6 shows that scores on the KLSI 4.0 are highly correlated with scores on the
previous KLSI 3.1 thusmaking validity research with previous LSI versions applicable to the
KLSI 4.0 maintaining the external validity that the instrument has shown over the years. The
average correlation between 3.1 and 4.0 scales equals .92.
Table 6.
KLSI 3.1
Concrete Experience
Pearson Correlation
Sig. (2-tailed)
KLSI 3.1
Reflective Observation
Pearson Correlation
Sig. (2-tailed)
KLSI 3.1
Abstract Conceptualization
Pearson Correlation
Sig. (2-tailed)
KLSI 3.1
Active Experimentation
Pearson Correlation
Sig. (2-tailed)
KLSI 3.1
Pearson Correlation
Sig. (2-tailed)
KLSI 3.1
Pearson Correlation
Sig. (2-tailed)
Correlation Studies of the LSI Scales.
Several predictions can be made from ELT about the relationship among the scales of
the Learning Style Inventory. These relationships have been empirically examined in two
ways—through a first order correlation matrix of the six LSI scales and through factor
analysis of the four primary LSI scales and/or inventory items.
ELT proposes that the four primary modes of the learning cycle—CE, RO, AC &
AE--are composed of two independent dialectic (bi-polar) dimensions--a “grasping”
dimension measured by the combination score AC-CE and a “transformation” dimension
measured by the AE-RO combination score. Thus, the prediction is that AC-CE and AE-RO
should be uncorrelated. Also, the CE and AC scales should not correlate with AE-RO and
the AE and RO scales should not correlate with AC-CE. In addition the dialectic poles of
both combination dimensions should be negatively correlated, though not perfectly since the
dialectic relationship predicts the possibility of developmental integration of the opposite
poles. Finally, the cross dimensional scales—CE/RO, AC/AE, CE/AE & AC/RO--should
not be correlated as highly as the within dimension scales.
Table 7 shows these critical scale inter-correlations for the total normative
sample of 10423. The correlations between AC-CE and AE-RO are significant but low. The
4.0 increases internal validity by increasing the statistical independence of the grasping (AC-
CE) and transforming (AE-RO) dimensions of the learning cycle. Independence of AC-CE &
AE-RO dimensions has increased reducing the negative correlation from -.27 in the 3.1 to -
.09 in the 4.0 RO is unrelated with AC-CE as ELT predicts, but correlations of AE with AC-
CE is correlated negatively with AC-CE (-.169). Correlations of AC and CE with AE-RO
are both very low as they should be. As predicted both AC & CE (-.369) and AE & RO (-
.418) are highly negatively correlated. The cross dimensional scales, CE/AE, CE/RO and
AC/RO have low correlations as predicted, but AC/AE has a higher negative correlation (-
.407) than predicted. Overall, with the exception of the negative correlation between AC and
AE, the scale inter-correlations demonstrate internal validity by showing excellent
correspondence with ELT predictions.
Table 7
Pearson Correlation
Sig. (2-tailed)
Pearson Correlation
Sig. (2-tailed)
Pearson Correlation
Sig. (2-tailed)
Pearson Correlation
Pearson Correlation
Sig. (2-tailed)
Pearson Correlation
Sig. (2-tailed)
**. Correlation is significant at the 0.01 level (2-tailed).
Factor Analysis Studies.
We have identified 17 published studies that used factor analysis to study the internal
structure of the LSI. Most of these studies have focused on the LSI 2, have studied different
kinds of samples and have used a number of different factor extraction and rotation methods
and criteria for the interpretation of results. Seven of these studies supported the predicted
internal structure of the LSI (Merritt & Marshall 1984, Marshall & Merritt 1985, Marshall &
Merritt1986, Katz 1986, Brew 1996, Yaha 1998, and Kayes 2005), four studies found mixed
support (Loo 1996 & 1999, Willcoxson & Prosser 1996 and Brew 2002), and six studies
found no support (Manfredo 1989, Newstead 1992, Cornwell, Manfredo & Dunlop 1991,
Geiger, Boyle & Pinto 1992, Ruble & Stout 1990 and Wierstra & de Jong 2002).
