1

The Kolb Learning Style Inventory—Version 3.1
2005 Technical Specifications
Alice Y. Kolb
Experience Based Learning Systems, Inc.
David A. Kolb
Case Western Reserve University
May 15, 2005

Abstract
The Kolb Learning Style Inventory Version 3.1 (KLSI 3.1), revised in 2005, is the latest revision of the original
Learning Style Inventory developed by David A. Kolb. Like its predecessors, KLSI 3.1 is based on experiential learn-
ing theory (Kolb 1984) and is designed to help individuals identify the way they learn from experience. This revi-
sion includes new norms that are based on a larger, more diverse, and more representative sample of 6977 LSI users.
The format, items, scoring and interpretative booklet remain identical with KLSI 3. 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 Educa-
tion (1999). Section 1 of the technical specifications describes the conceptual foundations of the LSI 3.1 in the theory
of experiential learning (ELT). Section 2 provides a description of the inventory that includes its purpose, history, and
format. Section 3 describes the characteristics of the KLSI 3.1 normative sample. Section 4 includes internal reli-
ability and test-retest reliability studies of the inventory. Section 5 provides information about research on the internal
and external validity for the instrument. Internal validity studies of the structure of the KLSI 3.1 using correlation
and factor analysis are reported. External validity includes research on demographics, educational specialization, con-
current validity with other experiential learning assessment instruments, aptitude test performance, academic perfor-
mance, experiential learning in teams, and educational applications.

© Copyright 2005: Experience Based Learning Systems, Inc. All rights reserved. 1

1. CONCEPTUAL FOUNDATION—EXPERIENTIAL LEARNING THEORY AND
INDIVIDUAL LEARNING STYLES
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 twentieth century scholars who gave experience a central role in their theories of human learning and
development-notably John Dewey, Kurt Lewin, Jean Piaget, William James, Carl Jung, Paulo Freire, Carl Rogers, and
others-to develop a holistic model of the experiential learning process and a multi-linear model of adult development.
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 scholars.

1. Learning is best conceived as a process, not in terms of outcomes. 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)
2. All learning is relearning. 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.
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. In the process of learning, one is
called upon to move back and forth between opposing modes of reflection and action and feeling and think-
ing.
4. Learning is a holistic process of adaptation to the world. It is not just the result of cognition but involves the
integrated functioning of the total person—thinking, feeling, perceiving, and behaving.
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.
6. Learning is the process of creating knowledge. ELT proposes a constructivist theory of learning whereby social
knowledge is created and recreated in the personal knowledge of the learner. This stands in contrast to the
“transmission” model on which much current educational practice is based, where pre-existing fixed ideas are
transmitted to the learner.

ELT defines learning as “the process whereby knowledge is created through the transformation of experience. Knowl-
edge results from the combination of grasping and transforming experience” (Kolb 1984: 41). The ELT model por-
trays two dialectically related modes of grasping experience-Concrete Experience (CE) and Abstract Conceptualization
(AC)-and two dialectically related modes of transforming experience-Reflective Observation (RO) and Active Experi-
mentation (AE). Experiential learning is a process of constructing knowledge that involves a creative tension among
the four learning modes that is responsive to contextual demands. This process is portrayed as an idealized learning
cycle or spiral where the learner “touches all the bases”—experiencing, reflecting, thinking, and acting-in a recursive
process that is responsive 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 cre-
ating new experiences (Figure 1). ELT proposes that this idealized learning cycle will vary by individuals’ learning style
and learning context.

2 LSI Technical Manual

Concrete
Experience

Testing Implications
Observation and
of Concepts in
Reflections
New Situations

Formation of Abstract
Concepts and
Generalization

Figure 1. 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)

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Figure 2. The experiential learning cycle and regions of the cerebral cortex.
Reprinted with permission of the author (Zull 2002)

ELT posits that learning is the major determinant of human development and that how individuals learn shapes the
course of their personal development. Previous research (Kolb 1984) has shown that learning styles are influenced by
personality type, educational specialization, career choice, and current job role and tasks. Yamazaki (2002, 2004a) has
recently identified cultural influences as well. The ELT developmental model (Kolb 1984) 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 orga-
nizational socialization forces shape the development of a particular, specialized learning style; and (3) integration in
midcareer and later life, where nondominant modes of learning are expressed in work and personal life. Development
through these stages is characterized by increasing complexity and relativism in adapting to the world and by increased
integration of the dialectic conflicts between AC and CE and AE and RO. Development is conceived as multi-linear
based on an individual’s particular learning style and life path—development of CE increases affective complexity, of
RO increases perceptual complexity, of AC increases symbolic complexity, and of AE increases behavioral complexity.

The concept of learning style describes individual differences in learning based on the learner’s preference for employ-
ing different phases of the learning cycle. Because of our hereditary equipment, our particular life experiences, and the
demands of our present environment, we develop a preferred way of choosing among the four learning modes. We
resolve the conflict between being concrete or abstract and between being active or reflective in patterned, characteris-
tic ways.

Much of the research on ELT has focused on the concept of learning style, using the Learning Style Inventory (LSI)
to assess individual learning styles (Kolb 1971, 1985, 1999). While individuals tested on the LSI show many differ-
ent patterns of scores, previous research with the instrument has identified four learning styles that are associated with
different approaches to learning—Diverging, Assimilating, Converging, and Accommodating. The following sum-
mary of the four basic learning styles is based on both research and clinical observation of these patterns of LSI scores
(Kolb1984, 1999a).


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 situa-
tions, people with the Diverging style prefer to work in groups, listening with an open mind to different points of view
and receiving personalized feedback.

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 it 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 impor-
tant 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 learn-
ing 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 logi-
cal 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.

5

FACTORS THAT SHAPE AND INFLUENCE LEARNING STYLES
The above patterns of behavior associated with the four basic learning styles are shaped by transactions between people
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, and 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 Diverging Assimilating Converging Accommodating
Introverted Introverted Extraverted Extraverted
Personality types
Feeling Intuition Thinking Sensation
Arts, English Education
Educational Mathematics Engineering
History Communication
Specialization Physical Science Medicine
Psychology Nursing
Social Service Sciences Engineering Sales
Professional
Arts Research Medicine Social Service
Career
Information Technology Education
Personal Information Technical Executive
Current Jobs
jobs jobs jobs jobs
Adaptive Valuing Decision Action
Thinking skills
Competencies skills skills skills

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) indi-
cate 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 Intui-
tive type with the LSI Assimilating style. MBTI Feeling types correspond to LSI Diverging learning styles, and Think-
ing 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, 83-85).

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, an increas-
ing process of specialization 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 such as medicine and engineering have Converging learning styles. Individuals with
Accommodating styles often have educational backgrounds in education, communications, 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 profes-
sional 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 Con-
verging learning styles tends to be dominant among professionals in technology-intensive fields such as medicine and
engineering. Finally, the Accommodating learning style characterizes people with careers in fields such as sales, social
service, and education.

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 orien-
tation to task accomplishment and decision making in uncertain emergent circumstances require an Accommodating
learning style. Personal jobs, such as counseling and personnel administration, which require the establishment of
personal relationships and effective communication with other people, demand a Diverging learning style. Informa-
tion jobs, such as planning and research, which require data gathering and analysis, as well as conceptual modeling,
require an Assimilating learning style. Technical jobs, such as bench engineering and production, require technical and
problem-solving skills, which 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 effec-
tive 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 (Kolb1984).

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2. THE LEARNING STYLE INVENTORY
PURPOSE
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 experi-
ence 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 lan-
guage 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 one
learns. Scores on the inventory should not be interpreted as definitive, but as a starting point for explora-
tion 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 dif-
ferent educational treatments, a process sometimes referred to as tracking. Such categorizations based on a single test
score amount to stereotyping that runs counter to the philosophy of experiential learning, which emphasizes indi-
vidual 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 1981a: 290-291)

The LSI is constructed as a self-assessment exercise and tool for construct validation of ELT. Tests designed for predic-
tive validity typically begin with a criterion, such as 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 correla-
tion 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 and
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, and Cook 1960: 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 devel-
opment, 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 theory.


HISTORY
Five versions of the Learning Style Inventory have been published over the last 35 years. During this time, attempts
have been made to openly share information about the inventory, its scoring, and its technical characteristics with
other interested researchers. The results of their research have been instrumental in the continuous improvement of
the inventory.

Learning Style Inventory-Version 1 (Kolb 1971, Kolb 1976)
The original Learning Style Inventory (LSI 1) was created in 1969 as part of an 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 learn.

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 who were asked 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 three 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 learned. From 1971 until it was revised
in 1985, there were more than 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). Several studies of the LSI 1 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 seventh 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, more than 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.

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Kolb Learning Style Inventory-Version 3 (Kolb 1999)
In 1999 the randomized format was adopted in a revised self-scoring and interpretation booklet (LSI 3) that included
a color-coded scoring sheet to simplify scoring. The new booklet was organized to follow the learning cycle, emphasiz-
ing the LSI as an “experience in learning how you learn.” New application information on teamwork, managing con-
flict, personal and professional communication, and career choice and development were added. The LSI 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 new LSI 3.1 described here modified the LSI 3 to include new normative data described below. This revision
includes new norms that are based on a larger, more diverse and representative sample of 6977 LSI users. The format,
items, scoring, and interpretative booklet remain identical to KLSI 3. The only change in KLSI 3.1 is in the norm
charts used to convert raw LSI scores.

FORMAT
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
and 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 versions of the LSI have had the same format—a short questionnaire (9 items for LSI 1 and 12 items for subse-
quent 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). Items in the LSI are geared to a seventh 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 to the more common normative, or free-choice, format, such as the
widely used Likert scale, which 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 make up 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 one’s preferred learning approach.

ELT defines learning style not as a fixed trait, but as a dynamic state arising from an individual’s preferential resolu-
tion 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 endur-
ing 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 actual occasions they live
through.” (Kolb 1984: 63-64)

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 had been the case when we used 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, that is, 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 analysis-independence of error variance. Cornwell and
Dunlap (1994) stated that ipsative scores cannot be factored and that correlation-based analysis of ipsative data pro-
duced uninterpretable and invalid results (cf. 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 detract from the validity of intra-individual comparisons (LSI
purpose one), they argue that ipsative scores are not appropriate for inter-individual comparisons, since inter-indi-
vidual 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 prefer-
ences that the theory seeks to assess.

The “pragmatic empiricists” argue that in spite of theoretical statistical arguments, normative and forced-choice varia-
tions of the same instrument can produce empirically comparable results. Karpatschof and Elkjaer (2000) advanced
this case in their metaphorically titled paper “Yet the Bumblebee Flies.” With theory, simulation, and empirical data,
they presented 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, in which 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 concluded that when sources of artifact are controlled, “individual differ-
ences 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 than 4% of the vari-
ance in LSI scores, in spite of the fact that individual LSI items all had very high social desirability.

11

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 reli-
ability and internal validity data but little or no external validity (Pickworth and Shoeman 2000, Geiger et al. 1993,
Romero et al. 1992, Marshall and Merritt 1986, Merritt and Marshall 1984).

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 that measure an individual’s preference for abstractness over con-
creteness (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 magni-
tude 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
that are not ipsative (Hicks 1970; Pathi, Manning, and Kolb 1989). To demonstrate the independence of the combi-
nation 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 intercorrelation 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’s 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.


3. NORMS FOR THE LSI VERSION 3.1
New norms for the LSI 3.1 were created from responses by several groups of users who completed the randomized LSI
3. These norms are used to convert LSI raw scale scores to percentile scores (see Appendix 1). The purpose of percen-
tile 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 2 shows the means and standard deviations for KLSI 3.1 scale scores
for the normative groups.

Table 2. KLSI 3.1 Scores for Normative Groups

SAMPLE N CE RO AC AE AC-CE AE-RO
TOTAL 6977 Mn. 25.39 28.19 32.22 34.14 6.83 5.96
NORM S.D. 6.43 7.07 7.29 6.68 11.69 11.63
GROUP
On-line 5023 25.22 27.98 32.43 34.36 7.21 6.38
Users 6.34 7.03 7.32 6.65 11.64 11.61
Research 288 23.81 29.82 33.49 32.89 9.68 3.07
Univ. 6.06 6.71 6.91 6.36 10.91 10.99
Freshmen
Lib. Arts 221 24.51 28.25 32.07 35.05 7.56 6.80
College 6.39 7.32 6.22 7.08 10.34 12.37
Students
Art 813 28.02 29.51 29.06 33.17 1.00 3.73
College 6.61 7.18 6.94 6.52 11.13 11.49
UG
Research 328 25.54 26.98 33.92 33.48 8.38 6.49
Univ. MBA 6.44 6.94 7.37 7.06 11.77 11.92
Distance 304 23.26 27.64 34.36 34.18 11.10 6.54
E-learning 5.73 7.04 6.87 6.28 10.45 11.00
Adult UG

TOTAL NORMATIVE GROUP
Normative percentile scores for the LSI 3.1 are based on a total sample of 6977 valid LSI scores from users of the
instrument. This user norm group is composed of 50.4% women and 49.4% men. Their age range is 17-75, broken
down into the following age-range groups: < 19 = 9.8%, 19-24 = 17.1%, 25-34 = 27%, 35-44 = 23%, 45-54 =
17.2%, and >54 = 5.8 %. Their educational level is as follows: primary school graduate = 1.2%, secondary school
degree = 32.1%, university degree = 41.4%, and post-graduate degree = 25.3%. The sample includes college students
and working adults in a wide variety of fields. It is made up primarily of U.S. residents (80%) with the remaining
20% of users residing in 64 different countries. The norm group is made up of six subgroups, the specific demo-
graphic characteristics of which are described below.

13

On-line Users
This sample of 5023 is composed of individuals and groups who have signed up to take the LSI on-line. Group users
include undergraduate and graduate student groups, adult learners, business management groups, military manage-
ment groups, and other organizational groups. Half of the sample are men and half are women. Their ages range as fol-
lows: <19 = .2%, 19-24 = 10.1%, 25-34 = 29.6%, 35-44 = 28.8%, 45-54 = 23/1%, >55 = 8.1 %. Their educational
level is as follows: primary school graduate = 1.7%, secondary school degree = 18.2%, university degree = 45.5%, and
postgraduate degree = 34.6%. Most of the on-line users (66%) reside in the U.S. with the remaining 34% living in
64 different countries, with the largest representations from Canada (317), U. K. (212), India (154), Germany (100),
Brazil (75), Singapore (59), France (49), and Japan (42).

