Analysis of learning style preferences in adult learners

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Analysis of learning style preferences in adult learners
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Thesis (Ph. D.)--University of Florida, 1992.
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Includes bibliographical references (leaves 93-105).
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by Oel G. Wingo.
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Typescript.
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Vita.

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ANALYSIS OF LEARNING STYLE PREFERENCES
IN ADULT LEARNERS














By

OEL G. WINGO















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

1992


UNIVERSITY OF FLOIPA LIBRARIES














ACKNOWLEDGMENTS

Special thanks go to my two best friends and most

ardent supporters, John Hotaling and my daughter, Eva Burk.

I am also most grateful to my boss, Dr. William Campion, for

his assistance and interest in my success. Finally, I wish

to express my appreciation to the members of my doctoral

committee, Dr. David Honeyman, chairman, Dr. James

Wattenbarger, Dr. David Miller, and Dr. Craig Wood for their

efforts on my behalf.















TABLE OF CONTENTS


page

ACKNOWLEDGMENTS........................................ ii

ABSTRACT................................................. v

CHAPTERS

1 INTRODUCTION.......................................... 1

Statement of the Problem ............................. 8
Justification of the Research........................ 9
Overview of the Methodology .......................... 9
Delimitations..................................... .... 12
Organization of the Dissertation..................... 13

2 REVIEW OF THE LITERATURE.............................. 14

The Nature of Learning Styles........................ 14
Dimensions and Elements of Learning Styles............ 19
Cognitive Dimension................................ 20
Affective Dimension ................................ 29
Physiological Dimension........................... 31
Diagnosis and Practical Applications of
Learning Style Research........................... 31
Learning Style Diagnostic Instruments
and Their Limitations.......................... 32
Instructional Approaches Utilizing
Learning Styles................. ................ 35
The Relationship between Academic Achievement
and Different Instructional Approaches.......... 40
Learning Style Research Related
to Adult Learners .............................. 43
Summary of Chapter 2................................. 46

3 MATERIALS AND PROCEDURES.............................. 50

Description of the Sample............................ 50
Basic Research Design and Analysis................... 51
Instrumentation....................................... 54
Administration and Use of the
Learning Style Profile ............................ 56
Treatment ............................................. 58

iii








4 RESULTS AND DISCUSSION................................ 65

5 SUMMARY, CONCLUSIONS AND RECOMMENDATIONS ............. 73

Conclusions................. ......................... 75
Implications and Recommendations ..................... 80

APPENDICES

A Sample Individual Learning Style Profile.......... 83

B Group One Learning Style Profile.................. 84

C Group Two Learning Style Profile.................. 85

D Group Three Learning Style Profile................ 86

E Group Four Learning Style Profile.................. 87

F Group Five Learning Style Profile................. 88

G Interpretation of the Learning Style Profile...... 89

H Sample Instructional Strategies
from the Learning Style Profile Handbooks....... 91


REFERENCES.............................................. 93

BIOGRAPHICAL SKETCH...................................... 106














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

ANALYSIS OF LEARNING STYLE PREFERENCES
IN ADULT LEARNERS

By

Oel G. Wingo

May, 1992

Chairman: David Honeyman
Major Department: Educational Leadership

The purpose of this study was to determine the

effectiveness of utilizing learning style profiles with the

adult learner. Furthermore, the study was an attempt to

determine whether there were differences in learning

outcomes when individualized instructional methods focused

on a single learning style dimension or a combination of all

three dimensions of learning style: cognitive, affective,

and physiological.

A sample (N=125) was randomly selected from a

population of adult learners enrolled in developmental

English education classes in a small, southern community

college. The research design was a pretest-posttest control

group design. Students were randomly assigned to one of

five experimental groups. A one-way analysis of covariance

was conducted to analyze the relative effectiveness of the

different instructional methods.








All groups completed a learning style inventory, The

Learning Style Profile, and a statewide standardized pretest

and posttest of English achievement level, Multiple

Assessment Programs and Services. Over a 12 week period,

all groups received self-paced computerized, competency

based instruction. Although a different instructor was

assigned to each group, instructional methods and content

were standardized across groups. Instruction varied only as

to what dimension, if any, of learning style was emphasized.

One group used strategies emphasizing all three dimensions

of learning style. Three groups emphasized strategies using

only a single dimension of learning style. One group acted

as a control and no strategies related to learning style

were used.

The analysis revealed a significant pretest by

treatment interaction effect. Upon examination of posttest

scores, only students in the group that focused on all three

learning style dimensions demonstrated mean achievement

levels that were significantly higher than those of the

other four treatment groups. The posttest means of the

cognitive and affective groups were not significantly

different, but were significantly higher than the

physiological and control groups.

Thus for adult learners, instructional methods that

focused on all three dimensions of learning style were more

effective than instructional methods that focused on a

single dimension of learning style or instruction that

ignored learning style.














CHAPTER 1
INTRODUCTION


Educators have long recognized the importance of

adapting or individualizing instructional approaches to

address individual differences in ability level,

developmental level, and level of prior knowledge. However,

over the past two decades, educational researchers and

practitioners have confirmed that individuals not only

differ in ability level, but individuals also differ

systematically in their patterns of behavior or responses to

similar situations, reflecting an underlying style. These

differences have important consequences for the ways in

which individuals learn and perform. The concept that

individuals have preferred approaches to learning based on

the individual's perception of a situation has been referred

to as learning style or cognitive style (Keefe, 1987).

Early researchers, prior to the 1940s, examined

patterns or styles of learning and identified individually

preferred perceptual modalities that influenced learner

performance. After World War II, with the growth of

cognitive psychology, the emphasis in style research shifted

to cognitive modes or patterns of behavior, attitudes or

strategies that determined how a person perceived,










interacted with and responded to situations. The outcomes

of this research were summarized by Messick (1976), who

listed more than 20 elements or traits of cognitive style

that had been identified in the literature. During the

1970s, physiological and affective elements became the focus

of learning style researchers. These researchers were

predominantly concerned with the interaction of

environmental variables on learner performance. Dunn and

Dunn (1978, 1979) and Dunn, Dunn and Price (1981) isolated

motivational processes that were influenced by external

variables as well as personality variables that affected

learning style.

The proliferation of research evidence identified

numerous variables, including cognitive, affective and

physiological elements, that were related to individual

learning style. As many as 30 models or approaches to the

study of learning style had been developed by the end of the

1970s (Ehrhardt & Corvey, 1980). After an extensive review

of the literature, Keefe (1979) adopted the approach that

the existing, identified learning style elements could

appropriately be assigned to three dimensions, cognitive,

affective, and physiological. Keefe (1979) defined learning

style as "characteristic cognitive, affective, and

physiological behaviors that serve as relatively stable

indicators of how learners perceive, interact with and

respond to the learning environment" (p. 11). Keefe's










definition encompassed both the internal and external

processes or traits determining an individual learner's

preferred mode of learning.

The construct of learning style developed in the

research identified individual differences in perception of

environmental stimuli. As learning style formed the basis

for the individual's perception and resultant interpretation

of the environment, the assumption was that individual

schemata contributed to academic achievement. Numerous

models and diagnostic instruments were developed to study

the learning style construct and how learning style affected

learner performance. The concept that individuals had

different approaches to learning based on an individual's

perception of the learning environment had profound

implications for instructional designs. Keefe (1979)

asserted,

Learning style diagnosis opens the door to placing
individualized instruction on a more rational basis.
It gives the most powerful leverage yet available to
educators to analyze, motivate, and assist students in
school. As such, it is the foundation of a truly
modern approach to education. (p. 132)

Diagnostic instruments available until recently focused

on only one or two dimensions of learning style or even

single elements or traits of learning style. Gregorc

(1979b) noted, "Instruments by their very nature are

exclusive, that is, they focus on certain variables and

therefore sacrifice other possibilities" (p. 19). However,

research utilizing each of the available instruments










documented a positive impact on academic achievement when

instructional methods were adapted to the individual

learning style or profile determined by that particular

instrument. Positive results were evidenced in the research

when only the cognitive dimension concentrating on even a

single element was the focus for adaptation (Gregorc, 1979b;

Letteri, 1985; Witkin, Moore, Goodenough, & Cox, 1977;

Witkin, Moore, Oltman, Goodenough, & Friedman, 1977).

Additionally, positive results were obtained when the

educational environment was adapted to learning style

profiles defined with only elements from the physiological

dimension or the affective dimension (Dunn & Dunn, 1987;

Hill, 1976; Rotter, 1966). The results of additional

research identified learning style elements and related

these to academic achievement where instructional

interventions were initiated based on the assessed

individual differences. There were no reported attempts to

determine those dimensions or elements within a dimension

that were best for improving academic achievement.

Applications of learning style research and learning

style diagnosis to the instructional environment were

conducted using varying approaches and producing diverse

results. Some educators indicated that the instructional

environment should be altered "to capitalize on the

learner's strengths"; others noted that the learner should

be taught to adapt to "nonpreferred styles" (Lindelow, 1983,










p. 14). Evidence from the research suggested that

individual differences in style did not necessarily affect

ability unless the instructor or the learner were not

flexible enough to adapt. There was sufficient evidence to

support not only adapting instruction to individual learning

style, but teaching individuals to adapt learning style

preferences to improve learning performance in certain

courses (Lindelow, 1983). Schmeck (1988a) suggested that

educators can

facilitate the development of this higher level skill
by periodically exposing students to contextual demands
that do not precisely match their styles. This must,
of course, be done very cautiously in order to avoid
instilling in the student a feeling of incompetence.
However, if we roughly match our instructional
technique to the student's style while simultaneously
providing experience in strategies that are outside
that style, we may prompt the development of
flexibility. This may be a greater service than always
structuring the context to match the student's
preferred style. (p. 15)

Regardless of the preferred instructional approach, a

review of the literature revealed that instructional methods

based on an understanding of individual learning style

improved learner performance (Dunn & Dunn, 1987; Hill, 1971;

Keefe, 1987; Letteri, 1985). However, the research was

limited to only one or two of the three dimensions of

learning style identified by Keefe or to single elements or

traits within each dimension. There has been no attempt to

systematically determine the efficacy of focusing on one

dimension to the exclusion of another or whether the

inclusion of all three dimensions of learning style is










preeminent to increasing learning performance. Until

recently, there was no diagnostic tool that was inclusive of

all three dimensions.

The National Association of Secondary School Principals

(NASSP) developed the first comprehensive diagnostic tool

for measuring all the major elements identified in the

literature. The inclusion of specific elements of style

within the NASSP model was based on the verifiability of the

existence of a given element in the literature and research

on the impact of the identified element on learning. Yet,

many of the elements lacked a research base. As with

previous research where different diagnostic instruments

were used, no effort was made to determine the significance

of one element over another. The assumption was made that

inclusion of all the elements identified would provide a

more comprehensive picture of the individual learner.

Additionally, at that time, the instrument had been tested

exclusively with elementary and secondary students (Keefe &

Monk, 1990). No research was reported on utilization of the

instrument with adult learners.

As of 1991, learning style research with adult learners

had utilized instruments focusing solely on one or two

elements in the cognitive domain. The various research

findings revealed that there were adult patterns of learning

that differed from youthful patterns of learning (Dorsey &

Pierson, 1984; Smith & Haverkamp, 1977; Witkin, Moore,










Goodenough, & Cox, 1977). Various researchers postulated /

that learning styles were developmental in nature or

progressed with age (Kirby, 1988; Pask, 1988; Schmeck,

1988c; Wallach & Kogan, 1965). Furthermore, these

researchers concluded, the experiential base of the

individual in conjunction with heredity may play a part in

the development of learning style. Thus as an individual

ages certain elements or style preferences are reinforced,

adapted, or modified (Schmeck, 1988a, 1988b).

Although learning style preferences may become more

firmly entrenched over time if flexibility is not

encouraged, researchers have confirmed that adult learning

styles can be modified even within a very short time period

(Schmeck, 1988c). Fourier (1983) determined that adult

learners cognizant of their learning style profile are

capable of adapting to a learning situation to improve

performance. Adaptation or modification of adult learning

styles affecting achievement level can occur within a

relatively short time frame, between 6 and 12 weeks J

(Reynolds & Torrance, 1978). Korhonen and McCall (1986)

concluded that creating an environment responsive to

individual adult learning styles or instructing adult

learners in their preferred styles of learning tends to

improve learning and increase academic achievement.

Determining those elements or dimensions most amenable to

adaptation or modifiability and most productive of improved










learner performance would appear to be essential to

understanding the adult learner.

