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Impact of analogical versus logical representations of theoretical concepts on recall and problem-solving performances of concrete and abstract thinkers
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Includes bibliographical references (leaves 118-127).
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by David N. Yonutas.
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IMPACT OF ANALOGICAL VERSUS LOGICAL REPRESENTATIONS OF
THEORETICAL CONCEPTS ON RECALL AND PROBLEM-SOLVING
PERFORMANCES OF CONCRETE AND ABSTRACT THINKERS














By

DAVID N. YONUTAS


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


2001





























This dissertation is dedicated to my children, Nathan and Claire, to
my wife, Linda, to my mother, Maria, and most of all, to the spirit of my
father, Nickolas.














ACKNOWLEDGEMENTS


I would like to thank the members of my doctoral committee for

their guidance and help in the writing of this dissertation. Dr. Lee

Mullally, chairperson of the committee, was always available when I

needed feedback and advice. His insight and belief in me were crucial

and I will always be grateful to him. Dr. David Miller's knowledge of

statistics and helpful recommendations for refining the analysis of my

data were invaluable. In addition, his willingness to join my committee

after the retirement of one of my original committee members is deeply

appreciated. I would like to thank Dr. Jeffery Hurt for his unflagging

encouragement throughout my doctoral program, especially when I felt

that my research was hitting a "dead end." Finally, I wish to thank Dr.

Patricia Ashton for her careful review of the manuscript and for her

painstaking corrections of grammatical and conceptual errors.

I would like to thank Mr. Paul Stephan, Mr. Thomas Heenen, Mr.

Jeff Majewski, Mr. Bill Cunningham, and Mr. Steve Bonett for their

review of the instructional packets and examination. Their thoughtful

recommendations improved the inter-packet consistency and helped

assure equivalency of content.








Although not a member of the committee, Dr. Stephanie Wehry

provided invaluable assistance in the statistical analysis of my data. Her

willingness to provide me with ongoing analyses every time another

"What if?" question sprang to mind was truly generous.

I especially extend my thanks to my children, Nathan and Claire

for their understanding when I was unable to be with them because of

dissertation deadlines.

Finally, my deepest gratitude is reserved for my wife, Linda, whose

unflagging support and belief in me kept me on course and enabled me

to complete my research and dissertation.















TABLE OF CONTENTS

Page
ACKNOWLEDGEMENTS ........................................................................ ii

LIST OF TABLES ................................................................................ i

LIST OF FIGURES ...............................................................................ix

ABSTRACT ...................................... ........... ............................... x

CHAPTER

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

Problem Statement ..................................................................... 2
Need for the Study ......................................................................... 4
D efin ition s ........................................................................................ 5
Delimitations ............................................................................ 7
Limitations and Assumptions........................................ ................ 8
Hypothesis.................................................... ............................. 8


2 REVIEW OF THE LITERATURE..................................... ........ .. 11

In trodu action ................................................................................... 11
Review of Learning Styles............................................................... 11
How Analogies Promote Learning ................................................... 20
Role of Analogies in Concept Attainment .... ....................... .......... 22
Attributes of Effective Analogies ................................................ 23
Advantages and Problems Associated with Analogies...................... 25
Visuals and Their Affect on Learning...... .................................... 27
Interpretation of Visuals ............................................... ............. 30
Problem Solving and Analogical Visualization................................. 34
Summary and Conclusions ....... ................................................ 37









3 METHODOLOGY........................................................................ 39

Research Design ............................................................................. 39
Population and Sample................................................................... 40
Materials and Measures................................................................. 41
Packet Design ................................................................................. 42
Kolb Learning Style Inventory: Validity and Reliability Issues......... 46
Procedure ........................................................ .............. ............ 52
Analyses ......................................................... ........................ 54
Summary.................................................................................. 54


4 RESULTS AND ANALYSIS............................................................. 56

Introduction ....................................................... ..................... 56
R esu lts .......................................................................................... 56
Additional Findings....................................... .................. ......... 61
Summary.... ........ ................. ........................................................... 69


5 CONCLUSIONS AND RECOMMENDATIONS.................................... 70

Introduction ....................................................... ..................... 70
Findings ......................................................... ......................... 71
Discussion ..................................................... .............. ......... 72
Implications................................................................................... .. 75
Recommendations for Future Research.......................................... 76
Summary.................................................................................. 81


APPENDICES

A Informed Consent Form ........................................... ......... ......... 84
B Packet 1: Analogical packet.......................................... .............. 87
C Packet 2: Mathematical packet....................................................100
D Examination ...................... ...... ..................................................107
E Information sheet supplied to participants............................... .....113


REFERENCE LIST ........ .... ........... ....................................................118


BIOGRAPHICAL SKETCH ...................................................................128














LIST OF TABLES


Table Page

2-1 Dunn, Dunn, and Price Learning Styles Inventory, Stimuli
and E lem ents........................................................................... 16

2-2 Categories of Visuals and Their Role in Knowledge Acquisition ... 28

3-1 Description of Participants in Sample......................................... 40

3-2 Hypothetical Results for Two Subjects with the Same Learning
Style (Diverger) but Different Learning Mode Scores ................ 50

4-1 Group Means and Standard Deviations on Recall and Problem-
Solving Questions ................................................................... 57

4-2 Analysis of Variance Summary Table-Recall Questions.......... 58

4-3 Analysis of Variance Summary Table-Recall Questions without
Treatment x PLS Interaction................................. ................... 59

4-4 Analysis of Variance Summary Table-Problem-Solving
Questions ...... ................................................................ ...... 60

4-5 Analysis of Variance Summary Table-Problem-Solving
Questions without Treatment x PLS Interaction....................... 60

4-6 Analysis of Variance Summary Table-Recall Questions Including
Treatment x Gender, PLS x Gender, Treatment x PLS and
Treatment x PLS x Gender Interactions ....................... .......... 62

4-7 Least Square (LS) Mean Data for Treatment/PLS/Gender
Combinations on Recall Questions ............................................ 63

4-8 Pair-Wise Comparisons of Least Square (LS) Means
(Treatment/PLS/Gender) and Probabilities that LS Means are
Equal at Alpha = .05 .................................................... ........... 64








4-9 Analysis of Variance Summary Table-Problem-Solving Questions
Including Treatment x Gender, PLS x Gender, Treatment x PLS,
and Treatment x PLS x Gender Interactions ............................... 65

4-10 Analysis of Variance Summary Table-Problem-Solving Questions
Including Treatment x Gender and PLS x Gender Interactions.... 66

4-11 Least Square (LS) Mean Data for Treatment/Gender
Combinations on Problem-Solving Questions ........................... 67

4-12 Pair-Wise Comparisons of Least Square (LS) Means
(Treatment/Gender) and Probabilities that LS Means are Equal
at Alpha = .05 ................................................... ................. 67

4-13 Least Square (LS) Mean Scores and Standard Errors Data for
Primary Learning Style/Gender Combinations on Problem-
Solving Questions .............................................. ......... ..... 68

4-14 Pair-Wise Comparisons of Least Square (LS) Means (Primary
Learning Style x Gender) and Probabilities that LS Means are
Equal at Alpha = .05 ................................. .............................. 68














LIST OF FIGURES


Figure Page

1-1 Experimental Model............................................................... 10

2-1 The Four Quadrants Comprising the Kolb Learning Style
Inventory.............................................. ................................. 18

2-2 Levie's Iconic Mode................................................ ................ 32

5-1 Mild (+3) and Strong (-27) Preferences for Concrete Experiences
Compared to Abstract Conceptualization.................................... 80














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

IMPACT OF ANALOGICAL VERSUS LOGICAL REPRESENTATIONS OF
THEORETICAL CONCEPTS ON RECALL AND PROBLEM-SOLVING
PERFORMANCES OF CONCRETE AND ABSTRACT THINKERS

By

David N. Yonutas

December 2001

Chairperson: Lee J. Mullally, Ph.D.
Major Department: School of Teaching and Learning

This study investigated gains in recall and problem solving based

on interactions between students' primary learning styles or PLS

(concrete experience and abstract conceptualization) and instructional

packet types or treatment (visual analogies and mathematical formulas).

The sample for this study was 75 students who were enrolled in, or who

had just completed, introductory anatomy and physiology courses at a

middle-sized community college in north-central Florida. Following their

informed consent, students completed the Kolb Learning Style Inventory.

Students then completed a pretest on arterial blood gas interpretation;

their scores indicated no prior knowledge of the material. At least one

week from the date of pretest, students were randomly assigned to

treatments. Students then completed a posttest, which tested recall and








problem-solving abilities. Two separate ANOVAS were performed, one for

the students' mean scores on the recall questions and one for the

problem-solving questions. The interactions between PLS and treatment

were investigated for both categories of questions.

There were no significant interactions between participants' PLS

and treatment, either on recall questions or questions requiring problem-

solving abilities. Interactions were noted for both recall and problem-

solving questions. For recall questions, a three-way interaction between

treatment, PLS, and gender was found at an alpha = .05. Males with

abstract PLSs who received mathematical packets scored higher than

females with concrete PLSs who received visual analogy packets.

For questions requiring problem-solving ability, there were

significant interactions between treatment and gender and between PLS

and gender. Males receiving packets using mathematical formulas had

higher mean scores on questions requiring problem-solving skills than

females receiving the mathematical packets. Males with a PLS of abstract

conceptualization had higher mean scores than did females with a PLS of

abstract conceptualization.

This study generated evidence that PLS and treatments do not

interact to affect performances on recall questions and questions

requiring problem-solving strategies. Exploratory analysis showed that

treatment and gender interactions, and PLS and gender interactions,

may affect performances on questions requiring problem-solving skills.








Further research is needed to determine whether these findings are

useful to teachers for the creation and selection of instructional

materials.














CHAPTER 1
INTRODUCTION

Visuals fulfill numerous roles in educational texts. One of the

functions of visuals is to serve as a "visual analogy." Analogies,

whether verbal or visual, are intended to concretize abstract material

into a more readily understood form. Duit (1991) defined analogies as

the comparison of structures between two domains. The domain that is

familiar to the learner is termed the analog and the unfamiliar domain

is the target. However, researchers have not investigated the

interaction between the learning styles of students and their ability to

interpret visual analogies. An individual's learning style refers to his or

her preferred method of gathering and processing information.

Conceivably, learners may not interpret visual analogies equally well.

Learners who prefer learning through concrete experiences may have a

difficult time learning abstract concepts without the use of visual

analogies, whereas learners who readily conceptualize abstract

concepts may not experience enhanced learning through the provision

of visual analogies.

Authors may use visuals for decorative purposes, to clarify or

reinforce textual-based concepts, or to serve as analogies and

mnemonic devices (Levin, Anglin, & Carney, 1987). Visuals, when used








appropriately, have been found to enhance retention of material and

problem-solving performance (Peeck, 1974). Paivio (1979) suggested

that visuals are stored in two channels within long-term memory,

providing for enhanced retention of factual material. On tests requiring

problem-solving skills rather than verbatim recall, Mayer (1989) found

that students who received labeled illustrations outperformed students

who did not receive labeled illustrations. Although they did not control

for learner characteristics, Bean, Searles, Singer, and Cowen (1990)

found that students receiving pictorial analogies improved their

understanding of biological concepts to a greater extent than did

students who received textual analogies.

Conceivably, individuals with preferences for concrete

experiences would benefit from the concretizing function of visual

analogies. Because of this concretizing function, visual analogies may

differentially improve abstract concept attainment and problem-solving

abilities in learners who prefer learning through concrete experiences,

compared to learning gains attained when using mathematical or

logical representations of physiologic concepts.


Problem Statement

Newby and Stepich (1987) noted that analogies may provide a

concrete substitute for abstract concepts and thus positively affect

concept attainment. Visuals also serve as analogies for abstract

concepts (Bean et al. 1990; Clement, 1988; Dwyer & Dwyer, 1989; Hurt,








1987; Issing, 1990; Messaris & Nielsen, 1989; Smith, 1989). However,

researchers have not explored whether students' learning styles affect

their ability to interpret visual analogies. Concretizing abstract

principles by providing visual analogies may enhance learning

outcomes in individuals who learn best through concrete experiences

and examples. Sensitivity to the differential affect of visual analogies

on learning, based on an individual's learning style, may have

important implications for the design of instructional materials.