Research University Freshmen
This sample is composed of 288 entering freshmen at a top research university. 53% are men and 47% are women.
All are between the ages of 17 and 22. More than 87% of these students intend to major in science or engineering.

Liberal Arts College Students
Data for this sample were provided by Kayes (2005). This sample includes 221 students (182 undergraduates and 39
part-time graduate students) enrolled in business courses at a private liberal arts college. Their average age is 22, rang-
ing from 18 to 51. 52% are male and 48% are female.

Art College Undergraduates
This sample is composed of 813 freshmen and graduating students from three undergraduate art colleges. Half of the
sample are men and half are women. Their average age is 20, distributed as follows: <19 =42.7%, 19-24 = 54.3%, 25-
34 = 2%, >35 = 1%.

Research University MBA Students
This sample is composed of 328 full-time (71%) and part-time (29%) MBA students in a research university manage-
ment school. 63% are men and 37% women. Their average age is 27, distributed as follows: 19-24 = 4.1%, 25-34 =
81.3%, 35-44 = 13.8%, 45-54 = 1%.

Distance E-learning Adult Undergraduate Students
This sample is composed of 304 adult learners enrolled in an e-learning distance education undergraduate degree pro-
gram at a large state university. 56% are women and 44% men. Their average age is 36, distributed as follows: 19-24
= 6.3%, 25-34 = 37.5%, 35-44 = 40.1%, 45-54 = 14.5%, and > 55 = 1.6%.

CUT-POINTS FOR LEARNING STYLE TYPES
The four basic learning style types—Accommodating, Diverging, Assimilating, and Converging-are created by divid-
ing the AC-CE and AE-RO scores at the ﬁftieth percentile of the total norm group and plotting them on the Learning
Style Type Grid (Kolb 1999a: 6). The cut point for the AC-CE scale is +7, and the cut point for the AE-RO scale is
+6. The Accommodating type would be deﬁned by an AC-CE raw score <=7 and an AE-RO score >=7, the Diverging
type by AC-CE <=7 and AE-RO <=6, the Converging type by AC-CE >=8 and AE-RO >=7, and the Assimilating type
by AC-CE >=8 and AE-RO <=6.


Recent theoretical and empirical work is showing that the original four learning styles can be refined to show nine
distinct styles (Eickmann, Kolb, and Kolb 2004; Kolb and Kolb 2005a; Boyatzis and 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 identi-
fied 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. Instead of dividing the grid at the fiftieth percentiles
of the 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 => +12) while the reflective regions are defined by percentiles less than 33.33% (<= +1). On the AC-CE
dimension, the concrete regions are defined by <= +2 and the abstract regions by => 13. For example the NW accom-
modating region would be defined by AC-CE raw scores <=2 and AE-RO scores =>12. (See Kolb and Kolb 2005a for
examples and details.)

4. RELIABILITY OF THE KLSI 3.1
This section reports internal consistency reliability studies using Cronbach’s alpha and test-retest reliability studies for
the randomized KLSI 3.1.

INTERNAL CONSISTENCY RELIABILITY
Table 3 reports Cronbach’s alpha coefficients for seven different studies of the randomized KLSI 3.1: the norm sub-
sample of on-line LSI users, Kayes (2005) study of liberal arts college students, Wierstra and DeJong’s (2002) study of
psychology undergraduates, Veres et al. (1991) initial and replication studies of business employees and students, and
two studies by Ruble and Stout (1990, 1991) of business students. Wierstra and DeJong and Ruble and Stout used
an LSI randomized in a different order than the KLSI 3.1. These results suggest that the KLSI 3.1 scales show good
internal consistency reliability across a number of different populations.
Table 3. Internal Consistency Alphas for the Scale Scores of the KLSI 3.1

Source N CE RO AC AE AC-CE AE-RO
On-line 5023 .77 .81 .84 .80 .82 .82
Sample
Kayes 221 .81 .78 .83 .84 .77 .84
(2005)
Wierstra & 101 .81 .78 .83 .84 .83 .82
DeJong
(2002)
Veres et al. 711 Initial .56 .67 .71 .52 — —
(1991)* 1042 Rep. .67 .67 .74 .58 — —
Ruble & 323 (1990) .72 .75 .72 .73 — —
Stout 403 (1991) .67 .78 .78 .78 — —
*Alpha coefficients are the average of three repeated administrations. Alphas for the initial administration were
higher (average = .70).

15

TEST-RETEST RELIABILITY
Two test-retest reliability studies of the randomized format KLSI 3.1 have been published. Veres et al. (1991) admin-
istered 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 retest. In these studies, test-retest correlation coefficients range from moderate to
excellent. The discrepancy between the studies is difficult to explain, although 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 1981a) or individuals’ ability to adapt their style to changing
environmental demands (Mainemelis, Boyatzis, and Kolb 2002; Jones, Reichard, and Mokhtari 2003).

Table 4. Test-Retest Reliability for the KLSI 3.1 (Veres et al. 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 – .97
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-Time 2, .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 another.

Table 5. Test-Retest Reliability for KLSI 3.1 (Ruble and Stout 1991)

Sample N CE RO AC AE AC-CE AE-RO
UG/Grad 253 .37 .59 .61 .58 .48 .60
Business
Majors

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.


5. VALIDITY
This section begins with an overview of validity research on the LSI 1 and LSI 2 from 1971 to the introduction of the
KLSI 3 in 1999. It is followed by internal validity evidence for the KLSI 3.1 normative group including correlation
and factor analysis studies of the LSI scales. The final part is focused on external validity evidence for the KLSI 3.1
and other LSI versions. It begins with demographic relationships of learning style with age, gender, and education
level. This is followed by evidence for the relationship between learning style and educational specialization. Concur-
rent 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.
Next, research on ELT and learning style in teams is presented. The final part presents evidence for the practical utility
of ELT and the LSI in the design and conduct of education in different disciplines in higher education.

AN OVERVIEW OF RESEARCH ON ELT AND THE LSI: 1971-1999
Since ELT is a holistic theory of learning that identifies learning style differences among different academic specialties,
it is not surprising to see that ELT/LSI research is highly interdisciplinary, addressing learning and educational issues
in several fields. Since the first publications in 1971 (Kolb 1971; Kolb, Rubin, and McIntyre 1971) there have been
many studies on ELT using the LSI 1 and LSI 2. The Bibliography of Research on Experiential Learning Theory and
The Learning Style Inventory (Kolb and Kolb 1999) included 1004 entries.

Table 6 shows the distribution of these studies by field and publication period. The field classification categories are:
Education (including K-12, higher education, and adult learning), Management, Computer/Information Science,
Psychology, Medicine, Nursing, Accounting, and Law. Studies were also classified as early (1971-1984) or recent
(1985-1999). The division makes sense in that the most comprehensive statement of ELT, Experiential Learning, was
published in 1984, and the original LSI was first revised in 1985.
Table 6. Early and Recent ELT/LSI Research by Academic Field and Publication

ELT/LSI Research Early Period Recent Period Total
(1971-1984) (1985-1999) (1971-1999)
By Academic Field
Education 165 265 430
Management 74 133 207
Computer Science 44 60 104
Psychology 23 78 101
Medicine 28 44 72
Nursing 12 51 63
Accounting 7 15 22
Law 1 4 5
Total 354 650 1004
By Publication Type
Journal Articles 157 385 542
Doctoral Dissertations 76 133 209
Books and Chapters 43 58 101
Other 78 74 152
Total 354 650 1004

Data Source: Kolb and Kolb 1999

17

Table 6 also shows the distribution of the 1004 studies according to the publication type. More than 50% of the
studies were published in journals, and another approximately 20% were doctoral dissertations. 10% of the studies
were either books or book chapters, and the remaining 150 studies were conference presentations, technical manuals,
working papers, and master theses. Numbers should be considered approximate, since a few recent citations have yet
to be verified by abstract or full text. Also, classification by field is not easy because many studies are interdisciplinary.
However, the 1999 Bibliography probably does give a fair representation of the scope, topics, and trends in ELT/LSI
research. The following is a brief overview of research activity in the various fields.

Education
The education category includes the largest number of ELT/LSI studies. The bulk of studies in education are in
higher education (excluding professional education in the specific fields identified below). K-12 education accounts
for a relatively small number, as does adult learning alone. However, in many cases adult learning is integrated with
higher education. A number of studies in the education category have been done with sample populations in U.K.,
Canada, Australia, Finland, Israel, Thailand, China, Melanesia, Spain, and Malta.

Many of the studies in higher education use ELT and the LSI as a framework for educational innovation. These
include research on the matching of learning style with instructional method and teaching style and curriculum and
program design using ELT (e.g., Claxton and Murrell 1987). A number of publications assess the learning style of
various student, faculty, and other groups. Other work includes theoretical contributions to ELT, ELT construct
validation, LSI psychometrics, and comparison of different learning style assessment tools. In adult learning there are
a number of publications on ELT and adult development, moral development, and career development. The work of
Sheckley and colleagues on adult learning at the University of Connecticut is noteworthy here (e.g., Allen, Sheckley,
and Keeton 1993; Travers, 1998). K-12 education research has been primarily focused on the use of ELT as a frame-
work for curriculum design, particularly in language and science. (e.g., McCarthy 1996, Hainer 1992)

Management
ELT/LSI research was first published in the management field, and there has continued to be substantial interest in the
topic in the management literature. Studies can be roughly grouped into four categories: management and organi-
zational processes, innovation in management education, theoretical contributions to ELT including critique, and
psychometric studies of the LSI. Cross-cultural ELT/LSI research has been done in Poland, New Zealand, Australia,
Canada, U.K., and Singapore. In the management/organization area, organizational learning is a hot topic. Dixon’s
book The Organizational Learning Cycle (1999) is an excellent example.

Another group of studies has examined the relationship between learning style and management style, decision
making, and problem solving. Other work has measured work- related learning environments and investigated the
effect of a match between learning style and learning environment on job satisfaction and performance. ELT has
been used as a framework for innovation in management education, including research on matching learning styles
and learning environments, program design, and experiential learning in computerized business games (e.g., Boyatzis,
Cowen, and Kolb 1995; Lengnick-Hall and Sanders 1997).

Other education work has been on training design, management development, and career development. Another area
of research has been on the development and critique of ELT. Most psychometric studies of the LSI in the early period
were published in management, while recent psychometric studies have been published in psychology journals. Hun-
saker reviewed the early studies of the LSI 1 in management and concluded, “The LSI does not demonstrate sufficient
reliability to grant it the predictive reliability that such a measurement instrument requires. The underlying model,
however, appears to receive enough support to merit further use and development.” (1981: 151)


Computer and Information Science
The LSI has been used widely in computer and information science, particularly to study end user software use and
end user training (e.g., Bostrom, Olfman, and Sein, 1990; Davis and Bostrom, 1993). Of particular interest for this
book on individual differences in cognitive and learning styles is the debate about whether these differences are suf-
ficiently robust to be taken into account in the design of end user software and end user computer training. Other
studies have examined the relationship between learning style and problem solving and decision making, on-line
search behavior, and performance in computer training and computer-assisted instruction.

Psychology
Studies in psychology have shown a large increase over time, with 77% of the studies in the recent period. Many of
these recent studies were on LSI psychometrics. The first version of the LSI was released in 1976 and received wide
support for its strong face validity and independence of the two ELT dimensions of the learning process (Marshall and
Meritt, 1985; Katz 1986). Although early critiques of the instrument focused on the internal consistency of scales
and test-retest reliability, a study by Ferrell (1983) showed that the LSI version 1 was the most psychometrically sound
among four learning instruments of that time. In 1985 version 2 of the LSI was released and improved the internal
consistency of the scales (Veres, Sims, and Shake 1987; Sims, Veres, Watson, and Buckner 1986). Critiques of this
version focused their attention on the test-retest reliability of the instrument, but a study by Veres, Sims, and Locklear
(1991) showed that randomizing the order of the LSI version 2 items resulted in dramatic improvement of test-retest
reliability. This finding led to experimental research and finally to the latest LSI revision, LSI Version 3 (Kolb 1999a).
The LSI version 3 has significantly improved psychometric properties, especially test-retest reliability (see Kolb 1999b).

Other research includes factor analytic studies of the LSI, construct validation studies of ELT using the LSI, and com-
parison of the LSI with other learning style and cognitive style measures. Another line of work uses ELT as a model
for personal growth and development, including examination of counselor-client learning style match and its impact
on counseling outcomes. Notable here is the work of Hunt and his colleagues at the Ontario Institute for Studies in
Education (Hunt 1992,1987).

Medicine
The majority of studies in medicine focus on learning style analysis in many medical education specialties-residency
training, anesthesia education, family medicine, surgical training, and continuing medical education. Of significance
here is the program of research by Baker and associates (e.g., Baker, Cooke, Conroy, Bromley, Hollon, and Alpert
1988; Baker, Reines, and Wallace 1985). Also Curry (1999) has done a number of studies comparing different mea-
sures of learning styles. Other research has examined clinical supervision and patient-physician relationships, learn-
ing style and student performance on examinations, and the relationship between learning style and medical specialty
career choice.

Nursing
ELT/LSI research has also increased dramatically with, 81% of the nursing studies in the recent period. In 1990
Laschinger reviewed the experiential learning research in nursing and concluded, “Kolb’s theory of experiential learn-
ing has been tested extensively in the nursing population. Researchers have investigated relationships between learning
style and learning preferences, decision-making skills, educational preparation, nursing roles, nursing specialty, fac-
tors influencing career choices, and diagnostic abilities. As would be expected in a human service profession, nursing
learning environments have been found to have a predominantly concrete learning press, matching the predominating
concrete styles of nurses. Kolb’s cycle of learning which requires the use of a variety of learning modalities appears to be
a valid and useful model for instructional design in nursing education.” (Laschinger 1990: 991)

19

Accounting
There has been considerable interest in ELT/LSI research in accounting education, where there have been two streams
of research activity. One is the comparative assessment of learning style preferences of accounting majors and prac-
titioners, including changes in learning style over the stages of career in accounting and the changing learning style
demands of the accounting profession primarily due to the introduction of computers. Other research has been
focused on using ELT to design instruction in accounting and studying relationships between learning style and per-
formance in accounting courses.

