brain hemispheric preferences of fourth- and fifth-grade

BRAIN HEMISPHERIC PREFERENCES OF FOURTH- AND FIFTH-GRADE SCIENCE TEACHERS AND STUDENTS IN TAIWAN: AN INVESTIGATION OF

THE RELATIONSHIPS TO STUDENT SPATIAL AND VERBAL ABILITY, STUDENT ACHIEVEMENT, STUDENT ATTITUDES, AND TEACHING PRACTICE

DISSERTATION

Presented in Partial Fulfillment of the Requirements for

the Degree of Doctor of Philosophy in the Graduate

School of The Ohio State University

By

Tzu-Ling Wang, M.S.

*****

The Ohio State University 2008

Dissertation Committee:

Professor Donna F. Berlin, Advisor

Professor Arthur L. White

Professor Garry D. McKenzie

Approved by _____________________

Advisor College of Education

Copyright by

Tzu-Ling Wang

© 2008

ii

ABSTRACT

This study is divided into two parts: the construction and validation of teacher and

student instruments translated into Chinese and the main study. After establishing the

validity and reliability of the instruments, the purposes of the main study were to: (a)

investigate the relationships among teacher and student hemispheric preferences, student

spatial and verbal abilities, student science achievement, and student attitudes toward

science class and (b) identify science teaching strategies and match them to teacher and

student hemispheric preferences. Four elementary school science teachers and 133

fourth- and fifth-grade students were selected from a school in Taiwan. Data were

analyzed using quantitative and qualitative procedures.

The major conclusions of the study are: (a) Science teaching strategies seem to be

related to gender differences. Male teachers preferred right-brain teaching strategies,

whereas female teachers preferred left-brain teaching strategies; (b) Verbal ability,

science achievement, and attitudes toward science class seem to be related to student

hemispheric preferences. Students with a stronger whole-brain preference tended to

exhibit better verbal ability, better science achievement, and more positive attitudes

toward science class. Male and fourth-grade students with a stronger whole-brain

preference tended to exhibit better science achievement and more positive attitudes

toward science class, respectively; (c) Student hemispheric preferences seem to be related

iii

to gender and grade level differences. The right hemisphere seems to develop earlier in

male students and the left hemisphere earlier in female students. For female students,

brain lateralization seems to develop earlier and they tended to exhibit a stronger whole-

brain preference. Fifth-grade students tended to exhibit stronger right-brain and left-brain

preferences compared to fourth-grade students suggesting greater brain lateralization in

older students. Fifth-grade males tended to exhibit a stronger right-brain preference

compared to fourth-grade males; fifth-grade females tended to exhibit a stronger left-

brain preference compared to fourth-grade females; (d) Spatial and verbal ability seem to

be related to gender differences. Male students tended to exhibit better spatial ability;

female students tended to exhibit better verbal ability; and (e) Science teaching strategies

were not generally related to student hemispheric preferences. Science teaching strategies

were partially related to student hemispheric preferences for 2 teachers.

iv

Dedicated to the memory of my parents

Sung-Hsin and Wu-Mei

for their endless love and support

v

ACKNOWLEDGMENTS

I would like especially to express my deepest appreciation to my advisor, Dr. Donna

Berlin, for her continuous advice, assistance, patience, and encouragement throughout my

doctoral study. Without her support and assistance, I could not have completed this

doctoral degree.

I would like to express sincere appreciation to Dr. Arthur White for his insightful

suggestions, kind encouragement, and expertise in statistics.

I would like to express grateful appreciation to Dr. Garry McKenzie for his expert

guidance, valuable feedback, and generous support.

I would like to express my thanks to Dr. Ching-Yang Chou for sharing his insightful

thoughts and encouragement throughout my academic career.

I also would like to express my thanks to Dr. Chao-Ti Hsiung for her continuous

support and encouragement.

I extend my sincere thanks to the principals, science teachers, students from the

elementary schools, and undergraduate and graduate students from the university that

participated in this study.

I wish to thank my friends in Taiwan and in the U.S. for their friendship and support

through my study. I am especially grateful to I-Chia who helped me with the translation

of the instruments.

vi

Finally, I would like to express my deep gratitude to my family for their unwavering

love and support. My elder sister, Su-Yu, has given her continuous encouragement and

has taken care of my daughter. My elder brother, Chien-Chia, has given his continuous

support and encouragement. My husband, Yi-Kuan, has given his endless love and

support. My daughter, Wan-Ching, has brought much joy and inspiration into my life.

vii

VITA

September 29, 1966………………… Born-Taipei, Taiwan, R.O.C.

1992……………………………….... B. Ed. Mathematics & Science Education National Taipei Teachers College, Taiwan

1995…………………………………. M. S. Science Education National Kaohsiung Normal University, Taiwan

2004-2007…………………………... Graduate Research Associate The Ohio State University

FIELDS OF STUDY

Major Field: Education

Specialization: Science Education

viii

TABLE OF CONTENTS

Page

Abstract ............................................................................................................................... ii Dedication .......................................................................................................................... iv Acknowledgments................................................................................................................v Vita.................................................................................................................................... vii List of Tables ..................................................................................................................... xi List of Figures .................................................................................................................. xiv Chapters 1. Introduction.................................................................................................................1

Theoretical framework.......................................................................................2 Hemispheric specialization .......................................................................2 Developmental and gender differences in hemispheric specialization.....4 Gender differences in cognitive ability.....................................................4 Left-brain and right-brain teaching strategies...........................................6 Hemisphericity and achievement..............................................................7

Significance of the study....................................................................................8 Statement of the problem and research questions..............................................8 Overview of the study......................................................................................10 Limitations of the study ...................................................................................12

2. Review of the literature.............................................................................................14

Brain structure..................................................................................................14 Brain development ...........................................................................................15 Hemispheric specialization ..............................................................................17 Hemispheric preference ...................................................................................21 Gender differences in brain structure, brain development, and cognitive ability ...............................................................................................................23

Gender differences in brain structure......................................................23 Gender differences in brain development ...............................................27 Gender differences in cognitive ability...................................................30 Spatial ability .................................................................................31 Verbal ability .................................................................................34

Nature and nurture ...........................................................................................35

ix

Left-brain and right-brain teaching strategies..................................................36 Hemisphericity and achievement.....................................................................40 Summary ..........................................................................................................46

3. Methodology............................................................................................................49

Construction and validation of instruments .....................................................49 Subjects ...................................................................................................49 Context....................................................................................................50 Teacher data sources ...............................................................................52 Human Information Processing (HIP) Survey................................52 Student data sources................................................................................55 Style of Learning and Thinking (SOLAT, Elementary Form) .......55 PMA Spatial Relations Test (Grades 4-6) ......................................57 Student Attitudes Toward Science Class Survey............................59

Main study .......................................................................................................64 Participants..............................................................................................64 Research design ......................................................................................65 Data collection procedures......................................................................66 Teacher data collection ...................................................................66 Data collection: Teacher Demographics Questionnaire .........66 Data collection: Human Information Processing Survey .......67 Data collection: Classroom observations................................67 Student data collection....................................................................71 Data collection: Style of Learning and Thinking....................71 Data collection: PMA Spatial Relations Test .........................71 Data collection: Chinese language midterm and final

exams ......................................................................................71 Data collection: Science midterm and final exams.................72 Data collection: Student Attitudes Toward Science Class

Survey .....................................................................................72 Data analysis procedures.........................................................................73 Teacher data analyses .....................................................................73 Data analysis: Teacher Demographics Questionnaire ............73 Data analysis: Human Information Processing Survey ..........73 Data analysis: Classroom observations ..................................74 Student data analyses ......................................................................75 Data analysis: Style of Learning and Thinking.......................75 Data analysis: Student multiple instruments...........................76 Teacher and student data analyses..................................................77 Data analysis: Classroom observations and Style of

Learning and Thinking ...........................................................77 Summary ..........................................................................................................77

x

4. Results of data analyses ............................................................................................79

Results of teacher data analyses.......................................................................79 Data results: Teacher Demographics Questionnaire...............................79 Data results: Human Information Processing Survey.............................81 Data results: Classroom observations .....................................................83