Kolb determined that individuals prefer one of four styles or

modes for learning new material (Smith & Kolb, 1996). These modes

are termed active experimentation, concrete experience, abstract

conceptualization, and reflective observation (Davidson, 1990;

Davidson, Savenye, & Orr, 1992; Matthews & Hamby, 1995; O'Brien,

1994). Conceivably, the method of presenting information may interact

with an individual's learning style and thus may influence learning and

concept attainment. Instructional approaches for teaching abstract

concepts frequently use mathematical formulas to model the

relationships among multiple variables. Learners who prefer concrete

experiences may have difficulty attaining or visualizing concepts when

these concepts are presented mathematically (Roark, 1998). Because

analogies concretize abstract concepts, learners who prefer concrete

experiences rather than abstractions should benefit from the use of

visual analogies. Failure to recognize this interaction of learning style








and instructional treatment could negatively affect learning outcomes

and increase the risk of student failure. Paris and Winograd (1990)

noted that "It is unreasonable to assume that one instructional technique ...

can be used with equal effectiveness for all kinds of tasks, for all kinds

of texts, and for all kinds of students" [italics added] (p. 42).

This study was designed to determine whether a correlation

exists between learning style and instructional treatment (e.g.,

supplying visual analogies rather than mathematical formulas). If such

a correlation exists, educators may facilitate learning by supplying

visual analogies to students who prefer concrete learning experiences.


Need for the Study

This study was designed to advance understanding of how

primary learning styles interact with instructional media to influence

learning outcomes. Clark (1972, 1994) stated that one of the most

important perspectives in the design of educational experiences is the

consideration of interactions between a mode of instruction and the

relevant individual differences of learners. Performance differences on

an examination designed to measure learning gains were used as the

indicator for interactions between primary learning styles and types of

instructional format (mathematical formulas or visual analogies).

Conceivably, educators should provide a variety of instructional

approaches that will help learners achieve significant learning

outcomes, regardless of the learners' preferred learning styles. This






5

study was designed to analyze whether multiple presentation formats

(mathematical formulas or visual analogies) would improve overall

learning outcomes in a sample of learners with preferences for either

concrete or abstract learning environments. The effectiveness of the

concretizingg" function of visual analogies for abstract concepts was

explored.

Finally, the experimental approach adopted for this study was

novel in that the identical concept homeostasiss) was presented in two

different formats, either as mathematical formulas or visual analogies.

Each mathematical formula had an. identical visual analogy associated

with it. However, participants only had access to one of the

instructional formats. Learning gains based on abstract presentations

of material (formula) and concrete representations of the concepts

(visual analogies) could then be isolated and analyzed.


Definitions

The following terms are used in this dissertation. These terms

are used to describe the characteristics of analogies. Terms relating to

learning styles are included.

Advance organizer. A familiar, related concept that is presented

before introducing a new concept, to facilitate the learning of the new

concept (Ausubel, 1960; Mayer, 1978; Mayer, 1979).








Analog. Subject matter that is familiar to a learner that serves as

the basis for an analogy (Newby, Ertmer & Stepich, 1995; Thagard,

1992).

Analogy. An explicit, nonliteral comparison between two objects,

or sets of objects, that describes their structural, functional, and/or

causal similarities (Stepich & Newby, 1988).

Base domain. Subject matter that is familiar to a learner that

serves as the basis for an analogy. Equivalent to an analog (Gentner,

1983; Newby et al., 1995).

Learning style. A unique way in which an individual gathers and

processes information (Davidson, 1990); a pattern in the way a person

accomplishes a particular kind of task that is somewhat resistant to

change (Schmeck as cited in Tendy & Geiser, 1997); a biologically and

developmentally imposed set of characteristics that affect the

effectiveness of instruction for a particular individual (Dunn & Griggs,

1988); and how we perceive new information or experience and how we

process what we perceive (Smith & Kolb, 1996).

Mapping. Connecting sets of attributes from a familiar domain to

an unfamiliar domain (Armbruster & Anderson, 1984; Gentner, 1983;

Gick & Holyoak, 1980; Gick & Holyoak, 1983; Holyoak, 1984; Thagard,

1992).

Primary learning styles. Learners' preferences for either concrete

experiences or abstract conceptualization as their primary learning








modes and active experimentation or reflective observation for

determining the validity of their experiences (Cornwell & Manfredo,

1994)

Target. An unfamiliar concept that is related to a known concept

or domain (analog) in an analogy (Gentner, 1983; Gick & Holyoak, 1980;

Gick & Holyoak, 1983; Holyoak, 1984; Newby et al., 1995; Newby &

Stepich, 1987; Wong, 1993).


Delimitations

The delimitations of this study include the population/sample,

treatments, setting and instrumentation. The population from which

the sample was drawn was students enrolled in a north central Florida

community college. The participants were enrolled in anatomy and

physiology classes or were dental hygiene students who had recently

completed their anatomy and physiology coursework. The treatments

used in the study were two instructional packets developed by the

investigator on the subject of arterial blood gas interpretation. One of

the packets used mathematical formulas to teach the topic; the second

packet utilized visual analogies to teach the same content. All testing

was performed in a classroom located in the health sciences building.

The instruments used in the study were the Kolb Learning Style

Inventory (Kolb, 1993) and an examination developed by the

investigator. The examination consisted of questions that required

factual recall and questions that required problem-solving strategies.








Limitations and Assumptions

Interpretation of the results from this study is subject to the

following limitations and assumptions.

The testing of the hypotheses relied on self-reported data and
performance on examinations that did not influence participants'
grades in courses they were taking. The investigator assumed that
participants answered all questions honestly, independently, and to
the best of their ability.

Conclusions from this study are limited to the population
represented by the sample. Generalizations to other populations
should be made with caution and should be subject to replication of
results.

The published validity and reliability data for the Kolb Learning Style
Inventory were assumed to be correct.

Instructional packets, which used either visual analogies or
mathematical formulas, were randomly assigned to the participants
in order to reduce threats to internal validity.

Hypotheses

This research focused on whether mathematical expressions and

visual analogies interact with learning preferences. Differential

learning outcomes based on learning styles were investigated. Two

presentation modes were used: mathematical formulas and visual

analogies. The investigator analyzed learning, as quantified by a

posttest administered after participants read their respective packets.

The examination consisted of two types of questions, those requiring

simple recall of factual information and those requiring problem-solving

abilities. The investigator conducted an analysis of variance on each

participant's performance for each category of question.








Hypothesis 1. Mean outcome scores on examinations testing the

recall of abstract concepts do not differ due to the interaction of

treatment and learning style.

Hypothesis 2. Mean outcome scores on examinations testing

problem-solving performance on questions relating to abstract concepts

do not differ due to the interaction of treatment and learning style.

The experimental design is shown in Figure 1-1. The

experimental model consists of two separate, 2 x 2 designs. The two

categories are primary learning style (CE or AC) and instructional

packet type (mathematical formulas or visual analogies). The

interaction between primary learning styles and packet types was

investigated for two types of questions (problem solving and simple

recall). Two separate analyses of variance were performed: one for

recall questions and one for questions requiring problem-solving skills.










Recall Questions


Concrete Experience
A I- _._. I i


Aostrac i


Conceptuaization


Mathematical Formulas Visual Analogies

Instructional Packet Type


Problem Solving Questions



Inrter FPnpriencr


Styles














Styles


Mathematical Formulas Visual Analogies

Instructional Packet Type


Figure 1-1. Experimental Model


Abstract Conceptualization


C.nr














CHAPTER 2
REVIEW OF THE LITERATURE

Introduction

This study focused on the interactions between learning styles

and visual analogies and whether visual analogies enhance recall and

problem-solving capabilities. This literature review surveys the

research on learning styles and how learning styles influence learners'

preferences for particular modes of learning. Next, studies

investigating the role of analogies in learning and concept attainment

are reviewed. The literature pertaining to the roles of visuals in

learning is discussed, focusing on how visuals may serve as analogues

for abstract concepts.


Review of Learning Styles

The perception that individuals learn differently is not a new one

(Fizzell, 1984). The ancient Hindus classified individuals along two

bipolar continue: people were either active or passive and either

emotional or thoughtful. This classification scheme led to the four

basic ways of practicing religion, the four yogas (or paths).

Researchers have defined learning styles in numerous ways.

Davidson (1990) defined a learning style as the unique way in which an

individual gathers and processes information. Schmeck (as cited in








Tendy & Geiser, 1997) defined style as a pattern in the way a person

accomplishes a particular kind of task that is somewhat resistant to

change. Focusing on the design of instruction, Dunn and Griggs (1988)

defined learning styles as "a biologically and developmentally imposed

set of characteristics that make the same teaching method wonderful

for some and terrible for others" (p. 3). Maintaining that cognitive style

is a permanent attribute of an individual, Messick (1979) stated that

cognitive styles are "information-processing consistencies reflective of

underlying personality trends. They are stable attitudes, preferences,

or habitual strategies determining a person's typical modes of

perceiving, remembering, thinking, and problem solving" (pp. 286-287).

He believed that cognitive styles are independent of the content of

cognition or the level of skill displayed in the cognitive performance.

Ultimately, a learning style reflects the means by which an individual

prefers to learn. Kolb defined learning style as how a person deals with

ideas and day-to-day situations (Smith & Kolb, 1996). Corno and Snow

(1986) determined that learning/cognitive styles were "propensities for

processing information in certain ways that develop around particular

ability-personality intersections" (p. 606). Perry (1994) noted that

learning styles arise through a combination of genetics and

experiences. "Over time as a result of the complex interaction of

inherited factors and experiences in a variety of learning situations, an

individual will consistently resolve the dialectic tension between the








polar opposite dimensions in a characteristic fashion and develop a

preferred learning style that emphasizes some characteristics over

others" (p. 4). Similarly, Cahill and Madigan (1984) defined

learning/cognitive style as the way an individual acquires, perceives

and processes information in a particular learning situation.

Learning style theorists targeted particular sets of learner

attributes when they formulated their theories. Letter (1980)

established a learning style categorization based on whether learners

were analytical processors (Type 1), global processors (Type 3) or a

combination of global and analytical (Type 2). Letter believed that

analytical processors tolerated ambiguity well and were highly

successful in school, compared to global processors who categorized

objects and concepts broadly and were intolerant of ambiguity. Type 3

processors frequently were not successful academically. Type 2

learners were moderately successful in school. Letter posited that

learners could have their learning type altered through training and

consequently improve their chances for academic success.

Anthony Gregorc (as cited in Tendy & Geiser, 1997) found that

some students preferred business-like instructors who presented

material in an orderly, step-by-step fashion. Other students preferred

instructors who personalized lessons, not focusing exclusively on the

text. Gregorc's model has perspective and temporal dimensions. The

perspective dimension ranges from concrete to abstract, whereas the








temporal dimension ranges from sequential to random (Benton, 1995,

Ferro, 1995, Steward & Felicetti, 1992). Gregorc (1984) believed that

successful students could alter their learning style to accommodate

learning environments that did not match their preferred mode of

learning, whereas students who could not accommodate alternate

environments were learning disabled.

Schmeck (as cited in Tendy & Geiser, 1997) similarly believed

that learning styles could be altered to fit a particular learning

environment. Schmeck defined style as a pattern in the way a person

accomplishes a particular kind of task that is somewhat resistant to

change. Schmeck proposed that cognitive style is developmentally

determined and ranges from global to analytical in nature. Self-

actualized individuals use both approaches to learning.

Hill developed his cognitive style model (as cited in Tendy &

Geiser, 1997) in order to elucidate cognitive processes and how they

help individuals in their search for meaning. He developed the

Cognitive Style Interest Survey, which explores how individuals

process theoretical and qualitative symbols, modalities of inference,

and cultural determinants of cognitive style. Through a detailed

analysis of cognitive types, Hill posited that there are 330 different

types of cognitive styles (Fizzell, 1984).

Fizzell (1984) developed a learning style assessment based on

nine variables. These variables included conceptual approach,








instructional mode, perceptual preferences, curricular interests, time

preferences, achievement level, counseling support, social orientation,

and social environment. Fizzell's research showed that students who

might fail in one program can succeed in another program that serves

different styles, even though the second program shares the same

goals and standards.

An individual's need for structure in learning activities and

whether a learner is peer- or adult-oriented served as the basis for

Hunt's learning-style inventory (Hunt, 1987). He proposed that

learners could be (a) concrete and impulsive, with a low tolerance for

frustration; (b) dependent on rules and authority; or (c) independent

and could require more freedom in the way they approached learning

tasks.

Dunn and Dunn (1993) developed a more complex model of

learning styles. The Dunn, Dunn, and Price Learning Styles Inventory

was used to analyze learners' responses to five stimuli: environmental,

emotional, sociological, physiological, and psychological. Each of these

stimuli has multiple elements (Table 2-1).

Dunn and Griggs (1988) believed that knowledge of an

individual's learning styles could provide guidance in terms of the way

classrooms should be organized and what type of assignments would

motivate a particular learner. Additionally, Rita Dunn recommended

that teachers (a) understand the concept of learning styles; (b) explain








the concept of learning styles to students, emphasizing that there are

no good or bad learning styles; and (c) have alternate instructional

methods available to accommodate different learning styles

(Shaughnessy, 1998). Dunn, Griggs, Olson, and Beasley (1995)

conducted a meta-analysis of research studies evaluating the

effectiveness of learner style accommodation. They found a large body

of research showing that students who had their learning styles

accommodated achieved approximately 75% of a standard deviation

higher on aptitude tests. This finding is particularly significant because

Backes (1993) found a correlation between high school dropout rates

and the students' learning styles among native American students.

