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LEARNING/COGNITIVE STYLES AND LEARNING PREFERENCES
OF STUDENTS AND INSTRUCTORS AS RELATED TO ACHIEVEMENT
IN RESPIRATORY THERAPY EDUCATIONAL PROGRAMS
By
MICHAEL J. BANNER
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
1989
UIIVERSITY OF FLORIDA LIBRARIES
This work is dedicated to my wife Tina Banner--a gentle
and thoughtful lady who is my best friend. Thank you
for your love, support, and understanding.
ACKNOWLEDGEMENTS
The author wishes to thank the members of his
doctoral committee for their guidance and assistance in
the writing of this dissertation. Dr. James W. Hensel,
chairman of the committee, was always available when I
needed feedback and provided sound advice. His input and
insightful comments were crucial and I will always be
grateful to him. Dr. Lois J. Malasanos, a lady and a most
respected professional in the fields of nursing and allied
health education, was very helpful in reading drafts of
the dissertation and providing feedback. Dr. C. Arthur
Sandeen, a gentleman and a scholar, asked pertinent
questions which forced me to think critically and
analytically.
Although not a member of the doctoral committee, the
author would like to especially acknowledge Dr. Linda M.
Crocker who was most helpful when things seemed to be
going wrong. Dr. Crocker provided feedback and made
suggestions that improved the study protocol as well as
providing expert advice regarding the statistical
analyses. The author would also like to acknowledge Mr.
David N. Yonutas, MS, RRT, Director of the Respiratory
Therapy program at Santa Fe Community College in
Gainesville, Florida. Mr. Yonutas was very cooperative by
allowing me access to his students for both the pilot
study and for the larger study. He also provided many
helpful suggestions and, in general, his assistance was
invaluable.
Finally, the author would like to acknowledge Ms.
Leila R. Cantara for her excellent typing work and
editorial assistance.
TABLE OF CONTENTS
Page
ACKNOWLEDGEMENTS .................................. iii
LIST OF TABLES .................... ................ vii
LIST OF FIGURES ....................... ............. viii
ABSTRACT ........................................... ix
CHAPTERS
1 INTRODUCTION ................ .................. 1
Instructors Teach in Ways That They Like
to Learn ................................ 8
Learning/Cognitive Styles and Learning
Preferences in Allied Health Education ... 10
The Problem .................................. 13
Hypotheses .................................. 14
Delimitations ................................ 16
Limitations .................................. 17
Need for the Study .... ..................... 18
Assumptions .................................. 21
Definition of Terms ....................... 21
2 REVIEW OF THE LITERATURE ................... 26
Introduction ............................... 26
Learning/Cognitive Styles ................. 29
Neurologic Basis of Learning/Cognitive
Styles: Left and Right Brain Hemispheric
Differences ............................... 50
Learning Preferences ....................... 55
Congruency--Student and Faculty Learning/
Cognitive Styles and Preferences ......... 68
Summary ...................................... 74
3 MATERIALS AND METHODS ...................... 76
Instrumentation .............................. 77
Pilot Study .................................. 80
Subjects and Study Design .................. 83
Collection of Data ......................... 85
4 RESULTS .................................... 86
Population ................................... 86
Analysis of Data .................... ......... 86
5 SUMMARY ...................................... 116
6 DISCUSSION, IMPLICATIONS, AND
RECOMMENDATIONS .......................... 122
Introduction ................................. 122
Discussion ................................... .. 124
Implications for Respiratory Therapy
Education .................................. 135
Recommendation for a Future Study ........... 136
Accommodating Various Student Learning/
Cognitive Styles ...... .................. 137
"Student-Driven" Teaching-Learning Process
Model .................................. 142
Recommendations ................ ............ 149
Leadership Implications ..................... 153
APPENDICES
A KOLB'S LEARNING STYLE INVENTORY ............... 157
B REZLER'S LEARNING PREFERENCE INVENTORY ...... 161
C ACHIEVEMENT TEST FOR RESPIRATORY THERAPY
STUDENTS ................................... 166
D SAMPLE LETTER TO DIRECTORS OF RESPIRATORY
THERAPY PROGRAMS PARTICIPATING IN THE
STUDY ... .................................. 178
E MEAN, STANDARD DEVIATION, AND CORRELATIONAL
ANALYSIS OF ALL VARIABLES ................ 181
REFERENCES ......................................... 183
BIOGRAPHICAL SKETCH ...... ........................ 195
LIST OF TABLES
Table Page
2-1 Cognitive Characteristics of Left and
Right Brain Hemispheres ............... 52
4-1 Student Demographic Data .................. 87
4-2 Multiple Regression of Learning/Cognitive
Styles Model (Reduced Model--Omitting
Learning Preference Dimensions) ........ 91
4-3 Beta Coefficients for the Learning/
Cognitive Styles Dimensions ............ 94
4-4 Multiple Regression of Learning Preferences
Model (Reduced Model--Omitting Learning/
Cognitive Style Dimensions) ............ 102
4-5 Beta Coefficients for the Learning
Preferences Dimensions ................ 104
4-6 Multiple Regression of Complete Learning/
Cognitive Styles and Preferences Model 108
4-7 Student Achievement Score by Program ...... 113
4-8 Step-wise Multiple Regression Model ....... 115
LIST OF FIGURES
Figure Page
1-1 Kolb's Experiential Learning Model ......... 5
1-2 A Model of Rezler's Learning Preference
Dimensions ............................. 7
4-1 Relationship Between Student Achievement
Score and Abstract Conceptualization
Discrepancy Score ....................... 95
4-2 Relationship Between Student Achievement
Score and Active Experimentation
Discrepancy score ....................... 96
4-3 Students Who Were "Matched" and "Mismatched"
With Their Instructor's Learning/
Cognitive Style Type .................... 98
4-4 Distribution of Student Learning/Cognitive
Style Types ............................. 99
4-5 Relationship Between Student Achievement
Score and Concrete Discrepancy Score .... 105
4-6 Relationship Between Student Achievement
Score and College Grade Point Average ... 112
6-1 Teaching-Learning Process Model .......... 144
viii
Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of
the Requirements for the Degree of Doctor of Philosophy
LEARNING/COGNITIVE STYLES AND LEARNING PREFERENCES
OF STUDENTS AND INSTRUCTORS AS RELATED TO ACHIEVEMENT
IN RESPIRATORY THERAPY EDUCATIONAL PROGRAMS
By
Michael J. Banner
August 1989
Chairman: James W. Hensel
Major Department: Educational Leadership
The purpose of the study was to determine whether a
relationship existed between achievement and the degree of
matching between students and their instructors based on
learning/cognitive style theory or learning preference
theory, and whether one of these learning theories was
better in predicting student achievement.
Learning/cognitive style is the manner in which an
individual acquires, perceives, and processes information in
the learning situation. Learning preference is the choice
that an individual makes for one learning situation over
another.
At the conclusion of a major course in the respiratory
therapy curriculum, students and their instructors from 11
respiratory therapy programs completed Kolb's Learning Style
Inventory (LSI) and Rezler's Learning Preference Inventory
(LPI); students also completed an achievement test.
Student-instructor discrepancy scores (student's score minus
instructor's score) calculated from the LSI and LPI
dimension scales were used to determine the degree of
matching, i.e., the lower the discrepancy scores the greater
the match. Multiple regression was used to analyze the
relationships between the degree of matching and the
students' achievement test scores.
Higher achievement scores correlated with greater
degrees of matching between students and their instructors
for the abstract conceptualization and active
experimentation dimensions of Kolb's theory and for the
concrete learning dimension of Rezler's theory. Using
Kolb's typology, students who were the same learning/
cognitive style type as their instructor had significantly
higher achievement scores than students who were a different
learning/cognitive style type. The learning/cognitive style
variables, as a group, accounted for a significant amount of
variance in predicting student achievement compared to the
learning preference variables. Thus, it may be inferred
that Kolb's theory of learning/cognitive style was better
than Rezler's theory of learning preferences in predicting
student achievement.
These data reveal that a learning environment congruent
with a student's predispositions for learning should be
provided to enhance learning and achievement. A
learning/cognitive style-responsive approach to education is
recommended.
CHAPTER 1
INTRODUCTION
Knowledge of the ways that individuals perceive and
process information and their predispositions for specific
types of learning situations is relatively new.
Consequently, college faculty members and administrators
have not considered using this knowledge as a possible
means to improve the quality of higher education.
Specific cognitive processes affecting one's
learning/cognitive style vary from student to student.
Learning/cognitive style is the manner in which an
individual acquires, perceives, and processes information
in the learning situation (Cahill & Madigan, 1984).
Students also have preferences in the ways in which they
learn. Learning preference is the choice that an
individual makes for one learning situation over another
(Rezler & Rezmovick, 1981).
Levine (1978) contended that one of the major
problems in higher education is that student differences
are not adequately taken into consideration. "Uniform
types of instruction produce widely divergent results in
different students" (p. 205). Levine stated that
different types of instructor relationships work best for
different types of students, and implied that students as
well as their instructors have different learning/
cognitive styles and preferences. Cross (1976) made a
series of recommendations regarding student differences:
that instructors and students should be helped to gain
some insight into learning and teaching styles; that most
individuals will be happier and more productive when they
are studying via a method compatible with their
learning/cognitive style and learning preference; that no
one teaching method should be regarded as effective for
all students in all situations; and that educators should
be aware of the cognitive styles of students in order to
provide appropriate kinds of support, motivation, and
reinforcement.
At least two schools of thought underlie the
processes of learning strategies and teaching regarding
learning/cognitive styles and learning preferences. One
school may be represented by Kolb (1984) and McKenny and
Keen (1974). This viewpoint is concerned with the
cognitive processes affecting one's learning/cognitive
style. The learner has discrete ways of perceiving,
organizing, and retaining information that are distinctive
and consistent. Two factors identified for human
cognitive processing are information gathering and
information evaluating (Dixon, 1982). Processing has also
been described as how an individual manipulates,
categorizes, and evaluates input information (Dixon,
1982). The basic concept is structuring of the teaching-
learning environment in a manner that is congruent with a
student's learning/cognitive style rather than forcing the
student to accommodate to a given teaching-learning
environment. Individual differences and
learning/cognitive styles are recognized with this
approach.
Kolb (1984) developed the Experiential Learning
Theory as a means of identifying learning/cognitive
styles. Kolb's model is portrayed as a cyclical process
with four cognitive dimensions: (a) concrete experience
(learning by feeling), learning from specific experiences,
relating to people, sensitivity to feelings and people,
and involving oneself fully, openly, and without bias in
new experiences; (b) reflective observation (learning by
watching and listening), learning by careful observation
before making a judgement, viewing things from different
perspectives, looking for the meaning of things, and
viewing experiences from many perspectives; (c) abstract
conceptualization (learning by thinking), learning by
logical analysis of ideas, systematic planning, acting on
an intellectual understanding of a situation, and creating
concepts that integrate one's observations into logically
sound theories; and (d) active experimentation (learning
by doing), learning related to the ability to get things
done, risk taking, influencing people and events through
action, and using previously learned theories to make
decisions and solve problems. For fully integrated
learning to occur, all dimensional activities eventually
must be utilized. Learning/cognitive style type is seen
as two-dimensional. One dimension relates to information
acquisition which ranges from concrete to abstract. The
other dimension relates to information processing ranging
from active to reflective. Four learning/cognitive style
types have been described: "diverger," "assimilator,"
"converger," and accommodatorr" (see Figure 1-1).
The other school of thought focuses on learning
preferences and may be represented by Rezler and Rezmovick
(1981) and Dunn and Dunn (1975). Learning preferences
refer to a student's preferred choices of methods of
instruction and relates to the "likes" and "dislikes" that
individuals have for particular modes of learning. This
frame of thinking is concerned with affective,
environmental, and, to some respect, cognitive aspects of
learning. Improvement and individualization of
instruction are accomplished by first identifying and then
providing methods of instruction that are appealing to the
learner. The same content of a course may be taught in
AGcOMMOTOR] | |
ACTVE REFLECTIVE
EXPERIMMENMnON OBSERMfON
(learning by doge) (IlfnTg by watching
& ring)
|CONMVERG ASSIR
I---- N ABSTmACT -4e
CONCETIUUZCAMnON
(lenming thinking)
Figure 1-1. Kolb's Experiential Learning Model. Learning
is a cyclical process of four cognitive dimensions:
concrete experience, reflective observation, abstract
conceptualization, and active experimentation (see text).