Results of student data analyses.......................................................................90 Data results: Style of Learning and Thinking .........................................90 Data results: Student multiple instruments .............................................96 Descriptive statistics .......................................................................96 Correlation analyses........................................................................99

Two-way Multivariate Analysis of Variance (MANOVA) ..........109 Results of teacher and student data analyses .................................................113

Data results: Classroom observations and Style of Learning and Thinking................................................................................................113

Summary ........................................................................................................118 5. Conclusions and discussion ....................................................................................123

Summary of the main study ...........................................................................123 Discussion of the research questions and the literature .................................126 Research question 1...............................................................................126 Research question 2...............................................................................128 Research question 3...............................................................................129 Research question 4...............................................................................130 Research question 5...............................................................................130 Research question 6...............................................................................131 Research question 7...............................................................................131 Research question 8...............................................................................133 Research question 9...............................................................................134 Research question 10.............................................................................135 Research question 11.............................................................................135 Research question 12.............................................................................136 Major conclusions..........................................................................................142 Implications....................................................................................................143

Teacher education programs .................................................................143 Curriculum developers..........................................................................144

Suggestions for future research......................................................................145 List of references..............................................................................................................147 Appendices

Appendix A: Student Attitudes Toward Science Class ..........................................158 Appendix B: Teacher Demographics Questionnaire ..............................................161

xi

LIST OF TABLES

Table Page 2.1 Information-processing styles of the left and right cerebral hemispheres ................20

2.2 Structural differences between male and female brains ...........................................27

2.3 Left-brain and right-brain teaching strategies...........................................................39

3.1 A summary of subjects and context for the construction and validation of teacher and student instruments ................................................................................52

3.2 Cronbach’s alpha coefficients for the Human Information Processing (HIP) Survey scales.............................................................................................................55

3.3 Description and sample item for each construct in the Student Attitudes Toward Science Class Survey...................................................................................60

3.4 Distribution of positive (+) and negative (-) items among the teaching materials, teaching strategies, and the three constructs of the Student Attitudes Toward Science Class Survey...................................................................................61

3.5 Cronbach’s alpha coefficients for student instruments.............................................64

3.6 Demographic information for Liang elementary school science teachers (n = 4) and elementary school students (n = 133) ................................................................65

3.7 Classroom observation schedule for the 4 elementary school science teachers .......68

3.8 Lesson topics and total class sessions for observations of the 4 elementary school science teachers .............................................................................................69

3.9 An example of the classroom observation field notes before coding and categorizing ...............................................................................................................70

3.10 An example of the classroom observation field notes after coding and categorizing...............................................................................................................75

3.11 A summary of data sources and data analyses for the main study............................78

4.1 Demographic information for the 4 elementary school science teachers .................80

xii

4.2 Standard scores on the HIP Survey left-brain, right-brain, and integrated-brain scales and brain hemispheric preference for the 4 elementary school science teachers .....................................................................................................................82

4.3 Teaching strategy code, teaching strategy category, and category abbreviation......84

4.4 Percentage of instructional time for teaching strategies related to left-brain, right-brain, and whole-brain processing for the 4 elementary school science teachers .....................................................................................................................85

4.5 Total percentage of instructional time for teaching strategies related to left-brain, right-brain, and whole-brain processing for the 4 elementary school science teachers.........................................................................................................88

4.6 Percentage of male and female students for left-brain, right-brain, and whole-brain preferences and no hemispheric preference for the four science classes.........91

4.7 Total percentage for male and female students for left-brain, right-brain, and whole-brain preferences and no hemispheric preference for the two grade levels .........................................................................................................................94

4.8 Means and standard deviations for the dependent variables for the male and female students..........................................................................................................98

4.9 Means and standard deviations for the dependent variables for grade 4 and grade 5 students.........................................................................................................99

4.10 Pearson correlation coefficients (r) for scores on the independent and dependent variables for elementary school students (n = 133)...............................100

4.11 Pearson correlation coefficients (r) for scores on the independent and dependent variables for elementary school male students (n = 68) ........................103

4.12 Pearson correlation coefficients (r) for scores on the independent and dependent variables for elementary school female students (n = 65).....................104

4.13 Pearson correlation coefficients (r) for scores on the independent and dependent variables for elementary school fourth-grade students (n = 65)............106

4.14 Pearson correlation coefficients (r) for scores on the independent and dependent variables for elementary school fifth-grade students (n = 68)...............108

4.15 Results of multivariate analyses of variance for all dependent variables by gender and grade level ............................................................................................110

4.16 Results of univariate analyses of variance for all dependent variables by gender and grade level ............................................................................................112

xiii

4.17 Confidence intervals for proportion tests for teaching strategies and student left-brain, right-brain, and whole-brain preferences in John’s class .............................114

4.18 Confidence intervals for proportion tests for teaching strategies and student left-brain, right-brain, and whole-brain preferences in Betty’s class......................115

4.19 Confidence intervals for proportion tests for teaching strategies and student left-brain, right-brain, and whole-brain preferences in Peter’s class ......................117

4.20 Confidence intervals for proportion tests for teaching strategies and student left-brain, right-brain, and whole-brain preferences in Lucy’s class ......................118

4.21 A summary of data sources, data analyses, and results for the main study ............119

5.1 A summary of the findings for the 12 research questions for the main study ........137

xiv

LIST OF FIGURES Figure Page 4.1 Standard scores on the HIP Survey left-brain, right-brain, and integrated-brain

scales for the 4 elementary school science teachers .................................................83

4.2 Percentage of instructional time for each teaching strategy category abbreviation related to left-brain, right-brain, and whole-brain processing for the 4 elementary school science teachers .................................................................87

4.3 Total percentage of instructional time for teaching strategies related to left-brain, right-brain, and whole-brain processing for the 4 elementary school science teachers.........................................................................................................90

5.1 An overview of the findings for the main study .....................................................141

1

CHAPTER 1

INTRODUCTION

Brain research has increased significantly since the 19th century. In particular,

research during the past 47 years has provided insight into cerebral specialization. Basic

to our understanding of brain functioning is the fact that the largest portion of the human

brain, the cerebrum, consists of two hemispheres. It has been found that each of the two

cerebral hemispheres has specialized functions and different styles for processing

information (Hider & Rice, 1986). The left cerebral hemisphere is commonly engaged in

activities related to written and spoken language, abstract symbolism, number operations,

linear processing, rational decision making, and deductive logic, while the right cerebral

hemisphere is primarily responsible for spatial skills, pattern recognition, creativity,

parallel processing, intuitive decision making, and inductive logic (Miller, 1988).

There is a growing body of research on hemisphericity and such research has

provided insight into possible explanations for the reason why many children are not

succeeding in today’s school (Fountain & Fillmer, 1987; Sonnier & Kemp, 1980). Many

educators believe that most contemporary schools are dominated by a left-brain

curriculum, and generally, teaching strategies and learning activities are based primarily

on a linear, sequential, and analytic way of thinking (Chapman, 1998; Chudzinski, 1988;

Cooke & Haipt, 1986; Grady, 1984; Lewallen, 1985; Marxer, 1988; Rubenzer, 1982;

2

Turner, 1999; Vitale, 1982). Rubenzer pointed out that there is a need to close the gap

between left-brain teaching strategies and right-brain learning styles because the

left-brain educational system handicaps many children who prefer right-brain teaching

strategies. Studies have revealed that a match between instructional practice and student

hemispheric preference appears to have positive effects on student achievement, whereas

a mismatch between instructional practice and student hemispheric preference appears to

have negative effects on student achievement (Douglass, 1979; Dunn, Sklar, Bruno, &

Beaudry, 1990; Jarsonbeck, 1984). These discoveries have shed new light on the way

children learn in school and suggest that teaching strategies and student hemispheric

preferences do affect learning. However, research on hemisphericity and learning is

inconclusive in many cases (Ellis, 2001; Van Giesen, Bell, & Roubinek, 1987) and

requires further investigation and refinement.