Statement of the Problem

The purpose of this study was to determine the

effectiveness of utilizing learning style profiles with the

adult learner. In order to determine the effectiveness,

learning outcomes of groups of adult learners taught through

four different instructional methods based on individual

learning style were compared to learning outcomes of adult

learners taught through instructional methods that

disregarded preferred learning style. Additionally, the

researcher attempted to determine whether there were

differences in learning outcomes when instructional methods

focused on a single learning style component or a

combination of all three components of learning style,

cognitive, affective and physiological, as defined by Keefe

(1979).

Specifically, the researcher attempted to answer the

following question: Are the methods of instruction used to

teach developmental English to the adult learners in this

study equally effective when adjusting for the influence of

the students' scores?

The null hypothesis for the question being investigated

in the study was: The mean posttest scores of the five

methods of instruction are not significantly different after

adjusting for the influence of the students' pretest scores.










Justification of the Research

Existing research reported that individual learners can

profit from instructional intervention or training that

optimizes learning style preferences and strengthens weak

styles (Keefe, 1987; Schmeck, 1988c). As of 1991, the

majority of the research had focused on the cognitive,

physiological, and affective variables that make up learning

styles as independent components of a given model or

classification system. The available research did not

report on the appropriateness of utilizing one variable over

the other when working with individual learners.

Until the 1980s, the majority of the research on

learning styles focused on the youthful learner. During the

1980s, researchers began to apply the findings from learning

style research to the adult learner. These attempts have

predominately focused on the application of cognitive models

of learning style to the adult learner. There was no

research reported utilizing the Learning Style Profile with

adult learners. Additionally, no researchers have examined

which components or dimensions of learning style were the

most effective for increasing academic achievement levels of

the adult learner.

Overview of Methodolovg

The sample (N=125) was randomly selected from a

population of adult learners enrolled in developmental

English education classes in a small southern community










college. The developmental English education classes were

required for all students who tested below a state

established cut-off score on a standardized test of basic

skills, Multiple Assessment Programs and Services (MAPS)

(Educational Testing Services, 1984). Additionally, older

students, returning to the educational setting after a long

absence from the classroom, often enrolled in the classes to

upgrade basic skills.

The study was designed to determine whether one of five

instructional methods used to teach developmental English to

adult learners was superior to another. The research design

for the study was a pretest-posttest control group design.

Students were randomly assigned to one of four experimental

groups and a control group. The randomization of student

assignment to groups increased the chances that the groups

were equivalent.

The groups were composed of 25 students each. All

groups met for a period of 12 weeks. All groups were

administered a learning style inventory, the Learning Style

Profile (LSP) (Keefe & Monk, 1989), and a pretest and

posttest of basic skill levels in English, the Multiple

Assessment Programs and Services test (MAPS). All groups

received self-paced, computerized, competency-based

instruction. Each group was taught by a different

instructor. In order to control for the confounding effect

of instructor and treatment, all instruction was uniform and










standardized. All groups used the same standardized,

computerized materials to learn course content. There was

no variation in instructional content. The only variation

between groups were the treatment conditions.

During the 12 week period of the study, students in the

experimental groups were given varying levels of

intervention based on which learning style component,

cognitive, affective, physiological, or all three combined,

was being emphasized in that particular group. Group one

received complete training in all three dimensions of the

learning style profile. In addition, the students were

taught strategies to improve skills related to all three

learning style dimensions. Group- wo received training in

only the soznitive dimension of the learning style profile.

Group three received training in only the affective

dimension of the learning style profile. Group four

received training in only the physiological dimension of the

learning style profile. Finally, group five was taught

using materials and instructional methods that disregarded

the learning styles of individual students in the group.

Different instructors were used for each experimental

group to control for the possible inadvertent transfer of

information regarding a dimension of learning style not

intended for that particular treatment group. Instruction

was computerized and standardized decreasing the chances of

confounding instructor and treatment. Given the focus of










the study on the impact different dimensions of learning

style might have on achievement, the researcher determined

that purity of treatment conditions by limiting instructor

knowledge to the treatment conditions for a given group was

essential to determining the outcomes of this particular

study.

A one-way analysis of covariance was conducted to

analyze the relative effectiveness of the different

instructional methods. The scores from the protests formed

the covariate'with the scores from the posttests as the

dependent variables and group as the independent variable.

Prior to using the covariance analysis, the assumption of

equal slopes and the assumption of linearity were examined.

The basic assumption of linearity was satisfied by the use

of a scatter plot. However, the check for equal slopes was

untenable, and interpretation of the results was based on

the resultant interaction effect.

Limitations

The following limitations existed in this study:

1. While every attempt was made to provide thorough

training for instructors and control external educational

conditions, treatment conditions could have been affected by

a variety of uncontrolled factors.

2. The test-retest method used could have affected

posttest scores since the interval between tests was short.








13

3. In spite of the standardization and uniformity of

instructional methods and content, the use of multiple

instructors may have confounded the treatment.

Organization of the Dissertation

Chapter 1 consists of the introduction, a statement of

the problem, the research questions, the purpose of the

study, the significance of the study, and the methods of

procedure. Chapter 2 contains a review of the literature

pertinent to this subject. Chapter 3 contains a description

of the methods of procedure for conducting this study, and

an analysis of the statistical treatment used for these

data. Chapter 4 includes a detailed analysis of the data

collected, and Chapter 5 consists of the conclusions and

recommendations gained from this investigation.














CHAPTER 2
REVIEW OF THE LITERATURE


The Nature of Learning Styles


Styles, as described by behavioral psychologists, are

internally consistent traits influenced by genetics and

prior experience. Gregorc (1979b) suggested "style appears

to be both nature/nurture in its roots. Patterns of

adapting to environments are apparently available to us

through our genetic coding system through our

environment and culture and within the subjective part

of our individual natures" (p. 20). Consistent patterns of

behaviors or invariable responses to similar situations,

reflecting an underlying style, have been confirmed in human

behavior research for years. Escalona and Heider (1959), in

studying developmental behaviors of children, stated, "As

one notes behavior alterations from infancy to later

preschool ages, one knows that not a single behavior has

remained the same, yet one is struck with the inherent

continuity of behavioral style and the child's pattern of

adaptation" (p. 9).

Learning style refers to individual preferences or

characteristic ways of behaving manifested by learners. The

term "learning styles" was first used, in 1954, by Thelen as








15

he related group dynamics to learning. Elements of learning

style appeared in the literature as early as 1892. Prior to

the decade of the 1940s, learning styles researchers focused

on identifying the one perceptual mode, auditory or visual,

which would best improve learner performance. Since then

numerous elements of learning style have been identified

(Keefe, 1979).

There was general agreement among researchers that

learning style is influenced by the individual's perception

of a situation. The individual classifies a situation based

on prior experiences and responds in ways that have proven

appropriate for that type of situation. Furthermore,

researchers suggested that learning styles are predictable

based on established patterns of responding (Schmeck, 1988a,

1988b, 1988c).

As to the modifiability of learning styles, the

research results have confirmed the existence of consistency

and variability in learning styles (Entwistle, Hanley, &

Hounsell, 1979). The apparent contradiction is explained by

the focus of the research. Questions regarding

modifiability or variability of learning styles appear to be

determined by whether the researcher focused on the

individual or the situation.

Kuchinskas (1979), focusing on the situational aspects

of the classroom, concluded that matching teacher style and

methods with learner cognitive style increased achievement.










Kuchinskas used cognitive style mapping to determine the

effect of cognitive style on activities in the classroom.

She focused on the style of both teacher and students and

instructional activities and materials used in the

classroom. From these studies, Kuchinskas concluded, style

mismatches resulted in a decrease or at best no change in

achievement level.

Reynolds and Torrance (1978) studied learning styles

from a different perspective than Kuchinskas. These

researchers focused on the individual, rather than the

situation, to determine if learning styles were modifiable

within a relatively short period of time, from 6 to 12

weeks. Employing both direct training in a specific style

and indirect training that exposed the learner to a variety

of styles and experiences, the authors concluded that it was

possible to modify an individual's learning style preference

within a relatively short time frame.

Schmeck (1988c), in Learning Strategies and Learning

Styles, discussed the varying theoretical perspectives

regarding the modifiability of learning styles. Entwistle

(1988), Marton (1988), and Ramsden (1988) argued that

improvements in learning are achieved by focusing on the

situation, that is, modifying the situation to fit the

perceptions of the learner. These authors contended that

style was a stable, fixed pattern of responding to the

environment. Das (1988), Kirby (1988), McCarthy and Schmeck








17

(1988), and Weinstein (1988) contended that it was possible

to develop cognitive skills regardless of the situation

because styles were flexible. Schmeck (1988c) concluded

that both perspectives on learning style, stability and

consistency versus flexibility and modifiability, must be

considered when developing instructional responses for

improving learning.

Researchers also differed regarding the consistency or

variability of learning style over time or as related to

growth and developmental changes. Tarule, Mattuck, and

Weathersby (1979) noted the consistency or stability of

cognitive styles over time while recognizing that styles may

be altered. The authors concluded "basic learning style

preferences probably remain consistent from childhood to

adulthood, although they may be expanded or deepened over

time as a result of socialization, specialization, or the

conscious acquisition of methods and procedures with

opposite strengths" (Tarule et al., 1979, p. 17).

Other researchers suggested that learning style differs

across grade, subject and achievement level. Carbo (1983,

1984), studying reading learning styles, concluded that

comparisons across grade level and achievement level

indicated significant differences in preferred learning

styles. Carbo utilized the Learning Style Inventory and

focused on the physiological and affective styles defined by

that instrument. Carbo concluded that changes in style are








18
related to growth and development and appear to parallel the

growth curve. Dunn, Dunn, and Price (1981) documented

additional research using the Learning Style Inventory that

confirmed Carbo's findings. However, Copenhaver (1979),

focusing on cognitive elements of style (scanning versus

focusing, and analytical versus global) discovered that

students' learning styles were consistent across subject

areas (English and mathematics) but were inconsistent across

time.

Other researchers have documented differences in

learning style among high and low achievers indicating

differences in developmental capacity depending on the

learning style. Shade (1983) found a significant

relationship between the cognitive style field independence

and high achievers and field dependence and low achievers.

Shade concluded that there existed a school oriented

learning style that enhanced school achievement.

The majority of the studies confirming an association

between achievement level and preferred style used the

Learning Styles Inventory (Dunn and Dunn, 1978; Marcus,

1979; Price, Dunn & Sanders, 1981; White, 1982). Griggs

(1984) reported the results of six studies, (Dunn & Price,

1980; Griggs & Price, 1980; Kreitner, 1981; Price, Dunn,

Dunn, & Griggs, 1981; Stewart, 1981; Wasson, 1980) focusing

on affective and physiological elements of style, that

suggested a relationship between selected learning style








19
elements and high achievement. Furthermore, the results of

these studies collectively implied that gifted learners were

independent learners, internally controlled, persistent,

perceptually strong, nonconforming, and highly motivated.

Dimensions and Elements of Learning Styles

As many as 30 models or approaches to the study of

learning style have been developed (Ehrhardt & Corvey,

1980). After an extensive review of the literature

available at the time, Keefe (1979) adopted the approach

that the existing, identified learning style elements could

appropriately be assigned to three dimensions: cognitive,

affective, and physiological. Keefe defined learning styles

as "characteristic cognitive, affective, and physiological

behaviors that serve as relatively stable indicators of how

learners perceive, interact with and respond to the learning

environment" (Keefe, 1979, p. 11). Keefe's definition

encompassed both the internal and external processes,

identified in the research, that appeared to determine an

individual's preferred mode of learning. Keefe was

convinced that all these factors interacted to influence an

individual's preferred style of learning. According to

Keefe, isolation of a single variable or factor would only

give a partial picture of the individual learner (Keefe,

1987).










Cognitive Dimension

Cognitive style has often been used interchangeably

with learning style in the literature although the two are

distinct concepts. Learning style is the broader term

encompassing cognitive, affective, and physiological styles.

As reported by Keefe (1979, 1987), the term cognitive style

was first used by Gordon Allport in reference to individual

adaptations to the environment based on personality types.