Factor analysis of the KLSI 4.0 total normative sample and sub-groups follows
recommendations by Yaha (1998). Principal components analysis with varimax rotation was
used to extract 2 factors using the 4 primary LSI scales. Analysis at the item level was not
done since it is not the item scores, but the scale scores that are proposed as operational
measures of the ELT learning mode constructs. Also, the -.33 correlation among the four
items in a set (resulting from the ipsative forced choice format) makes the interpretation of
item factor loadings difficult. Loo argues that the analysis by scale scores alleviates this
problem. “It should be noted that factoring scale scores (i.e. Yaha 1998) rather than item
scores bypasses the issue of ipsative measures when testing for the two bi-polar dimensions
(1999: 216).
ELT would predict that this factor analysis procedure would produce two bipolar
factors, one with AC & CE as poles and the other with AE and RO as poles, representing the
grasping and transforming dimensions of the learning cycle (See Figure 2). This is the result
for the total norm group, adult e-learning, managers, university undergraduates and
graduates. However, the medical, nursing and law student groups show a more mixed result
with the AC scale as one pole and a combination of CE and AE as the other in factor one.
Medicine and nursing show a clear AE-RO factor 2, while factor 2 in the law group has RO
as the dominant pole with AE only slightly higher than CE and AC. The percent of variance
explained by the two factors was about the same in all eight analyses with the total being
between 70 & 75%, factor one 36-41% and factor two 29-35%.
Table 8. Norm Group Factor Analysis of KLSI 4.0 Scales
Overall the results of correlation and factor analysis studies show similar results. As
Loo notes, “…with only four scale scores, factoring may be unnecessary because the factor
pattern structure can be accurately estimated from an inspection of the correlation pattern
among the four scales” (1999: 216). These data are better than previous versions of the LSI
(Kolb 1976b, 1985b) and give support for the ELT basis for the inventories.
Previous research with the LSI showed a linear increase in preference for learning by
abstraction with age as measured by the AC-CE scale and a curvilinear relationship with
learning by action as measured by AE-RO with middle age being the most active period of
life (Kolb 1976b, Kolb & Kolb 2005b). Results from the KLSI 4.0 normative sample with
much larger age cohort sample sizes than the LSI 1 norm group show a similar linear
relationship between AC-CE and seven age ranges--<19, 19-24, 25-34, 35-44, 45-54, 55-64
& >65. The AE-RO dimension shows a different pattern than previously with a decrease in
active orientation from the under 19 group to the 19-24 group (Similar to the increase in
reflection seen in college students over their four years (Mentkowski , M. and Associates
2000). AE-RO scores hold relatively constant through the adult years with a movement
toward action in the >65 group. See Figure 14 and Appendix 2 for complete descriptive
Figure 14.
Research with the previous LSI versions showed that males were more abstract that
females on the AC-CE scale and no significant gender differences on the AE-RO
dimension (Kolb 1976b, 1985b, Kolb & Kolb 2005b). Results from the KLSI 4.0
normative sample show similar results. See Figure 4 and Appendix 3 for complete
descriptive statistics. These results need to be interpreted carefully since educational
specialization and career choices often interact with gender differences making it difficult
to sort out how much variance in LSI scores can be attributed to gender alone and how
much is a function of one’s educational background and career (Willcoxson and Prosser
Under 19 19-24 25-34 35-44 45-54 55-64 65 & over
KLSI 4.0 Scores on AC-CE and AE-RO by Age Range
Abstract - Concrete Active - Reflective
1996). Also, statements like “Women are concrete and men are abstract” are unwarranted
stereotypical generalizations since mean differences are statistically significant but there
is considerable overlap between male and female distributions on AC-CE and AE-RO.
These consistent differences by gender on the LSI AC-CE scale provide a theoretical
link between ELT and the classic work by Belenky et al., Womens Ways of Knowing
(1986). They used gender as a marker to identify two different epistemological
orientations, connected knowing and separate knowing which their research suggested
characterized women and men respectively. Connected knowing is empathetic and
interpersonal and theoretically related to CE and separate knowing emphasizes distance
from others and relies on challenge and doubt, related to AC. Knight et al. (1997) tested
this hypothesized relationship by developing a Knowing Styles Inventory and correlating
separate and connected learning with the AC and CE scales of the LSI. They found no
relationship between AC and their measure of separate knowing for men or women and
no relationship between CE and connected knowing for women. However, they did find a
significant correlation between CE and connected knowing for men.
Figure 15.