In 1991 Ruble and Stout reviewed ELT/LSI research in accounting education. Reviewing the literature on predicting
the learning styles of accounting students, they found mixed results and concluded that low predictive and classifica-
tion validity for the LSI was a result of weak psychometric qualities of the original LSI and response set problems
in the LSI 1985. They tentatively recommended the use of the randomized version proposed by Veres, Sims, and
Locklear (1991). They wrote, “researchers who wish to use the LSI for predictive and classification purposes should
consider using a scrambled version of the instrument,” and note, “...it is important to keep in mind that assessing the
validity of the underlying theoretical model (ELT) is separate from assessing the validity of the measuring instrument
(LSI). Thus, for example, the theory may be valid even though the instrument has psychometric limitations. In such
a case, sensitivity to differences in learning styles in instructional design may be warranted, even though assessment of
an individual’s learning style is problematic” (p. 50).

Law
We are now seeing the beginning of significant research programs in legal education, for example the program devel-
oped by Reese (1998) using learning style interventions to improve student learning at the University of Denver Law
School.

Evaluation of ELT and the LSI
There have been two recent comprehensive reviews of the ELT/LSI literature, one qualitative and one quantitative. In
1990 Hickcox extensively reviewed the theoretical origins of ELT and qualitatively analyzed 81 studies in accounting
and business education, helping professions, medical professions, post-secondary education, and teacher education.
She concluded that overall 61.7% of the studies supported ELT, 16.1% showed mixed support, and 22.2% did not
support ELT.

In 1994 Iliff conducted a meta-analysis of 101 quantitative studies culled from 275 dissertations and 624 articles that
were qualitative, theoretical, and quantitative studies of ELT and the LSI. Using Hickox’s evaluation format, he found
that 49 studies showed strong support for the LSI, 40 showed mixed support, and 12 showed no support. About half
of the 101 studies reported sufficient data on the LSI scales to compute effect sizes via meta-analysis. Most studies
reported correlations he classified as low (<.5), and effect sizes fell in the weak (.2) to medium (.5) range for the LSI
scales. In conclusion Iliff suggests that the magnitude of these statistics is not sufficient to meet standards of predictive
validity.


Most of the debate and critique in the ELT/LSI literature has centered on the psychometric properties of the LSI.
Results from this research have been of great value in revising the LSI in 1985 and again in 1999. Other critiques,
particularly in professional education, have questioned the predictive validity of the LSI. Iliff correctly notes that the
LSI was not intended to be a predictive psychological test like IQ, GRE, or GMAT. The LSI was originally developed
as a self-assessment exercise and later used as a means of construct validation for ELT. 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 theory described in
Table 1.

Recent critiques have been more focused on the theory than the instrument, examining the intellectual origins and
underlying assumptions of ELT from what might be called a post-modern perspective, where the theory is seen as indi-
vidualistic, cognitivist, and technological (e.g., Kayes 2002, Vince1998, Holman et al.1997, Hopkins 1993).

INTERNAL VALIDITY EVIDENCE
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.

Correlation Studies of the LSI Scales
ELT proposes that the four primary modes of the learning cycle—CE, RO, AC, and AE-are composed of two inde-
pendent dialectic (bipolar) 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 nega-
tively correlated, though not perfectly, since the dialectic relationship predicts the possibility of developmental integra-
tion of the opposite poles. Finally, the cross-dimensional scales—CE/RO, AC/AE, CE/AE, and AC/RO—should not
be correlated as highly as the within-dimension scales.

Table 7 shows these critical scale intercorrelations for the total normative sample and the subsamples. Correlations
of AC and CE with the AC-CE dimension and AE and RO with the AE-RO dimensions are not included because
they are artificially inflated (all are above .8), because the combination score includes the scale score. The correlations
between AC-CE and AE-RO are significant but low. The correlation of .21 for the total norm group indicates that
the two scales share only 4.4% common variance. This correlation is somewhat higher than for the LSI 2 norm group
(-.09). RO has very low correlations with AC-CE, but correlations of AE with AC-CE are somewhat higher. Cor-
relations of AC with AE-RO are quite low, but with CE are somewhat higher. As predicted, both AC and CE and AE
and RO are highly negatively correlated. The cross-dimensional scales, CE/AE and AC/RO, have low correlations as
predicted, but the CE/RO and AC/AE have higher correlations than predicted.

Significance levels for correlations involving ipsative scales CE, RO, AC, and AE are not reported, since they are not
meaningful because of method-induced negative correlations.

21

Table 7. KLSI 3.1 Scale Intercorrelations

Sample N ACCE ACCE ACCE AERO AERO CE RO CE AC CE AC
/AERO /RO /AE /CE /AC /AC /AE /RO /AE /AE /RO
Total 6977 -.21 .10 -.26 .24 -.14 -.44 -.43 -.42 -.45 -.03 -.20
Norm p<.001
Group
On-line 5023 -.25 .13 -.30 .26 -.17 -.45 -.44 -.44 -.48 .00 -.18
Users p<.001
Research 288 -.02 -.06 -.10 .06 .01 -.41 -.41 -.28 -.34 -.20 -.34
University ns
Freshmen
Lib. Arts 221 -.14 .14 -.10 .15 -.08 -.34 -.48 -.42 -.35 -.18 -.20
College p<.05
Students
Art 813 -.25 .18 -.23 .30 -.14 -.35 -.38 -.52 -.44 -.06 -.18
College p<.01
UG
Research 328 -.20 .10 -.25 .17 -.18 -.45 -.45 -.36 -.46 -.07 -.16
University p<.01
MBA
Distance 304 -.12 -.01 -.22 .18 -.03 .37 -.36 -.36 -.41 -.08 -.31
E-learning p<.05
Adult UG
Significance levels for correlations involving ipsative scales CE, RO, AC, and AE are not reported, since they
are not meaningful because of method-induced negative correlations.

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 and Marshall 1984, Marshall and Merritt 1985, Marshall and Mer-
ritt 1986, Katz 1986, Brew 1996, Yaha 1998, and Kayes 2005); four studies found mixed support (Loo 1996, 1999;
Willcoxson and Prosser 1996; and Brew 2002); and six studies found no support (Manfredo 1989; Newstead 1992,
Cornwell, Manfredo and Dunlop 1991, Geiger, Boyle, and Pinto 1992; Ruble and Stout 1990; and Wierstra and de
Jong 2002).

Factor analysis of the total normative sample and subgroups 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 bipolar dimensions” (1999: 216).

ELT would predict that this factor analysis procedure would produce two bipolar factors, one with AC and CE as
poles and the other with AE and RO as poles. This is the result for the research university freshmen sample, the liberal
arts college sample, and the research university MBA students. However, the total normative sample, the on-line users,
and the distance e-learning students 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. In factor two, RO is the dominant pole and CE and AE are the other pole. The art
sample shows two different bipolar factors, with RO and CE as poles in factor one and AC and AE as poles in factor
two. The percent of variance explained by the two factors is about the same in all seven analyses, with the total being
between 70 and 75%—factor one 36-41% and factor two 29-35%.

Table 8. Norm Group Factor Analysis of KLSI 3.1 Scales

Sample Factor CE RO AC AE
Total Norm 1 .525 .053 -.988 .520
2 .438 -.998 .148 .475
On-line 1 .471 .056 -.991 .582
Users 2 .511 -.996 .120 .433
Research 1 .686 .152 -.945 .216
University 2 .116 -.906 .077 .760
Freshmen
Lib. Arts 1 .167 -.918 .041 .781
College 2 -.775 .044 .856 -.079
Students
Art College 1 .780 -.937 .048 .209
Undergrad 2 .180 .021 -.918 .752
Research 1 .665 .064 -.965 .339
University MBA 2 -.215 .952 -.030 -.694
Distance 1 .512 -.019 -.931 .613
E-learning 2 .397 -.992 .342 .333
Adult UG

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 inspec-
tion of the correlation pattern among the four scales” (1999: 216). These data are consistent with previous versions
of the LSI (Kolb 1976b, 1985b) and give qualiﬁed support for the ELT basis for the inventories. The support must
be qualiﬁed because the higher-than-predicted negative correlations between AC and AE, and CE and RO in the
KLSI 3.1 normative groups is not predicted and results in the slightly increased negative correlation between AC-CE
and AE-RO and the mixed-factor analysis results for all but the research university freshmen, the liberal arts college
students, and the distance e-learning sample.

23

EXTERNAL VALIDITY EVIDENCE
Age
Previous research with the LSI 1 showed that preference for learning by abstraction increased with age, as measured by
the AC-CE scale (Kolb 1976b). Preference for learning by action showed an initial increase (up to middle age) and a
subsequent decrease in later life, as measured by the AE-RO scale (Kolb 1976b). Results from the KLSI 3.1 normative
sample show similar significant relationships between the combination scores and six age ranges- <19, 19-24, 25-34,
35-44, 45-54, >55 -with much larger age cohort sample sizes than the LSI 1 norm group. See Figure 3 and Appendix
2 for complete descriptive statistics and ANOVA results.
Figure 3. KLSI 3.1
Scores on AC-CE and AE-RO by Age Range

9

8

7

Mean 6
Mean of AC-CE
Mean of AE-RO

5

4

3
<19 19-24 25-34 35-44 45-54 >55
Age Range

Gender
Previous research with the LSI 1 and LSI 2 normative groups showed that males were more abstract than females on
the AC-CE scale and that there were no significant gender differences on the AE-RO dimension (Kolb 1976b, 1985b).
Results from the KLSI 3.1 normative sample show similar significant gender differences on AC-CE and smaller but
significant differences on AE-RO. See Figure 4 and Appendix 3 for complete descriptive statistics and ANOVA
results. 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 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., Women’s 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. How-
ever, they did find a significant correlation between CE and connected knowing for men.


Figure 4. KLSI 3.1
Scores on AC-CE and AE-RO by Gender

9

8

7

Mean of AC-CE
Mean
Mean of AE-RO

6

5

4
Male Female
Gender

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 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 3.1 normative sample show the same linear relationship
between abstractness and level of education-from elementary to high school to university to graduate degree.

Differences among degree groups on the AE-RO dimension are smaller, with the largest difference being an increase in
active orientation from high school graduates to college graduates. This is similar to results with the LSI 1 normative
sample and is supported by longitudinal research that shows increasing movement in learning style from a reflective to
an active orientation through the college years (Kolb & Kolb 2005a, Mentkowski and Strait 1983, Mentkowski and
Associates 2000). See Figure 5 and Appendix 4 for complete descriptive statistics and ANOVA results.
Figure 5. KLSI 3.1
Scores on AC-CE and AE-RO by Level of Education

9

8

7

Mean 6 Mean of AC-CE
Mean of AE-RO

5

4

3
Elementary High School University Graduate
Education Level

25

Educational Specialization
A corollary of the ELT definition of learning as the creation of knowledge through the transformation of experi-
ence 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 varia-
tion 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 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 increas-
ingly 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
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 Prossor, 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 profession-
als 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 his/her 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 and 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 undergradu-
ate to graduate levels, and so on. For example, Nulty and Barrett (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 modes may be adopted which are not traditional to that discipline. Their study also suggests that learn-
ing styles are related to the stage the students have reached in their studies. While students in the first third of their
studies adopted learning styles that were similar to each other regardless of the discipline, 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 dif-
ferences 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 indicated that while there was no statistically sig-
nificant relationship between student learning styles in underclass students, there was a significant difference in mean
AC-CE scores among upperclass 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
the Truman State program better facilitates the academic requirements recommended by the Association of American
Colleges and Universities (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 research-
ers and educators also contend that understanding of the distribution of learning styles in one’s discipline and subspe-
cialty 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, and Sein 1990; Drew and Ottewill 1998; Fox and Ronkowski 1997; Kreber 2001;
Laschinger 1986; McMurray 1998; Rosenthal 1999; Sandmire, Vroman, and Sanders 2000; Sims 1983).

Results from the KLSI 3.1 on-line user normative subsample show similar results to earlier research on the relation-
ship between learning style and educational specialization. Figure 6 plots the mean scores on AC-CE and AE-RO for
respondents who reported different educational specializations and for the three specialized normative subgroups (in
bold). Appendix 5 shows the distribution of learning style types for each educational specialty.
Figure 6. KLSI 3.1 Scores on AC-CE and AE-RO

CONCRETE
Physical Education
0

Art Undergraduate
1

2
Communication
Applied & Fine Arts
3

Agriculture 4

Education
5
Psychology
6 Social Science
Literature Humanities
ACTIVE REFLECTIVE
12 11 10 9 8 7 Health 6 5 4 3 2 1 0
Business Law
Language
8 MBA
Medicine
Computer Sc/IS Architecture
9
Research University
Accounting
Undergraduate
10

11 Engineering

12 Science/Math

13

ABSTRACT

27

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, and Kolb 1995) and learn-
ing needs assessment (Rainey, Hekelman, Galaska, & Kolb, 1993; Smith 1990). Yamazaki et al. (2002) 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 9). In another study of 198 MBA students,
Mainemelis et al. (2002) found similar relationships between LSI 2 scores and the LSI clusters of Interpersonal, Infor-
mation, Analytic, and Behavioral learning skills (see Table 10).