Theoretical Framework

Hemispheric Specialization

The earliest evidence of hemispheric asymmetry came from observations of the

disorders produced by unilateral brain damage (Hellige, 1983). During the 1860s, Paul

Broca suggested the localization of speech production in the left hemisphere—Broca’s

area. During the 1870s, Karl Wernicke suggested the localization of speech

comprehension in the left hemisphere—Wernicke’s area (Finger, 1994). These clinical

reports indicated that different areas of the brain control specific functions. The notion of

localization of brain function emerged from early studies of brain-damaged individuals.

The most dramatic evidence for hemispheric asymmetry came from the study of

patients who had the corpus callosum severed in order to control severe epilepsy (Hellige,

3

1983). During the 1960s, Roger Sperry and two neurosurgeons, Philip Vogel and Joseph

Bogen, conducted a series of experiments with split-brain humans (Bogen, 1985). The

results showed that the left and right hemispheres process information differently. The

left hemisphere excels in dealing with verbal and analytic information, whereas the right

hemisphere excels in dealing with visuospatial, synthetic, and holistic information

(Springer & Deutsch, 1998). The initial idea of hemispheric specialization emerged from

studies of split-brain subjects.

More recently, hemispheric asymmetry has been investigated in studies on normal

people in a number of ways (e.g., tachistoscopic presentation, dichotic listening technique,

electroencephalography [EEG], functional magnetic resonance imaging [fMRI], and

positron emission tomography [PET]). The findings from studies on normal subjects

validated some of the insights about brain functioning gleaned from previous studies on

brain-injured individuals. They also offered subtle and new findings about brain

hemispheric differences (Springer & Deutsch, 1998). The notion of complementary

specialization of the cerebral hemispheres or the cooperative engagement of both

hemispheres emerged from the more recent research on normal individuals.

In conclusion, the findings from clinical observations and experimental

examinations support the notion that the left and right cerebral hemispheres in humans

process information differently from each other (Hellige, 1983). It is now widely

accepted that the left hemisphere is specialized for auditory, verbal, analytical, logical,

abstract, convergent, and deductive functions. The right side is specialized for visual,

motoric (tactile/kinesthetic), non-verbal, intuitive, creative, divergent, concrete, musical,

spatial, holistic, and inductive functions (Kane & Kane, 1979). The research data support

4

the notion that each hemisphere has its specialized functions in information processing

and thinking but both hemispheres usually work together when learning (Sousa, 2006).

Developmental and Gender Differences in Hemispheric Specialization

Researchers have suggested that the right hemisphere is superior in infants and

bilateral development gradually occurs until puberty or even later (Gurian, Henley, &

Trueman, 2001; Sonnier, 1982). Overall, female brains develop quicker than male brains

(Berlin, 1978; Gurian et al.; James, 2007). Further, the right hemisphere matures earlier in

males and the left hemisphere matures earlier in females (Berlin; Levy & Heller, 1992).

Epstein’s brain development theory of growth spurts and plateaus indicates that growth

spurts appear to occur between the ages of 3-10 months, 2-4 years, 6-8 years, 10-12+

years, and 14-16+ years. The last two growth spurts show clear differences in boys and

girls. Girls’ brains grew three times as fast as boys’ during the 10-12+ year spurt. During

the 14-16+ year spurt, the boys’ brains grew three times as fast as girls (as cited in

Lindelow, 1983; Rose, 1982). Sax (2005) reviewed a study conducted by researchers in

Virginia Tech who studied the brain activity in 508 normal children, 224 girls and 284

boys, ranging from 2 months to 16-years-old. They found that the various areas of the

brain develop in a different order, time, and rate in girls compared to boys.

Gender Differences in Cognitive Ability

According to Hines (2007), there are no significant sex differences in overall

intelligence tests. However, there are sex differences in some specific cognitive abilities.

In 1974, Maccoby and Jacklin published a systematic review of research which became a

milestone analysis. They concluded that males had an advantage in mathematical and

spatial ability, whereas females had an advantage in verbal ability. In addition, sex

5

differences in verbal ability begin to appear around the age of 11, and that sex differences

in spatial ability do not appear until adolescence (Hyde, 1990). Hyde (1981) published

the first meta-analysis using the studies that had been evaluated by Maccoby and Jacklin.

The results showed that males performed better than females in mathematical ability and

spatial ability, while females performed better than males in verbal ability.

Two major meta-analyses of sex differences related to spatial ability have been

conducted. Linn and Petersen (1985) conducted a meta-analysis of sex differences related

to spatial ability. The results showed that males have a moderate advantage with regard to

spatial perception, a clear advantage for mental rotation, and no difference for spatial

visualization. In addition, the results showed that sex differences in spatial ability seem to

be detected prior to adolescence for spatial perception and mental rotation. Voyer, Voyer,

and Bryden (1995) conducted a meta-analysis of sex differences related to spatial ability.

The results showed that sex differences were largest and most consistent for tests in the

mental rotation category (significant in each test), sex differences were large but

inconsistent for tests in the spatial perception category (significant in four out of seven

tests), and sex differences were highly changeable and frequently not significant for tests

in the spatial visualization category (significant in three out of eleven tests). The results

also showed that the age at which sex differences emerge is related to the test used (e.g.,

10-years-old for the generic mental rotation task and the PMA Spatial Relations subtest).

Based on the meta-analyses reported by Linn and Petersen and Voyer et al., a greater

male advantage appears to be for tasks involving mental rotation.

Hyde and Linn (1988) conducted a meta-analysis of sex differences related to verbal

ability. The results showed that all of the sex differences were small for tests of verbal

6

ability, favoring females with one exception—analogies. The largest sex difference was

for speech production, favoring females. More detailed analysis of various types of verbal

ability indicated that there were no sex differences at all ages from childhood to adult.

Left-Brain and Right-Brain Teaching Strategies

Research has shown that matching teaching strategies to student hemispheric

preference will enhance achievement (Beck, 2001; Saleh, 2001). In addition, research has

shown that educators must incorporate left-brain and right-brain teaching strategies to

stimulate both hemispheres to develop the whole brain, to reach all students, and to

achieve student highest potential (Beck; Lewallen, 1985; Williams, 1983). Researchers

have classified teaching strategies into left-brain and right-brain approaches. The

following left-brain and right-brain teaching strategies were derived from a review of the

literature (Beck; Bishop, 1978; Jackson, 1998; Lewallen; Marxer, 1988; McCarthy, 1987;

Williams). The left-brain teaching strategies include: lecture, recitation, textual readings,

oral reports, written reports, computation, multiple-choice tests, choosing only one

solution, inquiry, systematic experiments with teacher control, problem solving,

independent study, mastery learning, programmed learning, internet search, word

processing, convergent questions, and prompting questions. The right-brain teaching

strategies include: physical relaxation; visual thinking (e.g., pictures, maps, diagrams,

charts, mind maps, illustrations, flow charts, and graphs); brainstorming; fantasy; direct

experiences (e.g., field trips, manipulation of materials, primary sources, and real objects);

random experiments with student control; simulation; multisensory learning

(e.g., requiring visual, auditory, tactile, and/or kinesthetic senses); music and rhythm;

color discrimination; spatial imagery; creativity; art and design; analogy and metaphor;

7

poetry; mediation; open-ended questions; allowing many possible solutions; round table,

magic circle, fish bowl, and cooperative learning groups; dramatic and role play; video

conferencing; creative software; divergent questions; and probing questions.

Hemisphericity and Achievement

Hemispheric specialization has implications for educators. At the elementary school

level, the following studies have related teaching strategies (left-brain, right-brain, and

integrated-brain) and student hemispheric preference to students’ achievement and

attitudes. For example, student left-brain preference has been related to better

achievement and more negative attitudes in science, whereas student integrated-brain

preference has been related to better achievement and more positive attitudes in science

(Hider & Rice, 1986). Student left-brain preference has been related to greater reading

vocabulary achievement (Van Giesen et al., 1987). Student integrated-brain preference

has been related to higher general ability level, whereas student left-brain or right-brain

preference has been related to lower general ability level (Shannon & Rice, 1983).

Specific to fourth-grade students, integrated-brain preference has been related to higher

achievement in reading, mathematics, language, and a basic battery of tests (Fountain &

Fillmer, 1987).