Note. From the Learning Style Inventory (p. 9) by D. A.
Kolb, 1985, Boston: McBer and Company. Copyright 1981 by
D. A. Kolb. Adapted by permission.
several ways to make it more palatable to the learner.
Students have a predilection for specific learning
preferences which may influence the degree of learning
that occurs. It is believed that efforts should be made
to provide methods of instruction that match student
preferences in order to promote student satisfaction and
achievement in the learning situation.
Rezler's approach consists of identifying a person's
learning preference profile based on six dimensions: (a)
individual, a preference for learning or working alone,
with emphasis on self-reliance and solitary tasks such as
reading; (b)interpersonal, a preference for learning or
working with others, with emphasis on harmonious relations
between students and teacher and among students; (c)
student-structured, a preference for learning via student
organized tasks, with emphasis on autonomy and self-
direction; (d) teacher-structured, a preference for
learning in a well-organized teacher-directed class, with
expectations, assignments, and goals that are clearly
identified; (e) abstract, a preference for learning
theories and generating hypotheses with focus on general
principles and concepts; and (f) concrete, a preference
for learning tangible, specific, practical tasks, with
focus on skills. One's learning preference profile is
determined by the six dimension scores (see Figure 1-2).
It is recommended that appropriate methods of instruction
STUDENT-
STRUCTURED
ABSTRACT
IDIDUAL
k CONCRTEE
TEACHER -
STRUCTURED
Figure 1-2. A Model of Rezler's Learning Preference
Dimensions. Six dimensions are used to identify one's
learning preference profile: individual, interpersonal,
student-structured, teacher-structured, abstract, and
concrete (see text). As an example, the above profile is
indicative of a student who prefers learning abstract
concepts, learns without the aid of others, and who is
independent, autonomous, and learns best with minimal
guidance provided by the instructor.
should be provided on the basis of this profile. Because
of individual differences in learning preferences, one
method of instruction may be effective and please some
students but alienate others. Thus, it is felt that
learning is facilitated when methods of instruction are
provided that are compatible with one's learning
preferences.
Instructors Teach in Ways That They Like to Learn
Witkin (1976) suggested that instructors teach in a
manner similar to the way in which they prefer to learn.
An instructor's teaching style appears to be influenced by
his or her learning/cognitive style and learning
preferences (Smith, 1982). Teaching style refers to an
instructor's characteristic behavior in the teaching-
learning situation (Smith, 1982). Smith (1982) contended
that teachers conduct instructional sessions with the
kinds of learning activities that they prefer when
learning. Giunta (1984) noted that instructors' teaching
styles were congruent with their own learning/cognitive
styles. Brillhart and Debs (1982) evaluated the learning
preferences of classroom instructors and reported there
was a direct correlation between how instructors taught
and how they learned. It would seem, therefore, that the
manner by which an instructor acquires, perceives, and
processes information in the learning situation
(learning/cognitive style) and the choice of a particular
9
method of instruction (learning preference) are related to
how an instructor perceives, processes, interprets, and
articulates information in the teaching situation
(teaching style).
Using the Myers-Briggs Type Indicator, Lawrence
(1982) noted that the kinds of questions and the ways in
which they were stated reflected an instructor's
preference for sensing (emphasis on sense perception,
facts, details, and concrete events) or intuition
(emphasis on possibilities, imagination, meaning, and
seeing things as a whole). Sensing types asked questions
seeking facts and details to which responses were
predictable. In contrast, intuitive instructors asked
questions which called for synthesis and evaluation, as
well as imagination and hypothesis formulation. It was
noted also that sensing instructors neglected synthesizing
and evaluating, while intuitive types gave little
importance to facts and details.
As previously stated, instructors as well as students
have different learning/cognitive styles and learning
preferences (Levine, 1978). It was noted also that
different types of instructor relationships work best for
different types of students. In a study involving
undergraduate students, Brown (1978) noted that when the
instructor's teaching style complemented the
learning/cognitive style of the students, achievement
(i.e., grade point average) was greater. Kuchinskas
(1979) reported that matches of learning/cognitive style
between the instructor and student and complementary
methods of instruction resulted in increased achievement,
whereas mismatches resulted in the reverse. Blagg (1985)
showed that when there was a mismatch between the
learning/cognitive styles of students and instructors,
students were not sufficiently motivated and the necessary
involvement in learning (study/homework time) did not
occur. Using a learning preference approach, Adams (1983)
reported greater student satisfaction and achievement
(i.e., higher grade point average) when the learning
preferences of students and their instructors were
matched. A prior, this would suggest that the greater
the degree of matching or congruency between students and
their instructors on the basis of learning/cognitive style
theory or learning preference theory, the greater might be
the communication and understanding between them. This
might subsequently result in greater student involvement,
satisfaction, and enjoyment in the teaching-learning
situation, and ultimately higher student achievement.
Learning/Cognitive Styles and Learning Preferences
in Allied Health Education
Interest in learning/cognitive styles and learning
preferences and their impact on the teaching-learning
process in allied health education has been described
(Edge, 1988). Educators in allied health professions have
focused most of their attention on curriculum design and
establishment of educational programs leading to
professional certification. Interest has been
demonstrated in increasing teacher effectiveness and
student learning efficiency. A major problem in allied
health education is the lack of knowledge about how people
prefer to learn (Llorens & Adams, 1976). In many allied
health professions (nursing, occupational therapy,
physical therapy, medical technology, dental hygiene, and
medical dietetics) formal evaluations of student and
faculty learning/cognitive styles, learning preferences,
and their relationships to educational outcomes have been
conducted (Barris, Kielhofner, & Bauer, 1985; Blagg, 1985;
Carrier, Newell, & Lange, 1982; Highfield, 1988; Hodges,
1988; Merritt, 1983; O'Kell, 1988; Payton, Hueter, &
McDonald, 1979).
Individuals aspiring to practice in many of the
allied health professions generally matriculate into
community colleges or four-year college programs.
Graduates of such programs are qualified to take state
and/or national board licensure examinations to become
registered or certified in their respective fields.
Respiratory therapy is one allied health profession, for
example, in which graduates are qualified to take a
national board certification examination in order to
become certified respiratory therapy technicians (CRTT).
The examination certifies technical competency in
respiratory therapy and allows one to practice at the
clinical level.
Reviewing pass/failure statistics from 1977 through
1987 revealed that candidates attempting the respiratory
therapy board examination passed at an average rate of
approximately 50% (Filippi, 1988). It is disconcerting
that a large percentage of graduates of respiratory
therapy programs fail the board examination resulting in a
shortage of certified respiratory therapy technicians
(O'Daniel, 1987). Hospital administrators and medical
staffs are in a potentially precarious position, i.e.,
being forced to consider hiring noncertified respiratory
therapy technicians to meet burgeoning manpower demands.
It is difficult to assess whether less than optimal
patient care will result when administered by non-
certified respiratory therapy personnel unable to pass
their own societal board examinations. Professional
certification achieved through a standardized examination
process assures at least a minimal level of competency
which is designed to protect the general public. Ideally,
all graduates of respiratory therapy programs should be
able to pass the board examination to become certified
respiratory therapy technicians.
A study of the teaching-learning approaches used in
respiratory therapy educational programs and the effects
on student achievement may have implications relating to
the low pass rate on the board examination. Such a study
could result in the identification of problematic areas,
according to Dr. Robert M. Kacmarek, Chairman of the
National Board for Respiratory Care (NBRC), Clinical
Simulation Examination Committee (personal communication,
March 9, 1987). A study of this nature had never been
conducted, and the results of such a study should indicate
weaknesses in respiratory therapy educational programs and
provide a basis for redress.
The Problem
The degree of matching between students and their
instructors based on Kolb's theory of learning/cognitive
styles and Rezler's theory of learning preferences is a
factor which may influence student achievement in
respiratory therapy educational programs. Various degrees
of matching may predispose to differential levels of
student achievement. It was contended that the greater
the degree of matching, the greater the level of student
achievement and vice versa. However, it was not clear
whether a relationship between student achievement and the
degree of matching between students and their instructors
differs when based on learning/cognitive style theory or
learning preference theory. The problem centered on two
key questions: (a) is there a relationship between
student achievement and the degree of matching between
students and their instructors based on Kolb's theory of
learning/cognitive styles and Rezler's theory of learning
preferences, and (b) which of the two learning theories
involving the degree of matching between students and
their instructors is a better predictor of student
achievement. The study was designed to determine if a
significant relationship between student achievement and
the degree of matching based on learning/cognitive style
theory of learning preference theory would be demonstrated
for students enrolled in a major course in respiratory
therapy. It may then be hypothesized that greater overall
learning for the entire respiratory therapy program might
result if efforts are made to teach students in ways that
are compatible with their learning/cognitive styles or
learning preferences. Pedagogic and administrative
implications are that the application of one or both of
these learning theories may be appropriate in facilitating
and promoting greater learning.
Hypotheses
The following hypotheses were tested in this study:
Hypothesis 1. There is no significant relationship
between the actual level of student achievement and the
predicted level of achievement as a function of the
weighted linear combination of grade point average (GPA),
number of hours of homework per week, program, and the
concrete experience, reflective observation, abstract
conceptualization, and active experimentation
learning/cognitive style dimension discrepancy scores.
Hypothesis 2. There is no significant relationship
between student achievement and the student-instructor
discrepancy score (degree of matching between student and
instructor) on any of the four learning/cognitive style
dimensions.
Hypothesis 3. There are no differences in the actual
level of achievement for students who are the same
learning/cognitive style type ("matched") compared with
students who are a different learning/cognitive style type
("mismatched") from their instructor.
Hypothesis 4. There is no significant relationship
between the actual level of student achievement and the
predicted level of achievement as a function of the
weighted linear combination of GPA, number of hours of
homework per week, program, and the interpersonal,
individual, teacher-structured, student-structured,
abstract, and concrete learning preference dimension
discrepancy scores.
Hypothesis 5. There is no significant relationship
between student achievement and the student-instructor
discrepancy score (degree of matching between student and
instructor) on any of the six learning preference
dimensions.
Hypothesis 6. There is no significant change in the
proportion of explained variance (R2) in student
achievement when the learning/cognitive style set of
variables, as a group, are removed from the complete (all
variables) regression model.
Hypothesis 7. There is no significant change in the
proportion of explained R2 in student achievement when the
learning preference set of variables, as a group, are
removed from the complete (all variables) regression
model.
Delimitations
This study was confined to students enrolled in
Associate of Science and Bachelor of Science respiratory
therapy educational programs. A cluster sampling of 143
students and their instructors from 11 respiratory therapy
programs in the northern, southern, eastern, and western
regions of the United States was studied. The major
variables in the study were student achievement and the
degree of matching of students and their instructors on
the basis of Kolb's learning/cognitive style dimensions
and Rezler's learning preference dimensions for a major
course in respiratory therapy.
Limitations
1. In this descriptive, correlational, ex post facto
study, the researcher identified subjects in whom changes
in the independent variables had already taken place and
the researcher studied them in retrospect for their
possible effects on the dependent variable. The key
independent variables were the degree of matching between
students and their instructors on the basis of several
learning/cognitive styles and learning preference
variables. The dependent variable was the level of
student achievement for a major course in respiratory
therapy theory. The ex post facto design of this study
and the fact that learning/cognitive style and learning
preference inventories and an achievement test were
administered only once, precluded the advantages of an
experimentally designed study. The researcher was not
able to control all extraneous variables or to manipulate
the independent variables.
2. The results of this study were interpreted within
the limitations imposed by the validity and reliability of
the inventories and the achievement test used in the
investigation.
3. Factors that may affect the external validity,
generalizability, or representativeness of the data
include variations in the data base that could possibly
occur with different groups of students at different
colleges and in a different academic year.