Educational Level
ELT defines two forms of knowledge. Social knowledge is based on abstract
knowledge that is culturally codified in language, symbols and artifacts. An individual’s
personal knowledge is based on direct uncodified concrete experience plus the level of
acquired social knowledge that he or she has acquired. Hence, the theory predicts that
Abstract-Concrete Active -Reflective
KLSI 4.0 Scores on AC-CE and AE-RO by Gender
Male Female
abstractness in learning style is related to an individual’s level of participation in formal
education. Research relating educational level to learning style in the LSI 1 normative
sample (Kolb 1976b) showed the predicted linear relationship between amount of education
and abstractness. Data from the KLSI 4.0 normative sample show the same linear
relationship between abstractness and highest degree obtained—from Elementary to High
School to University to Graduate degrees. Differences among degree groups on the AE-RO
dimension are smaller indicating relatively little influence of educational level on orientation
toward action or reflection. See Figure 16 and Appendix 4 for complete descriptive statistics.
Figure 16.
Educational Specialization
A corollary of the ELT definition of learning as the creation of knowledge through the
transformation of experience is that different learning styles are related to different forms of
knowledge. Academic disciplines differ in their knowledge structure, technologies and
products, criteria for academic excellence and productivity, teaching methods, research
methods, and methods for recording and portraying knowledge. Disciplines even show socio-
cultural variation- differences in faculty and student demographics, personality and aptitudes,
as well as differences in values and group norms. For students, education in an academic
field is a continuing process of selection and socialization to the pivotal norms of the field
governing criteria for truth and how it is to be achieved, communicated, and used. The
resulting educational system emphasizes specialized learning and development through the
accentuation of the student’s skills and interests. The student’s developmental process is a
product of the interaction between his or her choices and socialization experiences in
Primary School Secondary School University Degree Masters Degree Doctoral Degree
KLSI 4.0 Scores on AC-CE and AE-RO by Level of Education
Abstract - Concrete Active - Reflective
academic disciplines. That is, the student’s dispositions lead to the choice of educational
experiences that match those dispositions. And the resulting experiences further reinforce the
same choice dispositions for later experiences. Over time the socialization and specialization
pressures combine to produce increasingly impermeable and homogeneous disciplinary
culture and correspondingly specialized student orientations to learning.
ELT (Kolb 1981b, 1984) provides a typology of specialized fields of study, learning
styles, and forms of knowledge and based on Pepper’s (1942) “world hypotheses”
framework. Social professions such as education and social work are typified by the
accommodating learning style, a way of knowing that is based on contextualism. The
science based professions such as medicine and engineering are characterized by the
converging learning style which is based on formism. The humanities and social sciences are
typified by the diverging learning style and are based on the world hypothesis of organicism.
Mathematics and the natural sciences are characterized by the assimilating learning style and
the world hypothesis of mechanism.
Overall, previous research with the LSI shows that student learning style distributions
differ significantly by academic fields as predicted by ELT. For example Willcoxson and
Prosser in their review of research on learning style and educational specialization using the
LSI 1 conclude that there is “some measure of agreement amongst researchers regarding the
learning style preferences typically found in specified disciplines and more agreement if
disciplines are subsumed under descriptions such as social sciences or humanities. It also
appears as specified by experiential learning theory that learning styles may be influenced by
environmental demands and thus results obtained for professionals and students in a
specified discipline may be dissimilar…in all studies the reporting of a numerical majority as
the predominant learning style obscures the range of styles found.” (1996: 249)
Their last point is important since ELT does not predict that a match between an
individual’s learning style and the general knowledge structure of their chosen field is
necessary for effectiveness; since learning is essential in all fields and therefore, all learning
perspectives are valuable. For example, a person in marketing with an assimilating style of
learning doesn’t match the typical accommodating style of marketing but, because of his or
her assimilating style may be more effective in communicating with research and
development scientists (Kolb 1976).
There is considerable variation in inquiry norms and knowledge structures within some
fields. Professions such as management (Loo 2002a, 2002b, Brown & Burke 1987) and
medicine (Sadler et al. 1978, Plovnick 1975) are multi-disciplinary including specialties that
emphasize different learning styles. Social sciences can vary greatly in their basic inquiry
paradigms. In addition fields can show variation within a given academic department, from
undergraduate to graduate levels and so on. For example, Nulty and Trigwell (1996) caution
that the learning style grouping should not be taken as absolute representation of a particular
student population, because different teaching strategies and discourse mode may be adopted
which are non-traditional to that discipline. Their study also suggests that learning styles are
related to the stage the students are in their studies. While students in the first third of their
studies adopted learning styles that were similar to each other irrespective of the disciplines,
learning styles of students in the final third of their studies tended to be related to the
learning requirement of their academic major.