Studies have revealed that a match between teaching strategies and student

hemispheric preference appears to have positive effects on student achievement in (a)

mathematics at the elementary school level (Jarsonbeck, 1984), (b) biology at the high

school level (Douglass, 1979), (c) mathematics at the college level (Dunn et al., 1990),

and (d) on student grades in an undergraduate psychology course (Gadzella, 1995).

However, some studies have revealed inconsistent results. For example, teaching

8

strategies (left-brain, right-brain, and integrated-brain) were not related to students’

achievement in science at the elementary school level (Sonnier & Kemp, 1980). In

addition, a match or mismatch between teaching strategies and student hemispheric

preference did not affect student achievement in mathematics at the senior high school

level (Brennan, 1984). Based upon the literature review, it appears that teaching strategies

and student hemispheric preference may affect learning, but the research results are not

consistent.

Significance of the Study

Children entering today’s schools encounter an unbalanced education. Teaching

materials and teaching strategies are primarily left-hemisphere oriented and almost totally

ignore the right hemisphere. Many learners who prefer right-brain teaching strategies are

particularly handicapped. Discoveries from the field of hemispheric specialization are

helpful in understanding this imbalance. Therefore, educators must incorporate left-brain

and right-brain teaching strategies to reach all students and adjust the curriculum to

involve both hemispheres in the learning process to develop the whole brain. The full

potential of the human brain can be reached only when both hemispheres work together

(Cooke & Haipt, 1986). A balanced curriculum is one that stimulates both hemispheres

(Grady, 1984) and is beneficial to all students and not just left-brain preference students.

Statement of the Problem and Research Questions

The purpose of this mixed methods study was first to investigate the relationships

among teacher hemispheric preference, student hemispheric preference, student spatial

ability, student verbal ability, student science achievement, and student attitudes toward

science class using teacher and student data from multiple instruments. The second

9

purpose was to identify teaching strategies in the science classroom using observation

data and then match these teaching strategies to teacher and student hemispheric

preferences. This study of elementary school science teachers and grade 4 and grade 5

students in Taiwan was designed to investigate the following research questions:

1. What is the relationship between science teacher hemispheric preference and

science teaching strategies? Is there a gender difference in the relationship?

2. What is the relationship between student hemispheric preference and spatial

ability? Is there a gender and/or grade level difference in the relationship?

3. What is the relationship between student hemispheric preference and verbal

ability? Is there a gender difference in the relationship?

4. What is the relationship between student hemispheric preference and science

achievement? Is there a gender difference in the relationship?

5. What is the relationship between student hemispheric preference and attitudes

toward science class? Is there a gender and/or grade level difference in the

relationship?

6. What is the relationship between teacher and student hemispheric preference? Is

there a gender and/or grade level difference in the relationship?

7. Are there any differences in student hemispheric preference by gender and grade

level?

8. Are there any differences in student spatial ability by gender and grade level?

9. Are there any differences in student verbal ability by gender?

10. Are there any differences in student science achievement by gender?

10

11. Are there any differences in student attitudes toward science class by gender and

grade level?

12. What is the relationship between science teaching strategies and student

hemispheric preference?

Overview of the Study

This study, approved by The Ohio State University Behavioral and Social Sciences

Institutional Review Board, is divided into two parts: the construction and validation of

teacher and student instruments translated into Chinese for use in Taiwan and the main

study to explore the relationships among the variables of interest. In the first part, various

instruments were acquired from commercial vendors or developed by the researcher. All

of the instruments were then translated into Chinese, back-translated, and reviewed by a

panel of experts for validity. The Chinese version of the Human Information Processing

(HIP) Survey was administered to 35 undergraduate students, 35 graduate students, and

39 elementary school science teachers in Taiwan. Cronbach’s alpha (n = 109) for the

Chinese version of the HIP Survey was 0.81 for the left-brain scale, 0.78 for the

right-brain scale, and 0.86 for the integrated-brain scale. The Chinese version of the Style

of Learning and Thinking (SOLAT, Elementary Form) was administered to 98

fourth-grade students and 101 fifth-grade students in Taiwan. Cronbach’s alpha (n = 199)

for the Chinese version of the SOLAT inventory was 0.75 for the left-brain scale, 0.74 for

the right-brain scale, and 0.83 for the whole-brain scale. The Chinese version of the

Primary Mental Abilities (PMA) Spatial Relations Test was administered to 98

fourth-grade students and 101 fifth-grade students in Taiwan. Cronbach’s alpha (n = 199)

for the Chinese version of the PMA Spatial Relations Test was 0.64 for fourth-grade

11

students and 0.79 for fifth-grade students. The Chinese version of the

researcher-developed Student Attitudes Toward Science Class Survey was administered

to 98 fourth-grade students and 101 fifth-grade students in Taiwan. Cronbach’s alpha

(n = 199) for the Chinese version of the Student Attitudes Toward Science Class Survey

was 0.90. In conclusion, all four instruments translated into Chinese are valid and reliable

for use in Taiwan.

In the second part, the main study was conducted. Four elementary school science

teachers and 133 fourth- and fifth-grade students participated in the main study during the

2007 fall semester. There were two classes of fourth graders (n = 65) and two classes of

fifth graders (n = 68). The four classes were taught by four science teachers. All the

teachers and students were purposively selected from Liang elementary school located in

northern Taiwan. There were three teacher data sources: (a) Teacher Demographics

Questionnaire to obtain teacher background information, (b) Human Information

Processing Survey to identify teacher hemispheric preference, and (c) classroom

observations to identify science teaching strategies. There were five student data sources:

(a) Style of Learning and Thinking to identify student hemispheric preference, (b) PMA

Spatial Relations Test to measure student spatial ability, (c) Chinese language midterm

and final exams to assess student verbal ability, (d) Science midterm and final exams to

assess student science achievement, and (e) Student Attitudes Toward Science Class

Survey to measure student attitudes toward science class.

Teacher data were analyzed in two ways. The Teacher Demographics Questionnaire

and Human Information Processing Survey were analyzed using descriptive statistics to

describe teacher background information and teacher hemispheric preference. Classroom

12

observations were analyzed by coding and categorizing science teaching strategies related

to hemispheric preference and descriptive statistics. Student data from multiple

instruments—Style of Learning and Thinking, PMA Spatial Relations Test, Chinese

language exams, Science exams, and Student Attitudes Toward Science Class Survey

were analyzed in four ways including descriptive statistics, Pearson Product Moment

Correlations, Multivariate Analysis of Variance (MANOVA), and Analysis of Variance

(ANOVA). Lastly, teacher data related to classroom observations and student data related

to student hemispheric preference were analyzed using Confidence Intervals for

Proportion tests.

Limitations of the Study

The following limitations are associated with the current study. These limitations

may affect the internal reliability and validity of the study as well as the generalizability

of the findings.

1. The participants were delimited to an available sample of elementary school

science teachers and grade 4 and grade 5 students in Taiwan. This sample may

not be representative of the elementary school science teachers and elementary

school students in Taiwan. Thus, generalizability of the results of this study may

be limited to the sample in the study. In addition, the results cannot be

generalized to other countries.

2. Four elementary school science teachers and 133 fourth- and fifth-grade students

were purposively selected for this study. The teachers self-selected and

volunteered to participate in the study. The non-random selection of subjects

from intact classrooms may limit the generalizability of the results of this study.

13

3. The study may be limited by the Chinese translation of the instruments used in

the current study. Instruments were commercial instruments or developed by the

researcher. All of the instruments were translated into Chinese, back-translated,

and reviewed by a panel of experts. However, the translations into Chinese may

not be completely accurate and may be affected by individual language and

cultural misconceptions. Thus, there may be validity issues associated with the

use of these instruments. In addition, the findings associated with teacher and

student responses to these instruments may be problematic.

4. Cronbach’s alphas were used to measure the reliabilities of the instruments in the

current study. Cronbach’s alpha measures internal consistency of the items on an

instrument. Other measures of reliability, such as test-retest analysis, may further

contribute to the reliability of the instruments. However, additional

administrations of the instruments were not feasible in Taiwan.

5. The instrument to measure student spatial ability may be measuring specific

aspects of this construct. For example, spatial ability includes spatial perception,

spatial visualization, or mental rotation. Other instruments may measure

different aspects of spatial ability.