Table 2-1

Dunn, Dunn, and Price Learning Styles Inventory. Stimuli and
Elements

Stimulus Elements

Environmental Sound, lights, temperature, and
design

Emotional Motivation, persistence, structure,
responsibility

Sociological Self, pairs, peers, team, adult,
varied

Physiological Perceptual, intake, time, mobility

Psychological Global, analytical, hemisphericity,
impulsive, reflective








Basing her system on Kolb's experiential learning model, Bernice

McCarthy developed her 4MAT Learning System and devised learning

activities that included right and left brain dominance exercises,

creativity, and movement (Samples, Hammond, & McCarthy, 1985;

Scott, 1994; Smith & Kolb, 1996). Type One learners were described as

concrete and perceptual, reflectively processing information. Type Two

learners were categorized as analytical, relying on abstract

conceptualization and reflective observation as their primary modes for

learning. Categorized as using "common sense" in their approach to

learning, Type Three learners rely on abstract conceptualization and

active experimentation. Finally, Type Four, or dynamic learners, prefer

to use concrete experience and active experimentation to assimilate

new information. McCarthy used her learning theory to devise activities

that would move learners through the concrete experience, reflective

observation, abstract conceptualization, active experimentation

learning cycle (Samples et al., 1985).

The experiential learning model serves as the theoretical basis

for the Kolb Learning Style Inventory (Smith & Kolb, 1996). The core of

the experiential learning model is the learning cycle, in which concrete

experiences are translated into concepts, which then are used to guide

choices for new experiences. With each new experience, the learner

tests previously formulated concepts and generalizations; and based on

outcomes, either retains or rejects these concepts. The experiential








learning model proposes that learners reflect on their concrete

experiences, devise theories or concepts to explain these experiences,

and then test these theories or concepts on new experiences.

When he developed the LSI, Kolb (Smith & Kolb, 1996) created a

grid that incorporated the four learning modes of experiential learning:

concrete experience (CE), reflective observation (RO), abstract

conceptualization (AC), and active experimentation (AE) (Figure 2-1).

Leonard and Harris (1979) referred to these learning modes as "feeling"

(concrete experience), "watching" (reflective observation), "thinking"

(abstract conceptualization), and "doing" (active experimentation).

Concrete Experience

Accommodator Diverger

Active Reflective
Experimentation Observation
Converger Assimmilator


Abstract Conceptualization

Figure 2-1. The Four Quadrants Comprising the Kolb Learning Style
Inventory

Davidson (1990) noted that learning mode scores are used to

compute two combination scores, AC-CE and AE-RO. These

combination scores are based on polar opposites comprising the active

cycle of learning. The AC-CE score reflects a learner's degree of

preference for abstract conceptualization compared to concrete








experiences. The AE-RO score shows the degree of preference for

active involvement versus passive observation. These combination

scores are plotted on a grid to identify an individual's "quadrant" or

preferred learning style. CONVERGERS' dominant learning abilities are

abstract conceptualization and active experimentation. They perform

well in situations using data and objects and where only one correct

answer to a problem exists. DIVERGERS' dominant abilities are

concrete experience and reflective observation. Their strengths lie in

their ability to generate ideas, see concrete situations from many

perspectives and work with people. ASSIMILATORS' dominant abilities

are abstract conceptualization and reflective observation; they excel in

inductive reasoning and assimilating disparate observations into

integrated explanations such as theories and models. Lastly,

ACCOMMODATORS prefer concrete experience and active

experimentation for optimal learning. They are frequently task oriented

and rely heavily on other people for information rather than their own

analytic ability to gather the information. Each of the four quadrants is

sub-divided into inner quadrants that show whether the learner ranks

as high, moderate, or low for that particular style.

Researchers have examined learning styles and how they

correlate with various student characteristics and academic success.

Titus, Bergandi and Shryock (1990) found that adolescents tended to

favor concrete learning styles rather than the abstract learning modes








seen in their adult sample. Witkin, Oltman, Raskin, and Karp (as cited

in Couch, 1991, p. 134) reported that, when instructional tasks

required restructuring in order to encode, store, and retrieve

information, field-independent subjects would outperform field-

dependent subjects. For example, the field-dependent, field-

independent nomenclature proposed by Witkin et al. (1977) was based

on a differential ability to view visuals. Field-independent learners

were characterized as analytical, self-referent, and impersonal;

whereas field-dependent learners were characterized as globally

oriented, socially-sensitive, and concerned with interpersonal

interactions (Messick, 1979).

Recent analyses of the Kolb LSI raised a number of concerns

regarding its validity and reliability. These concerns are reviewed in

Chapter 3 (p. 46).

Individuals' learning styles affect how they prefer to learn.

Analogies also have been shown to have positive effects on learning.

The following is a review of the literature regarding the nature and

instructional benefits of analogies.


How Analogies Promote Learning

Aristotle defined "classical analogies" as comparisons among

terms in the analogy, usually represented in the format of A:B::C:D

(Goswami, 1992). The relationship between the C and D terms should

be equivalent to the relationship linking the A and B terms. The ability








to conceptualize this quality of relations is recognized as the

"hallmark" of analogical reasoning. Similarly, Goswami (1992)

discussed the phenomenon of "problem analogies" in which the

characteristics of a solution for a base problem is applied to solving an

analogous target problem. Goswami (1992) reported that children

frequently were unable to see the intended relational correspondence

between the base and target problems, even though the relationship

was evident to the experimenters.

Research highlights the role of analogies in concept attainment.

When learners are confronted with unfamiliar material, provision of

advance organizers and analogies are thought to enhance learning.

Ausubel (1960) stated that advance organizers (AO) are useful for

learning unfamiliar but meaningful verbal material by providing relevant

subsuming concepts. Analogies promote learning by concretizingg"

abstract concepts for the learner, promoting the assimilation of

ambiguous or intangible concepts (Newby & Stepich, 1987). Frequently,

students are required to conceptualize processes not readily

perceivable and it is believed that analogies may help fulfill this role

(Lawson, 1993). Gick and Holyoak (1980) found that analogies enhanced

a subject's ability to critically analyze and suggest a solution to a

problem, as long as the subject recognized the similarities between the

problem and the analogy. Furthermore, if mapping support from an

analog to a target is not provided by the instructor, the potential for








misunderstanding is created (Duit, 1991). Mayer and Gallini (1990)

found that illustrations consistently improved performance on the

recall of conceptual rather than non-conceptual information and that

illustrations promoted creative problem solving rather than enhanced

verbatim retention.

Mayer (1979) found that advance organizers (AO) must meet four

criteria if they are to promote learning:

1. The AO facilitates comprehension of some or all of the relationships
found in the new material.

2. The AO helps the learner relate the unfamiliar material with
material that was previously learned.

3. The AO is easily learned.

4. The AO is likely to be used by the learner.

The similarities between the attributes required by effective

advanced organizers and attributes of effective analogies are apparent.


Role of Analogies in Concept Attainment

Mayer (1989) cited four criteria that must be met if conceptual

models are to be effective in promoting learning. Conceptual models

are words and/or diagrams that are intended to help learners build

mental models of the system being studied. Two of these criteria relate

directly to the design of the study: the material to be learned must be

explanative rather than descriptive or narrative in form and the major

dependent measures should focus on problem-solving performance

rather than verbatim recall. If post-testing consists of questions








requiring verbatim recall, conceptual models will not enhance learning

outcomes. Mayer also noted that the learners must be novices rather

than experts if the conceptual models are to result in improved

posttest scores.

With regard to conceptual models, visual analogies should

effectively function in this role by concretizingg" abstract concepts for

the learner, promoting the assimilation of ambiguous or intangible

concepts (Newby & Stepich, 1987). Gabel and Sherwood (1980) found

that students who were at the concrete-operational developmental

level improved performance in their chemistry through the use of

analogies. In contrast, students who were at the formal operational

level of development benefited most from the completion of additional

practice problems rather than from the analogies.


Attributes of Effective Analogies

In order for an analogy to be effective, regardless of whether the

analogy is text-based or visual, Gentner (1983) recommended that

analogies should meet the following criteria:

* The base domain should be familiar and understood by the learner
(base specificity).

* Object mappings should be precisely defined (clarity).

* There should be a large number of predicates (or characteristics)
that are mapped from the base to the target (richness).

* The greater the number of higher order relationships between the
base and target, the more abstract and effective the analogy or
mapping (abstractness).









* The predicates in the two domains must be correct (validity).

* There should be a large number of possible cases to which the
analogy can be applied validly (scope of applicability).

These recommendations were incorporated into the visual analogies

developed for this investigation.

Newby et al. (1995), Newby and Stepich (1987), Stepich and

Newby (1988a) and Stepich and Newby (1988b) reported similar

recommendations for analogy design. Additional considerations

regarding the student's learning level (Newby et al., 1995, Stepich &

Newby, 1988a) and the point in the learning process at which analogies

were presented (Newby & Stepich, 1987; Schustack & Anderson, 1979;

and Stepich & Newby, 1988a) were found to be significant.

Glynn (1991) developed the Teaching With Analogies (TWA) model

for using an analogy to explain science concepts, based on his research

of 43 science texts. For maximum effectiveness, the instructor should

ensure that six operations occur:

1. The target must be introduced.

2. Cue retrieval of the analog must occur.

3. Relevant features of the target and analog are identified.

4. Similarities between the target and analog are mapped.

5. Conclusions about the target are drawn.

6. Areas in which the analogy breaks down are described.








Glynn's TWA model contains similar recommendations as those made

in an instructional model for analogies developed by Radford (1989),

who stressed the role of an analogy as an advanced organizer.

Brown (1994) and Clement (1993) found that the use of multiple

bridging analogies between an anchor and target would facilitate

learning. For example, Brown investigated the physical concept of force.

The anchor illustration was a hand pressing down on a spring; the

target was whether a book resting on a table would "experience" an

upward force from the table. The bridging illustrations were a book on a

spring, a book on a flexible board between two saw horses, and a book

on a table. Brown found that concept attainment was enhanced

through the use of these bridging analogies, minimizing some of the

dangers inherent in the use of analogies.


Advantages and Problems Associated with Analogies

Five advantages of using analogies in teaching were noted by

Duit (1991), especially in relation to the constructivist view of learning

(p. 666):

1. They are valuable tools in conceptual change learning, which open
new perspectives.

2. They may facilitate understanding of the abstract by pointing to
similarities in the real world.

3. They may provide visualization of the abstract.

4. They may provoke students' interest and may therefore motivate
them.








5. They force the teacher to take the students' prior knowledge into
consideration. Analogy use may also reveal misconceptions in areas
already taught.

Duit (1991) also noted problems with the use of analogies. Only

the deep structure aspects of an analogy provide inferential power,

surface similarities providing little benefits in terms of learning. In

addition, because an exact fit between the analog and target never

exists, features of the analog may mislead students. Finally, students

must draw the intended analogies themselves. Dagher (1995) stated

the following:

While instructional analogies provide a bridge between what is
known and what is less known, some fear that this bridge has
an elusive quality that could lead those traversing it into side
tracks that interfere with their arrival to the intended
destination. (p. 296)

Thiele and Treagust (1994) found that teachers tended to select

analogs from their own experience, rather than using analogs that may

be more familiar to the students. As a result, students frequently had

difficulty in understanding the analog's relevance, especially when the

teachers did not explicitly map the relevant attributes and limitations

of the analog and target. Dagher (1995) stressed that teachers should

have an understanding of learners' prior knowledge so that they may

select analogs that are readily assimilated into existent knowledge

structures. In a study by Gabel and Sherwood (1980), the researchers

found that 48% of the students participating in the study did not








understand 90% of the analogies used by teachers in an experimental

treatment.

Although analogies are frequently text-based, visuals may also

serve an analogical role. A large body of research has examined the role

of visuals in education and in concept attainment.


Visuals and Their Affect on Learning

Levin et al. (1987) identified five categories of visuals used in

educational texts and discussed each of these categories' role in terms

of knowledge acquisition. Table 2-2 summarizes these categories and

their roles. Specifically, Levin et al. (1987) stated that transformational

visuals affect a student's memory through four mechanisms.