Need for the Study
A study identifying factors which may be influencing
student achievement in respiratory therapy educational
programs and also affecting the low pass rate on the
respiratory therapy board examination was needed so that
corrective interventions could be made. One may predict,
a prior, that the degree of matching between students and
their instructors based on learning/cognitive style or on
learning preference theories may be causal factors
affecting the student achievement. The greater the degree
of match between students and their instructor on the
basis of learning/cognitive style or learning preference
theory throughout the entire length of a respiratory
therapy program, the greater may be the degree of
achievement and possibly the greater likelihood of passing
the board examination. Results of this study should be
helpful in explaining the low pass rate on the board
examination. Respiratory therapy curriculum planners and
policy makers at the national and local levels could
benefit from the results of this study by using
information obtained from learning/cognitive style and
preference inventories to match students with appropriate
methods of instruction to improve student achievement.
Kolb's learning/cognitive style theory and Rezler's
learning preference theory were evaluated in this study.
A comparison of these related, but different viewpoints
could have important academic and practical implications.
Academically, such a study contributes to the scholarly
knowledge in the area of teaching-learning strategies used
in higher education. First, the relationship between the
degree of matching of students and their instructors,
based on Kolb's and Rezler's theories, and student
achievement had not been compared. If the degree of
matching based on one of these theories is associated with
greater student achievement, it could then be identified.
Second, if student achievement is favorably influenced by
matching students and instructors on the basis of
learning/cognitive style or learning preference theory,
then it may be possible to predict academic achievement
using multiple regression analysis. Predicting academic
achievement using Kolb's learning/cognitive style or
Rezler's learning preference variables is a novel approach
that heretofore had not been researched. Third, another
unique aspect of this research was the use of discrepancy
scores derived from the learning/cognitive style and
learning preference dimension scale scores as a means of
determining the degree of match between students and their
instructors. This technique allowed for various degrees
of matching to be recorded as a continuous variable based
on the discrepancy score, i.e., the smaller the
discrepancy score, the better the match (discrepancy score
= student's score on each dimension scale minus
instructor's score on each dimension scale). A fourth
point is that it may be possible, using step-wise
regression analysis, to select dimensions from Kolb's and
Rezler's theories which are significantly related to
student achievement. Thus, by combining select dimensions
from these two schools of thought, a new model may be
developed that is better than using either theory alone in
predicting student achievement. A fifth point was that
there was no reference in the literature describing the
learning/cognitive styles and learning preferences of
respiratory therapy students and their instructors and the
relationship these factors might have on student
achievement. In other allied health professions,
learning/cognitive styles and learning preferences had
been documented, but not in this major allied health
profession. Practically speaking, if student achievement
in respiratory therapy educational programs is related to
the degree of matching between students and their
instructors on the basis of learning/cognitive style or
learning preference theory, then application of one or
both of these approaches could have educational
implications. Such information would be useful in
evaluating current instructional practices and assessing
student achievement and the overall learning outcome in
respiratory therapy educational programs (i.e., pass/fail
rate on the national board examination).
Assumptions
The assumptions for this study were as follows:
1. The testing of the hypotheses relied on self-
reported data and it was assumed that subjects answered
all questions honestly. Systematic error caused by method
bias, therefore, may have affected any relationships that
were questioned.
2. It was assumed that the learning/cognitive style
and learning preference inventories used in this study
were appropriate for use with respiratory therapy
personnel and were applicable to this sample.
3. It was assumed that the published validity and
reliability data for the above inventories were correct.
4. It was assumed that, because of the study design,
generalizations to all students in the programs
represented could be made.
Definition of Terms
Allied health personnel are specially trained
individuals representing a variety of health related
professions who fulfill necessary functions, including
those of assisting, facilitating, and complementing the
work of physicians and other specialists in health care
systems.
The American Association of Respiratory Care (AARC)
is a national association of respiratory therapy personnel
formed to encourage, develop, and provide educational
programs for those persons interested in respiratory
therapy and diagnostics; advance the science, technology,
ethics, and art of respiratory care through institutes,
meetings, lectures, publications, and other materials;
facilitate cooperation and understanding among respiratory
care personnel, allied health professions, hospitals,
service companies, industry, government organizations, and
other agencies interested in respiratory care; and provide
education of the general public in pulmonary health
promotion and disease prevention.
The American Medical Association (AMA) is a national
association of medical practitioners and concerned
individuals which functions to promote standards of
medical and general health care. One function authorized
to this national association is to evaluate and approve or
disapprove graduate medical and undergraduate allied
health care educational programs.
Certification/registration is the process by which
states and professional bodies recognize the particular
competence of individual practitioners.
A certified respiratory therapy technician (CRTT) is
a graduate of an AMA accredited associate or bachelor of
science degree respiratory therapy program who has
successfully written the CRTT examination provided by the
National Board for Respiratory Care.
Degree of matching on the basis of learning/cognitive
style and learning preference dimensions refers to the
various degrees of matching between a student and his or
her instructor which are recorded as a continuous variable
based on the learning/cognitive style and learning
preference dimension discrepancy scores (discrepancy score
= student score on each dimension scale minus instructor
score on each dimension scale, i.e., the smaller the
discrepancy score, the better the match and vice versa).
Learning preference refers to the choice that an
individual makes for one learning situation over another.
The learning preference dimensions, as posited by
Rezler, refer to six dimension scales which describe an
individual's learning preferences: individual,
interpersonal, student-structured, teacher-structured,
abstract, and concrete.
The Learning Preference Inventory (LPI) is an
inventory developed by Rezler (1981) designed specifically
to identify the learning preferences of allied health
students. Results from the LPI may be used to match
learner preferences with learning conditions. An
individual's learning preferences are measured on six
scales representing three bipolar dimensions: individual
and interpersonal, student-structured and teacher-
24
structured, and abstract and concrete (see Figure 1-2 and
Appendix B).
Learning/cognitive style is the manner in which an
individual acquires, perceives, and processes information
in the learning situation.
The learning/cognitive style dimensions, as posited
by Kolb, refer to four dimension scales which describe an
individual's learning/cognitive style: concrete
experience, reflective observation, abstract
conceptualization, active experimentation.
The Learning Style Inventory (LSI) is an inventory
developed by Kolb (1985) to assess individual
learning/cognitive styles and is derived from Experiential
Learning Theory. The inventory measures an individual's
relative emphasis on four learning abilities: concrete
experience, reflective observation, abstract
conceptualization, and active experimentation (see Figure
1-1 and Appendix A).
Learning/cognitive style type is determined on the
basis of an individual's learning/cognitive style
dimension scale scores. Each person's learning/cognitive
style type is a combination of the four basic learning
modes of learning/cognitive style dimensions. Four
dominant learning/cognitive style types described by Kolb
are seen as two-dimensional: Diverger (concrete and
reflective), Assimilator (abstract and reflective),
Converger (abstract and active), and Accommodator
(concrete and active) (see Figure 1-1).
Licensure is the process by which states authorize
individuals to practice an otherwise restricted
profession.
"Matched" and "mismatched" on the basis of
learning/cognitive style types refer to either those
students who are of the same learning/cognitive style type
as their instructor and are thus classified as "matched,"
or those who are a different learning/cognitive style type
than their instructor and are classified as "mismatched."
The National Board for Respiratory Care (NBRC) is the
national certifying agency established by the AARC in
charge of administering the CRTT examination to graduates
of AMA accredited associate and bachelors in science
degree respiratory care programs.
Respiratory therapy (occupational description) is an
allied health specialty employed with medical direction in
the treatment, management, control, diagnostic evaluation,
and care of patients with deficiencies and abnormalities
of the cardiopulmonary system.
CHAPTER 2
REVIEW OF THE LITERATURE
Introduction
The notion that people learn differently is not a new
idea (Fizzell, 1984). Over 2,500 years ago, the ancient
Hindus viewed people as active or passive and as emotional
or thoughtful. Based on these characteristics, the Hindus
speculated that individuals needed four ways of practicing
religion that were congruent with their personality types-
-the four yogas or pathways--which are described in the
Bhagavad Gita. In the early 1900s, psychologists in
Germany were exploring the concept of cognitive style.
Carl Jung's work on "psychological types" was described in
1921. Gordon Allport used the word "style" to refer to
consistent patterns in individual behavior (Guild &
Garger, 1985). Allport (1937) described attitude,
interest, concept, and ideal as forms of mental
organization that result in and affect learning. In the
early 1940s, "concrete" and "abstract" styles of cognitive
functioning were described by Goldstein and Scheerer
(1941). Witkin and Asch (1948) postulated the bipolar
characteristic of being dependent or independent of
27
structure when discriminating figures against a background
relief as the manner in which individuals perceive and
relate to the world. Thurstone (1948) and later Guilford
(1959) recognized individual perceptual abilities and
flexibility as important factors in the teaching-learning
process.
In the 1920s Thorndike reported that a student's
achievement was highly correlated with intelligence
(Henson & Borthwick, 1984). Yet the conditions set for
these studies were such that all students were given the
same type of instruction and the same amount of time to
learn. Years later, in 1963, John B. Carroll provided
students with a variety of teaching approaches and as much
time as they needed (Henson & Borthwick, 1984). The
results were that students' aptitudes proved not to be a
major factor in determining academic achievement. A major
implication of Thorndike's and Carroll's data is that
given sufficient time and correct teaching methods, most
students can learn or master the material set before them
and achieve at a high level (Henson & Borthwick, 1984).
This approach to learning recognizes that individual
learners have their own learning/cognitive style and
preferences and that a responsible approach for teachers
is to modify their teaching styles to accommodate
students' learning/cognitive styles and preferences so as
to promote academic achievement.
Learning/cognitive style is the manner in which an
individual acquires, perceives, and processes information
in the learning situation (Cahill & Madigan, 1984; Rezler
& Rezmovick, 1981). Learning preference is the choice
that an individual makes for one learning situation over
another (Cahill & Madigan, 1984; Rezler & Rezmovick,
1981). Learning/cognitive styles and preferences are the
result of personality characteristics that distinguish
individuals in a teaching-learning situation. Such
characteristics include a variety of attitudes and values
individuals have about learning, how they think, and how
they want information presented. One approach to
individualizing instruction is to accommodate the
learners' styles and preferences for various instructional
methods. It is useful to determine whether learners with
different styles prefer different instructional
activities. Such information might be helpful in
modifying instructional activities in order to optimize
the teaching-learning environment. Within a course, for
example, two or three options might be provided to
accommodate different preferences.
Congruency between student and faculty
learning/cognitive styles and preferences for methods of
instruction are thought to improve the learning
experience. Thus, the learning/cognitive styles and
preferences of students appear to be relevant factors that
should be considered in the development of a curriculum.
When there is a mismatch between the learning/cognitive
styles or preferences of students and faculty, students
are not sufficiently motivated and the necessary
involvement in learning does not occur (Blagg, 1985).
Learning/Cognitive Styles
Learning/cognitive styles or thinking styles are
information-processing regularities that are related to
underlying personality traits (Corno & Snow, 1986;
Messick, 1984). As Corno and Snow (1986) pointed out,
learning/cognitive styles are conceptually at the overlap
between individual differences in intellectual abilities
and personality characteristics. When a person
demonstrates a predisposition to favor a particular
cognitive strategy while learning, then he or she is
manifesting a learning/cognitive style. Because a
cognitive strategy is defined as a pattern of information-
processing activities that a person engages in when
confronted with a learning task, then learning/cognitive
style refers to cognitive strategies that one uses with
some cross-situational consistency (Schmeck, 1986). A
variety of learning/cognitive styles have been
investigated (e.g., convergence-divergence and
assimilation-accommodation, field independence-dependence,
deep and surface processors, etc.).
Learning/cognitive style is the unique way each
individual gathers and processes information, i.e., how an
individual manipulates, categorizes, and evaluates input
information (McKenny & Keen, 1974). Learning/cognitive
style differs from ability or intelligence. One style is
not presumed better than another. When using scores from
intelligence tests, it is obviously preferable to have a
higher score. This is not true with learning/cognitive
style instruments. For example, a high score on a scale
of "independent learning/cognitive style" is not presumed
to be better than a high score on a scale of "dependent
learning/cognitive style" (Fuhrmann & Jacobs, 1980).