Messick (1976) defined cognitive styles as individually

preferred and consistent "ways of organizing and processing

information" (p. 8). According to Messick (1976), cognitive

styles are distinct from ability or level of skill, in that,

styles "are conceptualized as stable attitudes, preferences,

or habitual strategies determining a person's typical modes

of perceiving, remembering, thinking and problem solving"

(p. 8). Cognitive styles are more general and relate to how

information is processed. Abilities are more specific and

are related to the quantity of information an individual is

able to process. Additionally, cognitive styles are

described as bipolar or dualities, "ranging from one extreme

to the other" (Messick, 1976, p. 9). Witkin, Moore,

Goodenough, and Cox (1977) described this "value neutral"

characteristic of cognitive style.

With regard to value judgments, cognitive styles
are bipolar. This characteristic is of particular
importance in distinguishing cognitive styles from
intelligence and other ability dimensions. .
With cognitive styles, each pole has
adaptive value under specified circumstances, and










so may be judged positively in relation to those
circumstances. (p. 16)

Pask (1988), discussing the cognitive style element,

comprehension versus operation, insisted that learners

ultimately achieve the same thing whether they start from a

comprehension or operation style, confirming Witkin, Moore,

Goodenough, and Cox's (1977) position. However, other

researchers disagreed as to whether either extreme of the

cognitive style continuum was equally effective in

accomplishing its purpose. Kirby (1988) argued that the

task determined which mode of functioning would prove

better.

Several researchers have confirmed Kirby's (1988)

argument that task specific styles are more predictive of

learner success. For instance, Copenhaver (1979) confirmed

that styles defined as "scanners" or global learners were

more successful in English and "focusers" or detail specific

learners were more successful in mathematics. Onyejiaku

(1982) determined that analytical students scored

significantly higher than nonanalytic students in

mathematics. Shade (1983) concluded that field independent

or analytical learners were the high achievers as measured

by grades and achievement tests.

Messick (1976) noted cognitive styles were deeply

rooted in the personality structure, "developing around

underlying personality trends" (p. 9). In his conclusions,

Messick stated,










cognitive styles are thus intimately interwoven with
affective, temperamental, and motivational structures
as part of the total personality; they provide one
aspect of the matrix, as it were, that determines the
nature or form of adaptive traits, defense mechanisms,
and pathological symptoms. (pp. 6-7)

Evidence existed to suggest that the separation of cognitive

functions or styles from the context of the affective and

motivational conditions in which they occur may be a

mistake. There was growing evidence to indicate that "the

quality and nature of the cognitive function is influenced

by the quality and nature of the affective experience"

(Messick, 1976, p. 171). Specifically, the literature

referred to motivational/affective factors that might affect

learner performance regardless of learning style.

Messick (1976) described 20 elements of cognitive

style derived from the research. These elements are limited

to specific traits and abilities, as pointed out by Ash

(1986).

These formulations of cognitive style, are
atomistic in their concentration on single traits
and abilities and their lack of connection to a
holistic theory that purports to encompass the
major functional modes of learning. (p. 8)

Keefe (1987) attempted to develop a holistic approach

to learning style, building on his definition of learning

styles as comprising three dimensions. The myriad elements

identified in the research were classified under each

dimension. Keefe categorized these elements into either

receptive elements or concept formation and retention

elements of the cognitive dimension.










Receptive elements, according to Keefe (1987), were

concerned with individual differences in perception and

analysis of data. The receptive elements included:

perceptual modality preferences, field independence versus

dependence, scanning, constricted versus flexible control,

tolerance for incongruous or unrealistic experiences, strong

versus weak automatization, and conceptual versus perceptual

motor dominance. Concept formation and retention elements,

as defined by Keefe (1987), related to differences in

individual problem solving behaviors, and differences in

information processing and hypothesis generation. These

elements included: conceptual tempo, conceptualizing styles,

breadth of categorizing, cognitive complexity versus

simplicity, and leveling versus sharpening (Keefe, 1987).

Perhaps the most widely studied of the receptive

elements of cognitive style was field independence versus

field dependence (Witkin, Dyk, Faterson, Goodenough, & Karp,

1962; Witkin, Lewis, Hertzman, Machover, Meisener, & Wapner,

1954; Witkin, Moore, Goodenough, & Cox, 1977; Witkin, Moore,

Oltman, Goodenough, & Friedman, 1977). Kirby (1988) used

the terms analytical versus global approaches to the

environment. Field independent (analytic) learners

perceived objects as distinct from the background field,

while dependent (global) learners were influenced by

embedded context. The research indicated that field-

independent learners were more successful using high-level










structure knowledge to process information than were field

dependent learners (Brooks, Dansereau, Spurlin, & Holley,

1983; Spiro & Tirre, 1980). The field dependent (global)

learner was less competent in analytical functions, but

exhibited greater social orientation and social skills.

Bolocofsky (1980) discovered a significant interaction

between field dependence and competitive motivation. Field

dependent learners performed significantly better than field

independent learners in a competitive environment.

Barbe and Milone (1981) noted that Holzman, Gardner,

Schlesinger and others at the Menninger Foundation, focusing

on receptive elements of cognitive style, took the

perspective that cognitive style was influenced by a

complexity of cognitive controls. The cognitive control

elements identified by these researchers included such

elements as constricted versus flexible control, tolerance

for unrealistic experiences, leveling versus sharpening,

breadth of categorization, and differentiation and

equivalence range.

Researchers concluded that flexible individuals

restricted their attention to cues relevant to the

situation. The constricted learner was more easily

distracted by extraneous environmental cues (Gardner,

Holzman, Klein, Linton, & Spence, 1959; Jensen & Rohwer,

1966).










The tolerant individual had a propensity to accept

apparent discrepancies, while the intolerant individual was

more closely oriented to reality. The intolerant individual

had difficulty accepting ideas at variance with conventional

experience. Everything must fit an accepted pattern for the

intolerant individual (Gardner et. al, 1959; Klein, Gardner,

& Schlesinger, 1962).

Levelers confused objects or events with similar but

not identical objects or events from the past. Sharpeners

noted the differences between the identical and the similar

(Gardner et al., 1959, Holzman & Gardner, 1960; Holzman &

Rousey, 1971).

Breadth of categorization referred to preferences for

broad inclusiveness versus narrow inclusiveness. Broad

categorizers tolerated very little error, including all

items, preferring errors of inclusion. The narrow

categorizer was more tolerant of errors of exclusion,

preferring to exclude doubtful items (Bruner & Tajfel, 1961;

Messick & Kogan, 1966).

Kagan, Rosman, Day, Albert, and Phillips (1964) focused

on analytical styles of thinking related to concept

formation and retention elements of cognitive style. Kagan

and his colleagues identified a cognitive element referred

to as "reflection-impulsivity". Reflection versus

impulsivity pertained to the speed and adequacy with which

information was processed and hypotheses were formed.










Reflective individuals took time to form conclusions while

the impulsive moved quickly (Kagan et al., 1964; Kagan &

Kogan, 1970; Kagan & Messer, 1975). Odom, McIntyre, and

Neale (1971) confirmed that impulsive learners made more

errors than reflective learners. Additionally, reflective

learners required fewer attempts to learn a task.

The concrete random learner, as identified by Gregorc

(1979a, 1979b), utilized the trial and error approach in

learning, working well independently with little teacher

intervention. The abstract sequential learner preferred

logical rational presentations that had substance. Gregorc

further identified the selection and instructional

preferences of the abstract random learner and the concrete

sequential learner.

Wallach and Kogan (1965) used the term conceptualizing

styles to refer to the differences in individuals as related

to concept formation. The three identified conceptualizing

bases appeared to be developmentally related. Relational

conceptualizing or the use of thematic or functional

relations among stimuli was more directly associated with

children. The analytical-descriptive style was

representative of older children and adults. Categorical-

inferential conceptualizing, the inference of categorical

membership, was more characteristic of adults.

Cognitive complexity versus simplicity was another

concept formation element referring to differences in the








27

preferred patterns or modes of interpreting the world. This

style was also referred to as abstract versus concrete. The

abstract interpretation of the world was well integrated,

differentiated into many distinct dimensions, and

articulated to discriminate between even the fine detail

(Langley, 1971; Zimring, 1971).

After the decade of the 1960s, the concept of

cognitive style broadened to include different types of

selection strategies affecting individual learning

performance. Selection strategies included scanning and

focusing, open-closed mindedness, memory or retention

styles, risk taking versus cautiousness and perceptual

modality preferences.

Scanning and open-closed mindedness, both retention

elements, referred to differences in the way individuals

focus attention or perceive new situations, with a broad or

narrow viewpoint. There appeared to be similarities between

this element and breadth of categorization. The conclusion

was that the scanner could also be a narrow categorizer, as

scanners tended to exclude items. Likewise, the risk taking

versus cautiousness category related to these elements. The

risk taker was much more willing to take chances to achieve

a goal. Therefore, it was possible the risk taker was more

likely to be a scanner and narrow categorizer (Gardner &

Long, 1962a, 1962b; Kogan & Wallach, 1964).










The study of perceptual modality preferences was

extensive and has been consistently studied since learning

style elements first appeared in the literature. Although

Keefe (1987) classified these elements as cognitive and Dunn

(Keefe, 1987) included them in the physical domain, both

agreed on the general meaning of perceptual modality and its

impact on learning style. Researchers agreed that "A

modality is any of the sensory channels through which an

individual receives and retains information" (Barbe &

Swassing, 1979, p. 1). The research confirmed that

individuals assimilated information through the five primary

senses, developing preferences and responding in a preferred

learning pattern (Hill, 1971, 1976). Additionally,

researchers have confirmed that instructional techniques

matched to modality strengths improved performance (Barbe

and Milone, 1980; Carbo, 1980; Semple, 1982).

In summary, cognitive style, as defined by Keefe

(1987), is distinct from learning styles. Keefe (1987)

defined cognitive styles as the "information processing

habits representing the learner's typical mode of

perceiving, thinking, problem solving and remembering" (p.

34). The cognitive styles identified in the literature can

be divided into two distinct categories, according to Keefe,

styles that "deal with the perceptual and analysis of data"

or reception styles, and styles that "deal with hypothesis










generation, problem solving and memory processing" or

concept formation and retention styles (Keefe, 1987, p. 10).

Affective Dimension

Keefe's second major category is affective styles.

Affective styles encompass those personality variables

derived from empirical research related to attention,

emotion, and valuing that affect an individual's perception

of the environment. "Affective learning styles are the

offshoots of the same motivational processes viewed as the

learner's typical mode of arousing, directing, and

sustaining behavior. Motivation is the end product of

attention, emotion and valuing" (Keefe, 1987, p. 10).

Levels of motivation vary over time and in different

environments, but the affective style of the person remains

relatively stable.

Keefe (1987) classified 15 elements of affective styles

into two dimensions, attention elements and expectancy and

incentive elements. Attention referred to the general level

of responsiveness of the individual. Individual differences

in general responsiveness to the environment have been

related to levels of curiosity (Berlyne, 1954), anxiety and

frustration tolerance (Alpert & Haber, 1960; Waterhouse &

Child, 1953), ability to persevere or persist in the face of

failure (Carrol, 1963), responsibility levels and structural

needs (Dunn & Dunn, 1978; Hunt, Butler, Noy & Rosser, 1978).








30

Expectancy and incentive elements were related to the

degree of certainty with which an individual perceives an

expected outcome will follow a particular action. Variables

related to this style were locus of control (Rotter, 1966,

1971), level of self-actualization (Maslow, 1968), and level

and source of motivation (Alschuler, 1973; Maccoby &

Jacklin, 1974; McClelland, Atkinson, Clark, & Lowell, 1955;

Skinner, 1953; Riessman, 1962; Witty, 1961).

The motivational processes of the learner are closely

interrelated with the individual's preferred affective modes

of behavior. Variation in motivational processes are

influenced by the cultural environment, parental and peer

pressure, school influences and personality variables.

Individuals who are interested in excellence for its own

sake require very little motivation from outside.

McClelland et al. (1955) called this pattern of striving an

internal need for achievement. Some individuals are

motivated by competition while others thrive on group

support. Additionally, a learner's motivational level may

differ according to variations in social, racial/ethical and

cultural viewpoint. Finally, variations in level of

motivation exist as to the impact of negative and positive

reinforcement. Level of motivation changes from day to day.

Understanding individual differences in source and level of

motivation and the daily variation can make a difference in

learner performance (Hill, 1976; McClelland et al., 1955).










Physiological Dimension

Keefe's third category is determined by the

conventional functions of the human body. Physiological

styles are "biologically based modes of response that are

founded on sex-related differences, personal nutrition and

health, and accustomed reaction to the physical environment"

(Keefe, 1979, p. 23). The five physiological factors

identified were gender differences, health related behavior,

environmental variables (i.e., light, temperature and

sound), mobility, time rhythms, and intake needs. The

majority of the research documenting the influence of

environmental and physiological elements on learning style

was initiated during the 1970s (Dunn & Dunn, 1978).