1. Targeting the critical information to be learned.

2. Recoding the information into a more concrete and memorable
form.

3. Relating the separate pieces of information into a well-organized
context.

4. Providing for the retrieval of information when required (p. 61).

Interpretational pictures fulfill the requirements of an advance

organizer in that they provide "stage-setting support" for complex

concepts (Levin, et al, 1987, p. 58). In the absence of concrete or direct

experiences, inclusion of analogies should help prepare the learner for

more abstract, complex experiences (Curtis & Reigeluth, 1984, p. 108).

Similarly, Stencel (1997) referred to simple paper models that served as

analogies for cells and organic materials as "visual imprints" that








facilitated learning (p. 235). Gambrell and Bales (1986) found that poor

readers benefited from visual analogies because the visual constructs

served as anchors for newer concepts.

Table 2-2

Categories of Visuals and Their Role in Knowledge Acquisition

Type of Visual Role in Knowledge Acquisition

Decorative No role in knowledge acquisition

Representational Used to reinforce the major features or

topics contained within the narrative

Organizational Clarifies procedures that were outlined in

the text

Interpretational Serves to clarify difficult concepts or

passages contained in the narrative

Transformational Functions as a mnemonic device through a

number of mechanisms



Similarly, Ault (1985) stated that, in order for learners to grasp

concepts, those concepts must first be integrated into existing

memory. Because concrete concepts are easier to integrate, instructors

should initially present concrete images and concepts. Once these are

integrated into the learner's memory, the learner will be able to "map"

or attach new concepts to them. Analogies will help to concretize

abstract concepts (Newby & Stepich, 1987; Simons, 1984).








Other researchers categorized visuals into three categories:

realistic, logical, and analogical (Issing, 1990; Knowlton, 1966; Levie,

1987). A realistic image portrays the surface features of an object with

a high degree of fidelity. Illustrations frequently fall within this

category. Knowlton (1966) defined a logical picture as "a visual

representation wherein the elements are arbitrarily portrayed, while

pattern and/or order are isomorphic with the state of affairs

represented" (p. 178).

Logical (or arbitrary) images, include diagrams, graphs, maps,

circuit diagrams and flowcharts (Issing, 1990; Knowlton, 1966). If the

intent of the visual is to function as an analogy for a DNA molecule,

the visual would be analogical in nature. In effect, the "portrayed

objects "participate" in a manner common to the less familiar

process ... in the state of affairs that is of interest" (p. 177). Knowlton

found that this use of visuals is most dramatic when representing the

non-phenomenal world. A subset of analogical pictures includes

illustrations that are drawn to concretize a theory (such as a drawing of

the structure of an atom). Issing (1990) noted that the inclusion of

humorous or stimulating elements as components of the analogy would

increase their effectiveness.

The role of a particular visual may change, depending on the

intent of the author. For example, a picture of a cloth cutter using a








template would serve as a realistic picture if the text were referring to

a cloth cutter, clothing factory, or clothing templates (Knowlton, 1966).

The interpretational and transformational pictures of Levin et al.

(1987) and the analogical and logical visuals described by Knowlton

(1966) provide the learner with many of the benefits associated with

the inclusion of analogies. They help to concretize abstract concepts

and to relate new and unfamiliar material with material that is already

familiar to the learner. However, a learner must correctly interpret a

visual if the visual is to enhance learning.


Interpretation of Visuals

Defining visual literacy and analyzing how learners relate to

visuals are complex problems. A great deal of confusion exists because

establishing a definition of visual literacy is problematic (Seels, 1994).

Seels and Lenze and Dwyer (1993) divided the concept of visual literacy

into three sub-concepts: visual thinking, visual learning, and visual

communication. Debes (as cited in Levie, 1978) proposed one of the

most widely promulgated definitions of visual literacy, specifically that

a visually literate person can interpret "the visual actions, objects

and/or symbols, natural or man-made" encountered in the

environment. Levie (1978) pointed out that Debes' definition includes

the ability to read words and that reading ability, ultimately, accounts

for some of the difficulties associated with the research on visual

literacy:








It is, of course, this latter ability-verbal literacy-with which
visual literacy is often contrasted. The key problem with Debes'
definition is that it defines the stimuli of interest in terms of
a sensory modality rather than a symbolic modality. (p. 26)

Levie (1978) believed that the interpretation of pictures requires

fundamentally different mental processes than the interpretation of

words and other digital symbols. Expanding on the work of Paivio

(1979), Levie noted that images are concrete and processed

simultaneously, in contrast to words that are abstract in nature and

processed sequentially. Although both systems are interconnected,

they can work independently. Mental imagery and visual thinking are

the independent operations of the imaginal system. These independent

processes are the internal link within the "iconic mode." In the iconic

mode, images are stored or internalized as mental representations and

externalized in the form of pictures. Figure 2-2 shows the iconic mode

proposed by Levie. Similarly, Lenze and Dwyer (1993) viewed visual

thinking as the mental imagery of visual concepts and relate these to

the internal thought processes of the learner.

Many factors influence how readers interpret visuals, including

culture and experience (Goldsmith, 1987), cultural and ethnic

background (Hurt, 1989), general education (Messaris & Nielsen, 1989),

gender, learner, and ethnic influences (Randhawa, Back, & Myers,

1977), learner experience (Tierney & Cunningham, 1984), Piagetian

level (Gabel & Sherwood, 1980) and cognitive style (Witkin et al., 1977).









I Internalization

Perceptual Mental
Representation Representation
(Pictures) (Imagery)

SExternalization


Figure 2-2. Levie's Iconic Mode. Note. From "A prospectus for
instructional research on visual literacy," by W. Levie, 1978,
Educational Communication & Technology Journal. 26, p. 27. Copyright
1978 by the Association for Educational Communication and
Technology, Inc. Adapted with permission.


Seels (1994) defined visual thinking as the internal reaction

stage of visual literacy, involving "more manipulation of mental imagery

and more sensory and emotional association than other stages" (p.

104). She defined visual learning as the acquisition and construction of

knowledge as a result of interaction with visual phenomenon and

visual communication as "using visual symbols to express ideas and

convey meaning" (p. 109).

Research in learning theory has shown that learners view images

differently, based on their learning styles, educational levels, age, and

cultural backgrounds. Witkin et al. (1977) found differing abilities to

locate embedded figures, based on whether subjects were field-

independent or field-dependent. Macnab, Hansell and Johnstone (1991)

examined field-independence/ dependence in students ranging from 8

to 19 years of age and tested their ability to recognize microscopic

specimens in various orientations. The investigators found that few








field-dependent students were in upper level biology courses and

speculated that students who were field dependent were progressively

filtered out during their school years and in the early stages of tertiary

education. Messaris and Nielsen (1989) found a relationship between

educational level and a subject's ability to interpret an associational

montage, a juxtaposition of visual images that is intended as an

analogy.

In addition, investigators showed that age, cultural background,

and previous academic abilities influence learners' ability to interpret

visuals. Constable, Campbell and Brown (1988) found that very few 11

year-old British students could identify the cut surfaces of objects in

biological illustrations. Hurt (1989) posited that cultural background

influenced the ability of children to interpret visual conventions,

whereas Koran and Koran (1980) determined that inductive reasoning

scores were inversely related to performance gains associated with use

of diagrams. Low-ability subjects benefited most when a diagram was

included as an adjunct to the text, regardless of the diagram's position

in the text, whereas high-ability subjects performed best without the

drawing. Similarly, Lin, Shiau, and Lawrenz (1996) determined that

students taught with pictorial analogies scored significantly higher

than their counterparts, but that low achievers benefited more from

this approach than high achievers.








Problem Solving and Analogical Visualization

The history of scientific inquiry is replete with examples

illustrating the importance of analogies and visualization in problem

solving and cognition. Perhaps one of the earliest examples of

analogical reasoning and the solution of problem was reported by

Archimedes (Goswami, 1992). Archimedes was unable to determine

whether base metal had been substituted for gold in the construction

of an elaborate crown made for the king, although the weight for

volume of pure gold was known. The problem was how to determine the

volume of the intricately designed crown. It was not until Archimedes

stepped into his bath that he had his "Eureka" moment and was able

to solve the problem. Rieber (1995) described the process of

illumination, where solutions to problems came to scientists and

researchers in "sudden bursts of insight" (p. 48). For example, Kekule

reported that he frequently imagined atoms dancing before his eyes.

One day, while gazing into a fire, chains of atoms transformed

themselves into snakes that grabbed their own tails and started

spinning before his eyes. Through this utilization of visualization,

Kekule was able to elucidate the structure of benzene. Einstein

reported that many of his fundamental insights were the result of

experiments performed in his imagination, using creative imagery as

his laboratory (Finke, 1990).








Analogies, whether textual or visual, were found to improve

performance on examinations requiring problem solving or critical

thinking. Weinstein and Mayer (1986) included imaging as a cognitive

strategy that influences the learner's encoding process. Using

Duncker's radiation problem, Gick and Holyoak (1980, 1983) found that

investigator-supplied analogies increased the likelihood that learners

would arrive at a creative solution to the problem. Briefly, Duncker's

radiation problem describes a hypothetical patient with a large

abdominal tumor. Subjects were told that a beam of radiation with

enough energy to kill the tumor would also destroy the surrounding

tissue as it passed through it, resulting in the death of the patient. If

Gick and Holyoak first presented subjects with a story of an army that

split into smaller units to attack a castle from multiple routes, the

subjects suggested that the physician use multiple converging, lower-

energy radiation beams to destroy the tumor. However, without a hint

from the investigators to use the story, most learners did not

spontaneously use the analogy to solve the problem.

When learners are presented verbal and visual material, Mayer

and Anderson (1991) found that, in order for problem solving to occur,

the learners must construct representational and referential

connections. Representational connections are connections that are

made between verbal stimuli and verbal representations within long

term memory. Similar representational connections also are made








between visual stimuli and their visual representations. Mayer and

Anderson stated that problem solving requires that the learner create

referential connections between the verbal and visual representations

within long-term memory. In their study, college students were better

problem solvers when related verbal and visual stimuli were presented

simultaneously, rather than serially, due to the facilitation of

referential connections between visual and verbal stimuli. The

instruments developed for this study will present the visual and verbal

information simultaneously.

Lin, Shiau, and Lawrenz (1996) concluded that most of the

studies investigating the effects of analogies on learning focused

primarily on students' "algorithmic problem-solving ability" rather than

their "conceptual problem-solving ability." Students were able to

correctly apply formulas to solve problems in chemistry, even though

they did not learn the underlying concepts relevant to the problems.

They concluded that conceptual problem solving was enhanced through

the use of pictorial analogies, especially in low achievers. Research by

Nakhleh (1993) confirmed that students' conceptual problem-solving

abilities lagged far behind their algorithmic problem-solving abilities.

She found that college chemistry students were able to use formulas

and algorithms to solve problems, without being able to interpret

drawings that illustrated the problems' underlying physical concepts.

In his review of visualization and scientific inquiry, Rieber (1995)








concluded that problem solving through the use of imagery was

inappropriately discounted as unsophisticated and not as powerful as

more complex and abstract approaches.


Summary and Conclusions

Learning styles reflect the unique ways that individuals gather

and process information (Davidson, 1990). Learning-style theorists

categorized learners in many different ways, including preferences for a

particular learning strategy (Cahill & Madigan, 1984; Corno & Snow,

1986; Messick, 1979; Perry, 1994; Smith & Kolb, 1996) and learner

attributes (Dunn & Dunn, 1993; Fizzell, 1984; Gregorc, 1984; Letteri,

1980). Theorists differ on whether learning styles are permanent

attributes of an individual (Messick, 1979) or vary based on learning

task (Baker, Wallace, Bryans, & Klapthor, 1985; Gregorc, 1984).

Interactions between learning styles and instructional tasks were

found to affect academic success (Witkin, Oltman, Raskin, & Karp, as

cited in Couch, 1991). Dunn and Griggs (1988) posited that one mode of

teaching may be effective for some learners and "terrible" for learners

with different learning styles (p. 3).

David Kolb (Smith & Kolb, 1996) based the Kolb Learning Style

Inventory (LSI) on experiential learning theory. The Kolb LSI

determines an individual's relative emphasis on four learning modes or

orientations (concrete experience, active experimentation, reflective

observation, and abstract conceptualization) and on two combination








scores (Abstract Conceptualization-Concrete Experience) and (Active

Experimentation-Reflective Observation). Conceivably, students'

preferences for concrete learning experiences may adversely affect their

ability to learn concepts that are presented in an abstract fashion.

Analogies may serve as concrete substitutes for abstract concepts and

thereby positively affect concept attainment (Newby & Stepich, 1987).

Visuals also may function as analogies for non-phenomenal or the

abstract concepts (Issing, 1990; Knowlton, 1966; Levie, 1987).

The purpose of this research was to determine if students'

primary learning styles (abstract conceptualization or concrete

experience) interact with instructional packet types (mathematical

formulas or visual analogies), thereby enhancing students' recall of

factual information and/or improving their problem-solving skills. The

remainder of this paper will present the methodology for the research,

analysis of the results, and recommendations for future research.