Table 9. Relationship between Learning Skills Profile Scores and KLSI 3.1

Interpersonal learning skills (CE) Perceptual learning skills (RO) Analytical learning skills (AC) Behavioral learning skills (AE)

Variables Leadership Relationship Help Sense Information Information Theory Quantitative Technology Goal setting Action Initiative
& making gathering analysis building analysis &
understanding computer

2 2 2 2 2 2 2 2 2 2 2 2
β R β R β R β R β R β R β R β R β R β R β R β R

AC-CE -.14* .06 -.22*** .06 -.24*** .06 .06 .01 -.01 .00 .20*** .04 .30*** .10 .33*** .11 .21*** .04 .16** .04 .03 .01 -.15** .07

AE -RO .19*** .08 .07 .10 .04 .07 .10 -.01 .02 .13* .09 .22***

F 8.27*** 8.26*** 9.54*** 1.92 .26 6.58** 15.12*** 17.18*** 6.36** 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 < .01
*** p < .001

Table 10. Correlations Between LSI 2 and the Learning Skills Profile
(Mainemelis et al. 2002)

N Interpersonal Information Analytic Behavior Anal.- Interp. Behav.-
/CE /RO /AC /AE /AC-CE Info.
/AE-RO
198 .31 -.14 .54 .12 .57 .23

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 differ-
ent 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 individuals systematically change learning styles to respond to different learning situations in their lives.
Earlier studies found that adaptive flexibility is positively related to higher levels of ego development on Loevinger’s
instrument (Kolb and 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 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 unex-
pected 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 11 shows the correlations between these total ASI scores and the scales of the LSI 2, indicating high
concurrent validity between the two instruments.
Table 11. Correlations between LSI 2 and Adaptive Style Inventory Scale Scores

Source N CE RO AC AE AC-CE AE-RO
Mainemelis 198 .43 .37 .49 .42 .53 .44
et.al. (2002)
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 U.K. managers and of pragmatic value to them, not “some-
thing that was academically respectable” (1986: 5). While they based their learning styles on the learning cycle, they
defined 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
and Bokoros (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, 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”.

29

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

Source N LSI Version Activist-CE Reflector- Theorist-AC Pragmatist-
RO AE
Honey and 29 LSI 1 .23 .73 .54 .68
Mumford
1982
Sims, et al. 279 LSI 2 .22*** .28*** .11* .01
1989
Goldstein et 44 LSI 1 .23 .09 .36* .38*
al. 1992 LSI 2 .43** .14 .23 .38*

*** p < .001, ** p<.01,* p < .05 No sig. levels reported by Honey and Mumford

Aptitude Test Performance
Studies of the relationship between learning style and aptitude test performance have consistently found that individu-
als 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 showed 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
scored 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 perfor-
mance. While some studies show relationships between grades and the converging learning style (Rutz 2003, Boyatzis
and Mainemelis 2000), other studies indicate that these learning style differences in student performance may be a
function of the assessment technique used.

Lynch, Woelfl, Steele, and Hanssen explored the relationship between learning style and three different academic per-
formance 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 simula-
tions, 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 problem. Time advances
during the simulation in proportion to the time necessary to perform each examination, laboratory study, or interven-
tion (1998: 63). Of the 227 participants in the study, 102 (45%) were Converging learners, 59 (26%) Assimilating,
48 (21%) Accommodating, and 18 (8%) were Diverging learners. The result indicated that Converging and Assimi-
lating learners scored significantly higher on the two multiple-choice performance measures, while no learning style
difference was found on the CBX computer simulation. The authors concluded that the results support the Kolb
(1984) and Newland and Woelfl (1992) assertions that Converging and Assimilating learners may have a performance
advantage on objective, single-best-answer, multiple choice examination. They also concluded that the absence of
relationships between learning style and CBX simulation suggests that multiple- choice examination and clinical case
simulations measure different capabilities and achievements. Clinical management may require not only an abstract
orientation supporting the acquisition, organization, and synthesis of preclinical basic science data, but also a concrete
orientation involving pattern recognition and instinct. The data demonstrate the importance of evaluating learn-
ing outcomes by applying more than one type of examination format. Multiple-choice examinations favor abstract
learners; however, clinical performance requires additional cognitive skills and abilities, and behaviors that are not
adequately reflected in objective measures of performance.

Oughton and Reed (2000) measured the relationship between graduate students’ learning styles and performance out-
come in a hypermedia environment in which students were required to structurally map out their acquired knowledge
and grasp the interrelationships among various ideas and concepts. The dependent measures included the number
of concepts, number of nodes, number of links, number of bidirectional links, number of multiple concept nodes,
number of nodes with multiple links, levels of depth, preserved concepts, omitted concepts, and added concepts on
each student’s map. The results showed that Assimilating and Diverging learners were the most productive on their
concept maps. The authors concluded that this result can be attributed to the common traits shared by the two learn-
ing styles: the ability to see many perspectives and the ability to generate many ideas.

Holley and Jenkins (1993) examined the impact of learning style on four different accounting exam question formats:
multiple-choice theory (MCT), multiple-choice quantitative (MCQ), open-ended theory (OET), and open-ended
quantitative (OEQ). Their results indicated that there was a significant performance difference by learning style for all
but the multiple -choice quantitative format. On the active-reflective learning style continuum, there was a significant
difference in students’ performance on the multiple choice theory format (p< .01) and the open-ended quantitative
format (p< .05) with active students performing better. On the abstract-concrete learning style continuum, abstract
students performed better on the open-ended theory format (p< .062). The authors concluded that students with
different learning styles perform differently depending on the examination format, and that performance cannot be
generalized for similar subjects if the testing format varies.

This research suggests that educators need to exercise caution in evaluating performance based on a single outcome
measure. Diverse assessment strategies are required to adequately measure student overall competence and perfor-
mance.

Experiential Learning in Teams
Current research, involving different methodologies and different educational and workplace populations, has shown
that ELT is useful for understanding team learning and performance (Adams, Kayes, and Kolb 2005a). A number of
studies support the proposition that a team is more effective if it learns from experience and emphasizes all four learn-
ing modes. Summarized below are studies of team member learning style, team roles, and team norms.

31

Team Member Learning Style

In the first experimental study of the effect of learning styles on team performance, Wolfe (1977) examined how
homogeneous three-person teams of accommodators, divergers, assimilators, or convergers performed on a complex
computer business simulation compared with heterogeneous teams. The four groups of homogeneous teams had
similar performance results. However, the teams that had members with diverse learning styles performed significantly
better, earning nearly twice the amount of money of the homogeneous learning style teams. Similarly, Kayes (2001)
found that teams made up of members whose learning styles were balanced among the four learning modes performed
at a higher level on a critical thinking task than teams whose members had specialized learning styles.

Sandmire and Boyce (2004) investigated the performance of two-person collaborative problem-solving teams in an
allied health education anatomy, physiology, and pathology course. They compared a group of high abstract/high
concrete student pairs with a group of abstract pairs and a group of c oncrete pairs. The abstract/concrete pairs
performed significantly better on a simulated clinical case than the abstract pairs and slightly better than the concrete
pairs, indicating the value of integrating the abstract and concrete dialectics of the learning cycle. However, a similar
study by Sandmire, Vroman, and Sanders (2000) investigating pairs formed on the action/reflection dialectic showed
no significant performance differences.

Halstead and Martin (2002) found that engineering student teams that were formed randomly to include all learning
styles performed better than self-selected teams. Furthermore, in her studies of engineering students, Sharp stated,
“Classroom experience shows that students can improve teamwork skills with Kolb theory by recognizing and capi-
talizing on their strengths, respecting all styles, sending messages in various ways, and analyzing style differences to
resolve conflict and communicate effectively with team members” (2001, F2C-2). In his study of a six-week team
building program, Hall (1996) reported difficulty with self-selected teams that tended to group on the basis of friend-
ship. He advocated random team assignment, concluding, “If we had taken this approach there would have been more
disagreement to work through, personality clashes to cope with and conflict to resolve. The stress would have been
greater, but the learning probably more profound” (1996: 30).

Using another approach, Jackson studied the learning styles of ongoing workgroup team members who participated in
a paired team competition. The exercise was designed to require teamwork skills. Results showed that teams with bal-
anced learning styles performed better. In 17 of the 18 team pairs, the winning team’s average score was higher than
that of the losing team. Jackson concluded, “Designing teams that reflect the dynamic nature of team activities has
great appeal in that it gives all team members a more equal opportunity to contribute and a more equal opportunity
to be valued. . . . The process model advocates that different team members lead in different team activities or learning
situations (2002, p. 11).

Team Roles

Park and Bang (2002) studied the performance of 52 Korean industrial work teams using the Belbin team role model,
which is conceptually linked to ELT (Jackson 2002). They found that the best-performing teams were those whose
members adopted at a high level all nine of Belbin’s roles covering all stages of the learning cycle. They also found that
teams with roles that matched the particular stage of a team’s work/learning process performed best.

McMurray (1998) organized his English-as-a-foreign-language classroom using ELT principles. He divided his Japa-
nese students into four-person teams with maximally diverse learning styles. Students were assigned to one of four
roles that matched their strongest learning mode: leader (concrete experience), artist (reflective observation), writer
(abstract conceptualization), and speaker (active experimentation). The leader’s role was to direct classmates in com-
pleting assignments; the artist’s, to create ideas for presentations; and the writer’s, to compose messages for speakers to
read. Class lessons were organized to include all four stages of the learning cycle. Classroom observations supported
the idea that students benefited from the team role assignment and from accounting for learning style in the course
design.


Gardner and Korth used ELT, learning styles, and the learning cycle to develop a course for human resource develop-
ment graduate students that focused on learning to work in teams. They found strong relationships between learning
styles and preference for learning methods-assimilators preferred lectures, reading, writing, and individual work, while
accommodators and often divergers and convergers preferred partner and group work. They advocated providing
different student roles during team learning activities to develop appreciation for, and skill in, all learning styles. “Part
of the class could actively participate in a role play (accommodating), while a second group observes and provides
feedback to the participants (diverging), a third group develops a model/theory from what they have seen and shares it
with the class (assimilating, and the fourth group develops a plan for applying what they have seen to a new situation
and shares it with the class (converging)” (1999: 32).

Team Norms

Carlsson, Keane, and Martin used the ELT learning cycle framework to analyze the biweekly reports of research and
development project teams in a large consumer products corporation. Successful project teams had work process
norms that supported a recursive cycling through the experiential learning cycle. Projects that deviated from this work
process by skipping stages or being stuck in a stage “indicated problems deserving of management attention” (1976:
38).

Gardner and Korth used ELT to design a course in group dynamics, group development, and group effectiveness.
They taught student learning teams to use the experiential learning cycle to improve the transfer of learning. They
concluded, “The use of learning groups in conjunction with the experiential learning model enhances the learn-
ing process, reinforces the link between theory and practice, and facilitates the transfer of learning to the workplace”
(1997: 51).

Pauleen, Marshall, and Ergort used ELT to construct and implement web-based team learning assignments in a
graduate-level course in knowledge management. Students worked on projects in virtual teams. Follow-up student
evaluations indicated that 75% “agreed or strongly agreed that experiential learning was a valuable way of experiencing
and learning about a variety of communication channels in a team environment” (2004: 95); 99% found experiential
learning to be more valuable than simply reading about something.

Two studies have explicitly examined team conversational learning spaces with norms that support the experiential
learning cycle. Wyss-Flamm (2002) selected from a management assessment and development course three multicul-
tural student teams who rated themselves as high in psychological safety, defined as the ability of the team to bring
up and talk about difficult or potentially psychologically uncomfortable issues. Three of the teams rated themselves as
low in psychological safety. Through intensive individual and team interviews, she analyzed the teams’ semester-long
experience. In teams with high psychological safety, the conversations followed a recursive experiential learning cycle:
differences were experienced among team members, were examined through reflective juxtaposition that articulated
learning, and culminated in either an integration of the differences or an affirmation of the contrast. Teams with low
psychological safety tended to have early disturbing incidents that limited conversation and made the conversational
flow more turbulent and conflict-filled. Lingham (2004) developed a questionnaire to assess the norms of conversa-
tional space in a sample of 49 educational and work teams. He found that the more the teams supported the experien-
tial learning cycle through norms that focused their conversation on interpersonal diverging (concrete experience and
reflective observation) and task-oriented converging (abstract conceptualization and active experimentation), the better
they performed, the more satisfied they were with their membership on the team, and the more they felt psychologi-
cally safe to take risks on the team.

Based on the above research, a workbook of structured experiential learning exercises designed to promote team learn-
ing has been developed, The Kolb Team Learning Experience (Kayes, Kayes, Kolb, and Kolb 2004). The workbook
program uses the experiential learning cycle and members’ learning style information to help teams learn about their
purpose, work process, team membership, roles, context, and action plans. Initial research on the impact of this educa-
tional intervention suggests that the program is effective in promoting team learning in educational and organizational
settings (Adams, Kayes, and Kolb 2005b).

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Educational Applications
The primary purpose of the LSI and ELT is to increase individuals’ understanding of the process of learning from
experience and their unique individual approach to learning. 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. There have been many studies that have used ELT and the
LSI in this way to improve the learning process in education. The following two sections summarize some of this
work. The first section examines those studies that have used the LSI to understand and manage differences between
student and faculty learning styles. The second section describes studies in a number of different disciplines that have
used the experiential learning model in curriculum development. For a complete review of the applications of the LSI
and ELT in higher education, see Kolb and Kolb (2006).

Managing Faculty and Student Learning Style Differences

Several studies have examined the differences between faculty and student learning styles. These studies suggest that
educators need to adapt their teaching styles and instructional methods to facilitate the learning process by offering
a variety of learning opportunities appropriate to different student learning styles and to different subject matters.
(Baker, Simon, and Bazeli 1986; Buch and Bartley 2002; Cartney 2000).

In their study of learning style differences among pediatric residents and faculty, Kosower and Berman (1996) found
that that while most residents preferred accommodating or diverging styles (81%), most faculty preferred either
converging or assimilating learning strategies (73%). A longitudinal study comparing undergraduate nursing students’
learning styles and faculty learning styles reported similar results: nursing students preferred concrete thinking (59%)
over abstract thinking (41%), while their faculty preferred abstract thinking (82%) over concrete thinking (18%)
(Kalsbeek 1989).

Kruzich, Friesen, and Soest (1986) conducted a study of student and faculty learning styles in social work at two
universities and two private colleges and found significant learning style differences among undergraduate students,
graduate students, field instructors, and social work faculty. Overall, faculty most often had converging learning styles,
whereas the majority of graduate students and field instructors were diverging learners. The undergraduate students
were mostly accommodating learners, suggesting a preference for action.

In a similar study conducted in the field of social work, Raschick, Maypole, and Day (1998) found that students
whose learning styles were similar to their field supervisors along the active experimentation-reflective observation con-
tinuum would rate their field experience with them higher. The authors suggest that the finding is most relevant for
the supervisors at the beginning point of the learning cycle, when matching their teaching techniques to their students’
preferences presents with added benefits to encourage students to move through the rest of the learning cycle.