Diagnosis and Practical Applications of
Learning Style Research

Numerous diagnostic instruments were developed to

measure the elements identified in the research for

understanding the way students learned. Research utilizing

these instruments verified that individual academic

achievement improved when education was responsive to

individual learning styles. Furthermore, the literature

confirmed that education can be personalized to meet the

needs of the individual learner, based on the findings of

the diagnostic instrument used. Keefe (1979) commented on

the importance of learning style diagnosis to education.

"Learning style diagnosis opens the door to personalizing

education on a rational basis. It gives the most powerful










leverage yet available to educators to analyze, motivate,

and assist students in school" (p. 35).

Learning Style Diagnostic Instruments and Their Limitations

The learning styles diagnostic instruments that have

been developed measure a variety of dimensions and elements

of learning style. Most of the instruments were self report

inventories, a few were supplemented by teacher observation,

while some were solely teacher assessments. All of the

instruments varied in the extent and degree to which the

various elements of learning styles were measured (Dunn,

Debello, Brennan, Krimsky, & Murrian, 1981).

Several developers of the learning styles diagnostic

instruments concentrated solely on cognitive style and

included only a few elements. The Group Embedded Figures

Test, developed by Witkin, Oltman, Raskin, & Karp (1971),

measured only one cognitive element, the analytical versus

nonanalytical mode of information processing. Gregorc's

(1979b) Style Delineator measured concrete versus abstract

and random versus sequential styles. The Cognitive Style

Mapping Inventory, developed by Joseph Hill (1971), measured

various elements related to an individual's cognitive style.

Finally, Letteri's (1980, 1985) Cognitive Profile measured

seven bipolar styles.

Diagnostic instruments developed to measure affective

style elements include David Hunt's Paragraph Completion

Method (PCM) (Hunt et al., 1978) that measures conflict










resolution styles and the Internal-External (I/E) Scale

developed by Julian Rotter (1966) that measures locus of

control. The Learning Style Inventory (Dunn, Dunn, & Price,

1978) measures both affective and physiological variables.

This instrument focuses on 18 elements assessing

environmental, social, emotional and physical needs. The

Myers Briggs Type Indicator (Myers & Briggs, 1967) focuses

on four bipolar personality dispositions or preferences

related to the cognitive and affective dimensions.

Research utilizing each of the available instruments

has resulted in the documentation of a positive gain in

academic achievement when instructional methods were adapted

to the individual learning styles or learning styles were

modified. Positive results were evidenced in the research

when the learning style profile depicted either the

cognitive dimension, or some single cognitive element

(Gregorc, 1979b; Letteri, 1985; Witkin et al., 1971) or when

the profile depicted elements from either the physiological

dimension or the affective dimension (Dunn & Dunn, 1987;

Hill, 1976; Rotter, 1966, 1971). The researchers identified

learning style elements and related these to academic

achievement where instructional interventions were initiated

based on preassessed individual differences. Increases in

academic achievement were related to many different

elements. However, there has been no research that

attempted to determine which dimension or elements within a










dimension were the best for improving academic achievement

or whether all three dimensions were best.

The NASSP Learning Style Profile (Keefe & Monk, 1989)

was the first diagnostic instrument that was designed to

measure all the major identified elements in all three

dimensions of learning style. Inclusion of elements within

the instrument was based on the verifiability of the

existence of a given element in the literature and research

on the impact of the identified element on learning. In

many cases, the research on chosen elements was limited or

nonexistent. The assumption was made that inclusion of all

the identified elements would provide a more comprehensive

picture of the individual learner. No effort was made to

determine the effectiveness of one element over another or

of one dimension over another. The advantage of the LSP

over other instruments was that it provided a measure for

all three dimensions of learning style and the majority of

the elements identified in the literature. The instrument

was more inclusive of possible learning style variables than

other diagnostic instruments developed heretofore. However,

since the instrument was developed only recently, no

research has been documented other than the validation and

reliability studies completed by the authors.

As emphasized by Keefe in 1979, learning styles

diagnosis has had a profound influence on education.

Learning styles research and resultant diagnostic tools have










provided educators with a sound basis or framework for

making education more responsive to the individual. The

personalization or individualization of education based on

learning styles research has diverged into two opposing

views or approaches to education.

Instructional Approaches Utilizing Learning Styles

As of 1991, the prevailing views on the application of

learning style research to the personalization of the

educational environment differed based on whether the focus

was the educational setting, the learning situation or the

individual learner. Some proponents of individualization

based on learning styles analysis, believed that instruction

should be responsive to the individual learner. That is,

the environment should be adapted to fit the individual.

Hunt (1979) stated, "Matching educational approaches to

student learning style facilitates academic achievement" (p.

375). This approach assumed that student learning style was

fairly stable and the instructional setting should be

modified to fit the needs of the student (Dunn, Dunn, &

Price, 1978; Hill, 1971, 1976; Keefe, 1987).

Hill (1971, 1976) explored individual cognitive

orientations and affective or motivational behaviors that

determined a particular individual's style of learning.

Hill utilized extensive individual profiles of students,

"cognitive style mapping", to match the student to the most

appropriate learning environment. Utilizing the technique










of cognitive mapping developed by Hill (1971), Gerald

Kusler's work, as summarized by Keefe (1979), discussed the

application of prescriptive education to improve academic

performance. Cognitive style mapping provided a student

profile based on the interrelationship of symbols (words,

sensory data, and psychomotor representations), cultural

determinants (environmental influences and style

preferences) and modalities of influence (inductive and

deductive reasoning processes) that was used to determine

the most effective educational approach for the student.

Kusler determined that learning assistance centers, with a

variety of materials and alternative methods of teaching

that focused on the students cognitive map, provided the

most effective educational environment for promoting

academic achievement.

Dunn and Dunn (1978), using a self-inventory

instrument, determined that students can identify their own

learning styles. This research focused on input/output

factors as these related to learning styles. There was more

concern with external factors and the individual's emotional

response to those factors as they influenced learning than

with the impact of cognitive style elements on learning.

Sociological, environmental, physical and emotional

variables were considered the most important determinants of

learning style. Numerous studies, utilizing the LSI,

documented the impact of physiological and affective










preferences on learner performance (Carbo, 1980; DeBello,

1985; Della Valle, 1984; Domino, 1970; Hodges, 1985; Kroon,

1985; MacMurren, 1985; Martini, 1986; Murrain, 1984; Perrin,

1984; Shea, 1983; Virostko, 1983; White, 1982).

It was suggested that teaching approaches that do not

match an individual learner's style make "learning more

difficult, cause frustration, and decrease a youngsters

confidence in himself" (Dunn & Dunn, 1978, p. 22). Both

Domino (1970) and Farr (1971) concluded

"when exposed to a teaching style consonant with the
ways they believe they learn, students score higher on
tests of factual knowledge, have better attitudes and
are more efficient than when taught in a manner that is
dissonant with their learning style. It is
advantageous to teach and test students in their
preferred modalities" (Farr, 1971, p. 10).

Researchers have documented that when students learned in

ways that were natural or preferred, academic achievement

increased (Dunn & Dunn, 1978; Martin, 1977, Trautman, 1979),

and self-esteem was enhanced (Hudes, Saladino, & Siegler,

1977).

Other researchers indicated that the student should be

trained to become more adaptive to the existing environment.

This approach to individualization based on learning styles

analysis assumed that styles were flexible and modifiable.

Kolb (1987) stated, "We should not deny students the

opportunity to develop themselves fully by only exposing

them to educational environments that match their strengths"

(p. 373). Furthermore, according to Schmeck (1988c),










researchers hypothesized that some cognitive styles were

more productive of student learning and achievement than

others. Therefore, intervention strategies should be used

to foster the development of those styles. Generally, these

researchers agreed that learning styles were developmental

in nature. Schmeck (1988a) concluded that development of

flexibility was more valuable to the learner than

structuring the learning environment to the student's

preferred style.

[Educators can] facilitate the development of this
higher level skill by periodically exposing
students to contextual demands that do not
precisely match their styles. This must, of
course, be done very cautiously in order to avoid
instilling in the student a feeling of
incompetence. However, if we roughly match our
instructional technique to the student's style
while simultaneously providing experience in
strategies that are outside that style, we may
prompt the development of flexibility. This may
be a greater service than always structuring the
contest to match the student's preferred style.
(p. 15)

Letteri (1980, 1985), reinforcing Schmeck's (1988a)

conclusions, recommended that students should be encouraged

to use nonpreferred or "weaker" learning styles to

strengthen those modes of learning. Using the Cognitive

Profile Model, Letteri focused on the interrelationship

between seven elements of cognitive style and how they

effected learning performance levels. His research

confirmed that less responsive cognitive style traits can be

modified.








39

Gregorc (1979b) confirmed that individuals are capable

of adapting to the environment. Gregorc emphasized that

educators have a responsibility to help learners recognize

"their inherent mediation abilities and the demands being

placed upon them" (p. 26). Individuals must learn to adapt

their individual learning styles in order to cope with an

ever changing environment. However, Gregorc cautioned

educators against creating a mismatch between environment

and learner that leads to excessive frustration resulting in

avoidance or procrastination behaviors.

In summary, the research provided supporting evidence

for both training students to adapt preferred learning

styles and changing the environment to match student

learning styles. The conclusion can be reached that a

combination of approaches should be used to enhance learner

performance based on the individual learner's style

preferences (Keefe, 1987). Keefe quoted Piaget's theories

of accommodation and assimilation relating to the

adaptability of the learner to the environment to support an

educational approach that both challenges and adapts to the

individual learner. He concluded that "cognitive growth can

come from adapting the environment to the existing skills of

the learner (assimilation), or from helping the individual

adapt successfully to the demands of the environment

(accommodation)" (p. 34). Keefe referred to this approach

as a program-person continuum of learning strategies.










"Depending on the individual's age, developmental

capacities, and skill levels, both remediation of cognitive

skills and personalization of the learning environment can

be appropriate adaptive behaviors" (Keefe, 1987, p. 34).

The Relationship Between Academic Achievement and Different
Instructional Approaches

A large percentage of the available research on

learning styles provided evidence that instructional methods

based on an understanding of learning styles increased

academic achievement. The preponderance of the research

examining the relationship between learning style and

achievement focused on the physiological and affective

dimensions.

Dunn and Dunn (1987) reviewed 17 studies "that verified

students achieved significantly higher scores when taught

through methods that responded to their individual learning

styles" (p.60). The researchers found no research that

failed to support these findings. The Dunns (1978)

described four types of instructional programs and six

instructional methods or resources and corresponding

learning style characteristics that were most adaptable to

that method or resource. The Dunns believed the student

should be diagnosed and advised regarding style preferences

and the learning environment should be structured to

accommodate these preferences. Cafferty (1980) confirmed

the findings of the Dunns and concluded that the greater the








41

degree of congruence between teachers' and students' styles,

the higher the grade point average.

Cavanaugh (1979a, 1979b) outlined the diagnostic and

prescriptive steps, utilizing learning styles research,

taken at Worthington High School. Cavanaugh's work was part

of a limited research effort that attempted to look at all

three dimensions of learning style as they related to

academic achievement. However, the limitations of the

instruments utilized forced exclusion of a number of the

major elements of style. Using Hill's Cognitive Style

Mapping Inventory (1971) and Dunn's Learning Styles

Inventory (1978), students were diagnosed to determine

cognitive, physiological, and affective styles. Learning

styles were matched to instructional methods. Although the

number of elements of learning style studied were limited,

the result was increased academic performance as measured by

grade point average and standardized tests.

Douglass (1979) focusing on a single cognitive element,

field dependence, discovered a positive relationship between

instruction matched to the preferred style and academic

achievement. Douglass confirmed that analytical students

perform better with inductive materials and global learners

perform better with deductive materials.

By 1991, there was a growing body of research

documenting the relationship between instructional

strategies that promoted flexibility in learning style and










academic achievement or improved learner performance.

Fourier (1983) focused on cognitive elements and the affect

on academic achievement. A form of cognitive style mapping

was used to determine the style preferences of 554 students.

Students were placed in an experimental and control group.

The experimental subjects were educated regarding the

outcomes of their individual cognitive map. There was a

significant difference between the semester grade point

average of the two groups. Fourier concluded that simple

disclosure of cognitive style map information even without

additional training made a significant difference in

academic achievement.