CHAPTER 3
METHODOLOGY


This study was designed to determine if learning style influenced

a participant's ability to use visual analogies to comprehend new

information and whether the visual analogies enhanced the

participant's problem-solving skills. This chapter contains a description

of the research design and methods used to support the validity and

reliability of the measures used in the experiment.


Research Design

The interaction between primary learning styles and

representational packet type was investigated for two types of

questions (problem solving or simple recall). Two separate analyses of

variance were performed, one for recall questions and one for

questions requiring problem solving.

Each factor studied had two learning outcome measures. Factor A

(subject learning style) was either concrete or abstract. Factor B (type

of instructional packet) used a visual analogical approach to

instruction or a mathematical approach. Learning outcomes were the

subject's performance on recall questions and questions that require

problem-solving skills. Each subject was categorized under a level of








factor A (learning style) and randomly given one of the instructional

packets (factor B). Performance gains on specific types of questions

were determined when the examinations were scored. The investigator-

generated examination was used as the posttest.


Population and Sample

The sample for this study was 75 students enrolled in

introductory anatomy and physiology courses and dental hygiene

students who had completed college-level anatomy and physiology

coursework at a middle-sized community college located in north

central Florida. The number of males and females in the sample, their

primary learning styles, and the number of who were in the visual

group and in the mathematical group are presented in Table 3-1.

Table 3-1

Description of Participants in Sample

Males Females Total

Number 18 57 75

Concrete Learning Style 4 28 32

Abstract learning Style 14 29 43

Visual Analogy Packet 10 34 44

Mathematical Packet 8 23 31


The population from which the sample was selected, was chosen

for three reasons:








1. The students have a basic understanding of the structure and
function of the human body.

2. The students were unlikely to have a background in the
interpretation of arterial blood gases, the content used in the
treatment. Students who were enrolled in respiratory care,
paramedic, or nursing programs might have been exposed to that
content during classroom, laboratory, or clinical coursework.

3. The curriculum for anatomy and physiology does not require
students to interpret arterial blood gases as part of their
coursework.

Written, informed consent from the participants was required

due to the nature of the study (see Appendix A).


Materials and Measures

The Kolb Learning Style Inventory was used to determine the

students' primary learning styles (PLS). Primary learning styles are

defined as the individuals' preferences for either concrete experiences

or abstract conceptualization as their primary learning modes and

active experimentation or reflective observation for determining the

validity of their experiences. The PLS (along the abstract

conceptualization/concrete experience continuum) for each participant

was determined, but not their learning style type. In addition, the

scrambled version of the LSI was administered to eliminate the

possibility of response set threats to validity. The instructional packets

were developed by the investigator and covered the major concepts

important in the interpretation of arterial blood gas results. The works

of Gentner (1983), Newby et al., (1995), Ortony (1975), Smith (1989),








and Stepich and Newby (1988b), and Mayer and Anderson (1991) served

as guides for the construction of the analogies that were used in the

study. The investigator was unable to use any existing materials for

two reasons.

1. The literature currently does not use visual analogies to teach the
concept of arterial blood gas (ABG) analysis. As a result, this
approach had to be developed by the investigator.

2. The major focus of both packets was to provide a conceptual
framework for ABG interpretation rather than an exhaustive
treatise.

Packet Design

The instructional packets used in the study covered the following

topics:

The determinants of the partial pressure of carbon dioxide gas in
arterial blood (PaCO2) that include carbon dioxide production and
ventilation.

The determinants of the pH of arterial blood that include PaCO2 and
bicarbonate levels (HCO3).

Normal laboratory values associated with arterial blood, including
pH, PaCO2, HCO3, hemoglobin, and oxygen (PaO2).

* A method for predicting a patient's PaO2.

Information regarding the determinants of PaCO2 and pH was

presented in the form of visual analogies or as mathematical formulas

(see appendices B and C). Normal blood values and the method for

predicting a patient's PaO2 were presented as identical textual

passages in both the visual analogy and mathematical formula packets.








The visual analogy for the determinants of PaCO2 consisted of a

picture of a tub, a faucet, and a drain. A picture of a pointing hand

indicated the original level of fluid in the tub. Drops of water from the

faucet indicated the relative flow of water into the tub; drops of water

exiting the drain represented the relative flow of water out of the tub.

As long as the flow of water from the tub equaled the flow of water into

the tub, the water level would remain constant. Once the visual

analogy was presented, the narrative explained how the water flowing

into the tube was analogous to carbon dioxide production, the water

flowing out of the tub was analogous to ventilation, and the level of

water within the tub was analogous to PaCO2. Carbon dioxide

production and alveolar ventilation were the independent variables and

PaCO2 was the dependent variable.

The visual analogies for the determinants of arterial blood pH

were a series of scales with a balance indicator pointing at a "comfort

scale" that indicated a comfort zone or, to its left, acid indigestion. The

first visual analogy was a picture of the scale with nothing on either of

the balance plates. The second visual analogy showed a jalepeno

pepper on the left balance plate and an antacid tablet on the right

balance plate. The scale was balanced and the balance indicator

pointed into the "comfort zone." The next visual analogy revealed an

additional pepper added to the left balance plate; the scale tipped to

the left and now indicated acid indigestion. Once a second antacid








tablet was added to the right side of the scale, the scale was balanced

and the balance indicator again pointed into the "comfort zone." After

the presentation of these four visual analogies, PaCO2 was substituted

for the jalepeno pepper and HCO3 was substituted for the antacid

buffer tablet. The comfort scale changed to a pH scale, with a normal

pH in the middle of the scale, and acidotic pH on the left side of the

scale, and an alkalotic pH on the right side of the scale. PaCO2 and

HCO3 were the independent variables and pH was the dependent

variable.

In both of these visual analogies, the concept of homeostasis

was presented. By using images of common objects, a relatively

abstract concept was presented in a concrete fashion. The visual

analogies permitted subjects to mentally manipulate intangible

substances (carbon dioxide production and ventilation; PaCO2 and

HCO3) and to predict the effects of this manipulation on dependent

variables (PaCO2 and pH respectively).

The following discussion describes how the analogies developed

for this investigation possess the attributes of effective analogies.

* The base domain should be familiar and understood by the learner (base
specificity). The base domain for the determinants of PaCO2 of either
a tub with water flowing into it or out of it at varying rates is
conceptually simple. The base domain for the determinants of pH is
a balance, a conceptually familiar device.

* Object mappings should be precisely defined (clarity). In both analogies
(tub and balance), the description of the relationships are clearly
stated and multiple examples are provided.








* There should be a large number of predicates (or characteristics) that are
mapped from the base to the target (richness). All of the determinants of
PaCO2 and of pH are conveyed through the analogies. For example,
the determinants of PaCO2 are alveolar ventilation and carbon
dioxide production. Alveolar ventilation and carbon dioxide
production are represented in the base domain by the rate of
drainage from the tub and by the rate of water entering the tub from
the faucet.

The greater the numbers of higher order relationships between the base
and target, the more abstract and effective the analogy or mapping
(abstractness). The target domain consists of extremely abstract
processes (carbon dioxide production and alveolar ventilation),
processes that are not readily observable. The example of the tub,
faucet, and drain concretizee" these two abstract processes. The
balance analogy has a higher level correlation with increases in
PaCO2 in that the peppers are related to acid indigestion, a
condition associated with a drop in the pH of the stomach.
Similarly, as a patient's PaCO2 increases, his pH will fall.

The predicates in the two domains must be correct (validity). The
characteristics of the base and target domains are correctly stated
and accurately represented by the visual analogies. Levels of carbon
dioxide in arterial blood are determined by carbon dioxide production
and alveolar ventilation. In addition, levels of carbon dioxide and
bicarbonate (HCO3) determine the pH of the arterial blood.

There should be a large number of possible cases to which the analogy
can be applied validly (scope of applicability). Virtually all examples of
homeostasis may be represented by the analogy of the tub, faucet,
and drain. Similarly, pH levels fall whenever PaCO2 increases, while
pH will rise when HCO3 levels rise. For example, this visual analogy
would provide a conceptual basis for the determinants of a lake's
water level (flow of water in and out of the lake basin), bank
balances (equal deposits and withdrawals), and a person's weight
(caloric intake versus caloric expenditure).

Five registered respiratory therapists, who taught in respiratory

care programs, independently reviewed the two packets for equivalency

of content. The investigator developed the examination used in the

study (Appendix D). The examination contained 11 questions that were

recall-based and 14 questions that required problem-solving








(interpretive) skills. There were no references to the analogies in the

test or any questions that could not be answered from the non-

analogical (mathematics) learning packet. All five therapists were able

to differentiate the recall questions from those requiring problem-

solving skills. The recall questions on the posttest examination had a

reliability of 0.50 and a split-half analysis of the problem-solving

questions yielded a reliability of 0.48.

The concept underlying the visual analogies used in the analogy

packet was that of homeostasis. This concept was represented by two

different visuals: (a) a faucet, tub, and drain and (b) a balance. The

learner's first exposure to each of these visuals was the baseline

condition, in which all components are in balance and normal.

Alterations in the baseline condition for each of these visuals will be

illustrated with additional images.

Recently, a number of concerns were raised regarding the validity

and reliability of the Kolb LSI. The following discussion reviews these

concerns.


Kolb Learning Style Inventory: Validity and Reliability Issues

David Kolb revised his Original Learning Style Inventory (OLSI) in

1985 in response to criticisms regarding the OLSI's format, language,

reliability, normative samples, instructions and scoring (Smith & Kolb,

1996). The 1985 Learning Style Inventory (LSI 1985) was designed to

measure individuals' learning styles, styles that are derived from








Experiential Learning Theory. The test is a 12-item questionnaire that

requires respondents to rank-order four sentence endings that

correspond to the four learning modes (i.e., Concrete Experience,

Abstract Conceptualization, Active Experimentation and Reflective

Observation). Scoring the test reveals an individual's relative emphasis

on these four learning modes or orientations, and on two combination

scores (Abstract Conceptualization-Concrete Experience) and (Active

Experimentation-Reflective Observation).

The Kolb LSI has been widely studied in terms of its reliability.

Gregg (1989) used Cronbach's Standardized Scale Alpha and found that

the LSI displayed good internal reliability for the four basic scales and

the two combination scores. Smith and Kolb (1996) reported that the

1985 LSI had very good internal reliability, based on Cronbach's

Standardized Scale Alpha, especially for the abstract conceptualization-

concrete experience scale (.88). Tukey's Additivity Power test indicated

that the combination scores (abstract conceptualization-concrete

experience and active experimentation and reflective observation)

showed nearly perfect additivity (1.0 and 0.99 respectively). In addition,

Pearson correlations among the LSI 1985 scales showed the strongest

negative correlation between polar opposites on the experiential

learning cycle (abstract conceptualization and concrete experience and

active experimentation and reflective observation). No relationship








exists between the two combination scores, indicating statistical

independence.

Ruble and Stout (1990) found that factor analyses failed to

support the construct validity of the LSI 1985 and suggested that there

was a response set present. They attributed this possibility to the

columnar format for the revised instrument, in which words referring to

a particular attribute, (either feeling, doing, watching, or thinking)

occurred within the same column. Sims, Veres, Watson, and Buckner

(1986) voiced a similar concern. In response to these criticisms, Kolb

(Smith & Kolb, 1996) revised the LSI so that terms describing learner

attributes are scrambled, eliminating the threat to validity created by

response sets. The newest version of the LSI, LSI-IIA has a high test-

retest reliability (Hay/McBer, 2000). Veres, Sims, and Shake (1987)

found that the LSI 1985 had improved coefficient alpha estimates for

internal consistency over the original form for the four LSI scales.

Using two groups of subjects (college-aged students and individuals

working in industry). Veres et al. (1987) reported the following alpha

estimates (student/industrial): concrete experience: .76/.82; reflective

observation: .84/.85; abstract conceptualization: .85/.83; and active

experimentation: .82/.84. Similar findings were later reported by Smith

and Baker (1996).

Baker et al. (1985) stressed that learning styles are highly

situational. Focusing on anesthesia residents, Baker et al. (1985)








found that residents who had just spent 10 hours in the operating

room scored higher in terms of concrete experience learning

preferences when compared to residents who had just attended a

lecture. This finding could explain the lack of reproducibility of learning

styles when the LSI is administered over time (Sims et al. 1986).

However, Sims et al. did find that the internal consistency of the LSI

1985 was improved over the OLSI.