In their study of differences and similarities of perception of learning among internal medicine residents and faculty,
White and Anderson (1995) found that one of the restraining factors that prevented learning from occurring was
related to the discrepancies in what residents and faculty perceived to be the most relevant aspect of the learning pro-
cess. In most situations, faculty tended to focus on abstract and reflective modes of the learning process, while resi-
dents emphasized the concrete mode of learning.

Sadler, Plovnick, and Snope (1978) report some of the difficulties of teaching in an environment in which the learn-
ing style of the faculty and the students differ. Their study suggests that faced with such a situation, instructors may be
required to use instructional methods valuable to the students but not necessarily appealing or intellectually rewarding
to the instructors themselves.


To help students deal with difficulties caused by faculty/student learning style differences, John Reese at the University
of Denver Law School conducts “connecting with the professor” workshops in which students select one of four teach-
ing 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 differ-
ences 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).

Concerning whether faculty member are capable of learning to teach in ways that are incongruent with their own
learning styles, Kosower and Berman argue that “because we all engage in all of the strategies to some degree, it seems
to be more a matter of willingness to learn rather than ability”(1996: 217). Baker, Simon, and Bazeli (1987) contend
that teaching is an art requiring the instructor to select from among a wide variety of instructional strategies to reach
students with a diversity of learning preferences. Harrelson and Leaver-Dunn (2002) suggest that experiential learn-
ing requires that teachers assume a facilitator mind set, which might be a difficult mindset for some. Lipshitz (1983)
underscores the complexity of the role of an experiential teacher who needs to have a firm grasp of the relevant concep-
tual material and also develop sensitivity and skill in managing students’ emotional reactions to the learning process.

McGoldrick, Battle, and Gallagher (2000) indicate that the less control instructors exert on the students’ experiences,
the more effective the learning outcome will be. However, instructors may run the risk of losing control over course
structure as well as failing to keep the learning activities bounded within a specific time frame. Most risks associated
with the experiential method, contend the authors, can be mitigated through careful planning, unambiguous course
structure, establishing of clear expectations and firm deadlines for each class activity. Furthermore, students will
have varying levels of interest as well as difficulties with certain stages of the learning cycle. It is incumbent upon the
instructor to grasp the diverse needs of the students and be aware of the challenges certain students will face in the
various phases of the cycle. Students may also react to the shifting role of the instructors in the experiential classrooms
from that of a knowledge purveyor to one who creates the learning environment and facilitates the holistic learning
process. For example, November (1997) describes how difficult it was for many of his students to accept that he had
a different role: “I had stopped using the textbook. Instead I used lecture time for looking at common problems or
for experiential activities. Rather than being the fount of knowledge who dispensed what needed to be learned to get
through this course, I had become a ‘reaming facilitator.’ I created the situations from which students could learn if
they wished. Some students did not see this way, particularly in the early weeks of struggle, and they did not keep their
views to themselves.” (1997: 293)

Leonard and Harris (1979) used the knowledge of learning style in a small-group teaching and clinical supervision
conducted in a primary care internal medicine residency program at the University of Minnesota. They found that
knowledge of learning style can be effectively used to recognize distinct patterns of behaviors, attitudes, and reactions
learners exhibit in a given learning context and thus allow the teacher to flex his or her teaching approach to fit the
learner’s immediate learning needs. It is also important to point out that in clinical sessions, the instructor was equally
effective in creating learning situations in which students could function within the safety of their preferred learning
mode but also be challenged to recognize the weaknesses associated with those learning styles.

Some studies suggest that the identification of learning strategies best suited for different learning styles may increase
the learning effectiveness of each individual student and, conversely, increase students’ adaptive flexibility to alter their
learning styles to respond to the learning demand of a specific environment (Brenenstuhl and Catalanello1979; Curry
1999; Fritzsche 1977; Lynch, Woelfl, Steele, and Hanssen 1998). ELT posits that effective learners are able to flex their
learning styles according to the demands of different learning tasks. To the extent that the individual learning prefer-
ences are respected and recognized, it is also important for the students to be exposed to diverse learning situations
where their abilities and competencies can be stretched beyond the comfort of their preferred learning modes. Several
studies suggest that in fact students shift their learning strategies to match the learning demands of a particular disci-
pline (Cornett 1983, Entwistle 1981, Kolb 1984, Ornstein, 1977).

35

Jones, Reichard, and Mokhtari (2003) examined the extent to which community college students’ learning style prefer-
ences vary as a function of discipline. They found significant differences in students’ learning style preference across
four different subject-area disciplines: English, math, science, and social studies. The results indicate that 83% of the
103 participants switched learning styles for two or more disciplines, suggesting that students are capable of flexing
their learning strategies to respond to the discipline-specific learning requirements.

Stutsky and Laschinger (1995) examined the effect of the preceptorship experience on the learning styles, adaptive
competencies, and environmental press perception of senior baccalaureate nursing students to investigate the nature
of learning style/learning environment interaction in nursing education. The results, according to the authors, sup-
port Kolb’s (1984) assertion that an effective learner is able to apply skills from each of the learning modes in whatever
combination the learning requires. The study also suggests that students’ successful learning experiences are dependent
upon careful design and selection of instructional strategies that allow them to demonstrate mastery of knowledge and
skills associated with each learning mode.

Lengnick-Hall and Sanders (1997) designed a learning system in the graduate and undergraduate level management
courses structured around the learning cycle to give students a variety of ways to master each segment of the course
material. Results indicate that despite their wide variety in learning styles, experiences, academic levels, and interests,
students demonstrated consistently high levels of personal effectiveness, organizational effectiveness, ability to apply
course materials, and satisfaction with both course results and learning process. The study also showed learning style
differences in student ratings of various outcome measures: divergent learners rated their personal effectiveness higher
than the nondivergent learners, while assimilating learners rated the lowest on the same outcome measure. Converging
learners on the other hand, rated their ability to apply course material significantly higher than did the nonconverg-
ing learners, an indication of their tendency to seek out opportunities to apply what they have learned. Looking at the
positive learning outcomes generated by the courses, the authors contend that high-quality learning systems are ones in
which extensive individual differences are matched with a variety of options in learning methods, thus creating oppor-
tunities for student behavioral, emotional, and intellectual transformation in a lasting impact.

The Experiential Learning Model as a Guide for Curriculum Development

In this section we review studies of experiential learning methods applied in sixteen different professions and academic
disciplines in higher education. The studies reported here cover a broad range of applications using ELT and the LSI.
Some educators have used an experimental design to compare the effectiveness of an experiential learning method with
a more traditional course format, whereas others have developed and implemented assessment methods for teacher-
student interaction. While instructional strategies and methods were designed to fit the academic requirements of a
specific field, many of the experiential activities reported in the studies can be broadly applied to different fields with
adequate modifications.

Accounting. Siegel, Khursheed, and Agrawal (1997) conducted a controlled field experiment to test the effectiveness
of video simulation as a way to integrate experiential learning theory in the teaching of auditing in their accounting
course. The videotape used in the experiment followed the principles of experiential learning in teaching the funda-
mental steps in auditing. The experiment involved four sections of an undergraduate course in auditing. Two sections
were used as control groups and two others as the experimental group. The instructor presented the videotapes at
various times during the semester to the experimental group, while no videotape presentation was made to the control
group. Both groups were given identical assignments, problems, and lecture material covering audit procedures and
concepts. Examinations were scheduled and administered to both sections at the same time. The results of the experi-
ment indicated significantly higher examination scores for the experimental groups, supporting the value of experi-
ential learning for improving effectiveness in teaching auditing. In auditing courses, the authors suggest, the initial
learning phase, concrete experience, is often missing from the learning process.


Specht (1991) examined the effect of an experiential learning method in student learning in an undergraduate
accounting course compared with another class conducted using a traditional lecture method. The results were mea-
sured by quizzes in both classes to compare the students’ knowledge of concepts, both specific and general, directly
after the class and six weeks after the learning activities have taken place. The results revealed no significant differ-
ences in short-term learning between the two course formats; however, the experiential class demonstrated retention
of knowledge over a six-week period, whereas a significant decrease in the scores of the lecture class was observed. The
authors concluded that students in the experiential learning classroom may have formed a better understanding of the
concepts, thus successfully retaining knowledge better than students in the lecture class.

In applying experiential learning in his accounting course Umapathy (1985) underscores the importance of the role of
the experiential instructor for a successful adoption and implementation of experiential learning curricula. Experiential
exercises have proven to be effective in generating considerable student involvement and participation in the learning
process, with increased student capacity to retain knowledge for a longer period of time. However, for the experiential
curricula to be effective, the instructors need to be properly trained in the design and delivery of the experiential activi-
ties, if both instructors and students are to benefit from the experience.

Business and management. Certo (1976) designed a series of experiential training activities for an undergraduate
management course, based on the four dimensions of the learning cycle. In conducting those activities, the instructor
assumed the role of an experiential facilitator by “encouraging high levels of student participation; creating a learning
environment conducive to learn new behaviors; providing theoretical clarification; and emphasizing both content and
process” (Certo 1976: 22). In a later study he further articulated the value of experiential learning as a methodology of
education that focuses on the whole person and emphasizes the critical role of the facilitator as an active experiential
instructor who blends, with a proper balance experience, reflection, conceptualization, and action in the classroom
activities (Certo 1977).

In order to respond to mounting criticism of the inadequacy of business education Sims and Sauser (1985) proposed
the experiential learning model as a theoretical basis to design management curricula intended to develop managerial
competencies in business students. The authors offer seven core principles that need to be in place if such curricula are
to be successfully implemented: 1) ability to face new situation and problems; 2) emphasis on both theory and prac-
tice; 3) opportunity to have a direct managerial experience; 4) relevant and reliable assessment methods; 5) effective
feedback; 6) increased self-knowledge; and 7) reflection and integration as a key final step in the acquisition of com-
petency. In designing his organizational communication course, Pace (1977) emphasized the relevance of experiential
pedagogy that gives primacy to learners’ experience, action, and opportunity for students to test out newly learned
concepts and theories.

In their organizational behavior course McMullan and Cahoon (1979) applied Kolb’s (1971) experience-based learn-
ing evaluation instrument. The Personal Application Assignment (PAA) was designed to raise student awareness of
the distinct learning process involved at each step of the learning cycle. For example, students often have difficulty
in differentiating objective experiences from personal reactions to those experiences. Similarly, the tendency to focus
only on personally useful concepts makes it difficult for students to discriminate between abstract conceptualization
and active experimentation in a given situation. By discriminating between the abstract conceptualizing and the
active experimentation, students are forced to clarify the implicit assumptions and values that guide their actions. The
PAA requires students to rigorously evaluate their own learning process and encourage behavioral patterns that lead
to meaningful and purposeful actions. Such rigorous examination of one’s learning process was foreign to most of the
students and consequently frustrating to many. PAA activities made the familiar and obvious way of learning uncertain
and problematic for most of them. As the authors suggest, “such a situation is ripe for learning, challenging students to
move beyond the safety of their predictable and familiar ways of learning.” (McMullan and Cahoon, 1979: 457).

Lipshitz (1983) designed and implemented an experiential behavioral science course in the Israeli Military Academy
focused on the development of problem-solving, decision-making, and crisis-management skills in their officers. The

37

aim was to use the experiential learning model to counter the organizational environment of the Israeli Defense Force
characterized by overall “lack of proper training, job pressures such as uncertainty and overload, competitiveness
coupled with high regard for results, preference of action coupled with dislike of reflection, and preference for the con-
crete coupled with distrust of the abstract.” (Lipshitz 1983: 125) A key finding from the experiential courses was that
the success of the course was largely a function of the instructor, as students’ reactions ranged from enthusiasm to deep
disappointment. The instructors skilled in integrating the content of the course and the learning process were able to
generate high levels of satisfaction and achievement in their students. In those successful courses, there was a shift from
the student tendency to analyze a case study purely in terms of its outcome to a more careful and thoughtful attention
given to the problem-solving and decision-making process embedded in the case.

Gopinah and Sawyer (1999) developed a computer-based enterprise simulation based on experiential learning in a
business course to bridge the gap between knowledge and its application in the business world. The results of the sim-
ulation show that the recursive nature of experiential learning promotes strategic decision making and group behavior
consistent with long- term strategy.

Marketing. Dissatisfied with the application of experiential methods in business classrooms, Dyer and Schumann
(1993) developed an experiential learning laboratory classroom in their marketing course. In order to create a true
laboratory experience in marketing classrooms, the authors developed the Knowledge/Experience Integration Learn-
ing Model in a senior-level marketing advertising/promotion class. In this class, the text assignments and lectures were
integrated with experiences generated from two types of learning tasks, multiple group projects and multiple individ-
ual case studies. The traditional performance evaluations (multiple-choice and essay exams) were eliminated altogether
to give central focus to the recursive cycle of lecture, discussion, feedback, and hands-on experiences. At the comple-
tion of the course, students reported an increased level of critical thinking ability and capacity to apply and connect
theoretical knowledge with real-life business application.

Education. Svinicki and Dixon (1987) published an influential paper describing a comprehensive instructional model
to deal with the constraints and challenges instructors and students encounter in adopting experiential learning as an
instructional design framework. The instructional design model incorporates a broad range of classroom activities that
lead students through the full cycle of learning, thus presenting instructors with a rich array of instructional choices
that give students a more complete learning experience gained from multiple learning perspectives. It broadened the
scope of application of experiential activities to a wide range of academic fields by illustrating possible course design
options suited to the learning objectives of different disciplines. Using the model, instructors are able to design their
classroom activities based upon how much student involvement would be appropriate given the time constraints most
instructors face. The model has been successfully applied in various academic fields such as geography (Healey and
Jenkins 2000), theatre (Gressler, 2002), and political science (Brock and Cameron 1999) and affords instructors great
flexibility in designing courses based on the specific educational goals, knowledge, and skill demands of their academic
discipline.