6. Some of the instruments were self-report surveys that rely on the abilities of the

subjects to understand themselves and be aware of their own behavior. Also,

responses to the self-report surveys may be more positive as respondents may

have pre-conceived ideas as to the expectations of the researcher.

7. Classroom observations of the science teachers were dependent upon the ability

and objectiveness of the researcher.

14

CHAPTER 2

REIVEW OF THE LITERATURE

Brain Structure

The human brain weighs only about 3 pounds and represents only 2% of the body

weight; however, it consumes from 20 to 25% of the energy used by the body (Lucas,

2003; Sprenger, 2007). The brain consists of three main structures: the cerebrum, the

cerebellum, and the brainstem. The largest and most complex section of the brain is the

cerebrum which is covered by a thin layer called the cerebral cortex or neocortex (Lucas).

Because the cerebral cortex has a high density of cell bodies, it appears to be a grayish

brown mass called gray matter. Gray matter is formed primarily by neuron cell bodies,

whereas the white matter below the gray matter is formed of myelinated axons

(Gazzaniga, Ivry, & Mangun, 2002). Further, the cerebrum is divided into the right and

left hemispheres. Each consists of four lobes: the frontal, parietal, temporal, and occipital

lobes. Each lobe has distinctive functions. The left and right hemispheres are connected

by a thick bundle of nerve fibers called the corpus callosum, which enables

communication between the two sides of the brain (Hardiman, 2003). The left

hemisphere controls sensory input and motor output on the right side of the body, and the

15

right hemisphere controls sensory input and motor output on the left side of the body

(Halpern, 2000). The two hemispheres are asymmetrical in structure and have different

cognitive functions (Kandel, Schwartz, & Jessell, 1995).

The brain is made up of many cells, including neurons and glial cells. Neurons are

nerve cells that send and receive electrical signals to and from the brain and nerve system.

There are about 100 billion neurons in the brain. Glial cells provide support, protection,

and nutrition to the neurons, and are far more numerous than neurons (Hardiman, 2003;

Sprenger, 2007). Every neuron has a cell body, one axon, and a number of dendrites.

When two neurons communicate, an electrical impulse goes from the axon of the sending

neuron to the dendrite of the receiving neuron. During this process, axons and dendrites

never actually touch. The electrical impulse that flows from the axon travels over a small

gap called a synapse through chemical substances known as neurotransmitters. The action

inside the cell is electrical. The action between the cells is chemical. Learning takes place

when two neurons communicate (Bergen & Coscia, 2001; Estes, 2001; Hardiman;

Sprenger). Sprenger stated that when neurons continually connect to other neurons they

form neural networks.

Brain Development

Blakemore and Frith (2005) stated that an adult brain contains about 100 billion

neurons. At birth, a child’s cerebral cortex has almost all of his/her 100 billion neurons.

Researchers once believed that no new neurons are produced after birth but recent

evidence shows that the cerebellum and hippocampus appear to generate new neurons

after birth.

16

During the first year of life, the brain forms new synapses (connections between

neurons) at a high rate of speed. By age 2, children’s brains contain twice as many

synapses as the brains of normal adults (Halpern, 2000). While these new synapses

between neurons continue to form throughout life, they reach their highest synapses at

around the age of 2 and the number of synapses remains relatively stable until the age of

10 or 11 (Halpern; Nash, 1997). After this age, the number of synapses begins to drop and

continues to decline slowly into adult level. By late adolescence, half of all the synapses

have been pruned which leaves about 500 trillion synapses. This number remains

relatively constant throughout life although new connections continue to be formed.

Brain development is a dynamic process of growth and pruning (Shore, 2003).

The weight of the brain increases from about 1 pound at birth to 2 pounds by age 1

and to its adult weight of 3 pounds at age 16 (Lindelow, 1983). The growth of the brain

involves an increase in size of the neurons and the development of glial cells, myelination

of the axons, and dendrite extensions (Bergen & Coscia, 2001). One type of glial cell

wraps itself around long axons, forming an insulating layer called myelin, and this

insulation helps the rapid conduction of nerve impulses (Martin, 2006; Sylwester, 1995,

2005).

Thatcher, Walker, and Giudice (1987) used EEGs to measure cerebral hemisphere

development in 577 normal subjects, ranging from 2 months to early adult. The results

showed that brain growth spurts were associated with Piaget’s developmental stages. The

results also showed that different regions within the left and right hemispheres developed

at different times and different rates. The results supported that the left frontal-occipital

17

and left frontal-temporal parts developed earlier than the corresponding right

frontal-occipital and right frontal-temporal parts, however, the right frontal pole

developed earlier than the corresponding left frontal pole.

Hemispheric Specialization

The earliest evidence of hemispheric asymmetry came from observations of the

disorders produced by unilateral brain damage (Hellige, 1983). During the 1860s, Paul

Broca observed patients suffering from loss of speech, all with lesions on their left frontal

lobe. He concluded that there is a relationship between the left hemisphere and speech

production. During the 1870s, Karl Wernicke observed patients having difficulties in

understanding speech, all with lesions on their left temporal lobe. He described a

relationship between the left hemisphere and language comprehension (Finger, 1994).

These clinical reports indicated that different areas of the brain control specific functions.

These findings were confirmed and expanded in the 1950s and 1960s (Herrmann, 1990).

In summary, the notion of localization of brain function emerged from early studies of

brain-damaged individuals.

In many ways, the most dramatic evidence for hemispheric asymmetry comes from

the study of patients who have had the corpus callosum severed in order to control severe

epilepsy (Hellige, 1983). During the 1950s, Roger Sperry and his graduate student

Ronald Myers did a series of experiments on split-brain cats. The results showed that

severing the corpus callosum prevented the information received in one hemisphere from

reaching the other hemisphere. In addition, the animals appeared to have two independent

minds. This finding was confirmed with monkeys (Finger, 1994; Springer & Deutsch,

1998). During the 1960s, Sperry and two neurosurgeons, Philip Vogel and Joseph Bogen,

18

conducted a series of experiments with split-brain humans (Bogen, 1985). The results

showed that the left and right hemispheres process information differently. The left

hemisphere excels in dealing with verbal and analytic information, whereas the right

hemisphere excels in dealing with visuospatial, synthetic, and holistic information. These

studies also demonstrated the role of the corpus callosum in integrating the specialized

functions of the left and right hemispheres into unified behavior (Springer & Deutsch). In

summary, the initial idea of hemispheric specialization emerged from studies of

split-brain subjects.

More recently, hemispheric asymmetry has been investigated by studies on normal

people in a number of ways. Some of the techniques on normal people are similar to ones

previously used in clinical populations (i.e., tachistoscopic presentation and dichotic

listening technique), whereas other studies used advanced technologies to measure brain

activity (e.g., EEG, fMRI, and PET).

The tachistoscopic technique assumes that superior performance for a stimulus from

the left visual field indicates that the right hemisphere excels in dealing with the material

presented, and superior performance for a stimulus from the right visual field indicates

that the left hemisphere excels in dealing with the material presented. In dichotic listening

tests, different auditory stimuli are presented simultaneously to each ear, and subjects are

asked to report what they heard. Superior performance for a stimulus presented to the left

ear indicates right hemisphere superiority in processing the stimuli, and vice versa.

Findings related to contralateral hemispheric superiority in normal subjects using both the

tachistoscopic and dichotic listening techniques are highly consistent across numerous

clinical studies (Springer & Deutsch, 1998).

19

Recently, brain imaging has made visible how certain areas of the brain process

specific tasks. The common types of brain imaging include recordings of brain electrical

activity (e.g., electroencephalography [EEG]); mapping the blood-flow patterns in the

brain (e.g., functional magnetic resonance imaging [fMRI]); and mapping the blood-flow

or glucose metabolism patterns in the brain (e.g., positron emission tomography [PET]).

The findings from studies on normal subjects using brain imaging validated some of the

insights about brain functioning gleaned from previous studies on both brain-injured and

normal subjects. They also offered subtle and new findings about hemispheric differences.