The notion of learning/cognitive style attempts to
integrate learning theory in such a way as to make it
applicable to the educational process. Hilgard and Bower
(1966) discussed the concept of learning/cognitive style
and provided a means for integrating and differentiating
the major learning theories, i.e., cognition, motivation,
personality, and stimulus-response theories. Principles
emphasized in cognitive theory that apply to the learning
process include the following: (a) perceptual features,
how the individual takes in information from the
environment; (b) organization of knowledge for the
learning situation; (c) learning with understanding
(believed to be more permanent and more transferable than
rote learning by formula); (d) cognitive feedback, needed
L
to confirm whether knowledge is correct and to correct
faulty learning; (e) goal setting by the learner
(important motivator for learning, with successes and
failures determining future goals); and (f) divergent and
convergent thinking (divergent thinking leads to inventive
solutions to problems, while convergent thinking leads to
logically correct answers).
Hilgard and Bower (1966) pointed out that from
motivation and personality theory, these principles apply
to the learning process: (a) learners' abilities are
important and provisions must be made for differences in
these abilities; (b) postnatal development may be as
important as hereditary and congenital determiners of
ability and interest, the learner must be understood in
terms of the influences that have shaped the individual's
development; (c) learning is culturally relative; (d)
anxiety level may affect the learner's ability to learn;
(e) the same situation may tap different motives for
learning from one learner to another (situation
dependent); (f) organization of motives and values within
the learner are relevant to the learning situation; and
(g) atmosphere of learning, whether by competition versus
cooperation, authoritarianism versus democracy, or
individual versus group identification--affects both
learning satisfaction and the products of learning.
Concepts emphasized in stimulus-response theory, as
explained by Hilgard and Bower (1966), that may be
observed in the learning process follow: (a) active and
passive learning environments, whereby the learner is an
active doer rather than a passive listener; (b)
repetition, whereby the learner needs repetition to
acquire skill to a level that guarantees retention; (c)
reinforcement, whereby the learner needs reinforcement to
promote learning and retention; (d) practice, whereby
practice is needed in a variety of situations in order to
develop the ability to generalize and discriminate; (e)
models, whereby the learner needs models to imitate; and
(f) drive, whereby the intrinsic drive conditions of the
learner are important for the learning.
Jung (1976) explored the differences in the way
people perceive and process information. He defined four
categories: (a) feelers, transfer value for themselves to
what they experience; (b) thinkers, are more classically
rational, they engage in direct thinking and arrange what
happens and what they perceive into rational categories;
(c) sensors, they perceive consciously, but beyond reason,
and they apprehend the world by what they sense, see,
know, feel, smell, and reason; and (d) intuitors, they are
the reverse of sensors in that they unconsciously impose
control on perceptions, and also understand what they see
and feel in a whole and complete way.
Kolb, Rubin, and McIntyre's (1974) model of
learning/cognitive styles (see Figure 1-1) is based on
Experiential Learning Theory and is composed of four
dimensions or abilities: (a) concrete experience
(learning by feeling), (b) reflective observation
(learning by watching and listening), (c) abstract
conceptualization (learning by thinking), and (d) active
experimentation (learning by doing).
Experiential Learning Theory provides a model that
conceptualizes the learning process in such a way that
allows users to identify differences among individual
learning/cognitive styles and corresponding learning
environments (Kolb, 1984). The model is founded on the
Jungian concept of styles or types. The model's core is a
simple description of the learning cycle, showing how
experience is translated into concepts that, in turn,
guide the choice of new experiences. In this model
learning is conceived as a four-stage cycle. Immediate
concrete experience forms the basis for observation and
reflection. These observations are assimilated into
concepts from which new implications for action can be
deduced. These implications or hypotheses then serve as
guides in creating new experiences which the learner can
then test in more complex situations. The result is
another concrete experience, but this time at a more
complex level. Thus, the Experiential Learning Theory
model may be thought of as a "helix," with individuals
having new experiences, reflecting on them, deducing
generalizations about the experiences, and then using the
experiences as guides to further action at higher levels
of complexity (Claxton & Murrell, 1987).
The Experiential Learning Theory model may also be
viewed as a means to distinguish between what Kolb
describes as the two key elements in the learning process
(Kolb, 1984). The first is acquiring or grasping
information. Some people prefer acquiring information in
concrete ways (e.g., relating to people), while others
prefer abstract ways (e.g., logically sound concepts and
theories). The second element is processing or
transforming the information. Some people prefer to
reflect on experiences (e.g., contemplation, evaluation,
and judgment), while others transform experiences through
active experimentation (e.g., practical, "hands-on," doing
approach).
Kolb (1984) found that the four abilities described
combine to form four learning/cognitive style clusters.
Individuals who acquire information by relying on abstract
conceptualization and then process it through active
experimentation are classified as "convergers." They are
called convergers because such individuals like to find
specific, practical answers, and when presented with a
task they move quickly (converge) to find an answer. They
35
are relatively unemotional and prefer dealing with things
rather than people. "Assimilators" acquire information
through abstract conceptualization and process it through
reflective observation. Such individuals are called
assimilators because they like to assimilate disparate
pieces of information into a logical and integrated whole.
They are more interested in theoretical concerns and have
little interest in the practical application of ideas and
concerns about people. Their primary strength is their
ability to create conceptual and theoretical models.
Individuals who like to acquire information through
concrete experience and process it through reflective
observation are classified as "divergers." These
individuals are called divergers because they are good at
generating ideas and brainstorming. Their major strength
is their imaginative ability. Such individuals enjoy
working with people and tend to be emotional. Divergers
excel at examining concrete situations from many
perspectives and at generating ideas. Finally,
"accommodators" acquire information through concrete
experience and process it through active experimentation.
These individuals are called accommodators because they do
well in situations where they must adapt to meet new
experiences. These individuals are risk-takers, focus on
doing things, and having new experiences. They are
intuitive and often use a trial-and-error strategy when
solving problems. They are often impatient, and even
pushy, when confronted with a theory that does not match
the facts as they see them, whereby they tend to discard
theory (see Figure 1-1).
Kolb (1984) found that specific learning/cognitive
style types tend to gravitate to certain career fields.
He asserted that occupational disciplines attract
individuals with learning/cognitive styles congruent with
the structure of knowledge within the discipline.
Convergers typically have rather narrow interests and
often specialize in careers in technology, e.g.,
engineering, medicine, computer science, and physical
science. Assimilators are particularly good at research
and planning activities and usually gravitate toward
careers in science, e.g., teaching, mathematics, physical
sciences, and biology. Because of their people
orientation and ability to generate ideas, divergers enter
careers in service organizations, e.g., social work,
psychology, nursing, police, personnel management, and
organization development. Accommodators like a "hands-on"
approach and prefer action-oriented jobs such as careers
in business and promotion, e.g., marketing, government,
business, and retail.
These learning/cognitive style types suggest that
certain classroom procedures will fit certain student
types better than others (Baker & Marks, 1981; Baker,
Wallace, Bryans, & Klapthor, 1985; Carrier, Newell, &
Lange, 1982; Wunderlich & Gjerde, 1978). Convergers
probably would find laboratory experiments and problems
that have specific answers satisfying learning
experiences. Assimilators may make observations of
concepts in naturalistic settings, watch role plays and
simulations in class, and then generate concepts that
describe and tie together what has occurred. Divergers
may benefit more from discussion groups and working on
group projects. Accommodators may prefer problem-solving
activities and be good candidates to participate in
classroom role plays and simulation.
Kolb (1985) developed the learning style inventory
(LSI) (see Appendix A) to assess the four modes of
learning defined in his model of learning/cognitive style.
He suggested that the degree to which a person favors
particular stages of the cycle, indicated the
learning/cognitive style of that individual. Kolb's LSI
has been used extensively as the preferred
learning/cognitive style inventory for assessing the
learning/cognitive styles for both medical and allied
health personnel. The Kolb LSI has been examined as a
predictor for medical students' specialty choices
(Plovnick, 1975; Wunderlich & Gjerde, 1978) and has also
been used to determine the instructional preferences of
family practice physicians and internists (Leonard &
I
Harris, 1979; Sadler, Plovnick, & Snope, 1978; Whitney &
Caplin, 1978), as well as describing the
learning/cognitive styles of surgeons (Andrassy & Torma,
1982). Analyses of the learning/cognitive styles of
anesthesiologists have been reported in several reports
(Baker & Marks, 1981; Baker, Wallace, & Cooke, 1987;
Baker, Wallace, Cooke, Alpert, & Ackerly, 1986;
Eisenkraft, Reed, Eisenkraft, & Kaplin, 1985).
Highfield (1988) used the LSI to determine the
learning/cognitive styles of baccalaureate nursing
students. The assimilative style was the most dominant
learning/cognitive style type (55%), while a divergent
style (31%) was the second most frequently occurring type.
In a related study using Kolb's LSI to determine the
learning/cognitive style types of nursing students,
Laschinger and Boss (1984) found that divergers were the
most frequently occurring learning/cognitive style type.
Hodges (1988) indicated that the highest percentages of a
group of nursing students were divergers and
accommodators. O'Kell (1988) used the LSI to identify the
learning/cognitive styles of British nursing students.
Results indicated that approximately 66% of the students
had active learning/cognitive styles, characteristic of
accommodators and convergers. In a study designed to
assess the learning/cognitive styles of practicing nurses,
Christensen, Lee, and Bugg (1979) identified accommodation
and divergence as the predominant styles. The Kolb LSI
was used to determine the learning/cognitive styles of
nonregistered baccalaureate nursing students and
registered nurses enrolled in baccalaureate nursing
programs (Merritt, 1983). Results indicated that mean
score for reflective observation was significant for both
groups, suggesting divergent and assimilative types.
Thomas (1986) used the LSI to identify the
learning/cognitive style types of nurse administrators as
a means of assessing the nursing management team in one
institution. The director, associate, and assistant
directors of nursing who formed the top management group
were mostly convergers with some assimilators. In
contrast, the clinical coordinators were predominantly
accommodators while the clinical specialists and educators
were convergers, accommodators and divergers. In a study
of dental hygiene students and dental hygiene faculty,
Carrier, Newell, and Lange (1982) used Kolb's LSI to
determine the relationship of learning/cognitive styles to
preferences for instructional activities. The results
indicated that most students and faculty were located in
Kolb's accommodator and diverger categories. In a study
of occupational therapy students, the LSI was used to
examine the relationship between learning/cognitive styles
and student performance in academic and in clinical course
work (Cunningham & Trickey, 1983). Results showed no
significant correlation between the four
learning/cognitive style types and academic course work.
McKenney and Keen (1974) developed a cognitive style
model for the purpose of applying it to managerial
processes. Problem-solving and decision making are
conceptualized in the model. The authors "view problem-
solving and decision making in terms of the processes
through which individuals organize the information they
perceive in their environment, bringing to bear habits and
strategies of thinking" (p. 80). McKenney and Keen's
model addresses itself specifically to information
gathering and information evaluation. The information
gathering dimensions refer to individuals who are
predominantly perceptive or receptive. Perceptive
individuals focus on relationships between items and look
for deviations from or conformities with their
expectations. Receptive thinkers are sensitive to the
stimuli itself and focus on detail rather than
relationships. They derive the attributes of the
information from direct examination rather than from
fitting it into their precepts. The information
evaluation dimensions are related to problem-solving and
are identified as systematic and intuitive. The
systematic individual approaches a problem by structuring
it into some logical procedure or method. Intuitive
thinkers usually avoid committing themselves to a
L
41
formalized structure. They are more sensitive to cues and
are willing to jump from one method to another and to
discard information if the cues seem to indicate a change
would be advantageous.
McKenney and Keen's cognitive style model provides
some explanation of the ways individuals perceive and
process information in their environment. From a
managerial standpoint, the model describes a person's
unique cognitive style regarding problem recognition and
definition. The authors asserted that, ideally, the style
of the manager should "match" with the environment:
"There needs to be a fit between the decision maker's
cognitive style and the information-processing constraints
of his task. Given this fit, the manager is more likely
to gather environmental information that leads to
successful problem finding" (p. 82). As previously
described, "matching" a person's innate style to the
appropriate environment facilitates one to perform to his
or her full capability.