As part of a counseling curriculum, Pelsma and Borgers (1986) developed an experience-based ethics course around
the experiential learning cycle, with focus on the “how” rather than “what” of learning. The authors suggest that the
emphasis on four modes of the cycle promotes learning and development of skills for responsible, ethical reasoning.
McGlinn (2003) used experiential learning cycle in a teacher education program, emphasizing the reflective compo-
nent of the cycle to overcome students’ lack of reflection on their teaching. The author claims that the experiential
learning model is effective in promoting change and development in students’ self-knowledge about their teaching
practices by providing time for reflection. Similarly, Hatcher and Bringle (1997) report the effectiveness of the learn-
ing cycle in designing reflection activities in service learning settings. Sugarman (1985) promotes the usefulness of
the experiential learning model for curriculum planning, implementation, and evaluation in the counseling field. The
experiential learning framework, the author contends, helps students expand their repertoire of learning skills thor-
ough the conceptualization of the total learning process.


Nursing. Stiernborg, Zaldivar, and Santiago (1996) conducted a pre-test post-test quasi-experimental design study
to assess the comparative effectiveness of didactic teaching and experiential learning in a HIV/AIDS training pro-
gram for nursing students in the Philippines. The program focused on improvement of HIV/AIDS knowledge levels
and attitudinal change toward HIV/AIDS patients. The authors hypothesized that experiential learning would yield
significantly higher knowledge levels and favorable attitude changes in the students than didactic teaching. Three
groups of nursing students participated in the study: the first group received didactic teaching in the form of lectures,
while the second group had training with an experiential learning approach. Both groups included participation by a
person with HIV/AIDS. The third group served as a control group and did not receive any formal HIV/AIDS train-
ing. The didactic and experiential groups covered the same content, including AIDS epidemiology, infection control,
socio-ethical issues related to HIV infection, and nursing care of patients in the hospital and community. The didactic
group had a two-hour presentation by the instructors, followed by a 30-minute Q & A session on the presentation.
The session finished with 30 minutes with an AIDS patient. The experiential learning group had the presentation
and discussion of a number of short case situations and a number of role-plays with student participation. The session
ended with 30 minutes with an AIDS patient. Knowledge post-test scores indicate that both didactic and experiential
learning approach produced a significant increase in the students’ knowledge levels. However, the experiential learn-
ing group achieved a significantly higher knowledge level than the didactic group. While both groups reduced fear of
attracting HIV (an indication of a positive attitude change), only the experiential learning group showed a consistent
positive change on all attitudinal scales.

The authors concluded that the experiential learning approach was more effective than the didactic approach for
knowledge acquisition in five significant ways: first, the problem-posed approach prompted students to get actively
involved in the learning process through role-play. Second, it emphasized personal involvement through reflection.
Third, the cases reflected the real world and encouraged integration of theory and policy and their practical applica-
tions. Fourth, the experiential learning session was flexible and learner centered. Fifth, the participation of an AIDS
patient formed an integral part of the experiential learning session, whereas in the didactic session, the lecture, the Q
& A session, and the patient with HIV-AIDS testimonial were separate parts with no opportunity for integration.

Medicine. Cleave-Hogg and Morgan (2002) designed an anesthesia simulation based on experiential learning for
undergraduate medical students. Students reported high levels of satisfaction with the anesthesia simulation experience
based on three grounds: 1) it provides opportunity to activate relevant prior knowledge and raise awareness of the gaps
in their knowledge, 2) offers a learning context that closely resembles a real-life anesthesia practice, and 3) provides
freedom to integrate their knowledge, to improve their skills, and to exercise their judgment without endangering a
patient. The authors contend that the results of the study support the value of integrating the experiential simulation
exercise in the anesthesia undergraduate curriculum.

Sandmire and Boyce (2004) investigated the performance of two-person collaborative problem-solving teams in an
allied health education anatomy, physiology, and pathology course. They compared a group of high abstract/high
concrete student pairs with a group of abstract pairs and a group of concrete pairs. The abstract/concrete pairs per-
formed significantly better on a simulated clinical case than did the abstract pairs and slightly better than the concrete
pairs, indicating the value of integrating the abstract and concrete dialectics of the learning cycle. However, a similar
study by Sandmire, Vroman, and Sanders (2000) investigating pairs formed on the action/reflection dialectic showed
no significant performance differences.

Psychiatry. Milne, James, Keegan, and Dudley developed an empirical method of assessing the effectiveness of mental
health trainers’ transaction patterns and their impact on student learning. The instrument, Teacher’s PETS (Process
Evaluation of Training and Supervision), was derived through operationalization of the experiential learning theory
with the main purpose of providing empirically valid and reliable data on the trainers’ behaviors during training ses-
sions. The instrument was designed around the four dimensions of the learning cycle and “is an explicitly transactional
one in which learners play an essential role in relation to the trainers, who will be at times responsive and at times
proactive” (2000: 189) in any given learning situation. The key feature of the model, the authors suggest, is the fluid,

39

dynamic transactions that occur between the levels of the model, as learners move backward and forward between
different learning modes, and trainers using several methods to move the learner on to a new mode of the experiential
learning cycle. The authors summarize the usefulness of the instrument to measure trainers’ effective behaviors as fol-
lows:

“…the effectiveness of the trainer can be assessed functionally, in terms of the learner’s mini-outcomes (a ‘good’ profile
would show that the learner made use of all four learning modes); structurally, in terms of the trainer’s use of the
observed facilitating behaviors (i.e., a ‘good’ profile would tend to show that the leader had utilized a range of such
behaviors).” (2000: 91)

The study was conducted to assess one trainer’s performance in an eight-day in-service workshop on psychosocial
interventions for severe mental illness held at a psychiatric hospital in the U.K. Participants in the workshop were
31 mental health professionals who were allocated to two different training groups. The workshops lasted for three
months and were scheduled in four blocks of two days each. Two months elapsed between the first group’s workshops
and the second group’s. The study was conducted in three distinct phases: a baseline phase in which the workshop
leader served as his own control; intervention phase in which the leader received feedback from consultants on his per-
formance based on the PETS instrument, followed by discussion and modeling of alternative teaching techniques; and
finally, the maintenance phase in which the consultants were withdrawn from the training session. The workshop was
video-taped for the duration of the study. A random selection of segments of the training was analyzed using PETS.
Four baseline sample sessions, followed by two from intervention, and one from the maintenance period were selected
for analysis. The relevant behaviors of the trainers and the participants were coded from the tapes. The results of the
study indicate that during the baseline phase, the observed teaching method was primarily didactic in nature and
accounted for the greatest impact (46.4%) on learner behavior in the reflection mode of the learning cycle, followed by
smaller overall impacts on the remaining phases of the cycle (range from means of 12.2% for abstract conceptualiza-
tion to 5.7% for active experimentation). In the intervention phase, by contrast, the greatest impact of the trainer’s
behavior on learners was on the concrete experience (59. 5%), followed by reflective observation (33%) and active
experimentation (4.5%) phases of the learning cycle.

The authors conclude that the intervention phase produced trainer behaviors that promoted learners’ ability to take
advantage of the full range of the experiential learning cycles, thus maximizing their learning outcomes. Finally, PETS
yielded a good inter-rater reliability as well as adequate empirical and concurrent validity, indicating its effectiveness
as an observational instrument in educational settings. As such, PETS serves as an exemplary model for assessing and
enhancing trainers’ skills in mastering experiential learning methods that is applicable in diverse teaching and training
situations.

Engineering. In order to revitalize the engineering education, in 1989 the College of Engineering and Technology at
Brigham Young University initiated a faculty training program based on the experiential learning model (Harb, Terry,
Hurt, and Williamson 1995). Volunteer faculty members were introduced to the concepts of the experiential learning
model and methods of teaching to four different learning modes and asked to implement experiential methods in their
courses. Volunteers were encouraged to visit each other’s classes, individual teaching was videotaped for a later review,
and follow-up support was offered through peer discussions about the successes and problems encountered in their
teaching. The benefits of the program have been many. Several faculty members redesigned their courses to reach the
full spectrum of the experiential learning cycle, using a variety of teaching strategies. Furthermore, there was a renewed
interest in and enthusiasm for teaching throughout the engineering school, and students responded positively to the
new learning strategies used.

The School of Engineering at Murdoch University, in Australia, decided to include a section on “understanding your
learning styles” in the newly developed foundation units in the first year, with the aim of empowering students in their
pursuit of university and life-long learning requirements. Due to the broad interdisciplinary nature of the foundation
units, the school decided that students needed to master some fundamental meta-cognitive skills to succeed in various
courses (Fowler, McGill, Armarego, and Allen 2002).


Stice argues that the low knowledge retention rate of engineering students can be attributed to the ineffective teach-
ing methods used by most faculty in engineering courses. The most frequently used teaching methods rely heavily on
abstract ideas and concepts, without providing opportunities to test the practical value of a theory. As an alternative
teaching strategy, the author designed a mathematics course on differential equations to use all four stages of the learn-
ing cycle: beginning with lecture (RO); followed by the students’ thinking about the ideas presented (AC); completing
homework assignment (AE); and closing the cycle with demonstration (CE). At the end of the experiment, Stice con-
cluded, “the rewards are sizeable: students learn more and derive intellectual satisfaction from the experience” (1987:
296).

Engineering educators have also paid close attention to the impact of dialectical tension created by the diverse learn-
ing-style composition of the student teams and have capitalized on the differences to broaden students’ skill levels and
competencies associated with learning in teams. Halstead and Martin (2002) found that student engineering teams
that were formed randomly to include all learning styles performed better than self-selected teams. Furthermore, in
her studies of engineering students, Sharp stated, “Classroom experience shows that students can improve teamwork
skills with Kolb theory by recognizing and capitalizing on their strengths, respecting all styles, sending messages in var-
ious ways, and analyzing style differences to resolve conflict and communicate effectively with team members” (2001).

Mathematics. Travers (1998) investigated the impact of experiential learning methods on students’ self-regulation of
their own learning process in mathematics. The author contends that the critical difference between academically
low, and high-achieving students is the capacity to self-regulate their learning by actively processing and controlling
information, affect, and behavior to acquire critical knowledge and skills. The purpose of the study was to examine
whether the treatment group taught mathematics through an experiential learning method demonstrated higher levels
of self-regulation than the control group, which was taught mathematics through a traditional lecture format. The
results indicate that the experiential learning group demonstrated a higher level of self-regulation. The difference was
explained by how the two groups regulated the learning outcomes. Students in the traditional lecture format were
taught rule-based learning, in which the rules given by the teacher were the only guide to deal with the new experi-
ences. In the rule-based learning, students were given information about what to do, but not how to deal with unex-
pected situations when things did not work the way they expected. Students taught experientially, on the other hand,
were exposed to a variety of situations from which to compare a new experience with previous ones, thus developing
the ability to critically evaluate what worked and didn’t work in a given learning situation.

Political Science. Building on Svinicki and Dixon model, Brock and Cameron (1999) developed instructional
sequences for a political science course based on the experiential learning cycle. The authors contend that teaching to
all learning modes is crucial for students’ acquisition of higher-order thinking and problem-solving skills. They offer,
as an illustrative example, how each phase of the cycle can be designed as a process of exploration of the experience
of involvement in a political campaign. During the CE phase, students can explore their reactions about the various
experiences during the election: Did they vote on policy or personality? Was the ballot clear or confusing? If discrimi-
nation and representation are key themes of the course, the instructor can encourage students to consider what role the
race, gender, sexual orientation, or religion of the candidates and the voters played in the determining the outcome of
the election. In the RO phase of the class, discussion, brainstorming sessions, and journals can be used to encourage
reflection about the political situations or policies. The AC phase of the learning process can be devoted to intellectual
modeling by the instructors in lectures. It is very important, emphasize the authors, that students observe the instruc-
tor “thinking out loud,” for it is by seeing the instructor’s mind at work that students learn how to think like political
scientists. When instructors present only conclusions or solutions to problems, students’ ability to develop higher-
order thinking can be substantially diminished. Finally, in the AE phase of the learning cycle, students can be asked
to project the outcome of the election in a specific district using the data generated by the polling firms or an analysis
of socio-political and income profile of the target area. Next, they can track the fortunes of the parties following the
development of the campaign, adjust their model, and offer final predictions on voting day. The cycle can be restarted
when the election results are known (CE phase), and students are encouraged to reflect on the election outcomes (RO
phase) and analyze the strength and weakness of their prediction model. The authors concluded that, while there is
great merit in following the four-stage learning cycle, the purpose of the model is not to set a rigid learning pattern
that takes away spontaneity and flexibility from both students and instructors.

41

Geography. Healey and Jenkins (2000) applied the Svinicki and Dixon model to the teaching of a geography course. In
their view, two central practical applications of the experiential learning theory are relevant to different types of learn-
ing environment, be it a lecture course or a seminar-based course: 1) how a session or a whole course can be designed
to take students systematically around the learning cycle, and 2) the selection of teaching methods appropriate to dif-
ferent stages of the cycle.

Economics. McGoldrick, Battle, and Gallagher ( 2000) developed a managerial economics course based on experien-
tial methods applied to one form of service learning, student-based instruction. Service learning is an example of an
experiential activity many educators have embraced as a valuable venue to link the theory and its application to the
real world (Rubin 2000, Stanton and Grant 1999). While service learning creates powerful learning opportunities for
students outside of the classroom it also introduces new challenges to properly assess those learning experiences and
outcomes. The service learning course was aimed at engaging business students in a student-based instruction project
as an opportunity for them to master some fundamental economic concepts by teaching those concepts to second- and
third-grade students. The project was highly structured to respond to the high degree of coordination needed between
business school faculty, economics students, and grade-school teachers. The economic students were required to form
teaching groups, choose their economic topic and lesson, coordinate a teaching time and location, complete their
lesson plan, turn in all required materials, and they were asked to follow a strict deadline for the completion of each
component of the project. To assess the impact of student-based instruction on the economics students, each student
was required to complete a two-to-five page reflective summary of his or her teaching experience, consisting of two
main components: a description of the lesson and the teaching environment, a presentation of the student’s opinion
on the success of the project. In addition to student teahers’ reflective summaries, each elementary teacher was asked
to complete an evaluation form to assess the overall performance of each economics student, as well as to offer their
perspective on the quality of the experience for their own students.