For instance, using the PET scan in the study of face recognition has shown activation of

posterior right-hemisphere regions. The major technological advances for studying living

brains have pointed to the involvement of many areas of the brain in even the simplest

task. There is little evidence to support the idea that either one or the other hemisphere

performs a specific task all by itself (Springer & Deutsch, 1998). The notion of

complementary specialization of the cerebral hemispheres, or the cooperative

engagement of both hemispheres, emerged from more recent research.

Studies from different approaches to investigating brain function have reached

convergence and revealed the fact that each hemisphere specializes in certain types of

information. According to Springer and Deutsch (1993), the idea that the two

hemispheres are specialized for different functions has led to the concept of hemispheric

specialization.

The findings from clinical observations and experimental examinations support the

notion that the left and right cerebral hemispheres in humans process information

differently from each other (Hellige, 1983). The primary differences between the cerebral

hemispheres have been characterized in a number of ways by different researchers.

Left Hemisphere Right Hemisphere Verbal

Using words to name, describe, define. Nonverbal

Awareness of things, but with minimal connections with words. Recognizing music, environmental sounds.

Sequential Dealing with events and actions sequentially.

Parallel Dealing with events and actions simultaneously.

Temporal Keeping track of time.

Non-temporal Without a sense of time.

Analytic Figuring things out step by step and part by part, taking things apart.

Synthetic Putting things together to form wholes.

Linear Thinking part to whole, taking little pieces, lining them up, arranging them in logical order, and leading to a convergent conclusion.

Holistic Thinking whole to part, seeing whole things all at once, perceiving the whole pattern, and leading to a divergent conclusion.

Digital Using numbers as in counting.

Spatial Visualizing where things are in relationship to other things, and how parts go together to form a whole.

Symbolic Using a symbol to stand for something. For example, the drawn form stands for eye, the sign + stands for the process of addition.

Concrete Dealing with real objects. For example, learning by doing, touching, and moving.

Compositional Writing music scientifically.

Responsive Responding to tones and sounds.

Factual Using facts.

Visual Using imagery.

Systematic and formal Dealing with information and objects in a variety of systematic ways.

Casual and informal Dealing with information and objects according to the needs of the moment.

Table 2.1: Information-processing styles of the left and right cerebral hemispheres. 20

21

Table 2.1, derived from a review of the literature, shows that each of the cerebral

hemispheres has a different information-processing style (Botkin, Keen, McClellan, &

Robinette, 1980; Cherry, Godwin, & Staples, 1989; Richards, 1984; Vitale, 1982).

Other researchers have described the cerebral hemispheres in terms of specialized

functions. The right and left hemispheres have distinctly different cognitive functions.

Two separate and unique ways of processing stimuli exist within each person. Both

hemispheres receive and process sensory information from the surrounding environment

and each hemisphere processes the information separately (Sousa, 2006; Vitale, 1982). It

is now widely accepted that the left hemisphere is specialized for auditory, verbal,

analytical, logical, abstract, convergent, and deductive functions. The right side is

specialized for visual, motoric (tactile/kinesthetic), non-verbal, intuitive, creative,

divergent, concrete, musical, spatial, holistic, and inductive functions (Kane & Kane,

1979).

Hemispheric Preference

Reviewing the literature, Torrance (1982) stated that hemisphericity is defined as the

tendency for a person to rely more on one than the other cerebral hemisphere in

processing information. Saleh (2001) reviewed the literature and suggested that

hemisphericity is the tendency for an individual to process information through the left

hemisphere or the right hemisphere or in combination.

Further, research has indicated that the two hemispheres, to some degree, are

lateralized or dominant for different functions (Halpern, 2000). Research also has shown

that most people have a preferred (or dominant) hemisphere, and this hemispheric

preference affects personality, abilities, and learning style. However, some researchers

22

note that people should not overemphasize a rigid specialization. These specializations do

not mean exclusivity; that is, they are relative rather than absolute. One cerebral

hemisphere may be more active in most people, but only in varying degrees (Frank, 1984;

Sousa, 2006; Vitale, 1982). Grady (1984) stated that, in most people, there seems to be a

hemispheric preference for specific functions. At times, a function can be lateralized in

the opposite hemisphere or even show mixed dominance. According to Sousa, these

functions are rarely exclusive to only one hemisphere—it is possible for both

hemispheres to be involved. Many tasks can be performed by either hemisphere, although

one may be better at it than the other. In a typical individual, the results of the separate

processing are exchanged with the opposite hemisphere through the corpus callosum.

According to Cooke and Haipt (1986), the right and left hemispheres of the brain

complement, interact, and collaborate with each other via the corpus callosum or the

commissures or fibers that connect the hemispheres. This interaction contributes to

integrated human thought and behavior. In short, although each hemisphere has

specialized functions, once information enters the brain, both hemispheres can work

cooperatively to process it and they complement one another in almost all activities

(Grady; Sousa). Therefore, neither the right nor the left hemisphere is completely idle

during any task. Most activities involve both hemispheres interacting with each other

(Richards, 1993). In summary, more recent research has continued to move people away

from a theory of cerebral dominance to one of complementary specialization of the

cerebral hemispheres.

23

Gender Differences in Brain Structure, Brain Development, and Cognitive Ability

Gender Differences in Brain Structure

Gur et al. (1999) conducted a study about sex differences in brain structure and

concluded that women have a higher percentage of total gray matter (neuron cell bodies),

whereas men have a higher percentage of total white matter (myelinated axons). Men

have a higher percentage of gray matter in the left hemisphere; however, the percentage

of white matter is the same in both hemispheres. In women, the percentage of gray and

white matter is the same in both hemispheres. According to Sousa (2006), these sex

differences in gray and white matter support the idea that female brains have better

communication between the two hemispheres, whereas male brains have better

communication within each hemisphere.

Powell and Kusuma-Powell (2007) reviewed the research literature and concluded

that a female’s corpus callosum—the connecting bundle of nerve fibers between

hemispheres—is, on average, thicker than a male’s, up to 20% thicker in the female brain

than in the male brain. This structural difference enables more cross talk between

hemispheres in the female’s brain. Halpern (2000) reviewed a large body of research

literature and arrived at a similar conclusion. She concluded that there are sex differences

in the shape and the volume in the corpus callosum. On average, females have a larger

and more bulbous structure. This conclusion supports the idea that females may have

greater information communication between the left and right hemispheres. According to

Gurian and Stevens (2005), more cross talk between hemispheres leads to better

multitasking. Girls tend to be better at multitasking, whereas boys are more single-task

oriented.

24

The hippocampus is another long-term memory area in the brain (Gutman, 2001).

James (2007) stated that girls develop their hippocampus before boys. The hippocampus

is related to mathematical calculations, basic arithmetic, vocabulary, and reading. On

average, girls are cognitively more ready for school tasks than boys. According to Gurian

and Stevens (2004), a girl’s hippocampus is larger than a boy’s. This difference increases

memory storage in girls, leading to greater learning for girls, especially in language arts.

According to Fuster (1997), frontal functions deal with movement, speech,

reasoning, action initiating, decision making, and regulation of emotions. Gurian and

Stevens (2005) stated that a girl’s frontal lobe is generally more active than a boy’s and

develops at an earlier age. Also, girls tend to make less impulsive decisions compared to

boys.

James (2007) reviewed the literature and noted that Broca’s area and Wernicke’s

area are located in the left hemisphere of the brain, in the frontal and temporal lobes,

respectively. Broca’s area is responsible for grammar and the production of words,

whereas Wernicke’s area is responsible for the acquisition and understanding of words.

Gurian and Stevens (2005) stated that a girl’s Broca’s and Wernicke’s areas are more

active than a boy’s and develop at earlier ages. These two areas are the main language

centers of the brain. According to Gurian et al. (2001), in general, these differences

increase girls’ learning advantage for verbal communication skills.