Letteri (1980, 1985) also focused on cognitive

components. However, unlike Fourier, Letteri disclosed the

cognitive style information to students and trained students

to use their strongest mode of functioning, as well as

develop the weak styles to enhance adaptability. Letteri's

findings revealed positive relationships between academic

achievement and disclosure of individual learning styles

coupled with training in learning styles.

In summary, numerous researchers have documented the

relationship between academic achievement and instruction

adapted to individual learning styles. Additionally, this

review reported a growing volume of research that supported

the modifiability of learning styles indicating a

relationship between instruction adapted to strengthen weak










styles and improved learner performance. The focus of the

research, regardless of instructional approach, has been

limited to only one or two of the three dimensions

identified by Keefe, cognitive, affective, and

physiological, due to the limitations of the available

diagnostic instruments.

Learning Styles Research Related to Adult Learners

In a review of the literature, Ash (1986) indicated

three approaches to learning styles have gained prominence

in the field of education, particularly as related to adult

learners. These included Witkin's field dependence and

field independence dimension, Hill's cognitive style

mapping, and Kolb's experiential learning approach to style.

The majority of the reported research utilizing

Cognitive Style Mapping was completed at Oakland Community

College in Michigan where Dr. Joseph Hill was president.

One of the most revealing studies utilizing this method

indicated that disclosure of cognitive style map information

to students even without alternative instruction improved

academic achievement. Fourier's (1983) study of 554

students enrolled in humanities, life science, and

psychology substantiated the value of student knowledge of

cognitive style using cognitive style mapping. The students

were divided into experimental and control groups. The

control group was administered a self-report inventory while

the experimental group completed a cognitive style










inventory. Using semester grades as the indicator of

learner performance, experimental and control group means

were statistically significant at the .01 level. Fourier

concluded that simple disclosure of cognitive style map

information to individual learners had a positive impact on

academic achievement for adult learners. The implications

of this study were that adult learners, cognizant of their

learning style preferences, were capable of adapting to

instructional situations that are inflexible.

Witkin, Moore, Goodenough, and Cox (1977) reported that

field independent students were more often found to perform

better in mathematics and sciences. However, Witkin and

colleagues differed with other researchers, stating "We have

seen that field-dependence-independence does not show much

relation to overall achievement measures, such as college

grade-point average" (p. 45). In a longitudinal study,

Witkin, Moore, Oltman, Goodenough, and Friedman (1977)

indicated that choice of educational major was determined by

cognitive style and that when choices were congruent with

cognitive style, a positive relationship was reported

between style and achievement.

Additionally, Smith and Haverkamp (1977), utilizing

Witkin's Group Embedded Figures Test, reported that

understanding of one's cognitive style, particularly for

adults, contrary to Witkin's findings, did improve learning.

Smith and Haverkamp (1977) concluded "the adult learner










needs to develop awareness and understanding of himself as a

learner" (p.7). In conjunction with the adult learner's

need to be aware of learning style, the authors emphasized

the need for faculty to be sensitive to the differences

between field-dependent and field-independent students and

the requirements for improved learning.

Korhonen and McCall's (1986) research utilizing Kolb's

Learning Style Inventory confirmed an interaction between

the learning styles of adults and learning environment,

including the traditional lecture approach and

individualized instruction. The authors concluded that "the

determination of the learning style of adult students

entering an educational experience can provide an instructor

with valuable information for planning activities and

methods that will assist each student in the learning

process" (Korhonen & McCall, 1986, p. 23).

Dorsey and Pierson (1984) concluded that age and prior

experience affected learning style more than sex or

ethnicity. Using Kolb's four learning styles, the authors

found that "the accommodator style becomes more predominant

at about the age of thirty-three" (Dorsey & Pierson, 1984,

p. 10). In addition, adults move from "merely assimilating

facts to understanding and using the interrelationship

of information and ideas" (Dorsey & Pierson, 1984, p. 10).

The authors concluded that there were dominant adult










learning style profiles that could serve as a framework for

developing responsive educational programs.

In summary, the literature review revealed a limited

amount of research on adult learning styles. The focus of

the research was on the cognitive dimension of learning

style. The findings from the studies reviewed revealed that

there were adult patterns of responding to the learning

environment that differed from those reported for the

younger learner. Additionally, adult learners cognizant of

their learning style profile were capable of adapting to a

situation in order to improve performance. It is possible

to conclude that creating an environment responsive to

individual adult learning styles or instructing adult

learners in their preferred styles of learning tended to

improve learning and increase academic achievement.

Summary of Chapter 2

Learning styles are composed of cognitive, affective

and physiological elements that determine how a learner

perceives, interacts with and responds to the learning

environment. Researchers have confirmed the impact on

learner performance of major elements within each of the *

three dimensions.

Existing research results suggested that instruction

can be adapted to respond to individual learning styles and b

improve learner performance. Evidence from the research

confirmed that individual differences in styles do not










necessarily affect ability unless the instructor or the

learner lack sufficient flexibility to adapt. There

appeared to be evidence for not only adapting instruction to

individual learning styles, but teaching individuals to

adapt learning style preferences to improve learning

performance in certain courses and inflexible situations.

Although numerous elements of learning style have been

identified, the available research has focused on either one

or two of the learning style dimensions, cognitive,

affective, or physiological, or isolated elements or traits

and instructional interventions that enhanced learning

performance based on the diagnosis of those elements. There

has been no attempt to determine the efficacy of focusing on

one dimension to the exclusion of another or whether the

inclusion of all three would be preeminent to increasing

learner performance.

Various researchers, (Kirby, 1988; Pask, 1988; Schmeck,

1988; Wallach & Kogan, 1965) have postulated that learning

styles were developmental in nature or progressed with age.

Given that previous experiences in conjunction with heredity

play a part in the development of learning style, as an

individual ages some elements or style preferences are

reinforced, adapted or modified. The assumption was made

that many style preferences become more firmly entrenched

over time, due to their very nature, if flexibility is not

encouraged (Schmeck, 1988a, 1988b, 1988c). Determining










those elements or dimensions most amenable to adaptation or

modifiability and most productive of improved learner

performance was determined to be essential to understanding

the adult learner.

The current literature on learning styles research

indicated that all three dimensions, cognitive, affective

and physiological, were interrelated and exclusion of one

dimension provided an incomplete picture of the individual's

learning style. The only research that focused on all three

domains utilized secondary school students to validate

Keefe's Learning Styles Profile. This instrument provided

the diagnostic tool required to determine the efficacy of

adapting instruction based on the determination of one

dimension over another or the inclusion of all three for

adult learners in this study.

The focus of this research was on all three dimensions

of learning style, cognitive, affective and physiological,

identified in the literature. Additionally, this researcher

adopted Keefe's (1987) position regarding the most

appropriate instructional methods utilizing learning styles.

Keefe (1987) concluded that "depending on the individual's

age, developmental capacities, and skill levels, both

remediation of cognitive skills and personalization of the

learning environment can be appropriate adaptive behaviors"

(p. 34). The purpose of this research was to determine the

appropriateness of utilizing learning style profiles with








49

the adult learner. Additionally, the researcher examined

the efficacy of focusing on one of the three dimensions of

learning style to the exclusion of the other dimensions.

Specifically, the study was an attempt to determine which of

five instructional methods, varying in the degree to which

dimensions of learning style were emphasized, were the most

effective with adult learners.














CHAPTER 3
METHODS AND PROCEDURES


The purpose of this study was to determine the

effectiveness of utilizing learning style profiles with the

adult learner. In order to determine the effectiveness of

utilizing learning style profiles, learning outcomes of

groups of adult learners taught through instructional

methods based on individual learning style were compared to

learning outcomes of adult learners taught through

instructional methods that disregarded preferred learning

style. Furthermore, the researcher attempted to determine

whether there were differences in learning outcomes when

instructional methods focused on a single learning style

component or a combination of all three components of

learning style, cognitive, affective and physiological, as

defined by Keefe (1979).

Description of the Sample

The sample (N=125) was randomly selected from a

population of adult learners enrolled in developmental

English education classes in a small, southern community

college. The developmental English education classes were

required for all students who tested below a state

established cut off score on a standardized placement test








51

of basic skills. Additionally, older students, returning to

the educational setting after a long absence from the

classroom, often enrolled in the classes to upgrade basic

skills.

The service area of the college was predominantly rural

with one major urban area comprising 15% of the total

population. The service area was primarily white,

representing 83% of the total population and was heavily

represented by older adults, over age 65.

Basic Research Design and Analysis

The study was designed to determine whether one of five

instructional methods used to teach developmental English to

adult learners was superior. The research design was a

pretest-posttest control group design. Students were

randomly assigned to one of four experimental groups and a

control group. The groups were composed of 25 students

each. The randomization of student assignment to groups

increased the chances that the groups were equivalent.

Demographically, the experimental groups were each

approximately 66% female, 34% male, 67% white and 33% black,

hispanic or other. Average age of each group was

approximately 24. The learning style profiles for each

group can be found in Appendices B-F.

All groups met for a period of 12 weeks. All groups

received self-paced, computerized, competency-based

instruction. A different instructor was assigned to each








52

group. A standardized instructional approach was used in

each group to control for the confounding effect between

instructor and treatment. All groups used the same

instructional materials, as far as content of the course.

The materials and approach for teaching the materials were

in a standardized, computerized format. The only variation

among the groups was the treatment conditions.

Different instructors were used for each experimental

group to control for the possible inadvertent transfer of

treatment conditions between groups. Instruction was

computerized and standardized decreasing the chances of

confounding instructor and treatment. Given the focus of

the study on the impact different dimensions of learning

style might have on achievement, the researcher determined

that purity of treatment conditions by limiting instructor

knowledge to the treatment conditions for a given group was

essential to determining the outcomes of this particular

study.

All groups completed a learning style inventory, The

Learning Style Profile (Keefe & Monk, 1989) and a statewide,

standardized pretest and posttest of achievement level in

English, The Multiple Assessment Programs and Services test

(MAPS). The tests were administered by a single trained

test administrator to all groups to ensure uniformity of

testing procedures. The researcher used the students'

pretest MAPS scores as a covariate in the analysis. The










covariate was used to reduce the amount of unexplained

variation in posttest scores, thus increasing the

statistical power of the analysis.

Specifically, the researcher attempted to answer the

following question: Are the five methods of instruction

used to teach developmental English to the adult learners in

this study equally effective when adjusting for the

influence of the students' pretest scores? The null

hypothesis for the question investigated in the study was:

The mean posttest scores of the five methods of instruction

are not significantly different after adjusting for the

influence of the students' pretest scores.

A one-way analysis of covariance (ANCOVA) was conducted

to analyze the relative effectiveness of the different

instructional methods. The alpha level used to test the

null hypothesis was set at .01. The scores from the

protests formed the covariate with the scores from the

posttests as the dependent variables and group as the

independent variable.

Prior to using the ANCOVA, a test of the assumptions of

linearity and equal slopes was made. The basic assumption

of linearity was satisfied by the use of a scatter plot.

The test of equal slopes or homogeneous regression

coefficients indicated that the assumption of equal

regression slopes was untenable (F=2.60, df=4, 115,










p=.0399). The results revealed that an interaction effect

was present.

Instrumentation

Learner performance or achievement level was assessed

by the pretest and posttest administration of the Florida

Multiple Assessment Programs and Services (MAPS)

(Educational Testing Service, 1984. MAPS was designed by

the College Board and includes the Reading Comprehension

test from the Descriptive Tests of Language Skills (DTLS,

the Test of Standard Written English (TSWE), and the

Arithmetic Skills test and the Elementary Algebra Skills

test from the Descriptive Tests of Mathematics Skills

(DTMS). Each of the tests was designed to provide

information about students' readiness for a college entry-

level course. The Florida MAPS Technical Manual

(Educational Testing Service, 1984) contains the statement:

"It should be understood that these test scores reflect past

opportunities to learn and the scores should not be used as

the sole criterion in a placement decision. In no case

should they be used for admissions" (p. 5).

The reliability coefficients reported for the MAPS were

alpha reliabilities, "computed by a formula that uses

agreement between questions on one form to estimate the

correlation between that form and another form of the test"

(Education Testing Service, 1984, p. 28). The reliability

coefficient reported for the TSWE was .89. Evidence of








55
validity was reported. Positive correlations were obtained

between TSWE scores and instructors' judgements of the

quality of students' essay writing.