Regarding learning/cognitive style, an approach was
advanced by Pask (1976) who described two types of
learners: holists and serialists. Holists learn a topic
in a holistic manner or a global approach (seeking an
overall understanding). Such learners use a broad
framework of understanding into which they can fit more
detailed information. Holists study a subject from the
"top down"; i.e., they examine parts of the topic at the
higher levels of complexity and make connections between
them. Serialists focus their attention more narrowly on
pieces of information low in the hierarchial structure and
learn in a step-by-step manner (attempting to fully
understand each step before proceeding to the next). Such
learners use links to relate different aspects of the
subject, thus working in a "bottom up" approach so that
the overall picture is developed slowly, thoroughly, and
logically. The learning curve of the holist shows almost
no increase for a period of time and then makes a steep
incline as full understanding is achieved. The serialist
or step-wise learner has a more gradual and steady
increase in the learning curve. As each new understanding
builds upon the previous one, the curve gradually rises to
full understanding (Pask, 1976).
Witkin's (1977) dimensions of field-independence and
field-dependence are also important information processing
considerations. Individuals who are relatively
uninfluenced by the surrounding environment or field are
called "field-independent"; those who are heavily
influenced by the surrounding field are called "field-
dependent." Field-independent students perceive things
clearly from the background, while field-dependents are
influenced by the overall background and see parts of the
field as "fused" (Keefe, 1986). The field-independent
43
learner tends to be highly analytic and systematic, while
the field-dependent learner is more holistic in his or her
approach to learning. Field-dependents are more strongly
influenced by authority figures and by peer groups than
are field-independents. Witkin (1976) posited that field-
independent persons were encouraged at an early age to be
autonomous. Thus, child-rearing experiences appear to be
important factors causing individuals to be field-
independent or field-dependent.
Witkin (1977) developed the Group Embedded Figures
Test to identify field-independent and field-dependent
information processing types. His research revealed that
field-independent persons learned more if allowed to place
their own structure on information, while field-dependent
persons were dependent on the context in which the
material was embedded to provide structure. Thus, a
student who is field-independent may have a strong dislike
for self-paced modules which allow little room for the
learner to reorganize the ideas into his or her framework.
As with most learning/cognitive style considerations,
individuals who are field-independent are not better
learners than those who are field-dependent, although each
is more proficient at certain types of tasks.
Witkin (1976) described the concepts of field-
independence and field-dependence as important factors in
a student's selection of a major area of study, a course,
I
and a career. Field-independent students favored areas of
study that called for analytic skills, such as
mathematics, engineering, and science. Whereas field-
dependent students favored subject areas that involved
extensive interpersonal relations, such as social
sciences, humanities, counseling, teaching, and sales.
Among nursing students, for example, the more field-
independent students chose surgical nursing, while the
more field-dependent students chose psychiatric nursing
(Witkin, 1976). Regarding careers in teaching, Witkin
noted that teachers in the areas of mathematics and
science were more likely to be field-independent. Field-
independent teachers preferred the lecture method, while
field-dependent teachers preferred discussion methods of
teaching.
Hill (1976) developed a comprehensive approach to
assessing the cognitive components of learning/cognitive
styles known as cognitive mapping. This approach is based
on the fact that individuals have certain preferences for
the processes used to gather information, think and make
inferences, and make decisions. The individual's
learning/cognitive style includes the following
categories: (a) gathering information refers to the
sensory modalities that individuals prefer to use; (b)
thinking and making inferences refers to how individuals
process information; (c) decision making processes is the
extent to which individuals make decisions for themselves,
rather than consulting others; and (d) interest in self,
others, and objects. These four general categories are
expanded into 27 specific cognitive characteristics in
Hill's Cognitive Style Mapping Inventory. These aspects
have been used to assess classroom procedures and courses
compatible with a student's style.
Sims and Ehrhardt used Hill's cognitive style mapping
approach on community college students and subsequently
informed the students of their results (Claxton & Murrell,
1987). The students felt that their maps had given them
helpful information on how they learned, helped them adapt
to different teaching approaches, and many had actually
changed the way they studied. Using Hill's cognitive
style mapping, Fourier (1980) conducted a study to
determine whether undergraduate students who were mapped
and had their learning/cognitive style explained to them
would achieve better grades. The results were that the
students who had their learning/cognitive styles explained
to them achieved significantly higher grades than students
who were not aware of their learning/cognitive styles.
Ehrhardt (1983) discussed the possibility of using Hill's
cognitive style mapping approach as an aid to medical
students experiencing difficulty to help them deal with
their studies, and also an aid to the faculty by providing
them with a way to counsel the students. In another
study, Flippo and Terrell (1984) applied Hill's cognitive
style mapping to undergraduate students and subsequently
informed each student of his or her learning/cognitive
style type. The students indicated that knowledge of
their learning/cognitive styles was useful to them in
gaining greater skill in studying and college work in
general.
In other studies of student learning/cognitive
styles, Marton (1975) found two distinctive
learning/cognitive style approaches: deep level and
surface processing. The "deep level processing approach"
may be characterized as an active search for meaning. For
example, users of this learning/cognitive style approach
start with the intention of understanding an article; they
question the author's arguments and conclusions and try to
relate them to previous knowledge and previous experience
to enable them to appraise the validity of the author's
conclusions. In contrast, users of the "surface level
processing approach" try to memorize those parts of the
article on which they think they might be questioned.
They tend to focus on specific facts which may not be
connected and they seem anxious about the conditions of
the learning experience. The deep level processing
approach was found to be associated with deeper
understanding, and even after a five-week interval the
users of this approach had better recall of detail than
47
those who use the surface approach (Marton & Saljo, 1976).
Using GPA as an index of academic achievement in college
students, Miller, Alway, and McKinley (1987) tried to
identify the learning/cognitive styles associated with
high academic performance. It was found that the deep
processing style (which focuses differences and
similarities among subjects, organization of information,
and a critical analysis of relationships) and related
strategies seem to be the most efficient learning styles
in obtaining higher GPAs. An implication of this paper is
that programs designed to improve academic achievement
should address the relationships between
learning/cognitive styles and achievement.
Schmeck (1983) defined a learning/cognitive style
model as "a predisposition on the part of some students to
adopt a particular learning strategy regardless of the
specific demands of the learning task" (p. 233). Similar
to Marton's classification, two learning/cognitive styles
were identified in terms of how people process
information: "deep elaborative" information processors
and "shallow-reiterative" information processors. Deep-
elaborative information processors spend more of their
time thinking and less time repeating. They classify,
contrast, analyze, and synthesize information from
different sources. They elaborate by thinking of personal
examples and restating information in their own words.
48
They draw upon the depth and breadth of their experiences.
Shallow-reiterative information processors spend much of
their study time repeating and memorizing information in
its original form. They prefer to assimilate information
as given rather than rewording, restating, or rethinking
it (Schmeck, 1981). In terms of academic achievement,
Schmeck (1981) reported that students who were deep-
elaborative processors demonstrated faster learning,
better memory, and higher grade point averages.
Lawrence (1986) posited that learning/cognitive style
is one reflection of personality type. In describing the
relationship of personality type and learning/cognitive
style, he contended that the key element is the dominant
mental process in each personality. Using Jungian theory,
Myers (1962) developed an approach for identifying
personality types. The Myers-Briggs Type Indicator (MBTI)
identifies four matched sets of variables, or bipolar
characteristics related to personality type
identification, and is used to identify individuals
according to 16 different personality types. An
"extroversion/introversion" scale indicates the way in
which an individual directs interest and attention,
whether to the outer world of objects, people, and action
or to the inner world of ideas, theorizing, and
contemplation. A "sensing/intuitive" scale indicates an
individual's preference for perceiving things, either
through input through sensory modalities or through
intuition. A "thinking/feeling" dimension provides
information as to whether an individual prefers to analyze
and apply impersonal logic or prefers to be guided by
personal values and feelings. Lastly, a
"judging/perceptive" preference reflects one's attitude
toward approaching the outer world. Those who demonstrate
the judging component prefer to live in an organized,
planned, orderly manner; those with a perceptive component
prefer a more flexible, spontaneous, and open manner.
Lawrence (1986) described Myers's approach to
personality identification as one that can be related to
learning/cognitive style. If a student is "thinking"
dominant, learning occurs best when activities are
organized in a logical manner. In a "feeling" dominant
type, a caring relationship with the instructor is
essential to maintain motivation and achievement.
"Sensing" dominant individuals learn best in environments
where the subject matter is presented as practical and
functional. Lacking imagination, such individuals may
become lost if the instructor omits steps in his or her
explanation. These students lack the ability to make
conceptual leaps. For such students, concrete learning
activities are most appropriate. "Intuitive" dominant
individuals desire inspiration from the instructor more
than other types. Such students enjoy learning through
I
new ideas, projects, and planning. "Extrovert" dominant
individuals process information predominantly through
"thinking" or "feeling," while "introvert" dominant
personality types rely on "sensing" or "intuition" (Myers,
1962).
Lawrence (1982) reported that teachers of different
Myers-Briggs types are attracted to different levels and
different subject matter. Sensing teachers choose lower
levels of education and are more likely to teach practical
skills with facts and details, while intuitive teachers
are more likely to be found in colleges and universities
teaching courses rich in abstraction and theory.
McCaulley and Natter (1980) stated that "in essence,
teachers tend to understand and appreciate students whose
minds are like their own" (p. 185).
Neurologic Basis of Learning/Cognitive Styles:
Left and Right Brain Hemispheric Differences
The essence of how different people think and solve
problems and how individuals differ regarding
learning/cognitive styles as influenced by the left and
right hemispheres of the brain is captured in Blakeslee's
(1986) statement:
One can have a "feel" for throwing a ball which
involves many subtle, and intuitive corrections
for movement of the receiver, wind, sloping
terrain, etc. This is possible without any
verbal or analytical knowledge of the equations
or principles involved. On the other hand, the
mathematician who programs gunnery computers may
be an expert in the left brain knowledge of
trajectory--yet have no "feel" whatsoever for
throwing a ball. (p. 190).
The human brain is a composite of two interacting
systems--the left and right hemispheres--each capable of
it's own processing approach and mode of memory
functioning (Sinatra, 1986). Investigations of brain
function in normal people reveal that the two hemispheres
may be differentiated on the basis of cognitive processing
characteristics (see Table 2-1). The left hemisphere was
determined to be inductive, sequential, detailed, and
analytical in nature, storing and retrieving information
in code such as numbers or words. While the right
hemisphere was found to be deductive, observing whole
concepts, filling in gaps and storing and retrieving
information as images and pictures (Tipps, Sanders, &
Languis, 1982). It is felt that the learning process
cannot be accomplished by either side of the brain alone,
but represents an integrated activity of both hemispheres.
Levy (1986) described this style of information processing
as "interhemispheric integration." Both hemispheres play
critical roles in organizing the perceptual and cognitive
processes that are prerequisite to understanding and
learning (Levy, 1986). Zenhausern (1986) applied the term
"neuroeducation" to that aspect of education that focuses
on the interaction of brain behavior in the learning
situation.
Table 2-1
Cognitive Characteristics of Left and Right Brain
Hemispheres (Keefe. 1986)
Left Hemisphere
sequential processing style (organizes one fact after
another)
inductive (going from the parts to the whole)
analytic, parts-specific thinking style and mode of
problem solving
logical
reflective
verbal understanding of concepts
field-independent (perceives things clearly from a
background field)
Right Hemisphere
parallel processing style (all facts conceptualized
at once, as a whole)
deductive (going from the whole to the parts)
global or holistic mode of problem solving
emotional
impulsive
intuitive
nonverbal perceptual understanding (hunches or "feel"
for something)
field-dependent (influenced by the overall
organization of the background and sees parts of the
field as "fused")
There is evidence that individual differences among
people exist to the extent that one hemisphere is more
differentially aroused or active in the learning situation
(Zenhausern, 1986). Physiologic evidence supporting the
notion of hemispheric dominance has revealed that people
differ in the asymmetry of blood flow to the two sides of
the brain, and that those having an asymmetric flow in
favor of the right hemisphere perform better in learning
situations favoring right hemispheric activities (Levy,
1986). Bradshaw and Nettleson (1981) differentiated the
two hemispheres in terms of their respective processing
styles: sequential for the left hemisphere (organizes one
fact after another) and parallel for the right hemisphere
(all facts conceptualized at once, as a whole). The
learning/cognitive style of some students may favor
sequential processing while parallel processing may be
used more frequently by others (Zenhausern, 1986).