Using factor analysis, Cornwell, Manfredo, and Dunlop (1991) and

Geiger, Boyle, and Pinto (1992) found that the learning styles that

loaded as polar opposites were Active Experimentation and Abstract

Conceptualization, rather than Active Experimentation and Reflective

Observation or Abstract Conceptualization and Concrete Experience as

proposed by Kolb. In addition, Geiger et al. (1992) determined that

Concrete Experience and Reflective Observation loaded on the same

factor. These findings resulted in the authors questioning the

theoretical construct of Experiential Learning on which Kolb based the

LSI (Smith & Kolb, 1996).

The Kolb LSI uses ipsative measures, in which each score for an

individual is dependent on his or her own scores on other variables

(Ruble & Stout, 1994). However, scores obtained from ipsative

measures are independent of and not comparable with the scores of

other individuals (Hicks, 1970). Consequently, ipsative measures only

are useful for studying intra-individual preferences (Hicks, 1970 and








Pedhazur & Schmelkin, 1991). Pedhazur and Schmelkin noted that

ipsative measures cannot be meaningfully interpreted relative to a

group mean.

Ruble and Stout (1994) raised an additional concern regarding the

LSI's utilization of ordinal rankings and the loss of information

inherent to ordinal scales. For example, two subjects could have the

dramatically different learning mode scores (CE, RO, AC and AE) yet

have the same learning style (Table 3-2). Based on these results, these

two subjects would have the same learning style profile. However,

Subject 1 strongly prefers concrete experiences to other learning

approaches, whereas Subject 2 only has a mild preference for concrete

experiences. This limitation weakens the relationship between the

empirical indicators (numerical rankings) and the theoretical

constructs (the respondent's preferred learning ability). As a result, the

measurement's validity is reduced (Ruble & Stout, 1994).

Table 3-2

Hypothetical Results for Two Subjects with the Same Learning Style
(Diverger) but Different Learning Mode Scores

Subject 1 Subject 2

CE 80 32

RO 12 30

AC 6 20

AE 2 18








Cornwell & Manfredo (1994) validated the

discriminant/convergent validity of an individual's primary learning

style (his or her score on the abstract conceptualization/concrete

experience continuum). For this reason, this study focused on

subjects' primary learning styles and not the learning-style types

defined by Kolb accommodatorr, diverger, converger, assimilator). In

addition, the scrambled version of the LSI was administered, to remove

the possibility of response set threats to validity.

Cornwell & Manfredo (1994) validated the

discriminant/convergent validity of an individual's primary learning

style (his or her score on the abstract conceptualization/concrete

experience continuum). For this reason, this study focused on

subjects' primary learning styles and not the learning-style types

defined by Kolb accommodatorr, diverger, converger, assimilator). In

addition, the scrambled version of the LSI was administered, to remove

the possibility of response set threats to validity.

The Kolb LSI was used for determining the learning style of the

study participants for the following reasons:

* The learning style score of each individual was not compared to
other participants. It was used to determine the learning preference
for the particular individual (Hicks, 1970; Pedhazur & Schmelkin,
1991).

* The scrambled version of the LSI was used for the study, to
eliminate the possibility of a response set, a concern expressed by
numerous investigators (Ruble & Stout, 1990; Sims et al. 1986).








* Learning style types were not determined to the level of converger,
diverger, assimilator, or accommodator. Instead, I determined the
primary learning style of the participants, because these were
shown to possess discriminant/convergent validity (Cornwell &
Manfredo, 1994).

The LSI's two bipolar dimensions, abstract conceptualization/
concrete experience and active experimentation/reflective
observation, represent the degree to which a learner prefers to
learn, either through active involvement or through abstract
conceptualization. The purpose of this study was to determine
whether a learner who preferred concrete experiences would benefit
from a visual analogy, an instructional approach that concretizes
abstractions. Other learning style inventories analyze learning style
issues other than abstract conceptualization and concrete
experiences. For example, the Dunn, Dunn, and Price LSI considers
environmental, emotional, sociological, physiological, and
psychological factors (Dunn & Dunn, 1993). Hill's Cognitive Style
Interest Survey (as cited in Tendy & Geiser, 1997) explores how
individuals process theoretical and qualitative symbols, modalities
of inference, and cultural determinants of cognitive style.

Experiential Learning Theory (ELT), which serves as the basis for
the LSI, parallels the fundamental processes involved in the
utilization of analogies. Issing (1990) noted that learners compare
the base domain of an analogy to its target domain. During reflective
observation and abstract conceptualization, individuals formulate
new theories that are tested through active experimentation and
concrete experiences. If the theories predict the experimental
outcomes, the theories are retained. If the experimental outcomes
do not validate the theories, they are rejected (Smith & Kolb, 1996).
It is precisely these cognitive processes that learners use when
presented with an analogy to explain a new topic.

Procedure

The investigator explained the purpose and methodology of the

study to the Chair of Sciences for Health Programs, the Director of

Dental Hygiene, and to the individual anatomy and physiology and

dental hygiene instructors to obtain their permission to use volunteers

from their classes for this study. The investigator explained the








purpose of the study to the students who agreed to participate. After

receiving their Informed Consent Forms, students scheduled

appointments to take the Kolb Learning Style Inventory (LSI). The

investigator determined their primary learning styles (PLS).

Once the students completed the LSI, the instructor

administered a pretest on arterial blood gas interpretation. The pretest

consisted of 11 recall questions and of 14 questions that required

problem-solving skills. Cronbach alpha reliability analyses were

performed for the two types of questions contained within the pretest

and showed that students had no prior knowledge of the material and

randomly selected answers on the pretest. After a minimum of 1 week

from the date of pretest administration, students who preferred

concrete learning experiences received either instructional packets

that used a non-analogical (mathematical) approach to convey the topic

of arterial blood gas interpretation or packets that used visual

analogies. If the last four digits of a student's social security number

totaled to an even number, the student received a packet using the

visual analogy approach. If the last four digits of a student's social

security number totaled to an odd number, the student received a

packet using the non-analogical (mathematical) approach. The identical

procedure was used for students who favored abstract

conceptualization as their PLS. Both packets covered the topics of

carbon dioxide production, alveolar ventilation, oxygenation, factors








influencing the partial pressure of arterial carbon dioxide (PaCO2), and

blood pH. Students were allowed as much time as they needed to

review the packets. A posttest was then administered. This approach

prevented students from sharing the results of their packets with other

participants, a threat to the validity of the study. After completing the

posttest, each participant received an information sheet on the Kolb

LSI (Appendix E).


Analyses

The interaction between learning styles and packet types was

investigated for the two types of questions (problem-solving and simple

recall). Two separate analyses of variance (ANOVA) were performed, one

for recall questions and one for questions requiring problem solving.

ANOVAs were performed in order to compare the means students

attained on the examination following manipulation of a single

treatment variable (packet type) and to determine whether the

differences noted in means were due to systematic effects of the

treatment or occurred by chance. F values were calculated for students'

primary learning styles, treatment, and primary learning

style/treatment interactions.


Summary

This study was designed to determine if a student's primary

learning style interacts with instructional packet type to enhance the






55

student's recall of factual information and/or improve his or her

problem-solving skills. This study used the following methodology.

Step 1. Students completed Kolb's Learning Style Inventory.

Step 2. Students took a pretest to determine if they possessed
any prior knowledge in the area of blood gas analysis.

Step 3. On the basis of the last four digits of their social security
numbers, students received either the visual analogy packet or the
mathematics packet as the experimental treatment.

Step 4. Students took a posttest to assess their recall and
problem solving skills relating to information contained within the
instructional packets.

Step 5. Two separate ANOVA tests were performed, one for recall
questions and one for questions requiring problem solving.
Interactions between primary learning styles and packet types were
investigated.














CHAPTER 4
RESULTS AND ANALYSIS


Introduction

This study was designed to determine whether a participant's

primary learning style affects his or her ability to learn from a

particular instructional packet type and whether these interactions

influence his or her performance on recall questions and questions

requiring problem-solving abilities. Primary learning style (PSL) was

used as a between subjects attribute variable with two types, concrete

experience and abstract conceptualization (Smith & Kolb, 1996).

Instructional packet type was manipulated as a between-subjects

experimental condition with two types, visual analogies and

mathematical formulas.


Results

The experiment was conducted and data were collected as

outlined in the previous chapter. Table 4-1 reports the group means

and standard deviations on recall and problem-solving questions for all

gender/treatment combinations (males/visual analogies,

males/mathematical formulas, females/visual analogies, and








females/mathematical formulas). The data were analyzed using two

separate ANOVA tests.

Table 4-1

Group Means and Standard Deviations on Recall and Problem-Solving
Questions


Recall

Std. Dev.

1.87

2.29

2.13

1.90

1.75

1.91

2.30

1.92

1.89

1.88

2.20

1.93

0.71

1.80

1.41

1.95


Group

VA

MA

M

F

VA/M

VA/F

MA/M

MA/F

C/M

C/F

A/M

A/F

VA/C/M

VA/C/F

VA/A/M

VA/A/F


Mean

5.73

5.97

6.94

5.47

6.20

5.59

7.88

5.30

7.75

5.25

6.71

5.69

8.50

5.12

5.63

6.06


Problem Solving

Mean Std. Dev.

7.59 1.90

6.68 2.73

8.00 2.79

6.96 2.10

7.60 2.12

7.59 1.86

8.50 3.55

6.04 2.12

6.25 2.22

7.18 2.09

8.50 2.79

6.76 2.12

5.50 2.12

7.35 1.97

8.13 1.89

7.82 1.78

(Table 4-1-continues)








Table 4-1-Continued

Recall Problem Solving

Group N Mean Std. Dev. Mean Std. Dev.

MA/C/M 2 7.00 2.83 7.00 2.83

MA/C/F 11 5.45 2.07 6.91 2.34

MA/A/M 6 8.17 2.32 9.00 3.85

MA/A/F 12 5.17 1.85 5.25 1.60

Note: VA = Visual Analogy, MA = Mathematical Formula. M = Male, F =
Female, C = Concrete, A = Abstract

The first ANOVA analyzed the interaction between primary

learning style and instructional packet type (treatment) as it affects

performance on recall types of questions. The results are reported as a

source table in Table 4-2. The R-squared for the dependent variable

(performance on recall questions) and Treatment x Primary Learning

Style interaction was equal to 0.016. The report of results reviews the

null hypothesis tested.

Table 4-2

Analysis of Variance Summary Table-Recall Questions

Source SS df MS F Pr>F

Treatment 0.962 1 0.962 0.22 0.6369

Primary Learning 3.765 1 3.765 0.88 0.3514
Style (PLS)

Treatment x PLS 0.003 1 0.003 0.00 0.9773

Error 303.846 71 4.280








The Treatment x PLS interaction was removed from the

statistical model and the results reported as a source table in Table 4-

3. The R-squared was equal to 0.016. Scores on tests of recall were not

affected by treatment or PLS.

Table 4-3

Analysis of Variance Summary Table-Recall Questions without
Treatment x PLS Interaction

Source SS df MS F Pr>F

Treatment 1.002 1 1.002 0.24 0.6275

Primary Learning 3.845 1 3.845 0.91 0.3514
Style (PLS)

Error 303.850 72 4.220


Hypothesis 1 stated that mean outcome scores on examinations

testing the recall of abstract concepts do not differ due to the

interaction of treatment and learning style. The analysis of variance

produced an F value of 0.00 for this interaction that was not

statistically significant at the .05 alpha level. The null hypothesis was

not rejected. The interaction between treatment and primary learning

style did not influence the recall of abstract concepts.

The second test of ANOVA analyzed the interactions between

primary learning style and treatment as they affect performance on

questions requiring problem-solving skills. These results are reported

as a source table in Table 4-4. The R-squared for the dependent

variable (performance on problem solving questions) and Treatment x








Primary Learning Style interaction was equal to 0.06. The report of

results reviews the null hypothesis tested.

Table 4-4

Analysis of Variance Summary Table-Problem-Solving Questions

Source SS df MS F Pr>F

Treatment 12.165 1 12.165 2.34 0.1309

Primary Learning 0.511 1 0.511 0.10 0.7551
Style (PLS)

Treatment x PLS 6.240 1 6.240 1.20 0.2774

Error 369.789 71 5.208


The Treatment x PLS interaction was removed from the

statistical model and the results reported as a source table in Table 4-

5. The R-squared was equal to 0.042. Scores on tests of problem-

solving performance were not affected by instructional packet type or

PLS.

Table 4-5

Analysis of Variance Summary Table-Problem-Solving Questions
without Treatment x PLS Interaction

Source SS df MS F Pr>F

Treatment 15.288 1 15.288 2.93 0.0914

Primary Learning 1.381 1 1.381 0.26 0.6086
Style (PLS)

Error 376.029 72 5.223








Hypothesis 2 stated that mean outcome scores on examinations

testing problem-solving performance on questions relating to abstract

concepts do not differ due to the interaction of treatment and learning

style. The analysis of variance produced an F value of 1.20 for this

interaction that was not statistically significant at the .05 alpha level.