For the purposes of this study, the MAPS Test of

Standard Written English (TSWE) was used as an indicator of

achievement level in English. The TSWE is a 30-minute test

containing 50 five choice questions to measure student

achievement in college level English. The TSWE has been

administered since 1974 as part of the SAT. A single score

from 0 to 60 is reported on the TSWE (Educational Testing

Service, 1984).

Learning style was assessed using the Learning Style

Profile developed in 1986 by the National Association of

Secondary School Principals (NASSP). The principal authors

of the test were James W. Keefe and John S. Monk. The LSP

was developed to help teachers identify student strengths

and weaknesses for the purpose of reorganizing instruction

more effectively. Even though the instrument was developed

for use with secondary school students, the elements

represented on the test have been effectively utilized with

adults using other diagnostic tools (Ash, 1986).

The LSP contains 23 independent scales representing

four higher order factors, cognitive styles, perceptual

responses, study preferences and instructional preferences.

Readability level of the LSP was set at grade 5-6 and

affirmed by six selected samples. Data to support the










construct and concurrent validity of the LSP have been

reported. Positive correlations were obtained between

scores on the LSP and the Group Embedded Figures Test, the

Edmonds Learning Style Identification Exercise, the Learning

Style Inventory and the Kaufman Assessment Battery for

Children. Exploratory and confirmatory factor analysis was

utilized in the field testing of the instrument to ensure

inclusion of concepts and items that exhibited strong factor

loadings. Reliability data for the LSP have also been

reported. The average internal consistency reliability for

subscales is 0.63, with a range from 0.47 to 0.86 (Keefe &

Monk, 1990).

Administration and Use of the Learning Style Profile

A test administrator was trained in the proper

administration of the Learning Style Profile and

interpretation of the profile. Use of a single test

administrator for all five groups was designed to ensure

conformity of procedures. Profiles were administered by

group, not individually. The test administrator read the

questions, allowing time for response to each question, to

ensure that all instructions were understood and a response

was given for each question. Because the sample was drawn

from developmental English education students, reading the

questions was intended to allow a higher level of

consistency on the profile for poor readers. The

consistency scores for the five groups on the profile








57

averaged 2.5, a relatively high level of consistency. The

closer the consistency score is to zero, the more likely the

profile is an accurate reflection of style (Keefe & Monk,

1990).

The profile for each respondent was machine-scored

using a computer and National Computer Systems (NCS)

scanning equipment. The computer program for scoring was

obtained from the National Association of Secondary School

Principals. This program generated both individual student

profiles and class profiles. The class profiles allowed for

treatment group comparisons and the development of group

instructional strategies based on commonalities. The

profiles listed standard scores for each subscale based on

national norms adjusted for grade, race and sex. Each group

profile form and a description of the scoring process are

located in the Appendices.

The Learning Style Profile was used as a diagnostic

tool to enable instructors to identify learning style

strengths or weaknesses for each group of students.

Instructors were able to organize instruction around the

identified profiles of each group. The learning style

preferences in common to the group formed the basis for the

development of instructional strategies. Instructional

strategies were based on Keefe's (1987) program-person

continuum of learning strategies. These strategies included

adapting the instructional environment to the groups








58
preassessed learning styles and helping individuals to adapt

skills, preferences or styles to the existing environment.

Treatment

The researcher selected five instructors for the study.

Five different instructors were selected to ensure that

treatment strategies intended for one experimental group

were not unintentionally shared with another group. All

instructors had previous experience working in developmental

education with self-paced, computerized assisted programs.

Complete group learning style profiles including cognitive,

affective and physiological dimensions were developed for

all five experimental groups. (See appendix for group

profiles).

One instructor was assigned to a group that was taught

through standardized, computerized instructional

intervention techniques that disregarded group learning

style profiles. The instructor for this group received no

training in learning style profile interpretation or

instructional techniques for learning style. The learning

style profile administered to this group was developed

solely for group comparative purposes.

The remaining instructors were assigned to treatment

groups utilizing instructional methods focusing on learning

styles. The remaining instructors were administered the

learning style profile by the researcher. For those groups

with an emphasis on only a single dimension of the learning










style profile, only that portion of the profile was made

available to the instructor assigned to those particular

treatment groups. Limiting the information available from

the profiles to the treatment variable for that group was an

attempt to ensure that information on other components of a

group's profile were not inadvertently shared with the

group. Additionally, the instructors received training in

instructional applications only in the dimensions) of style

which applied to their group. These procedures were an

attempt to provide some control for experimenter effect.

In order to control for the potential effect individual

differences in instructors might have on each group, the

instructional methods and materials were standardized.

There was no flexibility allowed in approaches for each

group. The basic content of the course was the same across

all groups. Computers formed a core part of the

individualization of the program to allow students to go at

their own pace. Those instructors using instructional

strategies for adaptation to or enhancement of learning

styles worked from the same handbooks and guidelines

(Appendix H). The researcher held weekly meetings with the

instructors to discuss class assignments and ensure

conformity to the agreed upon strategies.

The instructional interventions lasted for a period of

12 weeks. Researchers have confirmed that learning style

can be modified sufficiently to affect achievement within a










relatively short time frame (Reynolds & Torrance, 1978).

During the 12 week period, students in the experimental

groups were given varying levels of instructional

intervention based on which learning style component,

cognitive, affective, physiological or all three combined,

was being emphasized in that particular group. Group one

received a complete interpretation of all three dimensions

of the learning style profile. In addition, the students

were taught strategies to improve skills that related to all

three learning style dimensions. Group two received an

interpretation of only the cognitive dimension of the

learning style profile and training in strategies related to

that dimension. Group three received an interpretation of

only the affective dimension of the learning style profile

and training in strategies related to that dimension. Group

four received an interpretation of only the physiological

dimension of the learning style profile and training in

strategies related to that dimension. Finally, the non-

treatment group, group five, was taught the same materials

through instructional methods that disregarded the learning

style profile of the group.

The strategies for each learning style dimension

incorporated several instructional approaches. First,

groups were given both a verbal and written explanation of 7

the skill, style or preference. Researchers have shown that

learning can be enhanced by simply advising a student










regarding their particular learning style and the strengths

and weaknesses in skills associated with that style

(Letteri, 1985). Examples and generic activities forN

practicing each skill were developed with the aid of

training materials from the National Association of

Secondary School Principals. Practice of skill activities

were incorporated into the regular classroom/lab activities.

Additionally, instructors were trained to recognize their \

own style preferences and how variation in teaching

approaches or matching to strong student preferences was

required to accommodate different styles. Since all work

with students was based on a group profile, isolation of

strategies for particular skills was simplified.

Preferences associated with physiological yle

included study time, posture, mobility, sound, lighting. and

temperature. Students in groups four and one were counseled

regarding their preferences and how best to adapt themselves

to instructional settings that can not be changed to fit

their preferred learning style. An emphasis was made on

helping the students to adapt to all learning environments.

However, the instructional environment established for these

particular groups reflected a design that was flexible

allowing different spaces for different physiological

preferences. Those spaces included variation in type of

lighting, dim and bright, type of seating, standard

classroom seats to sofas and lounges, places in which a










student could move around, and headphones for students who

preferred some music or sound although they worked.

Although the environment or instructional setting can

be adapted for physiological preferences, affective style

and cognitive skills are traits that require a different

approach. The teaching method can be adapted in some cases

depending on the skill or trait, but intervention for these

two styles focused on counseling and developing adaptive

skills for the affective components and training in weak

skill areas in the cognitive dimension.

Affective traits or preferences included persistence,

verbal risk taking as a measure of anxiety, introversion-

extroversion and risk taking, and grouping or social

motivation. To improve persistence and risk taking students

in group three and one were given periodic positive

feedback. To encourage persistence, these groups were

taught how to establish incremental goals, to break down a

large task into smaller components. Tasks were adapted to

appeal to the profiles of the group. Finally, students in

group three and one received counseling and work in smaller

groups to counter-balance high anxiety, excessive

introversion and inability to take risks. Those students

who worked better in smaller groups were accommodated.

Although weaknesses were accommodated in the beginning,

strategies were used to strengthen those weaknesses and

gradually expose the student to situations that required










more risk taking, increasing anxiety, but providing the

support required to be successful.

The research reported that individual learners appear

to be inherently stronger in certain cognitive skills

(Schmeck, 1988c). As students progress through school,

those strong skills are usually reinforced and often the

weaker skills are not developed (Schmeck, 1988c). The focus

for group two, the cognitive skills group, and group one,

the group emphasizing all dimensions of learning style, was

to teach the strategies associated with analytical or

problem solving skills, spatial reasoning ability,

discrimination and categorization skills, sequential and

simultaneous processing, and memory skills. In addition,

preferences for verbal, auditory, or kinesthetic approaches

to learning were examined. The research indicated that by

the time a learner reaches adulthood, these sensory

modalities are fairly well integrated with preferences for

one or two (Keefe, 1987). Strategies, outlined in the NASSP

instructional handbooks were used to accommodate modality

preferences.

r. Strategies developed by the NASSP in association with

the development of the Learning Style Profile were used

throughout the research project. Strategies from handbooks

and audio tapes developed by NASSP formed the core of the

instructional methods used in the four experimental groups.

Instructional handbooks included Developing Cognitive Skills








64

(1990) by John M. Jenkins, Charles A. Letter, and Patricia

Rosenlund and Accommodating Perceptual. Study and

Instructional Preferences (1990) by James W. Keefe. The

audio tape used by instructors was Learning Style and

Effective Instruction by John M. Jenkins (1990).














CHAPTER 4
RESULTS AND CONCLUSIONS


The purpose of this study was to determine the

effectiveness of utilizing learning style profiles with the

adult learner. In order to determine the effectiveness of

utilizing learning style profiles, learning outcomes of

groups of adult learners taught through instructional

methods based on individual learning style were compared to

learning outcomes of adult learners taught through

instructional methods that disregarded preferred learning

style. Furthermore, the researcher attempted to determine

whether there were differences in learning outcomes when

instructional methods focused on a single learning style

component or a combination of all three components of

learning style, as defined by Keefe (1979).

The study was designed to determine whether one of five

instructional methods used to teach developmental English to

adult learners was superior. The research design was a

pretest-posttest control group design. Students were

randomly assigned to one of five experimental groups. The

groups were composed of 25 students each.

Specifically, the researcher attempted to answer the

following question: Are the five methods of instruction used








66

to teach developmental English to the adult learners in this

study equally effective when adjusting for the influence of

the students' pretest scores? The null hypothesis for the

question investigated in the study was: The mean posttest

scores of the five methods of instruction are not

significantly different after adjusting for the influence of

the students' pretest scores.

While the intent was to conduct a one-way analysis of

covariance (ANCOVA) to analyze the relative effectiveness of

the different instructional methods, the interaction of the

pretest and the groups was significant (F=2.60, df=4, 115,

p=.039). Therefore, an interaction model, presented in

Table 4-1, was used. The alpha level used to test the null

hypothesis was set at .01. The scores from the protests

formed the covariate with the scores from the posttests as

the dependent variables and group as the independent

variable.


Table 4-1

Interaction Model for the Learning Style Profile Study


SOURCE D SS MS F

PRETEST 1 7707.60 7707.60 3244.66 *

GROUP 4 1362.83 340.71 143.43 *

PRE*GROUP 4 24.67 6.17 2.60 *

ERROR 115 273.18 2.38

* significant at p < .01











Table 4-2 presents the unadjusted means and standard

deviations of the different groups on the pretest and

posttests. All treatment groups experienced an increase in

achievement level as measured by the posttest. The greatest

difference between unadjusted pretest and posttest mean

scores occurred in group one all dimensions of learning

style, followed by group two cognitive, group three -

affective, group four physiological, and group five -

control, respectively.


Table 4-2

Means and Standard Deviations
for the Learning Style Profile Study (N=125)

Method Pretest Posttest

Mean SD Mean SD

Group One All Styles 28.6 7.95 40.24 6.98

Group Two Cognitive 27.68 8.15 34.76 8.04

Group Three Affective 30.04 9.16 35.96 9.09

Group Four Physiological 25.88 5.78 29.48 5.51

Group Five Control 26.08 8.17 28.24 7.94



The mean posttest score for students in group one,

focusing on all three dimensions of style, was 40.24. The

mean posttest score for group two, the cognitive style

group, was 34.76. The mean posttest score for group three,

the affective group, was 35.96. The mean posttest score for










group four, the physiological group, was 29.48. The mean

posttest score for group five, control group, was 28.24.