Research on sex differences in brain structure may be said to have begun in 1964,

when psychologist Herbert Lansdell reported the existence of sex differences in brain

anatomy (as cited in Sax, 2005). For example, he conducted research on people who had

epilepsy and had some part of their right hemisphere removed. Men with

25

right-hemisphere damage did badly on spatial IQ tests, whereas women with similar

right-hemisphere damage were hardly affected. These results provided evidence that a

man’s right hemisphere is very important for spatial ability, while a woman’s right

hemisphere is not. Lansdell then studied the left hemisphere, where language skills are

located. Men with left-hemisphere damage did badly on verbal IQ tests, whereas women

with similar left-hemisphere damage were hardly affected. This finding suggests that a

man’s left hemisphere is very important for verbal ability, while a woman’s left

hemisphere is not. Lansdell discovered that men and women who were damaged in the

same area were affected differently. This led Lansdell to the conclusion that in men

verbal skills are much more specifically located in the left hemisphere, spatial skills in

the right hemisphere, whereas in women verbal and spatial skills are located in both

hemispheres. It appears that women use both hemispheres for verbal and spatial skills,

whereas men do not (Moir & Jessel, 1992).

Over the next two decades, a series of studies showed that male brains were more

asymmetrically organized for cognitive functions—a more lateralized organization—and

female brains were more symmetrically organized for these functions—a more bilateral

organization (Halpern, 2000; Powell & Kusuma-Powell, 2007).

Voyer (1996) conducted a meta-analysis of 396 separate comparisons from a variety

of studies on the question of sex differences in lateralization. He concluded that males are

more lateralized for visual and auditory modalities. This extensive review of the literature

confirmed earlier findings.

Boys tend to lateralize brain activity, that is, compartmentalize activity into discrete

areas of the brain (Powell & Kusuma-Powell, 2007). Girls tend to be better at

26

multitasking, whereas boys are more single-task oriented. Girls tend to pay attention to

more information at a given time, with fewer attention span problems, whereas boys tend

to concentrate for long periods on one task. Girls have greater ability to make quick

transition between tasks, whereas boys take more time to transition between tasks

(Gurian & Stevens, 2004, 2005; King & Gurian, 2006; Powell & Kusuma-Powell).

Professor Doreen Kimura, a Canadian psychologist conducted a series of studies

during the 1980s and 1990s that showed a sex difference in the organization of cognitive

functions within the left hemisphere. She examined patients whose damage was restricted

to the anterior (front) region and posterior (back) region of the left hemisphere,

respectively. Kimura concluded that language functions in the female brain are more

focally organized in the front region of the left hemisphere, whereas the language

functions in the male brain are more diffusely organized in the front and back of the left

hemisphere (as cited in Halpern, 2000; Moir & Jessel, 1992).

Research has confirmed that male and female brains differ in the organization of

cognitive functions within a hemisphere (Halpern, 2000; Moir & Jessel, 1992). Table 2.2

was derived from a review of the literature and provides a summary of sex differences in

brain structure.

27

Females Males Gray matter ▪ Higher percentage, with same

percentage in both hemispheres

▪ Lower percentage, with more percentage in left hemisphere

White matter ▪ Lower percentage, with same percentage in both hemispheres

▪ Higher percentage, with same percentage in both hemispheres

Corpus Callosum ▪ Larger (thicker) and more bulbous

▪ Smaller (thinner) and less bulbous

Hippocampus ▪ Quicker development ▪ Larger

▪ Slower development ▪ Smaller

Frontal lobe ▪ More active ▪ Quicker development

▪ Less active ▪ Slower development

Broca’s area ▪ More active ▪ Quicker development


Wernicke’s area ▪ More active ▪ Quicker development


Lateralization (Verbal Ability)

▪ More symmetrical ▪ More bilateral ▪ Left and right hemispheres

▪ More asymmetrical ▪ More lateralized ▪ Left hemisphere

Lateralization (Spatial Ability)

▪ More symmetrical ▪ More bilateral ▪ Left and right hemispheres

▪ More asymmetrical ▪ More lateralized ▪ Right hemisphere

Intrahemispheric (Language)

▪ More focal ▪ Left hemisphere ▪ Front region

▪ More diffuse ▪ Left hemisphere ▪ Front and back regions

Table 2.2: Structural differences between female and male brains.

Gender Differences in Brain Development

Sonnier (1982) stated that evidence has shown that infants depend totally on their

right hemispheres. As infants grow into children, their brain develops more bilaterality

until about puberty. James (2007) suggested that, in general, girls develop earlier than

boys and this developmental difference includes the brain and the body. This

developmental superiority for girls begins soon after birth and continues until late

adolescence or even later. Gurian et al. (2001) suggested that, in general, female brains

28

develop quicker than male brains. The right hemisphere is superior in infants and

development gradually occurs in the left hemisphere. Females’ development of the left

hemisphere occurs earlier compared to males. Levy and Heller (1992) reviewed the

research literature and arrived at an important conclusion in understanding the emergence

of adult gender differences in cognition. The accumulating evidence supports that the

right hemisphere matures faster in males and the left hemisphere matures faster in

females.

Berlin (1978) investigated age and sex differences with regard to brain lateralization.

Male and female subjects volunteered from two kindergarten classrooms (ages 5-to-6)

and two sixth-grade classrooms (ages 11-to-12) in an urban, white, middle class, public

school in the midwestern United States. Brain lateralization of 79 right-handed subjects

was assessed by two left-hemisphere tasks, the WISC Digit Span and PMA Verbal

Meaning Tests, and two right-hemisphere tasks, the WISC Block Design and PMA

Spatial Relations Tests. The statistical analyses employed ANOVA procedures, post hoc

Scheffé tests, and Pearson Correlations.

The statistical results indicated that sixth graders outperformed kindergartners on all

of the tests but the Spatial Relations Test. This unexpected kindergarten superiority on the

spatial test possibility may have reflected a negative context effect for sixth graders who

took the Verbal meaning and Spatial Relations Tests during one testing session and in that

order. The only significant sex effect was for the Block Design Test which revealed a

male advantage at the sixth-grade level. Group differences indicated that kindergarten

females were superior to sixth-grade females on the Spatial Relations Test.

29

Based upon the results of her study, Berlin (1978, p. 104) suggested the following

conclusions.

1. Children improve their performance on left- and right- hemisphere tasks with

increasing age.

2. Kindergarten children do not exhibit sex differences with regard to

left-hemisphere and right-hemisphere tasks thereby indirectly indicating similar

neurological hemisphere organization.

3. Six-grade children do not exhibit sex differences with regard to left-hemisphere

tasks thought there is some support for earlier, greater left-hemisphere

lateralization for females.

4. Sixth-grade children do not exhibit sex differences with regard to

right-hemisphere tasks measured by standardized paper-and-pencil spatial ability

tests.

5. Sixth-grade children do not exhibit sex differences with regard to

right-hemisphere tasks measured by constructive visuo-spatial tests thereby

supporting earlier, greater male right-hemisphere lateralization for specific

aspects of spatial ability.

Dr. Herman T. Epstein’s brain development theory of growth spurts (brain growth

periods) and plateaus (non-brain growth periods) indicates that the brain grows in rapid

spurts rather than in a slow and continuous process. For most normal children, these

growth spurts appear to occur between ages of 3-10 months, 2-4 years, 6-8 years, 10-12+

years, and 14-16+ years. The last two growth spurts show clear differences for boys and

girls. Girls’ brains grew three times as fast as boys’ during the 10-12+ year spurt. During

30

the 14-16+ year spurt, boys’ brains grew three times as fast compared to the growth of

girls’ brains (as cited in Lindelow, 1983; Rose, 1982). Rose also pointed out that

Epstein’s brain development pattern of growth spurts and plateaus corresponded to Jean

Piaget’s stages of cognitive development.

Sax (2005) reviewed a study conducted by researchers at Virginia Tech who studied

the brain activity in 508 normal children, 224 girls and 284 boys, ranging from 2 months

to 16-years-old. They found that different areas of the brain develop in a different

sequence in girls compared to boys. The results showed that the areas of the brain

connected with language and fine motor skills mature about 6 years earlier in girls than in

boys, whereas the areas of the brain connected with throwing an object at a target and

spatial memory mature about four years earlier in boys than in girls. These researchers

concluded that the various areas of the brain develop in a different order, time, and rate in

girls compared to boys.

Gender Differences in Cognitive Ability

According to Hines (2007), there are no significant sex differences in overall

intelligence tests. However, there are sex differences in some specific cognitive abilities.