The null hypothesis was not rejected. The interaction between primary

learning style and instructional packet type did not influence the

performance on questions requiring problem-solving strategies.


Additional Findings

Although not proposed in the original design of the study,

analyses of the interactions among treatment, learning style, and

gender were explored, because gender-related differences in subjects'

performances on recall and problem-solving questions were noted.

Additional ANOVAs were performed that included interactions between

treatment and gender, learning style and gender, and treatment and

learning style. The three-way interaction among treatment, learning

style, and gender also was explored. The results for recall-based

questions are reported as source table and are discussed separately.

Neither Treatment x Gender, Primary Learning Style x Gender, or

Treatment x Primary Learning Style interaction for the ANOVA of

effects on recall performance reported in Table 4-6 was significant at

the .05 alpha level. However, the three-way interaction between








Treatment x Primary Learning Style x Gender was significant at the .05

alpha level.

The R-squared for the dependent variable (performance on recall

questions) and Treatment x Primary Learning Styles, Primary Learning

Style x Gender, Treatment x Primary Learning Styles, and Treatment x

Primary Learning Styles x Gender interactions reported as an ANOVA

source table in Table 4-6, was equal to 0.22. Recall performance was

affected by the eight combinations of variables. On the basis of these

findings, the least square means were calculated for the eight

combinations of variables and multiple comparisons were performed

and are reported in Table 4-7 and Table 4-8.

Table 4-6

Analysis of Variance Summary Table-Recall Questions Including
Treatment x Gender, PLS x Gender. Treatment x PLS and Treatment x
PLS x Gender Interactions

Source SS df MS F Pr>F

Treatment 0.149 1 0.149 0.04 0.8396

PLS 0.703 1 0.703 0.19 0.6607

Gender 35.464 1 35.464 9.81 0.0026

Treatment x Gender 1.610 1 1.610 0.45 0.5069

PLS x Gender 3.522 1 3.522 0.97 0.3273

Treatment x PLS 4.996 1 4.996 1.38 0.2440

Treatment x PLS x Gender 17.543 1 17.543 4.85 0.0311

Error 242.308 67 3.617








Table 4-7

Least Square (LS) Mean Data


on Recall Questions


PLS

Concrete

Concrete

Abstract

Abstract

Concrete

Concrete

Abstract

Abstract


Gender

M

F

M

F

M

F

M

F


r of Treatment/PLS/Gender Combinations


LS
Mean

8.500

5.118

5.625

6.059

7.000

5.455

8.167

5.167


Standard
Error

1.345

0.461

0.672

0.461

1.345

0.573

0.776

0.549


Number of
Subjects

2

17

8

17

2

11

6

12


TRT

VA

VA

VA

VA

Math

Math

Math

Math


LS
Mean #

1

2

3

4

5

6

7

8


Note. VA = Visual analogies, Math = Mathematical formula, M = Males,
and F = Females.


--








Table 4-8

Pair-Wise Comparisons of Least Square (LS) Means


(Treatment/PLS /Gender) and Probabilities


that LS Means are


Alpha = .05

i/j 1 2 3 4 5 6 7 8

1 -- -- -- -- -- -- -- --

2 0.020

3 0.060 0.536

4 0.091 0.154 0.596

5 0.433 0.190 0.364 0.510

6 0.041 0.649 0.848 0.415 0.294

7 0.831 0.001 0.016 0.023 0.455 0.007

8 0.025 0.946 0.599 0.218 0.211 0.718 0.002


Similar analyses for the problem-solving questions were

performed. The results for questions requiring problem solving are

reported as source tables in Table 4-9, Table 4-10, and Table 4-1 land

are discussed separately.


Eaual at








Table 4-9

Analysis of Variance Summary Table-Problem-Solving Questions
Including Treatment x Gender. PLS x Gender, Treatment x PLS. and
Treatment x PLS x Gender Interactions

Source SS df MS F Pr>F

Treatment 0.261 1 0.261 0.06 0.8122

PLS 7.458 1 7.458 1.63 0.2064

Gender 3.310 1 3.310 0.72 0.3983

Treatment x Gender 18.362 1 18.362 4.01 0.0493

PLS x Gender 21.342 1 21.342 4.66 0.0345

Treatment x PLS 4.792 1 4.792 1.05 0.3101

Treatment x PLS x Gender 1.430 1 1.430 0.31 0.5782

Error 306.887 67 4.580


The R-squared for the dependent variable (performance on

questions requiring problem solving) and Treatment x Gender, Primary

Learning Style x Gender, Treatment x Primary Learning Style, and

Treatment x Primary Learning Style x Gender interactions reported as

an ANOVA source table in Table 4-9 was equal to 0.22. The interactions

between Treatment x Gender and Primary Learning Style x Gender were

significant at the .05 alpha level. On the basis of the data reported in

Table 4.9, the Treatment x Primary Learning Style and Treatment x

Primary Learning Style x Gender interactions were removed from the

statistical model. The results are reported as an ANOVA source table

in Table 4-10. The R-squared for the dependent variable (performance








on questions requiring problem solving) and Treatment x Gender and

Primary Learning Style x Gender interactions reported as an ANOVA

source table in Table 4-10, was equal to 0.178. Based on the results

reported in Table 4-10, there was a significant interaction between

treatment and gender and primary learning style and gender at the .05

alpha level.


Table 4-10

Analysis of Variance Summary Table-Problem-Solving Questions
Including Treatment x Gender and PLS x Gender Interactions

Source SS df MS F Pr>F

Treatment 0.906 1 0.906 0.19 0.6613

Primary Learning 9.530 1 9.530 2.04 0.1580
Style (PLS)

Gender 3.535 1 3.535 0.76 0.3877

Treatment x Gender 21.809 1 21.809 4.66 0.0343

PLS x Gender 18.701 1 18.701 4.00 0.0495

Error 322.728 69 4.677


The relationships between treatment and gender and primary

learning style and gender were explored by comparisons of least

squares means for participant performance on problem-solving

questions. Table 4-11 presents the least square means and standard

errors for the least square means for the four treatment/gender

combinations. A pair-wise comparison of least square means was








conducted at an alpha of .05. The probabilities that the means are

equal for each of these pair-wise comparisons are presented in

Table 4-12.

Table 4-11

Least Square (LS) Mean Data for Treatment/Gender Combinations on
Problem-Solving Questions

Treatment Gender LS Mean Standard Error LS Mean #

Visuals Male 6.90 0.777 1

Visuals Female 7.59 0.371 2

Math Male 7.92 0.824 3

Math Female 6.05 0.451 4


Table 4-12

Pair-Wise Comparisons of Least Square (LS) Means
(Treatment/Gender) and Probabilities that LS Means are Equal at
Alpha = .05

i/j 1 2 3 4

1 -- -- -- --

2 0.4289

3 0.3262 0.7152

4 0.3466 0.0105 0.0508

The relationships between primary learning style and gender

were explored by comparisons of least squares means for performance

on problem-solving questions.









Table 4-13 presents the least square means and standard errors

for the least square means for the four primary learning style/gender

combinations. A pair-wise comparison of the least square means was

conducted at an alpha of .05. The probabilities that the means are

equal for each of these pair-wise comparisons are presented in Table

4.14.

Table 4-13

Least Square (LS) Mean Scores and Standard Errors Data for Primary
Learning Style/Gender Combinations on Problem-Solving Questions

Treatment Gender LS Mean Standard Error LS Mean #

Concrete Male 6.25 1.081 1

Concrete Female 7.01 0.413 2

Abstract Male 8.57 0.583 3

Abstract Female 6.63 0.405 4


Table 4-14

Pair-Wise Comparisons of Least Square (LS) Means (Primary Learning
Style x Gender) and Probabilities that LS Means are Equal at Alpha =
.05

i/j 1 2 3 4

1 -- -- -- --

2 0.5115

3 0.0628 0.0326

4 0.7456 0.5009 0.0077








Summary

To summarize, the analyses of data collected for this study

resulted in the following findings. There were no statistically

significant interactions between PLS and instructional packet types

and performances on recall questions or questions requiring problem-

solving abilities. For questions requiring simple recall, a significant

three-way interaction was found for Treatment x Primary Learning Style

x Gender. There were significant interactions between instructional

packet type (treatment) and gender and primary learning style and

gender on questions requiring problem-solving abilities.

This study was designed to determine whether a learner's

primary learning style affects his or her ability to learn from an

instructional packet type and whether these interactions influence his

or her performance on recall questions and questions requiring

problem-solving abilities. What are the implications of these results

for the design of instructional materials that will most facilitate the

learning of students who differ in learning style and gender? Are

animations a more effective method of presenting dynamic processes

than a series of static images? Are actual scores on the abstract

conceptualization/concrete experience continuum more predictive of

learning benefits than the qualitative categorizations of abstract or

concrete primary learning styles? These questions are addressed in the

final chapter.














CHAPTER 5
CONCLUSIONS AND RECOMMENDATIONS

Introduction

The purpose of this study was to investigate whether material

presented in the form of mathematical expressions or visual analogies

interact with an individual's learning preference to enhance recall and

problem solving. Interactions between instructional packet type and

learners' primary learning styles (PLS) were investigated on two types

of multiple choice questions, those requiring simple recall of

information and those requiring problem solving.

The experimental model consisted of two separate 2 x 2 designs.

The two categories were primary learning style (concrete experience or

abstract conceptualization) and instructional packet type

(mathematical formulas or visual analogies). The interaction between

primary learning styles and learning packet types was investigated for

two types of questions (recall and problem solving). Two separate

analyses of variance were performed, one for recall questions and one

for questions requiring problem solving. After the collection of the data,

additional interactions between treatment and gender, primary learning

style and gender, and treatment and primary learning style, and gender

were explored.








To implement this study, students enrolled in anatomy and

physiology courses and dental hygiene students who had completed

anatomy and physiology were chosen as the population from which the

sample was chosen. The Kolb Learning Style Inventory (LSI) was used

to determine the students' PLS. Materials were developed to present

important concepts in blood gas interpretation using either

mathematical formulas or visual analogies. To determine if participants

had any prior knowledge relating to blood gas interpretation, a pretest

on the subject of blood gas analysis was developed and administered

that contained 11 recall questions and 14 questions requiring problem-

solving skills. The pretest was administered immediately after

participants completed the LSI. After a minimum of 1 week,

participants were randomly assigned to one of the two types of

instructional packets. After completing the packets, participants took a

posttest that was identical to the pretest. Expert opinions were used

to substantiate the equivalency of content contained in the

mathematical packets and visual analogy packets. In addition, expert

opinion was used to substantiate the categorization of test questions

as either problem solving or recall in nature.


Findings
The study was implemented, data were collected, and the data

were analyzed in terms of the stated hypotheses. The research

questions had been stated by the following null hypotheses:








Hypothesis 1. Mean outcome scores on examinations testing the

recall of abstract concepts do not differ due to the interaction of

treatment and learning style. This hypothesis was not rejected.

Hypothesis 2. Mean outcome scores on examinations testing

problem-solving performance on questions relating to abstract concepts

do not differ due to the interaction of treatment and learning style.

This hypothesis was not rejected.

Specifically, no interactions were found between primary learning

style (concrete experience or abstract conceptualization) and

instructional treatment analogicall or mathematical packet) on

participants' performances on recall and problem-solving questions.


Discussion

Although none of the null hypotheses were rejected, interactions

were noted for both recall and problem-solving questions. A significant

three-way interaction between Treatment x Primary Learning Style x

Gender was found at an alpha = .05. After controlling for family error

rates, a significant difference in least square means was found at an

alpha level of .05 (.05/28 = .002). Pair-wise comparisons of least square

mean scores showed that males with abstract primary learning styles

who received mathematical packets scored significantly higher than

females with concrete primary learning styles who received visual

analogy packets.








Significant interactions were found between treatment and

gender and primary learning style and gender relating to performances

on problem-solving questions. Treatment and gender and primary

learning style and gender interactions had significance at an alpha =

.05. Two sets of pair-wise comparisons of least square means were

conducted to determine significant differences between subjects'

performances on questions requiring problem-solving skills.

The first set of pair-wise comparisons compared least square

means for the four combinations of treatment and gender

(visuals/males, visuals/females, math/males, and math/females).

After controlling for family error rates, no significant differences in

least square means were found at an alpha level of .05 (.05/6 = .0083).

The second set of pair-wise comparisons compared least square

means for the four combinations of primary learning style and gender

(concrete/males, concrete/females, abstract/males, and

abstract/females). After controlling for family error rates, a significant

difference in least square means were found at an alpha level of .05

(.05/6 = .0083) between abstract males and abstract females.

These data show that males with abstract learning styles

performed significantly better than females with abstract learning

styles on examination questions that required problem-solving skills.