Table 4-3

Separate Regression Equations for Each Treatment Condition
for the Learning Styles Profile Study


Group One All three 16.01 + .97 X pretest

Group Two Cognitive 7.80 + .97 x pretest

Group Three Affective 6.28 + .99 x pretest

Group Four -Physiological 6.94 + .87 x pretest

Group Five Control Group 2.98 + .85 x pretest



Regression equations were calculated for each treatment

condition. The regression equations are presented in Table

4-3. The predicted posttest scores, based on pretest scores

ranging from zero and sixty were calculated for each

treatment group (Table 4-4). The highest predicted posttest

score with a pretest of 60 was 74.21 for treatment group one

emphasizing all dimensions of learning style. The lowest

predicted posttest score at pretest equal to 60 was group

five, the control group, at 53.98. Group two, cognitive,

and group three, affective, had approximately equal

predicted posttest scores (at a pretest of 60), of 66 and

65.68, respectively. Group four, physiological, with a

posttest score of 59.14, at a pretest of 60, had a higher

predicted posttest score than the control group, but a lower










posttest score than group two and three. The predicted

scores were used to plot an interaction graph.


Table 4-4

Predicted Posttest Scores at a
Pretest Ranging from Zero to Sixty
for the Learning Styles Profile Study


Pretest = 0 Pretest = 60

Group One All three 16.01 74.21

Group Two Cognitive 7.80 66.00

Group Three Affective 6.28 65.68

Group Four Physiological 6.94 59.14

Group Five Control Group 2.98 53.98



The graph on which these predicted scores were plotted

is presented in Figure 4-1. The pretest scores are scaled

along the x-axis and the posttest scores are placed on the

y-axis. Scores ranged from zero to sixty. The resultant

graph and the equations in Table 4-3 indicated that the

control group and the physiological group had much flatter

lines than the other three groups. Lines plotted for all,

cognitive and affective indicated a steeper slope.

The interaction graph revealed that the posttest scores

of those groups that focused on all three learning

dimensions (group one) the cognitive dimension (group two)

or the affective dimension (group three) increased the

fastest as the students' pretest scores increased. The





























10 20 30 40 50
PRETEST SCORES


CONTROL
- COGNITIVE


PHYSIOLOGICAL E AFFECTIVE

SALL DIMENSIONS


Figure 4-1 Interaction Graph
Learning Styles Profile Study








71
posttest scores of those groups that ignored learning styles

or focused only on the physiological dimension of learning

styles increased less rapidly. Students in the group that

focused on all three learning style dimensions demonstrated

posttest scores which were significantly higher than those

of the other four treatment groups at all levels of the

pretest. The posttest scores of the cognitive and affective

groups were not significantly different from one another,

but were significantly higher than the posttest scores of

the control group at all pretest levels. The cognitive and

affective group posttest scores were significantly higher

than the physiological group for higher pretest scores.

However, cognitive, affective and physiological posttest

scores were approximately equal for the lower pretest

scores.

In summary, all experimental groups experienced

increases in posttest scores after treatment. However, at

pretest equal to zero, group one, the group emphasizing all

dimensions of learning style, had the highest predicted

posttest scores for all students regardless of achievement

level, as measured by the pretest. Group five, the control

group, had the lowest predicted posttest scores for all

students regardless of achievement level. Group two,

cognitive, group three, affective, and group four,

physiological were, approximately, equally effective, for

students at a lower achievement level. However, the










cognitive and the affective group were more effective than

the physiological group as students' achievement level

increased.

The hypothesis stated that there would be no

significant difference between the five instructional

methods. The data analysis revealed a statistically

significant (p <.01) interaction effect. Therefore, the

null hypothesis stating there would be no difference between

the five instructional methods was rejected. The five

methods of instruction are not equally effective for adult

learners. The results from this study revealed that the

treatment group that emphasized all three dimensions of

learning style was the most effective instructional method

for teaching developmental English to adult learners.

Furthermore, the results revealed that instructional methods

emphasizing single dimensions of learning style, while not

as effective as methods emphasizing all dimensions of

learning style, are more effective than methods that ignore

learning style. Finally, the results revealed some

variations in effectiveness between methods based on

students' achievement level.














CHAPTER 5
SUMMARY, CONCLUSIONS AND RECOMMENDATIONS


The purpose of this study was to determine the

effectiveness of utilizing learning style profiles with the

adult learner. In order to determine the effectiveness of

utilizing learning style profiles, learning outcomes of

groups of adult learners taught through instructional

methods based on individual learning style were compared to

learning outcomes of adult learners taught through

instructional methods that disregarded preferred learning

style. Furthermore, the researcher attempted to determine

whether there were differences in learning outcomes when

instructional methods focused on a single learning style

component or a combination of all three components of

learning style, cognitive, affective, and physiological, as

defined by Keefe (1979).

A review of the literature indicated that, as of 1991,

very few studies had focused on the use of learning styles

to enhance learning in the adult learner. The preponderance

of that research related the cognitive dimension of learning

styles to achievement in adult learners. Additionally, no

studies had attempted to isolate the three dimensions of

learning style, as defined by Keefe (1979) and determine










which dimension or if a combination of all three dimensions

was better for increasing learner performance.

This study was designed to determine whether one of

five instructional methods used to teach adult learners was

superior. The research design was a pretest-posttest

control group design. Students were randomly assigned to

one of five experimental groups. The groups were composed

of 25 students each. Instructional methods varied only in

the emphasis given to the three dimensions of learning

style.

A review of the literature provided supporting evidence

for both teaching students to adapt preferred learning

styles and for changing the environment or instructional

approach to match student learning styles (Schmeck, 1988).

The instructional methods adopted for this study were based

on Keefe's (1987), Schmeck's (1988) and Letteri's preference

for using a combination of instructional approaches to

enhance learner performance. As noted by Keefe (1987),

"cognitive growth can come from adapting the environment to

the existing skills of the learner, or from helping the

individual adapt successfully to the demands of the

environment" (p. 34). Therefore, for the purposes of this

study, the treatment conditions consisted of both matching

the environmental or instructional approaches to a group's

preferred learning style profile and teaching the group to

adapt learning styles to the instructional environment.










Specifically, the researcher attempted to answer the

question: Are the five methods of instruction used to teach

the adult learners in this study equally effective when

adjusting for the influence of the students' pretest

scores? A one-way analysis of covariance (ANCOVA) was

conducted to analyze the relative effectiveness of the

different instructional methods. The alpha level used to

test the null hypothesis was set at .01. The scores from

the protests formed the covariate with the scores from the

posttests as the dependent variables and group as the

independent variable.

Prior to using the ANCOVA, a test of the

assumptions of linearity and common slope were made. The

basic assumption of linearity was satisfied by the use of a

scatter plot. The test of common slope or homogeneous

regression coefficients indicated that the assumption of

equal regression slopes was untenable (F=2.60, df=4, 115,

p=.039). Therefore, an interaction model was utilized.

Conclusions

The data analysis revealed a statistically significant

(p <.01) interaction effect. Therefore, the null hypothesis

stating there would be no difference between the five

instructional methods was rejected. The five methods of

instruction are not equally effective for adult learners.

The results from this study revealed that the treatment

group that emphasized all three dimensions of learning style










was the most effective instructional method for teaching

developmental English to adult learners. Instructional

methods focusing on single dimensions of learning style were 1

more effective than instructional methods that ignored

learning style preferences. Instructional methods that

ignored learning style preferences were the least effective

for adult learners. The conclusions to be drawn from these

findings are that instructional methods that employ

strategies emphasizing learning style may lead to an

increase in academic achievement for adult learners.

This research confirmed the findings of Dunn and Dunn

(1987), Cavanaugh (1979a), Douglass (1979), Fourier (1983),

Letter (1985) and others documenting the relationship

between instructional methods focusing on learning styles

and improved learner performance. However, this research

took the concept one step further to examine whether there

was a difference in learner performance when one dimension

of learning style is the focus of a particular instructional

method to the exclusion of another. This research concluded

that there is a difference. Instructional methods focusing

on all three dimensions are more effective than those

focusing on single dimensions of style.

Furthermore, it can be concluded from the findings of

this study that instructional methods utilizing learning

styles have varying degrees of effectiveness depending on

the achievement level of the student. The interaction graph










revealed that the posttest scores of those groups that

focused on all three learning dimensions (group one) the

cognitive dimension (group two) or the affective dimension

(group three) increased the fastest as the students' pretest

scores increased. The posttest scores of those groups that

ignored learning styles or focused only on the physiological

dimension of learning styles increased less rapidly.

The findings revealed that at pretest equal to zero,

group one, the group emphasizing all dimensions of learning

style, had the highest predicted posttest scores for all

students regardless of achievement level, as measured by the

pretest. Group five, the control group, had the lowest

predicted posttest scores for all students regardless of

achievement level. Group two, cognitive, group three,

affective, and group four, physiological were,

approximately, equally effective, for students at a lower

achievement level. However, the cognitive and the affective

group were more effective than the physiological group as

students' achievement level increased.

In summary, students in the group that focused on all

three learning style dimensions consistently demonstrated

posttest scores that were significantly higher than those of

the other four treatment groups regardless of achievement

level. The posttest scores of the cognitive, affective, and

physiological groups were not significantly different from

one another for low academic achievers. However, the










posttest scores for the high academic achievers in the

affective and cognitive groups were significantly higher

than the posttest scores of the high academic achievers in

the physiological group. The posttest scores of the control

group were significantly lower than posttest scores of all

other groups at all academic achievement levels.

Previous research has reported differences in learning

styles between low and high achieving students as measured

by grade point average or selected standardized tests.

(Griggs, 1984). The fact that these differences exist may

account for the increasing effectiveness, as achievement

level increases, of instructional methods focusing on

affective, cognitive, or all three learning style dimensions

discovered in this study. Griggs (1984), summarizing the

results of several studies, concluded that a broad range of

techniques and strategies were preferable for the high

achiever, particularly strategies focusing on the cognitive

skills. Furthermore, research (Schmeck, 1988a) has

confirmed that as an individual ages certain elements or

style preferences are reinforced, adapted or modified. The

developmental nature of certain dimensions of learning style

may account for the variation in effectiveness found in this

study for different academic achievement levels.

The findings suggested that as student achievement

level increased, instructional methods focusing on

physiological style preferences were not as effective as










methods focusing on all learning style dimensions, the

cognitive dimension, or the affective dimension. These

findings indicate that physiological preferences, for the

higher achieving adult learner, may not be as relevant to

increased learner performance as other dimensions of

learning style. These findings are contrary to the research

utilizing the Learning Style Inventory (Dunn, Dunn & Price,

1978) that indicated physiological preferences for high

achieving elementary and secondary students was a

significant factor in the improvement of learner

performance. Maturity may be a factor in considering the

importance of physiological style preferences for the adult

learner, particularly the high achiever. The higher

achieving adult learner may have learned to adapt

physiological preferences to the structured, standardized

nature of the classroom environment over the years. Lower

achieving adult learners may still be adapting or modifying

physiological preferences to the standard classroom setting.

In conclusion, the results of this study confirm the

effectiveness of instructional methods utilizing learning

style profiles for the adult learner. The evidence clearly

supports the positive impact on learner performance of

instructional methods based on an understanding of learning

styles. The findings confirm that instructional methods

that emphasize all dimensions of learning style are the most

effective. Furthermore, the study indicated that










instructional methods emphasizing even one dimension of

learning style are more effective than instructional methods

that completely ignore learning style.

Implications and Recommendations

This study demonstrated the effectiveness of utilizing

learning style profiles with adult learners. Instructional

strategies based on learning style that have previously

proven effective for increasing the academic achievement of

elementary and secondary school children proved to be

effective with adult learners. These findings should be of

particular significance to educational institutions that

focus on the adult learner. The assessment of learning

styles with the Learning Style Profile would enable

instructors to develop instructional strategies that would

improve adult learner performance. Furthermore, instruction

does not have to be individually prescribed to be

successful. Development of instructional strategies can be

based on group learning style profiles.

This study has implications for further research

utilizing the Learning Style Profile with adult learners.

Analysis of the individual learning style profiles of

varying groups of adult learners based on achievement level,

gender and even race would provide additional data for

understanding how individual adult learners differ.

Understanding of these individual differences has

consequences for the way in which individuals interact with










the educational environment thus impacting learner

performance.