As an experiential activity outside the classroom, student-based instruction has a number of positive learning outcomes
for business students, elementary teachers, and college faculty, concluded the authors. The students need to master the
basic economic concepts as well as develop lesson plans appropriate to the intellectual level of their young audience.
The elementary teachers benefit from an exposure to alternative lesson topics for teaching young children and take
advantage of the resources available to support their classroom activities. The schoolchildren gain knowledge about
the world in which they live through examples of the economic decision-making process drawn from the most basic
aspects of everyday life. Finally, by expanding their learning activities into the real world, college professors can enrich
their students’ experiences in a way that cannot be replicated in the classroom. The main challenge to the student-
based instruction project is the significant start-up costs involved. Still, the project is worth the effort, contend the
authors, given the support that can be drawn from outside organizations and “the potential for self perpetuation once a
network of local school teachers are enlisted.” ( McGoldrick, Battle, and Gallagher 2000: 49)

English. In her undergraduate course on Shakespeare, Rustici (1997) recounts a note one student had left in her mail
box at the end of the semester: “I did want to mention that I really like the concept of experiential learning. After
reading the sonnets I just had a natural impulse to want to create one of my own. The reading left a rhythm in my
head that my mind naturally sought words to fill out with meaning. I am sure that this is not an uncommon experi-
ence...” The student’s experience was a byproduct of the author’s sonnet-writing course, which experientially guided
the students through the learning cycle: Students were invited to draw upon their own personal stories, attitudes, and
emotions to compose their sonnets (concrete experience); to shift their perspective from poet to critic and describe the
connections between the form and content of their sonnets (reflective observation); to grasp the precise metrical and
rhythmic pattern of the sonnets through the systematic planning and manipulation of symbols (abstract conceptualiza-
tion); and attempt to create something unfamiliar and determine the intended effect of their sonnet (active experimen-
tation). Expanding on Kolb’s (1984) observation that “all learning is relearning... Thus one’s job as an educator is not
only to implant new ideas but also to dispose of or modify old ones,” the author often encourages students to move
beyond preconceived ideas about poetry by several forms of modeling: adopting self playful languages that students
are asked to try out, employing culinary metaphors to explain “how to whip up a sonnet,” and offering some of the
best essays from previous semesters as a guide.


History. Sprau and Keig, contend that “for many undergraduates, history courses are inherently uninteresting and
the required papers are boring”(2001:101). According to the author, students overall lack of interest in history can
be attributed to the way courses are generally taught. History educators have typically relied on lectures, note taking,
textbooks, tests, and term papers as the main teaching methods in the history course. What is often lacking is the
mechanism that allows for students’ emotional as well as intellectual engagement in the learning process. They suggest
that appealing to students’ hearts and their minds both deserve consideration by history teachers; teaching devoid of
emotion is quite dull, not to mention virtually impossible.” (2001:103)

In their attempt to create an intellectually stimulating as well as emotionally appealing learning experience, the authors
introduced films in the history survey courses based on experiential learning model. Each film served as a tool for the
students to distinguish between historical fact and fiction, reflect upon its themes and characters, research an issue
from it, and write an analysis paper based on those reflections and research. The authors recommend that the experien-
tial learning model can best serve the students’ interests if instructors envision the learning cycle not as simple stages to
be followed sequentially but as a conical structure, so that students are guided to acquire higher-order thinking skills to
deal with subsequent learning experiences.

Theatre. In his book Theatre as the Essential Liberal Art in the American University, Gressler makes a compelling argu-
ment that theatre is the only liberal arts discipline that is almost entirely based on an experiential learning approach to
education. It requires students, whether working individually or in groups, to integrate all its parts in order to com-
municate the end result to the audience. “Fortuitously, nearly every theatre course and production activity I can think
of disallows passivity; nearly every course and activity follows the active-based, experiential learning patterns proposed
by Kolb and others. For example, the acting student has 1) personal involvement with a script, 2) reflects on its mean-
ing by searching for internal and external evidence, 3) decides logically as well as intuitively how it should be played,
and 4) offers these conclusions to the class or audience. Their responses or non-responses and critiques help inform
the next scene or play that student reads/and or acts. The costume design student experiences a play in a manuscript
form, reflects on its meaning by investigating internal and external sources, draws logical conclusions as to the form,
color, line, and texture that will most accurately reflect new and more informed perceptions, or to an audience or critic
whose response indicates whether or not those conclusions were logical, acceptable, and valid.” (2002: 79-80)

For those who want to adopt experiential learning methodologies in the classrooms, Gressler has one important mes-
sage to share. “One caveat must be mentioned when accepting the superior quality of experiential learning method-
ologies: they take time. More active strategies, such as experiential learning techniques, take more time because there is
more active exploration, testing, discovering, and hypothesizing. However, there are also likely to be higher retention
rates, a higher degree of motivation, and more potential for integrating new ideas into the learner’s store of knowledge.
It seems clear that, because the modern world has made comprehension of all knowledge a useless quest, as measured
by standardized tests, it may be more efficacious to study through methods that are apprehensive, as measured by
motivation levels, retention levels, and integrative capabilities.” (2002: 84)

Arts Education. In her book dedicated to building a bridge between art inquiry and student processes of self-under-
standing, Rasanen developed a model of experiential art interpretation in which students reflect and construct aesthetic
meaning through an integration of art history, criticism, and aesthetics guided by the experiential learning model. The
author suggests that “experiential art interpretation increases students’ expressive skills and results in products that are
meaningful both to their makers and others.” (1997: 9)

43

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55

APPENDIX 1. KLSI 3.1 Raw Score to Percentile Conversion
Concrete Experience

Cumulative
Raw Score Frequency Percent Valid Percent Percent
12 12 .2 .2 .2
13 56 .8 .8 1.0
14 72 1.0 1.0 2.0
15 103 1.5 1.5 3.5
16 178 2.5 2.6 6.0
17 223 3.2 3.2 9.2
18 315 4.5 4.5 13.7
19 342 4.8 4.9 18.6
20 360 5.1 5.2 23.8
21 423 6.0 6.1 29.9
22 450 6.4 6.4 36.3
23 468 6.6 6.7 43.0
24 410 5.8 5.9 48.9
25 444 6.3 6.4 55.3
26 399 5.6 5.7 61.0
27 368 5.2 5.3 66.3
28 334 4.7 4.8 71.0
29 309 4.4 4.4 75.5
30 246 3.5 3.5 79.0
31 234 3.3 3.4 82.4
32 209 3.0 3.0 85.4
33 202 2.9 2.9 88.2
34 160 2.3 2.3 90.5
35 131 1.9 1.9 92.4
36 123 1.7 1.8 94.2
37 88 1.2 1.3 95.4
38 63 .9 .9 96.3
39 57 .8 .8 97.2
40 54 .8 .8 97.9
41 40 .6 .6 98.5
42 30 .4 .4 98.9
43 33 .5 .5 99.4
44 15 .2 .2 99.6
45 12 .2 .2 99.8
46 6 .1 .1 99.9
47 4 .1 .1 99.9
48 4 .1 .1 100.0
Total 6977 98.7 100.0
Missing System 89 1.3
Total 7066 100.0


Reﬂective Observation

Valid Cumulative
Raw Score Frequency Percent Percent Percent
12 8 .1 .1 .1
13 47 .7 .7 .8
14 53 .8 .8 1.5
15 75 1.1 1.1 2.6
16 111 1.6 1.6 4.2
17 130 1.8 1.9 6.1
18 159 2.3 2.3 8.4
19 205 2.9 2.9 11.3
20 216 3.1 3.1 14.4
21 288 4.1 4.1 18.5
22 310 4.4 4.4 23.0
23 337 4.8 4.8 27.8
24 348 4.9 5.0 32.8
25 365 5.2 5.2 38.0
26 362 5.1 5.2 43.2
27 354 5.0 5.1 48.3
28 332 4.7 4.8 53.0
29 350 5.0 5.0 58.0
30 346 4.9 5.0 63.0
31 305 4.3 4.4 67.4
32 287 4.1 4.1 71.5
33 305 4.3 4.4 75.9
34 283 4.0 4.1 79.9
35 235 3.3 3.4 83.3
36 230 3.3 3.3 86.6
37 188 2.7 2.7 89.3
38 170 2.4 2.4 91.7
39 145 2.1 2.1 93.8
40 123 1.7 1.8 95.6
41 93 1.3 1.3 96.9
42 69 1.0 1.0 97.9
43 43 .6 .6 98.5
44 38 .5 .5 99.0
45 29 .4 .4 99.5
46 28 .4 .4 99.9
47 7 .1 .1 100.0
48 3 .0 .0 100.0
Total 6977 98.7 100.0
Total 7066 100.0

57

Abstract Conceptualization

Cumulative
12 5 .1 .1 .1
13 8 .1 .1 .2
14 7 .1 .1 .3
15 16 .2 .2 .5
16 23 .3 .3 .8
17 33 .5 .5 1.3
18 67 .9 1.0 2.3
19 100 1.4 1.4 3.7
20 107 1.5 1.5 5.2
21 120 1.7 1.7 7.0
22 177 2.5 2.5 9.5
23 201 2.8 2.9 12.4
24 243 3.4 3.5 15.9
25 245 3.5 3.5 19.4
26 287 4.1 4.1 23.5
27 301 4.3 4.3 27.8
28 313 4.4 4.5 32.3
29 334 4.7 4.8 37.1
30 351 5.0 5.0 42.1
31 335 4.7 4.8 46.9
32 352 5.0 5.0 52.0
33 351 5.0 5.0 57.0
34 315 4.5 4.5 61.5
35 351 5.0 5.0 66.5
36 290 4.1 4.2 70.7
37 280 4.0 4.0 74.7
38 271 3.8 3.9 78.6
39 251 3.6 3.6 82.2
40 225 3.2 3.2 85.4
41 201 2.8 2.9 88.3
42 173 2.4 2.5 90.8
43 144 2.0 2.1 92.8
44 131 1.9 1.9 94.7
45 114 1.6 1.6 96.3
46 110 1.6 1.6 97.9
47 84 1.2 1.2 99.1
48 61 .9 .9 100.0
Total 6977 98.7 100.0
Total 7066 100.0


Active Experimentation
Cumulative
12 2 .0 .0 .0
13 1 .0 .0 .0
14 8 .1 .1 .2
15 15 .2 .2 .4
16 10 .1 .1 .5
17 16 .2 .2 .7
18 19 .3 .3 1.0
19 51 .7 .7 1.7
20 52 .7 .7 2.5
21 98 1.4 1.4 3.9
22 105 1.5 1.5 5.4
23 114 1.6 1.6 7.0
24 139 2.0 2.0 9.0
25 150 2.1 2.1 11.2
26 195 2.8 2.8 14.0
27 238 3.4 3.4 17.4
28 228 3.2 3.3 20.7
29 282 4.0 4.0 24.7
30 308 4.4 4.4 29.1
31 333 4.7 4.8 33.9
32 328 4.6 4.7 38.6
33 317 4.5 4.5 43.1
34 386 5.5 5.5 48.7
35 418 5.9 6.0 54.7
36 431 6.1 6.2 60.8
37 370 5.2 5.3 66.1
38 357 5.1 5.1 71.2
39 360 5.1 5.2 76.4
40 342 4.8 4.9 81.3
41 312 4.4 4.5 85.8
42 282 4.0 4.0 89.8
43 241 3.4 3.5 93.3
44 183 2.6 2.6 95.9
45 148 2.1 2.1 98.0
46 77 1.1 1.1 99.1
47 46 .7 .7 99.8
48 15 .2 .2 100.0
Total 6977 98.7 100.0
Total 7066 100.0

59

Abstract Conceptualization – Concrete Experience
Cumulative
-35 1 .0 .0 .0
-31 1 .0 .0 .0
-30 2 .0 .0 .1
-29 1 .0 .0 .1
-28 3 .0 .0 .1
-27 6 .1 .1 .2
-26 5 .1 .1 .3
-25 7 .1 .1 .4
-24 10 .1 .1 .5
-23 12 .2 .2 .7
-22 20 .3 .3 1.0
-21 12 .2 .2 1.1
-20 19 .3 .3 1.4
-19 23 .3 .3 1.7
-18 31 .4 .4 2.2
-17 32 .5 .5 2.7
-16 36 .5 .5 3.2
-15 53 .8 .8 3.9
-14 54 .8 .8 4.7
-13 65 .9 .9 5.6
-12 81 1.1 1.2 6.8
-11 73 1.0 1.0 7.8
-10 86 1.2 1.2 9.1
-9 87 1.2 1.2 10.3
-8 114 1.6 1.6 12.0
-7 121 1.7 1.7 13.7
-6 131 1.9 1.9 15.6
-5 129 1.8 1.8 17.4
-4 162 2.3 2.3 19.7
-3 140 2.0 2.0 21.7
-2 165 2.3 2.4 24.1
-1 186 2.6 2.7 26.8
0 179 2.5 2.6 29.3
1 210 3.0 3.0 32.4
2 218 3.1 3.1 35.5
3 193 2.7 2.8 38.2
4 213 3.0 3.1 41.3
5 192 2.7 2.8 44.1


Cumulative
6 243 3.4 3.5 47.5
7 206 2.9 3.0 50.5
8 220 3.1 3.2 53.6
9 225 3.2 3.2 56.9
10 234 3.3 3.4 60.2
11 216 3.1 3.1 63.3
12 228 3.2 3.3 66.6
13 231 3.3 3.3 69.9
14 192 2.7 2.8 72.6
15 197 2.8 2.8 75.5
16 171 2.4 2.5 77.9
17 149 2.1 2.1 80.1
18 176 2.5 2.5 82.6
19 181 2.6 2.6 85.2
20 162 2.3 2.3 87.5
21 133 1.9 1.9 89.4
22 123 1.7 1.8 91.2
23 118 1.7 1.7 92.9
24 89 1.3 1.3 94.1
25 90 1.3 1.3 95.4
26 71 1.0 1.0 96.4
27 64 .9 .9 97.4
28 58 .8 .8 98.2
29 29 .4 .4 98.6
30 30 .4 .4 99.0
31 19 .3 .3 99.3
32 17 .2 .2 99.6
33 15 .2 .2 99.8
34 11 .2 .2 99.9
35 3 .0 .0 100.0
36 2 .0 .0 100.0
Total 6976 98.7 100.0
Total 7066 100.0