The distinct value systems and educational goals of each educational institution also
exert significant influence on differences in students’ learning styles. To investigate the
relationship between the way a major is structured and student outcomes, Ishiyama and
Hartlaub ( 2003) conducted a comparative study of student learning styles in two different
political science curricular models at two Universities. The results indicate that while there
was no statistically significant relationship between student learning styles in underclass
students, there was a significant difference in mean AC-CE scores among upper class
students between the two universities. Students taking the highly structured, concept-
centered political science curriculum at Truman State University demonstrated higher
abstract reasoning skills than did students enrolled in the flexible, more content-oriented
major at Frostburg State University. The authors suggest that Truman State program better
facilitates the academic requirements recommended by Association of American College and
University (AACU) to promote abstract reasoning skills and critical thinking skills necessary
for the rigors of professional and graduate education than the flexible curriculum structure at
Frostburg State. Other researchers and educators also contend that understanding of the
distribution of learning styles in one’s field of discipline and sub-specialty is crucial for the
improvement of the quality of instructional strategies that respond to the individual need of
the learner as well as the optimal level of competency and performance requirement of each
profession (Baker, Simon, and Bazeli 1986, Bostrom, Olfman, & Sein 1990, Drew and
Ottewill 1998; Fox and Ronkowski,1997; Kreber, 2001; Laschinger, 1986; McMurray,1998;
Rosenthal, 1999; Sandmire, Vroman, & Sanders 2000; Sims, 1983).
Results from the KLSI 4.0 normative group show similar results to earlier research on
the relationship between learning style and educational specialization. Figure 17 plots the
mean scores on AC-CE and AE-RO for respondents who reported different educational
specializations on the KLSI 4.0 and Appendix 5 shows the distribution of learning style types
for each educational specialty.
A number of comparative studies using KLSI found significant differences in the learning
style preferences among the samples from different countries. Yamazaki’s (2005) meta-
analysis provides a summary of some of these studies. He compiled Yamazaki’s and Kayes’
(2005) study on Japanese and American mangers, Fridland’s (2002) study of Chinese and
American teachers, Barmeyer’s (2004) study of students from France, Quebec and Germany,
Auyeung’s and Sand’s (1996) study of accounting students from Australia and Hong Kong,
and Hoppe’s (1990) study of managers from 19 countries. Fig. 17 is a graphic representation
of the mean scores on AC-CE and AE-RO of the samples from these studies. The cut-off
Figure 17
point for AC-CE was 4.3 and for AE-RO 5.9 following the KLSI 2.0 norms that were used in
the reported studies.
Figure 18. Yamazaki’s Meta-analysis of Learning Style and Culture Studies
Joy and Kolb (2009) examined the role that culture plays in the way individuals learn using
the KLSI 3.1 to assess differences in how individuals learn and the framework for
categorizing cultural differences from the Global Leadership and Organizational Effectiveness
(GLOBE) study where national cultures are examined by cultural clusters and individual
cultural dimensions. The first part of the study assesses the relative influence of culture in
comparison to gender, age, level of education and area of specialization of 533 respondents
born in and currently residing in 7 nations. Figure 19 shows the KLSI 3.1 scores for the seven
Figure 19. Learning Styles of Respondents in
Poland, Italy, Brazil, USA, India, Germany and Singapore
This study to examine the influence of culture on learning style while examining some
of the other factors known to influence an individual’s approach to learning. Results of the
study indicate that culture as measured by the GLOBE country clusters and by representative
countries from each cluster does indeed significantly influence learning style, particularly the
extent to which individuals rely on concrete experiences versus abstract concepts in the way
they learn. On the AC-CE dimension of the KLSI, culture in the cluster sample accounted for
22% of the explained variance as compared with 17% for gender and 39% for educational
specialization while in the country sample the percentages of explained variance were 28%
for culture, 8.6 % for gender, 18% for level of education and 32 % for educational
specialization. Thus, in both samples while educational specialization accounted for the most
variance in AC-CE, culture ranked second ahead of gender, educational level, and age.