This study did not attempt to test the effectiveness of

instructional methods focusing on all the possible

combinations of the three learning style dimensions. For

instance, the effectiveness of an instructional method that

isolated the cognitive and affective dimensions of learning

style was not considered. Future studies should examine

instructional methods that include all the possible

combinations of the three dimensions of learning style to

further isolate those variables that are the most effective

for improving learner performance.

A replication of this study might also take into

consideration the potential impact of using multiple

instructors. A future study might focus on the use of a

single instructor to counter the possible confounding of

instructor and treatment. While strenuous controls were

placed on the instructional environments, possible

confounding may have occurred.

Given the success of this study with adult learners

enrolled in developmental education classes for college

level English, the obvious expansions of this investigation

would include:

1. Replication of the study in other content areas.








82
2. Examination of the effectiveness of utilizing

learning styles in college level courses as compared to

developmental courses.

3. Longitudinal studies or studies of longer duration

to determine if the relationship between the different

instructional methods remains consistent across time.

4. Comparison of the learning style profiles of varying

achievement levels of adult learners to clarify the

differences existing between the learning styles profiles of

high and low achievement levels.

















APPENDIX A
SAMPLE INDIVIDUAL LEARNING STYLE PROFILE

LEARNING STYLE PROFILE


THIS PROFILE IS FOR:
BIRTHDATE: SEX: GRADE:
DATE: SCHOOL: CLASS:

SKILLS--GENERAL APPROACH TO
SCORE WEAK


RACE:


PROCESSING INFORMATION
AVERAGE STRONG


Analytic
Spatial
Discrimination
Categorization
Sequential
Memory
Simultaneous


PERCEPTUAL RESPONSES--INITIAL RESPONSE TO VERBAL INFORMATION
SCORE WEAK AVERAGE STRONG


Visual
Auditory
Emotive


ORIENTATIONS AND

SCORE

Persistence
Verbal Risk
Manipulative
Study Time:
Early Morning
Late Morning
Afternoon
Evening

SCORE

Verbal-Spatial
Grouping
Posture
Mobility
Sound
Lighting
Temperature


PREFERENCES--PREFERRED RESPONSE TO STUDY OR
INSTRUCTIONAL ENVIRONMENT
WEAK AVERAGE STRONG


HIGH NEUTRAL HIGH


CONSISTENCY SCORE: NORMATIVE SAMPLE:

Source: National Association of Secondary School Principals, Reston, Va.
1990

















APPENDIX B
GROUP ONE LEARNING STYLE PROFILE

LEARNING STYLE PROFILE

THIS PROFILE IS FOR: GROUP ONE ALL DIMENSIONS


SKILLS--GENERAL APPROACH TO PROCESSING INFORMATION
SCORE WEAK AVERAGE STRONG


Analytic
Spatial
Discrimination
Categorization
Sequential
Memory


PERCEPTUAL RESPONSES--INITIAL RESPONSE TO VERBAL INFORMATION


SCORE WEAK


AVERAGE


STRONG


Visual
Auditory
Emotive

ORIENTATIONS AND

SCORE

Persistence
Verbal Risk
Manipulative

Study Time:
Early Morning
Late Morning
Afternoon
Evening

SCORE

Verbal-Spatial
Grouping
Posture
Mobility
Sound
Lighting
Temperature


t------ ---xxx----------

PREFERENCES--PREFERRED RESPONSE TO STUDY OR
INSTRUCTIONAL ENVIRONMENT


WEAK AVERAGE STRONG



_ xxxx -


------ ------ -xxx x ----
---------- xx~xxx ------

HIGH NEUTRAL HIGH

Spatial xx Verbal
Small xxLarge
Informal -xx--- Formal
Stillness ----xxxx Movement
Quiet xx --Sound
Dim -- Bright
Cool x Warm


CONSISTENCY SCORE: 3 NORMATIVE SAMPLE: 1986 -- National

Source: National Association of Secondary School Principals, Reston, Va.
1990


~


~

















APPENDIX C
GROUP TWO LEARNING STYLE PROFILE


LEARNING STYLE PROFILE

THIS PROFILE IS FOR: GROUP TWO COGNITIVE DIMENSIONS


SKILLS--GENERAL APPROACH TO PROCESSING INFORMATION
SCORE WEAK AVERAGE STRONG


Analytic
Spatial
Discrimination
Categorization
Sequential
Memory


Visual
Auditory
Emotive


PERCEPTUAL RESPONSES--INITIAL RESPONSE TO VERBAL INFORMATION
SCORE WEAK AVERAGE STRONG


S- xx
I ------ I ------ I-xxi--I ------


ORIENTATIONS AND PREFERENCES--PREFERRED RESPONSE TO STUDY OR
INSTRUCTIONAL ENVIRONMENT
SCORE WEAK AVERAGE STRONG

Persistence xxxx
Verbal Risk
Manipulative xx


Study Time:
Early Morning
Late Morning
Afternoon
Evening

SCORE

Verbal-Spatial
Grouping
Posture
Mobility
Sound
Lighting
Temperature


HIGH NEUTRAL HIGH


Verbal
Large
Formal
Movement
Sound
Bright
Warm


CONSISTENCY SCORE: 3 NORMATIVE SAMPLE: 1986 -- National

Source: National Association of Secondary School Principals, Reston, Va.
1990


Xxxx_


















APPENDIX D
GROUP THREE LEARNING STYLE PROFILE

LEARNING STYLE PROFILE

THIS PROFILE IS FOR: GROUP THREE AFFECTIVE


SKILLS--GENERAL APPROACH TO PROCESSING INFORMATION
SCORE WEAK AVERAGE STRONG


Analytic
Spatial
Discrimination
Categorization
Sequential
Memory


PERCEPTUAL RESPONSES--INITIAL RESPONSE TO VERBAL INFORMATION
SCORE WEAK AVERAGE STRONG


Visual
Auditory
Emotive

ORIENTATIONS AND

SCORE

Persistence
Verbal Risk
Manipulative

Study Time:
Early Morning
Late Morning
Afternoon
Evening


SCORE

Verbal-Spatial
Grouping
Posture
Mobility
Sound
Lighting
Temperature


-- xxxx
-xxxx
xxxx

PREFERENCES--PREFERRED RESPONSE TO STUDY OR
INSTRUCTIONAL ENVIRONMENT
WEAK AVERAGE STRONG



xxxx



-- -- x x

~x--x


HIGH NEUTRAL HIGH


Spatial
Small
Informal
Stillness
Quiet
Dim
Cool


Verbal
Large
Formal
Movement
Sound
Bright
Warm


CONSISTENCY SCORE: 2 NORMATIVE SAMPLE: 1986 -- National

Source: National Association of Secondary School Principals, Reston, Va.
1990


xxxx-


-- xxxx- -
------ --- x xx ----------

---xxxx-

















APPENDIX E
GROUP FOUR LEARNING STYLE PROFILE

LEARNING STYLE PROFILE

THIS PROFILE IS FOR: GROUP FOUR PHYSIOLOGICAL


SKILLS--GENERAL APPROACH TO PROCESSING INFORMATION


SCORE WEAK


Analytic
Spatial
Discrimination
Categorization
Sequential
Memory


AVERAGE


STRONG


xm


PERCEPTUAL RESPONSES--INITIAL RESPONSE TO VERBAL INFORMATION
SCORE WEAK AVERAGE STRONG

------ xxxx-
xxxx-


Visual
Auditory
Emotive


ORIENTATIONS AND PREFERENCES--PREFERRED RESPONSE TO STUDY OR
INSTRUCTIONAL ENVIRONMENT
SCORE WEAK AVERAGE STRONG


Persistence
Verbal Risk
Manipulative

Study Time:
Early Morning
Late Morning
Afternoon
Evening

SCORE

Verbal-Spatial
Grouping
Posture
Mobility
Sound
Lighting
Temperature


o xxx




~~---yti


HIGH NEUTRAL HIGH


Spatial
Small
Informal
Stillness
Quiet
Dim
Cool


Verbal
Large
Formal
Movement
Sound
Bright
Warm


CONSISTENCY SCORE: 2 NORMATIVE SAMPLE: 1986 -- National

Source: National Association of Secondary School Principals, Reston, Va.
1990

















APPENDIX F
GROUP FIVE LEARNING STYLE PROFILE


LEARNING STYLE PROFILE

THIS PROFILE IS FOR: GROUP FIVE CONTROL


SKILLS--GENERAL APPROACH TO PROCESSING INFORMATION
SCORE WEAK AVERAGE STRONG


Analytic
Spatial
Discrimination
Categorization
Sequential
Memory


Visual
Auditory
Emotive


PERCEPTUAL RESPONSES--INITIAL RESPONSE TO VERBAL INFORMATION
SCORE WEAK AVERAGE STRONG

F --- -
-------- ~ ~ ~ ------ -x x -- ----
-------------- x xx ----- ------


ORIENTATIONS AND PREFERENCES--PREFERRED RESPONSE TO STUDY OR
INSTRUCTIONAL ENVIRONMENT
SCORE WEAK AVERAGE STRONG


Persistence
verbal Risk
Manipulative

Study Time:
Early Morning
Late Morning
Afternoon
Evening

SCORE

Verbal-Spatial
Grouping
Posture
Mobility
Sound
Lighting
Temperature


t~tI --~
xxoox


HIGH NEUTRAL HIGH


Verbal
Large
Formal
Movement
Sound
Bright
Warm


CONSISTENCY SCORE: 3 NORMATIVE SAMPLE: 1986 -- National

Source: National Association of Secondary School Principals, Reston, Va.
1990


3--
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APPENDIX G
INTERPRETATION OF THE LEARNING STYLE PROFILE


For interpretation purposes, a mark falling in the

first box, to the far left, places a student in a group

containing only 16% of the population. The two middle boxes

include approximately 68% of the population. Finally, the

fourth box, to the far right, places the student in a group

containing approximately 16% of the normative population.

(Keefe and Monk, 1990)

The consistency score on the bottom of the profile

estimates how consistently a student responded to the

questions. The closer the consistency score is to zero, the

more dependable the profile is as representing a student's

style. Consistency level can be affected by the amount of

attention or responsibility a student brought to the task,

random responses to the answer sheet or individual reading

problems. A consistency score from one to seven is

considered good. The consistency scores for all groups in

this study were good, either a 2 or 3 was calculated for all

groups.

The five groups in this study had similar profiles.

The profiles for all five groups in cognitive skills,

modalities or perceptual responses, affective orientations,








90

and study time preferences were in the middle range, falling

approximately where 68% of the population falls. All

physiological preferences, except sound and posture also

fell in the middle or neutral range. All five groups

measured in the neutral range for lighting, temperature,

mobility, grouping and verbal-spatial preferences. All five

groups, in this study, preferred quiet, formal surroundings

for study.














APPENDIX H
SAMPLE INSTRUCTIONAL STRATEGIES FROM
THE LEARNING STYLE PROFILE HANDBOOKS

DE'VELC'PI[NG COGNITIVE SKILLS


1. Begin with a verbal and written definition of the
cognitive skill.

2. Provide examples of the cognitive skill: (i.e. examples
of analysis include sorting things, breaking fractions into
component parts, identifying parts of speech).

3. Ask students to identify things they can do with the
particular cognitive skill, examples of the skill or other
activities using the particular skill.

4. Ask students to identify ways that particular skill can
be helpful in daily life, in various subject matters, or
problem solving.

5. Provide students with generic activities from handbook to
teach step-by-step approach to completing a task.

6. Provide practice activities from handbook related to
specific subject matter to insure carry-over of skill to
specific subject related tasks.


ACCOMMODATING PERCEPTUAL. STUDY AND
INSTRUCTIONAL PREFERENCES


1. Provide a flexible instructional environment to
accommodate posture, mobility, sound, lighting and
temperature preferences. Seating arrangements are the best
way to accommodate these preferences. Sound accommodations
are possible with carpeting, use of ear plugs, seating away
from activity areas.

2. Grouping preference can be accommodated by using small
group instruction. Follow the handbooks guidelines for
different type of small group instruction. Focus on process
as well as content.








92

3. Provide videotape presentations, audio-books, cassette
tapes, word processing equipment, calculators, etc. for
groups with a strong manipulative preference. These
learners pre 'hands-on' activities.

4. Improve persistence orientation with the Triad Planning
Groups described in the handbook. Allow students to self-
pace or take frequent breaks if necessary.

5. Utilized instructional approaches, described in handbook,
which accommodate the visual, auditory and emotive learners.
Instructional methods should reflect activities which meet
all three preferences.

Source: Jenkins, Letteri, & Rosenlund, 1989














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