Although a treatment x gender effect was noted, a comparison of least








square means did not reveal a significant difference in performances on

questions requiring problem-solving skills.

The better problem-solving performance by males who favor

abstract conceptualization over females who favor abstract

conceptualization is difficult to explain solely on the basis of this

study. Enns (1993), in her review of literature related to learning styles

and gender differences, determined that the characteristics of

reflection and abstraction are traditionally associated with the

masculine gender, whereas concrete experience and active

experimentation are more closely aligned with the female gender.

Seventy-eight percent of male subjects who participated in this study

had a preference for abstract conceptualization as their favored mode of

obtaining new information, whereas only 50% of the females displayed

a preference for abstract conceptualization. Enns' findings would

explain the gender-related distributions of primary learning styles

observed in this study, but not why females who prefer abstract

conceptualization would perform less well than their male

counterparts.

Although males who received the mathematics packet had a least

square mean nearly 25% higher than females receiving the

mathematics packet, the results were not significant statistically. Lack

of statistical significance may be due to the small number of males






75

participating in this study (N=18, with eight males receiving the

mathematics packet).

Furthermore, females who received the visual analogy packet had

a least square mean approximately 20% higher than females receiving

the mathematics packet. Once again, the results were not statistically

significant and may be due to disparities in the number of females

receiving each of the treatments (34 females received the visual

analogy packet whereas only 23 females received the mathematics

packet). Nevertheless, the results are suggestive of gender/treatment

interactions that were confirmed in the ANOVA.


Implications

These results add to the knowledge base used by educators in

the design of instructional materials. Although primary learning style

(PLS) and instructional treatment interactions were not statistically

significant, interactions did occur between gender and treatment and

learning style and gender. Whereas this study did not show that

learners with concrete primary learning styles would benefit from the

use of visual analogies, or that learners favoring abstract

conceptualization would benefit with either visual analogies or

mathematical formulas, the study did show that gender and treatment

did interact significantly. Primary learning styles and gender did

interact significantly as well. Awareness of such interactions heightens

the need for the inclusion of multiple strategies in the delivery of








instruction. Illustrating abstract concepts through the use of visual

and/or verbal analogies may result in greater learning gains for women,

especially when compared to mathematical formulas. Even though the

number of male subjects in this study was small (N=18), males appear

to perform equally well on examinations testing problem-solving skills,

regardless of whether they receive the material in the analogical or

mathematical formats. In an attempt to apply information gained about

learners' preferred styles to instructional strategies, Lockitt (1997)

identified learning activities that would appeal to learners with the

attributes identified by the LSI. However, Tendy and Geiser (1997)

noted that considerable debate still exists on whether instruction

should focus on matching a learner's preferred style or altering it.


Recommendations for Future Research

One method of improving the power of this study would be to use

an appropriate covariant. Cronbach alpha reliability analyses were

performed for the two types of questions contained within the pretest

and showed that students had no prior knowledge of the material and

randomly selected answers on the pretest. Consequently, the pretest

could not be used as a covariant. Another ability indicator, such as

quantitative achievement scores on the ACT, SAT, or computerized

placement test (CPT), or students' mathematics GPAs, could serve as

appropriate covariants.








Also, the randomization technique used to assign packet types

that was based on the sum of the last four digits of the student's

social security number, resulted in a disparity in the number of

analogical and mathematical (logical) packets assigned (44 and 31

respectively). A technique such as having a subject flip a coin on

entering the room to determine whether he or she receives the visual

analogy packet or the mathematics packet and then assigning the other

packet type to the next subject entering the room, would eliminate this

problem. However, the independence of packet assignment may be

compromised by the coin-flip approach and adversely affect the validity

of the study.

The visual-analogy instructional packet used a series of static

images to convey the relationships between independent and

dependent variables. Images of pointing hands and dashed lines

indicating initial and final levels of independent variables were used to

highlight alterations discussed in the text (see Appendix B). However,

the process of homeostasis is inherently a dynamic, continuous

phenomenon. Rather than occurring in discrete steps, infinitesimal

alterations in carbon dioxide production and/or alveolar ventilation

result in an infinite number of possible carbon dioxide levels in the

arterial blood. The concept of homeostasis may therefore be conveyed

more accurately if animation is used to show the interactions between

independent variables and their effects on a dependent variable. Using








an interactive animation that would allow the subject to vary the flow

of water into the tub (carbon dioxide production) and/or alter the flow

of water out of the tub (alveolar ventilation) may have enhanced

concept attainment. A similar animation could be used to show the

relationship between alterations in PaCO2 and/or bicarbonate levels

(HCO3) and the effect of these alterations on blood pH. The use of

animations may support a cognitive process that is different from the

cognitive process supported by static, visual analogies. Although each

presentation format (static images versus animations) could use

attention focusing mechanisms, they would still represent different

instructional methods (Clark, 1994).

An additional strategy that may improve performance on the

examination would be to refer to the analogies used in the

instructional packets during the examination. Gick and Holyoak (1980,

1983) found that investigator-supplied analogies increased the

likelihood that learners would arrive at a creative solution to a problem

(Duncker's radiation problem, see p. 35). However, without a hint from

the investigators to use the story, Gick and Holyoak found that most

learners did not spontaneously use the analogy to solve the problem.

The current study could be modified to determine if explicit references

to the visual analogies would influence participants' performances on

the examination. Rather than comparing learning gains from visual

analogies and mathematical formulas, concrete learners could receive








the same treatment (visual analogies) and take one of two versions of a

posttest, one with references to the analogies and one examination

without the references. Lack of reference to the analogies used in the

visual analogy packet was not viewed as a limitation for this study due

to the short time delay between treatment and posttest (all

participants took the posttest immediately after the treatment).

Consequently, short-term memory should have allowed the

participants to remember the visual analogies. In addition, Gick and

Holyoak (1980) never stated that the analog to their multiple small

armies was the radiation beam or that the analog to the castle was the

large abdominal tumor. In contrast, the relationships between analogs

and targets were explicitly stated in the visual analogy packets used in

this study.

The current study considered primary learning styles (PLS)

qualitatively as concrete experience (CE) or abstract conceptualization

(AC). A more quantitative approach could be adopted in which the

subject's actual value on the AC-CE continuum would be used in the

analysis. Conceivably, only individuals with strong preferences for

concrete experiences may benefit from the use of visual analogies.

Figure 5-1 illustrates this concept. For example, a subject with an

AC-CE score of +3 (very close to the intersection of the concrete-

abstract and active experimentation-reflective observation continue)

may have equal preferences for concrete experiences and abstract








conceptualization. A subject with an AC-CE score of -27, in contrast,

would have a much stronger preference for concrete experiences and,

conceivably, benefit from the visual analogies to a greater degree.

Ruble and Stout (1994) raised a similar concern for the characterization

of both subjects as having concrete experiences as their primary

learning style.

Concrete Experience
-27


Active _+3 Reflective
Experimentation Observation




Abstract Conceptualization

Figure 5-1. Mild (+3) and Strong (-27) Preferences for Concrete
Experiences Compared to Abstract Conceptualization

A major concern that arose during this study involved the

motivation of the individuals who volunteered to participate in the

research. Analysis of the data revealed that 21 of the participants

scored lower on the posttest than they did on the pretest, even though

a Cronbach alpha run on the pretest showed that participants

randomly selected their answers. Guessing was permitted because the

participants had no prior exposure to the subject matter. However, on

the posttest, six participants selected answer "c," "d," or "e" on a

multiple-choice question that had only two choices. Evidently, these








participants did not read the question and distracters prior to marking

their response sheets. One participant repeated the sequence of "a,"

"b," "c," "d," and "e" as answers from the beginning to the end the

response sheet. Once again, the conclusion could be that the subject

did not make a concerted effort to answer the questions carefully. As

stipulated in the informed consent form, participation in the research

was voluntary. However, the validity and reliability of the conclusions

reached in this study would have been enhanced by sincere efforts on

the parts of the subjects. Obtaining the sample from a population of

students who had to perform their own research studies may increase

their motivation to perform to the best of their abilities.

This study only evaluated short-term recall of factual information

and problem-solving ability related to arterial blood gas interpretation.

Newby et al. (1995) found that retention scores were better for

students receiving analogies than students who did not receive the

analogies. Although the visual analogies did not result in significant

learning gains in terms of short-term performance on the examination

used in this study, long-term learning may be enhanced due to the

dual encoding of visuals proposed by Peeck (1974) and Paivio (1979).


Summary

This study investigated learning gains based on the interactions

between students' primary learning styles (concrete experience and

abstract conceptualization) and instructional packet types (visual








analogies and mathematical formulas). The sample for this study was

75 students enrolled in introductory anatomy and physiology courses at

a middle-sized community college located in north central Florida and

dental hygiene students who had completed college-level anatomy and

physiology coursework. The experimental model consisted of two

separate, 2 x 2 designs. The two categories were primary learning style

(concrete experience or abstract conceptualization) and instructional

packet type (mathematical formulas or visual analogies). The

interaction between primary learning styles and packet types was

investigated for two types of questions (problem solving or simple

recall). Two separate analyses of variance were performed, one for

recall questions and one for questions requiring problem-solving skills.

There were no statistically significant interactions between

students' primary learning styles (PLS) and instructional packet types.

There were no statistically significant interactions between PLS and

instructional packet types and performances on recall questions or

questions requiring problem-solving abilities. A significant three-way

interaction was found for instructional packet type (treatment), primary

learning style, and gender for recall questions. There were significant

interactions between treatment and gender and primary learning style

and gender on questions requiring problem-solving abilities.

In conclusion, primary learning styles and instructional

treatments do not interact to affect performances on recall questions








and questions requiring problem-solving strategies. Instructional

packet type (treatment), primary learning style, and gender interact to

affect students' performances on recall questions. The results from

this study show that instructional treatments do interact with gender

to affect performances on questions requiring problem-solving skills.

Furthermore, performances on questions requiring problem-solving

skills were affected by primary learning style and gender interactions.

Conclusions from this study were restricted to the variables

manipulated in the study and limited to the populations represented by

the sample participating in the study. Further research questions were

presented based on findings from this study and serve as directions for

future research.















APPENDIX A
INFORMED CONSENT FORM








Informed Consent


Protocol Title: Differential Impact On Recall And Problem Solving
Performances Of Concrete And Abstract Thinkers Resulting From
Analogical Versus Logical Representations Of Theoretical Concepts

Please read this consent document carefully before you decide to participate in
this study.

Purpose of the research study:
I am studying whether students' ways of thinking affect how useful
visual analogies will be on their learning of new material. A visual
analogy is a way of presenting new information by comparing it with
illustrations of common objects or processes. Because they already
understand the common objects or processes, this knowledge should
help them learn the new information. You are being asked to take the
examination that will be used in the study in order to test the
examination's reliability.

What you will be asked to do in the study:
If you agree to be in this study, you will be given a test on arterial
blood gas interpretation. Once you complete the test, you will return it
to your instructor. Your name will not be used.

Total time required:
15 minutes.

Risks and Benefits:
There is no risk or physical discomfort associated with this study.
There are no benefits to you for participation.

Compensation:
You will not receive compensation for participating in this research.

Confidentiality:
Your identity will be kept confidential to the extent provided by law.

Voluntary participation:
Your participation in this study is completely voluntary. There is no
penalty for not participating. Your decision to participate or not
participate in this study will not affect your grade in any course.

Right to withdraw from the study:
You have the right to withdraw from the study at anytime without
consequence.








Whom to contact if you have questions about the study:
David N. Yonutas, MS, RRT; phone number: (352) 375-6924

Whom to contact about your rights as a research participant in
the study:
UFIRB Office, Box 112250, University of Florida, Gainesville, FL 32611-
2250; phone number: (352) 392-0433.

Agreement:
I have read the procedure described above. I voluntarily agree to
participate in the procedure and I have received a copy of this
description.

Participant: Date:

Principal Investigator: Date:














APPENDIX B
PACKET 1: ANALOGICAL PACKET











Blood Gas Analysis

A Dripping Faucet and Draining Tub


imagine a faucet, a tub of water, and a drain. In Figure 1, the amount of water entering the
tub i s exactly equal to the amount of water exiting the tub through the drain. The level of
water in the tub would consequently remain constant and is indicated by the pointing hand. This
is the baseline condition.



Figure 1: Baseline condition Water exiting
6 tub equals water entering tub

_4


6

If the drain becomes clogged, resulting in less water flowing out of the tub, the level of water will
increase (Figure 2). The shadow pointing hand represents the original water level.


Figure 2: Less water exiting tub than water
entering tub resulting, in an increase in the
water level within the tub.


6
6
An~~c


In Figure 2, increasing the flow of water into the tub without altering the flow of water out of the
tub would also increase the level of water.




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