Along this line of thinking an indictment of our
school system has arisen; the traditional approach to
education is geared too much in favor of the left-brained,
analytic, inductive learner (Blakeslee, 1986; Brennan,
1986; Levy, 1986; Sinatra, 1986; Zenhausern, 1986).
Brennan (1986) noted that in any given classroom there are
probably an approximately equal number of left-brained
dominant and right-brained dominant students. "We present
all parts of a given lesson and expect students to be able
to piece the puzzle together and 'get the picture'" (p.
212). Thus, it is not surprising that right-brained
dominant learners fail to develop in such an educational
environment. In order to improve the quality of
education, consideration should be given to meet the
learning predispositions and needs of all students. Hart
(1978) stated that on the basis of neurologic studies, all
students cannot be expected to learn in traditionally
structured classrooms. In another report, Hart (1986)
stated that "to expect that the brain is to be comfortable
with typical schooling is absurd. We need brain-
compatible schools that accept the brain as it is" (p.
199). It has been well established that people do not
process information in the same way (Kolb, 1984). A goal
of education based on learning/cognitive styles should
therefore be to make the teaching strategies of the
instructor compatible with the learning/cognitive styles
of the students (Zenhausern, 1986).
Blakeslee (1986) pointed out a potential complication
of employing an educational approach that attempts to
match too closely the teaching strategies and educational
environment to the learning/cognitive styles of the
student. He contended the possibility of "one-sided
development" of the brain using the learning/cognitive
styles concept. He stated that
because a student tends to favor a left- or
right-brain approach does not mean that we
develop only the favored approach. To do so
is to reinforce habit. The ideal would be
to develop both halves of the brain and their
ability to work together. (p. 191)
Further, he posited that educators should not classify
students as left-brain or right-brain types and then teach
accordingly, for such an approach would allow the weaker
mode of brain processing to atrophy--a grievous mistake.
Sinatra (1986) contended that "the development of the
diverse potential of both brain hemispheres is preferable
to the development of one hemisphere" (p. 205). If one
accepts this position, then it would seem that if one side
of the brain was overlooked, the student may never realize
his or her full potential.
Learning Preferences
Learning preference models have been an emerging area
in the study of student learning. Investigators in this
area have been interested in discovering the preferences
that students have for study methods, instructional media,
course format, general environmental and sociological
learning preferences, and other dimensions of classroom-
related learning. The instruments that have been
developed to identify these learning preferences are
grounded directly in the classroom experiences of the
students. Many authors have described student learning
preference models by inappropriately referring to them as
"learning style" models. Such a designation is misleading
because these authors do not base their theoretical
premises on discernable cognitive patterns of information
acquisition and processing, but rather on preferred
classroom-related learning activities. Thus, the scales
on learning preference inventories refer to specific
aspects of the classroom and do not have the general
nature of the items found in Kolb's, Witkin's, or Hill's
models for example.
A learning preference theory developed by Rezler and
Rezmovick (1981) gives a specific classroom frame of
reference to several learning preference dimensions.
Their approach is based on six dimensions or three bipolar
pairs of dimensions to register preferences for the
following kinds of learning: (a) individual, (b)
interpersonal, (c) student-structured, (d) teacher-
structured, (e) abstract, and (f) concrete (see Figure
1-2).
Rezler and Rezmovick's learning preference theory is
a potentially useful approach whereby health professions
educators could identify the learning preferences of their
students. Assessment of learning preferences would help
to match students with learning conditions that they find
rewarding. Health professions educators could also use
information about student learning preferences to assign
the same task under different conditions to different
students. For example, the same problem solving task may
be addressed by some students individually and by others
in small groups. By the same token, some students may be
given complete freedom in selecting a task while others
may prefer highly structured assignments given by the
teacher.
The Learning Preference Inventory (LPI) was
constructed specifically to measure the affinity for
different modes of learning of allied health students
(Rezler, 1983; Rezler & French, 1975) (see Appendix B).
Rezler and French (1975) used the LPI to assess the
learning preferences of students in six allied health
professions (i.e., medical art, medical dietetics, medical
laboratory sciences, medical record administration,
occupational therapy, and physical therapy). The primary
conclusion was that it is more important to identify
individual differences within a health profession than to
identify differences among the professions, if teachers
wish to adjust their teaching styles to student
preferences in learning. The majority of all six groups
preferred to devote their attention to concrete tasks
assigned by the teacher. In a related study using a
learning preference inventory similar to that described by
Rezler, Ostmoe (1984) evaluated the learning preferences
of students in various allied health programs. It was
found that most students preferred learning strategies
which were traditional in nature, teacher-directed, and
highly organized with learning experiences of a concrete
rather than an abstract nature. Ferrell (1978)
investigated the learning preferences of adult learners
returning to school to earn an associate degree in
nursing. The findings were that the students preferred
traditional strategies of drill, recitation, and lecture.
Rogers and Hill (1980) administered Rezler's LPI to
students enrolled in bachelor's and master's degree
programs in occupational therapy and found that both
groups of students preferred learning experiences that
were teacher-structured, concrete, and interpersonal. In
another study involving baccalaureate students in
occupational therapy (Cahill & Madigan, 1984), the
influence of curriculum format on the stability of
learning preferences was examined. It was found that
learning preferences (as identified by the LPI) remained
stable over a prolonged period of time, even though the
students were exposed to different modes of instruction.
Barris, Kielhofner, and Bauer (1985) administered the LPI
to undergraduate and graduate occupational therapy
students and to undergraduate physical therapy students in
order to assess the relationships between learning
preferences, values, and student satisfaction. Although
all three groups conformed to a profile of preferring
teacher-structured, concrete, interpersonal learning, the
graduate occupational therapy students appeared to give
greater emphasis to universal social values and to have a
stronger preference for abstract learning than both groups
of undergraduates. The results revealed that learning
preferences of individual students should be considered
when designing learning experiences in order to match
teaching methods with learning preferences which may
result in greater learning and retention.
Dunn and Dunn (1975) advocated matching various
student learning preferences with appropriate teaching
approaches. When students learned in ways that were
natural to them, the outcomes usually were increased
academic achievement, improved self-esteem, a liking for
learning, improved basic skills, stimulated creativity,
and gradually increased learner-independence. Conversely,
when students were forced to adjust their learning
preferences to whatever teaching approaches were used,
learning was made more difficult. This caused frustration
and a decrease in students' confidence in themselves.
Dunn and Dunn devised a learning preferences model based
on student preferences for environmental stimuli (sound,
light, temperature, and classroom design); emotional
stimuli (motivation, persistence, responsibility, and
structure); sociological stimuli (prefers to work alone,
with another peer, with a small group of peers, with the
teacher or with another adult); and physical stimuli
(perceptual preferences, food intake, time of day, and
mobility).
Canfield's (1980) model of learning preferences deals
with learners' preferences in formal instructional
situations for various aspects of the conditions under
which learning takes place and some sensory system modes
of learning. Four conditions of learning are seen as
influencing individual responsiveness to the teaching-
learning situation: (a) affiliation, a desire for
friendly relations with peers and instructors; (b)
structure, a desire for orderly and well-defined course
structure and detailed information relative to
requirements; (c) achievement, a desire for freedom to set
one's own goals and work independently in a study
situation; and (d) eminence, a desire for opportunities to
compare one's performance with others, have order strictly
maintained, and learn from a knowledgeable instructor.
The four sensory modes of learning or learning preferences
are defined as (a) listening, a desire to learn through
hearing content presented; (b) reading, a desire to learn
through examining print media; (c) iconics, a desire to
learn through viewing content presented in media such as
slides and films; and (d) direct experience, a desire to
learn through handling content-related material or active
participation in exercises.
Llorens and Adams (1976) used Canfield's model on
undergraduate and graduate occupational therapy students
and showed that students preferred teaching conditions
that permitted more personal relationships with
instructors. In addition to having opportunities to set
their own objectives and to work alone and independently,
the students showed a high preference for working with
people and for direct experience in their learning modes.
Using Canfield's model, Ommen, Brainard, and Canfield
(1979) compared the learning preferences of older (greater
than or equal to 28 years of age) and younger (less than
or equal to 23 years of age) college students. Older
students preferred traditional instructional formats
(listening, reading, organized and detailed materials, and
less independence), while younger students preferred
iconics and direct experience as learning modes.
Grasha and Riechmann (1974) developed a social-
interaction approach to learning preferences based on how
students interact with their instructor, other students,
and the methods of instruction used. Six types of
students were identified: (a) competitive, exhibited by
students who learn material in order to perform better in
the class, such individuals feel they must compete with
other students in class for rewards, they regard the
classroom as strictly a win-lose situation in which they
must win; (b) collaborative, typified by students who feel
62
they can learn the most by sharing ideas and talents with
others, they cooperate with teachers and peers, such
students see the classroom as a place for learning and for
interacting with others; (c) avoidant, demonstrated by
students who are not interested in learning course content
in the traditional classroom, they do not participate with
students and teachers in the classroom, (d) participant,
manifested by students who want to learn course content
and like to go to class, they assume responsibility for
learning from classroom activities, such students
participate with others when told to do so; (e) dependent,
manifested by students who show little intellectual
curiosity and who learn only what is required, they see
the teacher and peers as sources of structure and support,
these students look for authority figures for guidelines,
and want to be told what to do; and (f) independent,
exhibited by students who like to think for themselves,
they prefer to work on their own but will listen to the
ideas of others in the classroom, such people learn the
content they feel is important and are confident in their
learning abilities. Most individuals are seen as having a
learning preference profile composed of all roles with
some used more often than others.
Subsequently, Riechmann and Grasha (1974) determined
classroom activity learning preferences for teaching
methods for each student type. Competitive students were
63
comfortable with a variety of teaching methods, so long as
the focus is instructor-centered rather than student-
centered. These types of students enjoyed serving as
group leaders in classroom projects and discussions.
Collaborative students preferred lectures with class
discussion in small groups. Avoidant students were
generally negative about any classroom activities. Such
students preferred self-evaluation and did not like
enthusiastic instructors. Participant students preferred
lectures with discussions and enjoyed instructors who
could analyze and synthesize material well. Dependent
students preferred the instructor to outline all
assignments and always use instructor-centered classroom
methods. Independent students preferred self-paced
instruction, assignments that gave them a chance to think
for themselves, and a student-centered rather than an
instructor-centered classroom setting.
Eison and Moore (1980), using Grasha and Riechmann's
model, asked the question whether the learning preferences
of traditional college age students (18 to 22 years of
age) differed from those of adult students. It was found
that younger students were more likely to experience
greater tension and anxiety than older students. Adult
students were more likely to be oriented toward the
pursuit of knowledge than for course grades. Further,
traditional age students were more of the avoidant
(uninterested or overwhelmed by what goes on around them)
and competitive types (view the classroom as a win-lose
situation, in which they must always win). These students
generally had lower levels of interest in the course and
decreased interest in getting the most out of class. In
contrast, adult students scored higher on the participant
scale and indicated that they wanted to participate as
much as possible in classroom activities. In terms of
learning activities, traditional age students preferred
short and frequently administered quizzes drawn from
clearly specified study questions, graded assignments, and
extra credit activities. Adult students were less
concerned with the instructor's testing policy, enjoyed
less structured learning opportunities, and worried less
about grades.