61

Active Experimentation – Reﬂective Observation
Cumulative
-32 1 .0 .0 ..0
-28 3 .0 .0 ..1
-27 2 .0 .0 ..1
-25 5 .1 .1 ..2
-24 2 .0 .0 ..2
-23 19 .3 .3 ..5
-22 17 .2 .2 ..7
-21 19 .3 .3 1.0
-20 32 .5 .5 1.4
-19 28 .4 .4 1.8
-18 40 .6 .6 2.4
-17 40 .6 .6 3.0
-16 37 .5 .5 3.5
-15 63 .9 .9 4.4
-14 77 1.1 1.1 5.5
-13 89 1.3 1.3 6.8
-12 92 1.3 1.3 8.1
-11 89 1.3 1.3 9.4
-10 119 1.7 1.7 11.1
-9 114 1.6 1.6 12.7
-8 127 1.8 1.8 14.5
-7 148 2.1 2.1 16.7
-6 138 2.0 2.0 18.6
-5 137 1.9 2.0 20.6
-4 156 2.2 2.2 22.8
-3 168 2.4 2.4 25.3
-2 169 2.4 2.4 27.7
-1 155 2.2 2.2 29.9
0 175 2.5 2.5 32.4
1 171 2.4 2.5 34.9
2 196 2.8 2.8 37.7
3 170 2.4 2.4 40.1
4 200 2.8 2.9 43.0
5
228 3.2 3.3 46.2
6 196 2.8 2.8 49.0


Valid Cumulative
Raw Score Frequency Percent Percent Percent
7 209 3.0 3.0 52.0
8 224 3.2 3.2 55.3
9 214 3.0 3.1 58.3
10 233 3.3 3.3 61.7
11 214 3.0 3.1 64.7
12 222 3.1 3.2 67.9
13 192 2.7 2.8 70.7
14 199 2.8 2.9 73.5
15 209 3.0 3.0 76.5
16 211 3.0 3.0 79.5
17 184 2.6 2.6 82.2
18 194 2.7 2.8 85.0
19 155 2.2 2.2 87.2
20 143 2.0 2.0 89.2
21 147 2.1 2.1 91.3
22 163 2.3 2.3 93.7
23 104 1.5 1.5 95.2
24 78 1.1 1.1 96.3
25 64 .9 .9 97.2
26 63 .9 .9 98.1
27 39 .6 .6 98.7
28 32 .5 .5 99.1
29 25 .4 .4 99.5
30 15 .2 .2 99.7
31 6 .1 .1 99.8
32 8 .1 .1 99.9
33 7 .1 .1 100.0
35 1 .0 .0 100.0
Total 6977 98.7 100.0
Total 7066 100.0

63

APPENDIX 2. Learning Style and Age

Std. Std. 95% Conﬁdence Interval
Age Range N Mean Deviation Error for Mean
Lower Upper
Bound Bound
Concrete <19 631 26.23 6.882 .274 25.69 26.77
19-
1155 25.66 6.484 .191 25.28 26.03
24
25-
1839 24.74 6.017 .140 24.47 25.02
34
35-
1573 25.22 6.237 .157 24.91 25.53
44
45-
1171 25.66 6.665 .195 25.28 26.04
54
>55 398 26.13 7.219 .362 25.42 26.84
Total 6767 25.39 6.437 .078 25.24 25.54
Reﬂective <19 631 29.79 7.046 .281 29.24 30.34
19-
1155 28.84 7.285 .214 28.41 29.26
24
25-
1839 27.73 7.180 .167 27.40 28.06
34
35-
1573 27.68 6.790 .171 27.35 28.02
44
45-
1171 28.02 6.959 .203 27.62 28.42
54
>55 398 27.67 7.030 .352 26.98 28.37
Total 6767 28.15 7.079 .086 27.98 28.32
Abstract <19 631 30.80 7.153 .285 30.24 31.36
19-
1155 30.83 6.958 .205 30.43 31.23
24
25-
1839 32.59 7.178 .167 32.27 32.92
34
35-
1573 32.66 7.356 .185 32.30 33.02
44
45-
1171 32.87 7.428 .217 32.45 33.30
54
>55 398 34.19 7.663 .384 33.43 34.94
Total 6767 32.28 7.313 .089 32.11 32.46


Active <19 631 33.08 6.452 .257 32.57 33.58
19-
1155 34.62 6.542 .192 34.24 35.00
24
25-
1839 34.87 6.415 .150 34.57 35.16
34
35-
1573 34.40 6.768 .171 34.06 34.73
44
45-
1171 33.43 6.866 .201 33.04 33.82
54
>55 398 32.01 6.482 .325 31.37 32.65
Total 6767 34.13 6.654 .081 33.97 34.29
AC-CE <19 631 4.54 11.922 .475 3.61 5.47
19-
1154 5.15 11.162 .329 4.51 5.80
24
25-
1839 7.85 11.188 .261 7.34 8.36
34
35-
1573 7.44 11.715 .295 6.86 8.02
44
45-
1171 7.22 12.163 .355 6.52 7.91
54
>55 398 8.06 12.763 .640 6.80 9.31
Total 6766 6.89 11.703 .142 6.61 7.17
AE-RO <19 631 3.26 11.409 .454 2.36 4.15
19-
1155 5.81 11.649 .343 5.14 6.48
24
25-
1839 7.14 11.601 .271 6.60 7.67
34
35-
1573 6.72 11.580 .292 6.15 7.29
44
45-
1171 5.41 11.831 .346 4.73 6.09
54
>55 398 4.34 11.139 .558 3.24 5.43
Total 6767 5.99 11.656 .142 5.71 6.27

65

ANOVA
Sum of Mean
Squares df Square F Sig.
Between
1647.824 5 329.565 7.995 ..000
Groups
Within Groups 278696.69 6761 41.221
Total 280344.51 6766
Between
3019.612 5 603.922 12.152 ..000
Groups
Within Groups 336016.02 6761 49.699
Total 339035.63 6766
Between
6085.709 5 1217.142 23.133 ..000
Groups
Within Groups 355729.44 6761 52.615
Total 361815.15 6766
Between
4448.907 5 889.781 20.387 ..000
Groups
Within Groups 295080.66 6761 43.645
Total 299529.56 6766
Between
9818.528 5 1963.706 14.480 ..000
Groups
Within Groups 916731.00 6760 135.611
Total 926549.52 6765
Between
9489.806 5 1897.961 14.104 ..000
Groups
Within Groups 909809.15 6761 134.567
Total 919298.95 6766


APPENDIX 3. Learning Style and Gender
Descriptives
Std. Std. 95% Conﬁdence Interval
Gender N Mean Deviation Error for Mean
Lower Upper
Bound Bound
Concrete M 3134 24.70 6.252 .112 24.48 24.92
F 3203 26.04 6.525 .115 25.82 26.27
Total 6337 25.38 6.426 .081 25.22 25.54
Reﬂective M 3134 28.11 6.782 .121 27.87 28.35
F 3203 28.29 7.357 .130 28.03 28.54
Total 6337 28.20 7.079 .089 28.02 28.37
Abstract M 3134 33.45 7.241 .129 33.20 33.71
F 3203 31.00 7.133 .126 30.75 31.24
Total 6337 32.21 7.290 .092 32.03 32.39
Active M 3134 33.67 6.660 .119 33.44 33.91
F 3203 34.65 6.584 .116 34.42 34.88
Total 6337 34.17 6.639 .083 34.00 34.33
AC-CE M 3134 8.75 11.548 .206 8.35 9.16
F 3202 4.94 11.477 .203 4.55 5.34
Total 6336 6.83 11.668 .147 6.54 7.12
AE-RO M 3134 5.56 11.438 .204 5.15 5.96
F 3203 6.38 11.836 .209 5.97 6.79
Total 6337 5.97 11.647 .146 5.68 6.26

67

ANOVA
Sum of Mean
Between
2872.164 1 2872.164 70.314 ..000
Groups
Within
258769.956 6335 40.848
Groups
Total 261642.119 6336
Between
49.364 1 49.364 .985 ..321
Groups
Within
317430.710 6335 50.107
Groups
Total 317480.074 6336
Between
9568.749 1 9568.749 185.273 ..000
Groups
Within
327182.431 6335 51.647
Groups
Total 336751.180 6336
Between
1511.274 1 1511.274 34.466 ..000
Groups
Within
277778.087 6335 43.848
Groups
Total 279289.361 6336
Between
22993.472 1 22993.472 173.499 ..000
Groups
Within
839434.413 6334 132.528
Groups
Total 862427.885 6335
Between
1070.165 1 1070.165 7.897 ..005
Groups
Within
858488.492 6335 135.515
Groups
Total 859558.657 6336


APPENDIX 4. Learning Style and Educational Level

Std. Std. 95% Conﬁdence Interval for
Educational Level N Mean Deviation Error Mean
Lower Upper
Highest Degree Bound Bound
Concrete Elem. 83 25.77 6.554 .719 24.34 27.20
H. S. 2078 26.08 6.397 .140 25.80 26.35
Univ. 2756 25.08 6.316 .120 24.85 25.32
Grad. 1688 25.15 6.649 .162 24.83 25.47
Total 6605 25.42 6.445 .079 25.27 25.58
Reﬂective Elem. 83 29.65 7.440 .817 28.03 31.28
H. S. 2078 28.88 7.062 .155 28.58 29.19
Univ. 2756 28.09 7.014 .134 27.83 28.36
Grad. 1688 27.27 7.044 .171 26.94 27.61
Total 6605 28.15 7.069 .087 27.98 28.32
Abstract Elem. 83 28.89 6.847 .752 27.40 30.39
H. S. 2078 30.91 7.160 .157 30.60 31.22
Univ. 2756 32.29 7.324 .140 32.02 32.57
Grad. 1688 34.14 7.214 .176 33.80 34.48
Total 6605 32.29 7.348 .090 32.11 32.46
Active Elem. 83 35.69 6.802 .747 34.20 37.17
H. S. 2078 34.00 6.505 .143 33.72 34.28
Univ. 2756 34.52 6.602 .126 34.27 34.77
Grad. 1688 33.44 6.815 .166 33.11 33.76
Total 6605 34.10 6.644 .082 33.94 34.26
AC-CE Elem. 83 3.12 11.460 1.258 ..62 5.62
H. S. 2078 4.83 11.447 .251 4.34 5.32
Univ. 2755 7.20 11.679 .223 6.77 7.64
Grad. 1688 8.99 11.827 .288 8.43 9.55
Total 6604 6.86 11.754 .145 6.58 7.14
AE-RO Elem. 83 6.04 12.445 1.366 3.32 8.75
H. S. 2078 5.12 11.448 .251 4.63 5.61
Univ. 2756 6.44 11.610 .221 6.00 6.87
Grad. 1688 6.17 11.813 .288 5.60 6.73
Total 6605 5.95 11.634 .143 5.67 6.23

69

ANOVA
Sum of Mean
Between
1340.678 3 446.893 10.806 ..000
Groups
Within Groups 272997.555 6601 41.357
Total 274338.233 6604
Between
2611.000 3 870.333 17.548 ..000
Groups
Within Groups 327386.205 6601 49.596
Total 329997.205 6604
Between
10701.061 3 3567.020 68.073 ..000
Groups
Within Groups 345892.256 6601 52.400
Total 356593.317 6604
Between
1458.381 3 486.127 11.062 ..000
Groups
Within Groups 290091.955 6601 43.947
Total 291550.336 6604
Between
17716.736 3 5905.579 43.571 ..000
Groups
Within Groups 894552.211 6600 135.538
Total 912268.947 6603
Between
2169.205 3 723.068 5.353 ..001
Groups
Within Groups 891650.670 6601 135.078
Total 893819.875 6604


APPENDIX 5. Learning Style Type and Educational Specialization

EDUCATIONAL SPECIALIZATION LEARNING STYLE TYPE Total
Accom. Diverge Converge Assim.
Accounting Count 39 26 42 42 149
% 26.2% 17.4% 28.2% 28.2% 100.0%
Architecture Count 2 0 1 4 7
% 28.6% .0% 14.3% 57.1% 100.0%
Business Count 290 165 215 259 929
% 31.2% 17.8% 23.1% 27.9% 100.0%
Count 54 17 20 19 110
% 49.1% 15.5% 18.2% 17.3% 100.0%
Computer Count
54 35 55 62 206
Sci./IS
% 26.2% 17.0% 26.7% 30.1% 100.0%
Education Count 92 46 41 61 240
% 38.3% 19.2% 17.1% 25.4% 100.0%
Engineering Count 103 50 145 138 436
% 23.6% 11.5% 33.3% 31.7% 100.0%
App. & Fine Count
23 20 12 20 75
Arts
% 30.7% 26.7% 16.0% 26.7% 100.0%
Health Count 82 48 59 72 261
% 31.4% 18.4% 22.6% 27.6% 100.0%
Humanities Count 28 24 19 40 111
% 25.2% 21.6% 17.1% 36.0% 100.0%
Language Count 8 4 5 9 26
% 30.8% 15.4% 19.2% 34.6% 100.0%
Law Count 29 16 23 42 110
% 26.4% 14.5% 20.9% 38.2% 100.0%
Literature Count 5 15 8 10 38
% 13.2% 39.5% 21.1% 26.3% 100.0%
Medicine Count 88 50 96 82 316
% 27.8% 15.8% 30.4% 25.9% 100.0%
Other Count 301 213 185 248 947
% 31.8% 22.5% 19.5% 26.2% 100.0%
Phys. Count
12 5 3 4 24
Education
% 50.0% 20.8% 12.5% 16.7% 100.0%
Psychology Count 53 40 15 52 160
% 33.1% 25.0% 9.4% 32.5% 100.0%
Science/Math Count 53 35 88 110 286
% 18.5% 12.2% 30.8% 38.5% 100.0%
Social Count
68 51 38 72 229
Sciences
% 29.7% 22.3% 16.6% 31.4% 100.0%
Agriculture Count 6 6 6 1 19
% 31.6% 31.6% 31.6% 5.3% 100.0%
Total Count 1390 866 1076 1347 4679
% 29.7% 18.5% 23.0% 28.8% 100.0%

71

Hay Resources Direct

116 Huntington Avenue
Boston, MA 02116
617 927 5080
800 729 8074
Fax 617 927 5008
www.hayresourcesdirect.haygroup.com

33 Grosvenor Place
London SW1X 7HG
United Kingdom
00 44 (0) 20 7856 7575
Fax 00 44 (0) 20 7856 7103
E-mail: sales_europe@haygroup.com

MCB 101K

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