Analysis of the GLOBE country ratings on individual cultural dimensions suggests that
individuals tend to have abstract learning styles in countries that are high in uncertainty
avoidance, future orientation, performance orientation and institutional collectivism.
Individuals from Italy and Brazil had the most concrete learning styles and those from
Singapore and Germany had the most abstract learning styles.
On the AE-RO dimension of the KLSI, in the cluster sample only age had a significant
influence on individuals’ emphasis on action versus reflection in learning, accounting for 45%
of the explained variance. In the country sample age accounted for 36% of the explained
variance and educational specialization accounted for 23%. The influence of culture was
marginally significant (p<.07) and accounted for 34% of explained variance. Analysis of the
GLOBE country ratings on individual cultural dimensions suggests that individuals tend to
have reflective learning styles in countries that are high in uncertainty avoidance and active
learning styles in countries that are high in in-group collectivism, Individuals from Germany
had the most reflective learning styles and those from Poland had the most active learning
Other Experiential Learning Assessment Instruments.
The Learning Skills Profile
The Learning Skills Profile (LSP, Boyatzis and Kolb 1991a, 1991b, 1995) was
developed to assess systematically the adaptive competencies associated with learning style
(Kolb 1984). The LSP uses a modified Q-sort method to assess level of skill development in
four skill areas that are related to the four learning modes--Interpersonal Skills (CE),
Perceptual/Information Skills (RO), Analytical Skills (AC) and Behavioral Skills (AE).
Several studies have used the LSP in program evaluation (Ballou, Bowers, Boyatzis, & Kolb,
1999; Boyatzis, Cowen, & Kolb, 1995) and learning needs assessment (Rainey, Hekelman,
Glazka, & Kolb, 1993; Smith 1990). Yamazaki et al. (2003) studied the relationship between
LSP and LSI 3.1 scores in a sample of 288 research university freshmen. AC-CE was
negatively related to the interpersonal skills of leadership, relationship and help and
positively related to the analytic skills of theory building, quantitative analysis and
technology as predicted. The AE-RO dimension did not relate to the perceptual/information
skills of sense making, information gathering and information analysis but did relate to the
behavioral skills of goal setting and initiative as predicted (See Table 10). In another study of
198 MBA students, Mainemelis et al. (2002) found similar relationships between LSI 2
scores and the LSI clusters of Interpersonal, Information, Analytic and Behavioral learning
skills (See Table 11).
Table 9. Relationship between Learning Skills Profile scores and KLSI 3.1
AC-CE and AE-RO Scales (Yamazaki et al. 2003)
Table 10. Correlations between LSI 2 and The Learning Skills Profile
(Mainemelis et al. 2002)
r’s> .14 p< .05, r’s>.24 p<.001 two-tailed
The Adaptive Style Inventory
The Adaptive Style Inventory (ASI) was developed to assess situational variability in
learning style in response to different kinds of learning task demands (Kolb 1984). It uses a
paired comparison method to rank learning preferences for the four learning modes in eight
personalized learning contexts. It measures adaptive flexibility in learning, the degree to
which one systematically changes learning style to respond to different learning situations in
their life. Earlier studies found that adaptive flexibility is positively related to higher levels
of ego development on Loevinger's instrument (Kolb & Wolfe, 1981). Individuals with high
adaptive flexibility are more self-directed, have richer life structures, and experience less
conflict in their lives (Kolb, 1984).