Milton, Polio, and Eison (1986) identified learning
preferences in terms of students' attitudes toward
learning and grading and devised a typology consisting of
four student types: (a) high learning orientation/high
grade orientation, such students are highly motivated both
to learn and to achieve high grades, e.g., pre-medicine
and pre-law students; (b) high learning orientation/low
grade orientation, these students are interested in
educational enrichment and personal growth; (c) low
learning orientation/high grade orientation, the primary
interest of these students is to achieve a good grade; and
(d) low learning orientation/low grade orientation, such
students attend college to have a good time or to avoid
getting a job.
Friedman and Stritter (1981) developed a learning
preference inventory focusing entirely on preferences for
various types of instructional processes. The
Instructional Preference Questionnaire is a 57-item
instrument on which students are asked to agree or
disagree with certain items or to indicate how beneficial
they would be to their learning. Five primary dimensions
are measured by the instrument: (a) involvement in
determining course content, (b) preferred instructional
media, (c) formal course structure, (d) discovery
learning, and (e) reality testing.
Gregorc (1979) developed a four-scale dimensional
model to determine preferences for learning approaches:
concrete sequential, concrete random, abstract sequential,
and abstract random. The concrete sequential learning
preference is characterized by a finely turned ability to
derive information through direct, hands-on experience.
These students exhibit a high level of sensory
sensitivity, they prefer touchable concrete materials in
the classroom and specific step-by-step directions. Such
individuals prefer order and logical sequence in the
presentation of material. These students prefer
workbooks, demonstration teaching, programmed instruction,
and well-organized field trips. The concrete random
learning preference is characterized by an experimental
trial-and-error attitude in which the individual
demonstrates an uncanny ability to make intuitive leaps in
exploring unstructured problem-solving experiences. These
students do not prefer step-by-step learning methods that
deny them opportunities to find their own way. Such
individuals work well independently or in small groups and
prefer games, simulations, independent study projects, and
problem-solving activities. The abstract sequential
learning preference exhibits excellent decoding abilities
in the areas of written, verbal, and image symbols. These
types of learners tend to think abstractly and use
conceptual "pictures" as they learn. They prefer to learn
through reading and listening and desire orderly, rational
presentations. The abstract random learning preference is
distinguishable by the attention given human behavior and
an extraordinary ability to sense and interpret
"vibrations." This type of student is "tuned" to nuances
of mood and atmosphere. This type of learner associates
the medium with the message in that the speaker's manner
of presentation and personality is closely tied to the
message being delivered. Thus, these students globally
evaluate the learning experience. Such students prefer to
learn in an unstructured manner and like group
discussions, question-and-answer sessions, movies, and
television (Gregorc & Ward, 1977).
Fuhrmann and Jacobs (1980) developed a model that
discriminates three classroom learning preference styles:
(a) dependent style refers to the learner's needs for
structure, direction, external reinforcement, and
encouragement; (b) collaborative style refers to the
learner's needs for interaction, practice, probing self
and others, observation, participation, peer challenge,
peer esteem, and experimentation; and (c) independent
style refers to the learner's needs for internal
awareness, experimentation, time, and nonjudgmental
support. In a situation where students have little or no
prior experience, a dependent learning preference style is
appropriate. In a course or curriculum that emphasizes
group problem solving, a collaborative style is suitable.
Students with an independent style may opt for courses
where options are available to choose independent means of
accomplishing learning objectives. Fuhrmann and Jacobs
(1980) stated that individuals learn in all three styles
but may prefer a particular style in a given situation,
based on personal preferences and the unique
characteristics of the subject to be learned or the
activity in which to be engaged. In this model, no one
style is better than another, although one may be more
appropriate for a given individual or in a given
situation.
In addition to describing the learner's needs,
Fuhrmann and Jacobs described the instructor's role and
the appropriate teaching behavior for each learning
preference style (Fuhrmann & Grasha, 1983). For the
dependent style learner, the instructor's role is one of
expert and the teaching behavior is lecturing,
demonstrating, and assigning. For the collaborative style
learner the instructor's role is co-learner, environment-
setter, and one of participation and the teaching behavior
is interacting, questioning, providing resources,
modeling, providing feedback, coordinating, evaluating,
and managing. Regarding the independent style learner,
the instructor's role includes internal awareness and
nonjudgmental support and the teaching behavior includes
allowing, providing requested feedback, providing
resources, consulting, negotiating, and evaluating.
Congruency--Student and Faculty Learning/Cognitive
Styles and Preferences
Greater learning appears to result when teaching
styles of the faculty are congruent with the
learning/cognitive styles and preferences of the students
(Eisenkraft, Reed, Eisenkraft, & Kaplin, 1985). Cafferty
(1981), using Hill's cognitive style mapping approach,
reported a higher GPA for students who were more closely
matched to the learning/cognitive styles of their
instructors. Conversely, the greater the dissonance
between the two, the lower the GPA. In another study
using Hill's cognitive style matching approach, Terrell
(1976) found that students whose learning/cognitive style
matched the instructional mode tended to achieve higher
grades and experienced greater reduction in anxiety than
nonmatched students. Fiske (1981) described how "teachers
adjusted schooling to fit the students "individuality" by
responding to students' learning/cognitive styles. Using
this approach, significant gains in reading scores and
other traditional measures of academic performance
resulted. Others demonstrated that when faculty and
students had similar learning/cognitive styles and
preferences, the faculty were more attuned to the needs of
the students and greater learning resulted (Carrier,
Newell, & Lange, 1982). Deep (1988) indicated that when
there is a mismatch between the teaching style used and
the learning/cognitive styles and learning preferences of
students, the result is a loss of motivation (less
study/homework time) and a failure to achieve (GPA).
Baker, Wallace, Cooke, Alpert, and Ackerly (1986), using
Kolb's model, reported that resident physicians
(anesthesiologists) enrolled in a formal postdoctoral
training program scored higher in clinical skills and
knowledge when matched with faculty members of the same
learning/cognitive style type. Hunt (1975) and Witkin
(1977), using Witkin's model, indicated that greater
learning took place when the learner was matched with the
appropriate process, and that time needed for learning was
reduced. Witkin (1976) noted that when students and
teachers were matched and mismatched in terms of field-
dependence and field-independence, the matched students
described each other positively, and the mismatched
subjects described each other negatively. When the
teachers described their students' abilities, they placed
greater value on the attributes of students who were like
themselves. Similarly, the students felt more positively
about the teachers who were like themselves in terms of
learning/cognitive styles. Douglass (1979) identified
students who favored inductive or field-independent (left
hemisphere) and deductive or field-dependent (right
hemisphere) processing styles and then matched and
mismatched instruction accordingly. When inductive
students used inductive materials and when deductive
students used deductive materials, achievement increased;
when the students and the resources were mismatched, lower
academic achievement was realized. Pask (1976) showed
that by assessing students' learning/cognitive styles with
a learning/cognitive styles inventory and providing
complementary materials, that individuals learned more
quickly and thoroughly and retained information much
longer. Conversely, when mismatched, learning time was
increased and thoroughness and retention decreased.
Sadler, Plovnick, and Snope (1978), using Kolb's model,
noted that the faculty at one institution had an abstract,
more reflective learning/cognitive style while the
students preferred learning situations which offered
concrete examples and active participation in the learning
process. Role-playing, simulation, participation in the
clinical area, and learning exercises which place students
in positions of active learning appeared to be preferable
to the more passive and abstract features of lectures and
literature reviews.
The research on learning preference models reveals
that matching instructional methods and the learning
environment to students' learning preferences leads to
improved learning. Rezler and French (1975) posited that
students may be more motivated to study (hours
study/homework time) and achieve at higher levels (GPA) if
given as much opportunity to learn according to their own
learning preferences. Evidence has shown that students
who appear to be admirably suited to function in a health
related field can be deprived of personal and professional
fulfillment because the manner in which they are taught
and tested is incompatible with their most effective
manner of functioning (Rezler & French, 1975). Adams
(1983), using Canfield's model, showed that, for a large
group of community college students, those well matched
with their instructors on the basis of learning
preferences received higher GPAs and fewer lower grades.
Ware and Williams (1975) examined the learning preferences
of podiatry students and found that higher achievement
(GPA) and more satisfaction resulted when instructional
methods matched their learning preferences. Pizzo (1981),
using Dunn's model, indicated that when students were
matched with their learning preferences (their need for
either sound or quiet preferences), significantly higher
reading and attitude scores resulted, and students who
were mismatched achieved significantly below the matched
students. Domino (1971), using Dunn's model, demonstrated
that when college students were taught in ways they
preferred, they scored higher on tests, fact knowledge,
attitude, and efficiency than those taught in a manner
dissonant from their preferred ways of learning. In a
related study, Dunn (1983) noted that academic achievement
increased when students took their most difficult courses
and study during their preferred time of day.
Statistically significant differences in achievement
resulted whenever students were taught in ways that
complimented their learning preference (Dunn, 1983).
Murrain (1983), using Dunn's model, reported that students
performed better in a classroom environment that matched
their thermal preferences. Dunn, Krimsky, Murray, and
73
Quinn (1985), using Dunn's model, reported on the validity
of students' illumination preferences in the classroom and
academic achievement. In this experimental investigation,
the effects of matching and mismatching students with
their learning preferences for lighting were examined.
Results revealed that predispositions for illumination
significantly affected the reading achievement of students
for whom light was an important learning preference
variable.
Using Grasha and Riechmann's model, Andrews (1981)
studied the interrelationship of teaching methods,
learning preferences, and learning outcomes. College
level students in a course were randomly assigned two
types of instruction: instructor-centered (the instructor
provided mini-lectures, answered questions, worked
problems, and questioned students, i.e., the instructor
provided a central role in guiding the class), and peer-
centered (the instructor served more as a facilitator and
a resource, emphasizing student responsibility for
presentations and student-to-student teaching). The
results were that collaboratively-oriented students
learned better using the peer-centered approach, while
competitive individuals learned better using the
instructor-centered type of instruction. Thus, students
learned best and benefited more from classroom methods
that closely matched their learning preferences.
Summary
Exhortations to meet the needs of individual learners
are commonplace in the educational literature. Evidence
reveals that when the learning/cognitive styles and
preferences of students and their instructors were
matched, greater learning tended to occur and when they
were mismatched, students appeared to be at a
disadvantage. These studies demonstrated also that for
the teaching-learning process to be successful, faculty
should be sensitive to the needs of students with
different levels of experience and different
learning/cognitive styles and preferences.
Students have different learning predispositions,
needs, experiences, and abilities. If the educational
process is to be successful in helping each student to
achieve the maximum possible growth, educators must first
recognize individual differences. Specific knowledge of
each student is necessary to both individualize and
diversify instruction. This knowledge should be available
and understandable to both the educator and the student,
and should increase the student's awareness of himself or
herself as a learner as well as the teacher's awareness of
individual similarities and differences among students.
The interactions between the teacher and the student are
of critical importance, i.e., different kinds of teacher
75
behavior and instructional methods influence the learning
and academic achievement of students.
CHAPTER 3
METHODOLOGY
The purpose of the study was to determine whether a
relationship existed between achievement and the degree of
matching between students and their instructors based on
learning/cognitive style theory or learning preference
theory, and whether one of these learning theories was
better in predicting student achievement. A relationship
among these factors may have implications regarding the
low pass rate on the national board examination for
graduates of respiratory therapy programs.
This investigation was descriptive, correlational,
and ex post facto in design. Descriptive research studies
are designed to obtain information concerning the current
status of phenomena. Such studies are directed toward
determining the nature of a situation as it exists at the
time of the study. Correlational studies provide
information on the presence and strength of a relation
between variables. The ex post facto study starts with
groups that are different and tries to determine the
antecedents of these differences. Possible cause-and-
effect relationships are investigated by observing some
77
existing consequences and searching back through data for
plausible causal factors (Ary, Jacobs, & Razavieh, 1985).