Mainemelis, Boyatzis and Kolb (2002) employed the LSI 2, the Adaptive Style
Inventory (Boyatzis and Kolb 1993), and the Learning Skills Profile (LSP, Boyatzis and
Kolb 1991, 1995, 1997) to test a fundamental ELT hypothesis: The more balanced people are
in their learning orientation on the LSI, the greater will be their adaptive flexibility on the
ASI. To assess a balanced LSI profile two different indicators of a balanced learning profile
using absolute LSI scores on the Abstract/Concrete and Active/Reflective dimensions were
Goal setting
AC-CE -.14* .06
.06 -.24*** .06 .06
.20*** .04 .30***
.33*** .11 .21*** .04 .16** .04 .03 .01 -.15** .07
.19*** .08 .07 .10 .04 .07 .10 -.01 .02 .13* .09 .22***
F8.27*** 8.26*** 9.54*** 1.92 .26 6.58** 6.39** .89 11.08***
df 2, 285 2, 285 2, 285 2, 285 2, 285 2, 285 2, 285 2, 285 2, 285 2, 285 2, 285 2, 285
N = 288
* p < .05
** p < .0 1
*** p < .0 01
Interpersonal learning skills (CE)
Perceptual learning skills (RO)
Analytical learning skills (AC)
Behavioral learning skills (AE)
developed. The results supported the hypotheses showing that people with balanced learning
profiles in both dimensions of the LSI are more adaptively flexible learners as measured by
the ASI. The relationship was stronger for the profile balanced on the Abstract/Concrete
dimension than the active/reflective dimension. Other results showed that individuals with
specialized LSI learning styles have a greater level of skill development in the commensurate
skill quadrant of the LSP. The study also produced some unexpected results. For example,
while it was predicted that specialized learning styles would show less adaptive flexibility on
the ASI, the results showed that this is true for the abstract learning styles but not for the
concrete styles.
The ASI also produces total scores for the sum of the eight different learning contexts
on the four basic learning modes. Table shows the correlations between these total ASI
scores and the scales of the LSI 2 indicating high concurrent validity between the two
Table 11. Correlations between LSI 2 and Adaptive Style Inventory Scale
r’s>.28 p< .001 two-tailed
The Honey-Mumford Learning Styles Questionnaire
Honey and Mumford (1982, 1992) developed the Learning Styles Questionnaire
(LSQ) based on ELT with the aim to create an instrument that was phrased in the language of
UK managers and of pragmatic value to them, not “something that was academically
respectable” (1986: 5). While they base their learning styles on the learning cycle they
define the four learning modes somewhat differently. Three of the learning modes on the
face of it appear similar to ELT; Reflector and RO, Theorist and AC and Pragmatist and AE;
but the fourth mode Activist and CE is not, confusing concrete experience and active
experimentation. This appearance is supported by a cluster analysis and factor analysis of
the LSQ by Swailes and Senior (1999) who found a three stage learning cycle of action,
reflection and planning instead of the ELT four stage cycle. Honey and Mumford’s (1982)
correlation of the LSI 1 and the LSQ is also consistent although the sample is quite small. In
a larger study of undergraduate students by Sims Veres and Shake (1989) there was very
little relationship between any of the LSI 2 and LSQ scales. Another study by Goldstein et
al. (1992) of 44 students and faculty found similar small correlations between the LSQ and
LSI 1 and LSI 2 scales (See Table 12). They argued with some justification that the proper
correspondence between the LSQ and LSI is between the LSQ scales and the LSI learning
style types (eg. Activist = Accommodating) but found little evidence to support it. Only 41%
were correctly classified with the LSI 1 and 29% with LSI 2. In addition a factor analysis of
the LSQ by De Ciantis and Kirton (1996) failed to support the two bipolar dimensions, AC-
CE and AE-RO predicted by ELT; as did a study by Duff and Duffy (2002). Finally,
Mainemelis (2002)
Mumford in Swailes and Senior (2001:215) stated, “the LSQ is not based upon Kolb’s bi-
polar structure as the academic community seems to think”.
Given these results, caution should be used in equating scores from the LSI and LSQ
and in interpreting LSQ research as either confirming or disconfirming ELT.
Table 12. Correlations of the Honey-Mumford Learning Styles Questionnaire
with the LSI 1 and LSI 2
*** p < .001, ** p<.01,* p < .05 No sig. levels reported by Honey & Mumford
Multiple Intelligences
Narli, S., Ozgen, K., & Alkan, H. (2011) examined the relationship between
individuals' multiple intelligence areas and their learning styles using the mathematical
concept of rough sets. Multiple intelligence areas and learning styles of 243 mathematics
prospective teachers studying at a state university were identified using the "Multiple
Intelligence Inventory for Educators" developed by Armstrong (2000) and the KLSI 3.1.
The authors conclude, “Given that the data analysis of this study revealed that intelligence
areas together could explain learning styles at 0.794 level, we tend to take the position that it
is unacceptable to believe that learning styles and intelligence areas are totally different from
and irrelevant of each other…On the contrary, the findings of this study could be argued to
present results in line with the researchers…who believe that multiple intelligence and
learning styles should be explored together. These results also largely overlap with Gardner's
approach that learning styles and multiple intelligence types are different and a learning
style could be related to more than one intelligence area’.
Epistemological Beliefs Questionnaire
Tumkaya, S. (2012) investigated the epistemological beliefs of university students
according to their genders, classes, fields of Study, academic success and learning styles.
This study was carried out with 246 females and 242 males university students using the
Epistemological Beliefs Questionnaire.(Shommer 1990, EBQ)and the Kolb Learning Style
Inventory 2.0 translated into Turkish. The EBQ had a structure of three factors and consisted
of 34 items. There were 17 items in the first factor named “the belief concerning that earning
depends on effort”, 9 items in the second factor named the belief concerning that learning
depends on ability” (Range 9-45) and 8 items in the third factor named the belief
Honey &
et al. 1992
concerning that there is one unchanging truth” Results indicated that students who have
diverging learning styles believe more strongly that learning depends on ability and that there
is one unchanging truth more strongly than students who have assimilating, accommodating
and converging learning styles.
Aptitude Test Performance
Studies of the relationship between learning style and aptitude test performance have
consistently found that individuals with abstract, and sometimes active, learning styles
perform best on tests of this type. Boyatzis and Mainemelis (2000) found significant
correlations (p<.001) between the total GMAT scores of MBA students and their LSI 2
scores on AC-CE (.16 for 576 full time students and .19 for part time students) and on AC
(.23 FT and . 21 PT). Data from the research university freshmen normative sample shows
significant correlations (p<.001) between their total SAT scores and the KLSI 3.1 AC-CE
(.32) and AC (.37) scales. Kolb (1976b) reported significant correlations between the LSI 1
and the LSAT for a sample of 43 law students for RO (-.29 p< .05) and for AC (.30 p<.05)
Two studies have examined the relationship between the Wonderlic test of general
mental ability and the LSI. Kolb (1976b) reported data from 311 industrial managers
indicating significant positive relationships between the LSI 1 AC-CE (.18 p<.01) and AE-
RO (.24 p<.001) scales and Wonderlic scores. Cornwall and Manfredo (1994) studied the
relationship between learning style and the Wonderlic in a group of 74 students and young
working professionals. They scored the LSI 2 using a nominal scoring method and found that
those whose primary learning mode was AC score significantly higher than those with the
other primary learning modes.
While some have concluded that these relationships between AC and aptitude test
performance indicate that abstract persons have greater mental ability (eg. Cornwall and
Manfredo 1994) it is also possible that the one best answer format of tests of this type is
biased toward the converging learning style (See below).
Assessment of Academic Performance.
A number of studies have examined the relationship between learning style,
assessment method and academic performance. While some studies show relationships
between grades and the converging learning style (Rutz 2003, Mainemelis et al. 2000), other
studies indicate that these learning style differences in student performance may be a
function of the assessment technique used.
Tucker (2009) found that design students in architecture change towards the learning
styles of design teachers as they progress through their studies, producing a statistically
significant relationship between learning styles and academic performance in design
assignments. They found that successful architecture students’ learning styles were located in a
southerly direction or south of the AE-RO bi-polar dimension or in the converging and
assimilating quadrants as the skill sets and ways of thinking about implementing architecture
reflect these two learning styles. Weaker students had learning styles north of the AE-RO bi-
polar dimension or in the accommodating and diverging quadrants.
Lynch, Woelfl, Steele, & Hanssen explored the relationship between learning style and
three different academic performance measures in a third-year surgery clerkship in a medical
school. Two cohorts of third-year medical students took the United States Medical
Licensing Examination step1 (USMLE 1), the National Board of Medical Examiners
(NBME), and NBME computer-based case simulations (CBX). The USMLE 1 and NBME
subject examination rely on a single best answer, multiple-choice question format to assess
performance, whereas CBX is a complex computer simulation intended to measure clinical
management skills: The CBX consists of eight patient management simulations, each
involving a patient with an unknown surgical problem. The simulation allows the student to
obtain results of the history and physical examination, to order laboratory studies, to request
radiology procedures, and to perform invasive/interventional procedures of surgeries.
Beyond the presenting complaint, management is unprompted, and the student must balance
the clinical evaluation with the acuity and progression of the clinical pro