Instrumentation
Kolb's learning/cognitive style inventory (LSI)
(Kolb, 1985) was used to identify the learning/cognitive
styles of all subjects in the study. This
learning/cognitive style inventory was chosen for this
study because it has been used extensively for assessing
the learning/cognitive styles of medical and allied health
personnel (see Appendix A). Kolb's research has revealed
four distinct learning/cognitive style types which are
labeled as divergers, assimilators, convergers, and
accommodators (see Figure 1-1). The LSI is a self-report
instrument that can be administered in approximately 5 to
10 minutes. The instrument contains 12 items, each
consisting of four words. The respondent is asked to rank
order the words according to how well each characterizes
his or her learning/cognitive style. One word in each
item represents one of the four learning/cognitive style
dimensions, i.e., concrete experience (CE), reflective
observation (RO), abstract conceptualization (AC), and
active experimentation (AE). The LSI was designed to
measure the individual's relative emphasis on the four
learning/cognitive style dimensions (score range per
dimension is 12 to 48) and on two combinations scores that
indicate the extent to which the individual emphasizes
78
abstractness over concreteness (AC CE) and the extent to
which the individual emphasizes action over reflection
(AE RO). The combination scores are then used to
determine one's learning/cognitive style type.
Reliability and validity data on the LSI have been
determined. Reliability of the four basic scales and the
two combination scores all show very good internal
reliability as measured by Cronbach's alpha. Reliability
coefficients for the scale scores range from .73 to .83,
and for the two combination scores, .81 (AE RO) and .88
(AC CE) (Kolb, 1985). Acceptable levels of construct
and concurrent validities of the LSI have been assessed by
studying the relationship between learning/cognitive style
and several variables including age, educational level,
scores on creativity tests, general aptitude tests,
personality tests, preferences for types of learning
situations, academic specialization, undergraduate
interests, and career choices (Leonard & Harris, 1979).
The Learning Preference Inventory (LPI) devised by
Rezler and French (1975) was selected as the instrument
for measuring learning preferences because it was
constructed with the specific learning requirements of the
allied health fields in mind (Rezler, 1983). The LPI is a
self-report instrument that can be administered in about
10 minutes and requires the respondent to rank order
specific items (see Appendix B). It was designed to
assess an individual's choice for methods of instruction
on six dimensions (or three bipolar pairs of dimensions)
of learning preferences: individual and interpersonal,
student-structured and teacher-structured, and abstract
and concrete (score range per dimension is 15 to 90).
Reliability and validity data for the LPI have been
performed. Internal consistency reliabilities for the six
scales range from .72 to .88. The six scales of the LPI
have been supported by factor analysis, thus establishing
content validity. Construct validity has been
demonstrated in studies with allied health and pharmacy
students (Rezler & Rezmovick, 1981).
Finally, an achievement test was devised to assess
the level of student achievement for a major course in
respiratory therapy theory. The theory and operation of
mechanical life-support ventilators was the subject matter
of the test. (This subject area constitutes a major
section of the respiratory therapy board examination.)
The test consisted of 40 multiple-choice items with five
possible choices per item (see Appendix C).
Content validity for the achievement was assessed by
a group of experts, i.e., two physicians (medical
directors of the respiratory therapy departments at two
university hospitals) and three faculty members of a
community college respiratory therapy program. This group
agreed that the achievement test represented the content
of interest and was content valid for a course on
mechanical life-support ventilators. Test reliability was
determined using the Kuder-Richardson 20 method on a group
of respiratory therapists who were recent graduates of
accredited respiratory therapy programs; the reliability
coefficient was .74.
Pilot Study
A pilot study at a community college respiratory
therapy program (Santa Fe Community College, Gainesville,
Florida) was conducted, in 1988, to assess the
relationship between student achievement and the degree of
matching of students and their instructor based on Kolb's
learning/cognitive style theory and Rezler's learning
preference theory. Whether learning/cognitive style
and/or learning preference variables could be used to
predict academic achievement through multiple regression
techniques was also determined.
The course instructor and participating students (N =
17) of a major course in respiratory therapy (theory and
operation of mechanical life-support ventilators)
completed the LSI and LPI instruments; the students also
completed the aforementioned achievement test at the end
of the course. For all subjects, the learning/cognitive
style and learning preference dimension scores, as well as
the learning/cognitive style types, were determined. The
discrepancy scores for each student were calculated for
L
all learning/cognitive style and learning preference
dimension scales. The achievement tests were scored and
the student's overall college GPA and number of hours of
homework per week devoted to the course on mechanical
life-support ventilators were obtained. Data were
analyzed using multiple regression and an unpaired t-test.
Alpha level was set at .05 for all tests of significance.
Two multiple regression models were evaluated: one
model used the four learning/cognitive style dimension
discrepancy scores; the other model used the six learning
preference dimension discrepancy scores as variables.
Both models used the variables college GPA and the number
of hours homework per week for the course. The
learning/cognitive styles regression model was significant
(E < .01), while the learning preferences regression model
was not significant. Inspection of the beta coefficients
that were significant for the learning/cognitive style and
learning preference dimension discrepancy scores in both
regression models indicated relationships between student
achievement and the degree of matching between the
students and their instructor. (The lower the dimension
discrepancy score between the student and the instructor,
the better the degree of matching.) Thus, the more
negative the beta coefficient between student achievement
(Y axis) and a dimension discrepancy score (X axis), the
better the relationship between student achievement and
the degree of matching between the students and the
instructor.) The beta coefficients for the "active
experimentation" and "abstract conceptualization"
dimensions in the learning/cognitive styles regression
model were -1 (p < .01) and -.35 respectively.
The relationship between the predicted and actual
levels of student achievement was calculated using the
learning/cognitive styles regression model. When the
predicted and actual values for student achievement were
regressed the relationship was positive and highly
significant (r = .87, E < .0001).
Regarding the learning/cognitive style types, the
course instructor as well as six students were the
"diverger" type (an individual who acquires new
information in a concrete manner and processes it by
observation and reflection). The remainder of the
students were of the accommodatorr" (N = 2), "assimilator"
(N = 3), and "converger" (N = 6) learning/cognitive style
types. Those students who were the same, or of a
different learning/cognitive type from the instructor were
labeled "matched" and "mismatched" respectively. The
achievement scores for students in the "matched" and
"mismatched" groups were compared (unpaired t-test).
Students who were the same learning/cognitive style type
as their instructor scored significantly higher (87.3 +
83
4.3) than those who were of a different learning/cognitive
style type from their instructor (80.3 7.3) (p < .048).
The results can be viewed as suggesting that students
having similar learning/cognitive styles as their
instructor learned more and performed better on tests than
students having dissimilar learning/cognitive styles as
their instructors. Moreover, the greater the degree of
matching between students and their instructors on the
basis of Kolb's learning/cognitive style theory, the
greater student achievement. Specifically, the greater
the degree of matching for the active experimentation
learning/cognitive style dimension, the greater was the
level of achievement. From these data it may be inferred
that students may not achieve or develop to their full
potential when taught in ways that are different from the
manner in which they innately perceive and process
information (i.e., learning/cognitive style). Based on
the results of the pilot study, it was deemed appropriate
that a study utilizing a larger sample size be conducted.
Subjects and Study Design
Subjects for this study included students and their
instructors from associate and baccalaureate degree
respiratory therapy programs in the United States
(accessible population). A sample size of 115 students
was required to achieve a 95% confidence interval. This
sample size was calculated using the method described by
Marks (1982).
Over 250 respiratory therapy programs based in
community colleges and universities operated in the United
States at the time of this study. In order to obtain the
calculated sample size, 11 institutions (cluster sampling)
from various regions of the country participated in the
study. These 11 institutions were the following:
1. Macomb Community College, Michigan
2. Washtenaw Community College, Michigan
3. Mayo Clinic, Minnesota
4. Jefferson Community College, Kentucky
5. Santa Fe Community College, Florida
6. Valencia Community College, Florida
7. Miami-Dade Community College, Florida
8. Tallahassee Community College, Florida
9. Louisiana State University Medical Center,
(Shreveport and New Orleans campuses), Louisiana
10. Loma Linda University, California
11. Pueblo Community College, Colorado
In each program, students in their final year of study and
their instructor from a major course in respiratory
therapy theory answered the LSI and LPI instruments.
(Final year students were selected since at that stage of
the curriculum they were enrolled in major courses in
respiratory therapy theory.) A major course in
respiratory therapy (i.e., the theory and operation of
mechanical life-support ventilators) that was common to all
programs was selected and an achievement test specific to
that subject matter was prepared. At the end of the
course, students in all programs answered the multiple-
choice achievement test.
Collection of Data
All data were collected in the Fall semester of 1988
and Spring semester of 1989. Near the end of the
semesters, the LSI, LPI, and achievement test forms were
mailed to the directors of all programs. (Prior approval
to use the LSI and LPI in this study was obtained from the
authors of the instruments.) A letter explaining the
purpose of the study and guaranteeing anonymity to all
subjects participating in the study was included (see
Appendix D). The director of each program distributed the
LSI and LPI inventories to all final year students and
their instructor for the major course in respiratory
therapy theory and then collected these materials when
completed. The program director also distributed the
aforementioned achievement test to the students and
collected the tests when completed. The directors then
returned all completed materials to the investigator for
collation and analysis. A data base was developed using
the results from the learning/cognitive styles and
learning preferences inventories and the achievement test.
CHAPTER 4
RESULTS
Population
Directors of 13 respiratory therapy programs in the
United States were contacted and 11 respondents agreed to
participate for an overall response rate of 84%. The
study population consisted of 143 students and their
instructors (one instructor per program). There were 84
males and 59 females in the study. The majority of the
students were white (78.3%), while black, hispanic, and
oriental students comprised the remainder of the study
population (see Table 4-1). Student ages ranged from 18
to 42 years, with a mean age of 24.5. Traditional college
age students (18-22 years) represented 30.8% of the
population; 35% were between 23 and 27 years, while the
remaining 34.2% were 28 years of age or older (see Table
4-1).
Analysis of Data
All data were analyzed using the data analysis
software SAS (SAS Institute, 1985). Multiple regression
was performed to analyze the data unless indicated
otherwise. Step-wise regression was performed to
determine the best prediction model.
Table 4-1
Student Demographic Data
Category Count Percent
Gender
Male
Female
Race
White
Black
Hispanic
Oriental
58.7
41.3
78.3
11.2
8.4
2.1
Age
18-22 years 44 30.8
23-27 years 50 35.0
28-32 years 32 22.4
33-37 years 11 7.6
38-42 years 6 4.2
(Mean age + 1 standard deviation was 24.5 4.3)
From the data base, all hypotheses were tested.
Hypothesis 1. There is no significant relationship
between the actual level of student achievement and the
predicted level of achievement as a function of the
weighted linear combination of GPA, number of hours of
homework per week, program, and the concrete experience,
reflective observation, abstract conceptualization, and
active experimentation learning/cognitive style dimension
scores.
This was a test to determine if there was a
relationship between the actual level of student
achievement (Y) and the predicted level of student
achievement based on the learning/cognitive styles
(Y'L/CS) multiple regression model. (Using SAS and the
General Linear Model procedure, this hypothesis was tested
using the F-test for the multiple regression model. The
F-test for the hypothesis was that R2 = 0.) The following
variables constituted the learning/cognitive styles
multiple regression model:
Y'L/CS = B1 (GPA) + B2 (Number of hours homework per week
for the course) + B3 (Program) + B4 (Concrete
experience discrepancy score) + B5 (Reflective
observation discrepancy score) + B6 (Abstract
conceptualization discrepancy score) + B7 (Active
experimentation discrepancy score)
In addition to the learning/cognitive style
variables, other variables examined in this study were
college GPA and the number of hours of homework per week.
College GPA is frequently selected as an index and
predictor of student achievement. The number of hours of
homework per week was chosen as an assessment of
motivation in a course. It was contended that the more
enjoyable the student found the teaching-learning
environment, the greater the motivation to study the
subject matter in the course. There may have been other
intervening variables affecting student achievement in
addition to the degree of matching between students and
their instructors based on learning/cognitive styles or
learning preferences, e.g., IQ, level of cognitive
development on entering the program, motivational factors,
personal study habits, previous experience, and the type
of respiratory therapy degree program (e.g., more emphasis
on theory than practice). These variables were not
evaluated because the purpose of this study was to focus
on select variables in the teaching-learning process,
i.e., student and instructor learning/cognitive styles and
learning preferences. Regarding the variable "program,"
because 11 respiratory therapy programs were involved in
the study, the programs were coded as a dummy variable in
the model.
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