In 1974, Maccoby and Jacklin published a systematic review of research which

became a milestone analysis. They reviewed more than 1,000 research reports on sex

differences in cognitive abilities, personality, and social behavior that had been conducted

between 1966 and 1973. With regard to the cognitive domain, they concluded that males

had an advantage in mathematical and spatial ability, whereas females had an advantage

in verbal ability. With regard to developmental trends, they concluded that sex differences

in verbal ability begin to appear around the age of 11, and that sex differences in spatial

31

ability do not appear until adolescence. However, this is an oversimplification. In the

1980s, researchers used the meta-analysis technique to combine evidence from a larger

sample of studies. In meta-analysis studies, many researchers use a rubric generated by

Cohen. An effect size of 0.2 is considered small, an effect size of 0.5 is considered

moderate, and an effect of 0.8 is considered large (Hyde & McKinley, 1997). This new

statistical method has radically changed the analysis of sex differences with regard to

cognitive abilities (Hyde, 1990).

Hyde (1981) published the first meta-analysis using the studies that had been

evaluated by Maccoby and Jacklin. Hyde computed effect size for the studies and showed

that the average effect size was -0.24 for verbal ability, 0.43 for mathematical ability, and

0.45 for spatial ability. The positive values indicated higher scores for males and negative

values indicated higher scores for females. The results showed that males performed

better than females for mathematical ability and spatial ability, while females performed

better than males for verbal ability. The results confirmed Maccoby and Jacklin’s

conclusion that males had an advantage for mathematical and spatial abilities, whereas

females had an advantage for verbal ability. According to Cohen’s scheme, the sex

difference in verbal ability was small, while the sex differences in mathematical and

spatial ability were moderate.

Spatial Ability

Two major meta-analyses of sex differences related to spatial ability have been

conducted. Linn and Petersen (1985) conducted a meta-analysis of sex differences related

to spatial ability. They examined 172 studies published between 1974 and 1982 and

classified spatial tests into three distinct categories: spatial perception, mental rotation,

32

and spatial visualization. Spatial perception was defined as the ability to determine spatial

relationships in the presence of distracting information (e.g., Rod-and-Frame Test).

Mental rotation was defined as the ability to imagine what a two- or three-dimensional

figure would look like if rotated in space (e.g., Spatial Relations subtest of the Primary

Mental Abilities Test). According to Crawford and Chaffin (1997), spatial visualization is

defined as the ability to find a simpler figure in a more complex figure (e.g., Embedded

Figures Test). For these tests, the effect size for spatial perception was 0.44; for mental

rotation, it was 0.73; and for spatial visualization, it was 0.13. The results showed that

there was a moderate difference for spatial perception, a rather large difference for mental

rotation, and no difference for spatial visualization. That is, males have a moderate

advantage with regard to spatial perception, a clear advantage for mental rotation, and no

difference for spatial visualization. They also examined the data for developmental trends

in sex differences related to spatial ability. The results showed that a male advantage for

spatial perception was present in childhood and may increase with age, the male

advantage for mental rotation was consistent across ages, and there were no age

differences for spatial visualization. Thus, gender differences in spatial ability seem to be

detected prior to adolescence for spatial perception and mental rotation. The

meta-analysis results did not support Maccoby and Jacklin’s conclusion that sex

differences in spatial ability emerged in adolescence.

Voyer et al. (1995) conducted a meta-analysis of 286 studies published between

1974 and 1993. For these tests, the effect size for spatial perception was 0.44, for mental

rotation was 0.56, and for spatial visualization was 0.19. Because the values of d

( mean differencedSD

= ) were not homogeneous, Voyer et al. partitioned the studies by

type of tests, age of participants, and a number of procedural variables. According to

Crawford and Chaffin (1997), the results of the Voyer et al. meta-analysis showed that

sex differences were largest and most consistent for tests in the mental rotation category

(significant in each of six tests); sex differences were large but inconsistent for tests in the

spatial perception category (significant in four out of seven tests); and sex differences

were highly changeable and frequently not significant for tests in the spatial visualization

category (significant in three out of eleven tests). According to the more detailed analysis,

the results showed that all of the sex differences were in the same direction, favoring

males. The results also showed that the age of emergence of sex differences was related

to the test used such as 7-years-old for the Rod-and-Frame Test, 9-years-old for the Water

Level Test, 10-years-old for the generic mental rotation task and the PMA Spatial

Relations subtest, 13-years-old for the DAT Spatial Relations subtest, and 14-years-old

for the Embedded Figures Test. These results indicated an absence of reliable sex

differences in early childhood.

In conclusion, based upon the meta-analyses reported by Linn and Petersen (1985)

and Voyer et al. (1995), a greater male advantage appears to be for tasks involving mental

rotation. However, there are many different types of spatial ability and different

measurement instruments, and other differences may exist within particular categories

(Berlin, 1978).

33

34

Verbal Ability

Hyde and Linn (1988) conducted a meta-analysis of 165 studies of sex differences

related to verbal ability. Over all studies, the mean effect size was 0.11. In this

meta-analysis, the positive values indicated higher scores for females and negative values

indicated higher scores for males. The results indicated a slight female advantage for

verbal ability. Hyde and Linn argued that the difference was so small that it indicated that

there was no sex difference related to verbal ability. Because the values of d were not

homogeneous, Hyde and Linn partitioned the studies by type of test, age of participants,

cognitive process, and date of study. The results showed that the mean effect size was

0.02 for vocabulary, -0.16 for analogies, 0.03 for reading comprehension, 0.33 for speech

production, 0.09 for essay writing, 0.22 for anagrams, and 0.20 for general verbal ability.

All of these sex differences were small, favoring females with one exception—analogies.

The largest sex difference was for speech production, favoring females.

With regard to developmental trends, all tests showed that the effect size was 0.13

for the 5-year-old and younger group, 0.11 for the 11- to 18-year-old group, and 0.20 for

the 26-year-old and older group. The effect sizes were close to zero for the intervening

two age groups. Vocabulary tests showed that the effect size was -0.26 for the 6- to

10-year-old group and 0.23 for the 19- to 25-year-old group. The effect sizes were close

to zero for the other three age groups. Reading comprehension tests showed that the

effect size was 0.31 for the 5-year-old and younger group but it was not reliable because

this was based on a single study. The effect sizes were close to zero for other age groups.

More detailed analysis of various types of verbal ability indicated that there were no sex

35

differences at all ages from childhood to adult. Thus, the results did not support Maccoby

and Jacklin’s (1974) conclusion that sex differences in verbal ability begin to appear

around the age of 11.

In conclusion, there does not appear to be substantial evidence for sex differences

related to most types of verbal ability. The one possible exception is speech production

which may favor females. In addition, gender differences were consistently small at all

age levels, suggesting that there are no gender differences in verbal ability at all ages

from childhood to adult.

Nature and Nurture

In the 1960s, Marian Diamond and her colleagues at the University of California at

Berkeley conducted experiments with rats on the effects of environmental stimulation and

deprivation on the development of the cerebral cortex. Diamond placed one group of rats

in an enriched environment—cage mates, novel toys; another group was put in an

impoverished environment—no cage mates, no novel toys. The results showed that the

rats living in the enriched environment developed a thicker and heavier cerebral cortex as

the result of an increase in the size of the nerve cells, gaining more dendrite branching,

and an increase in the number of glial cells (Diamond, 1988). Diamond’s study

demonstrated that brain structures in rats were modified by the environment. Her research

led to the concept of brain plasticity, the brain’s amazing ability to change the structure of

the brain through experience (Wolfe & Brandt, 1998).

Some educators and researchers believe that brain development is genetically

programmed (Shore, 2003). Diamond’s (1988) research challenges this assumption. Her

research illustrates that experience appears to be able to change the physical structure of

36

the brain. In recent years, researchers have presented a holistic view, believing that nature

and nurture are inseparable. That is, one cannot separate the environmental effects from

the biological ones related to brain development (Smilkstein, 2003). The nature-nurture

dichotomy has ended and has been replaced with a dynamic model that emphasizes that

brain development is based on a complex interaction between genes and experiences

(Halpern, 2000).

Jensen (1998) stated that Diamond’s discovery of brain plasticity illustrated that the

brain can grow new connections with environmental stimulation. Sprenger (2007)

suggested that learning takes place when connections between neurons form. Experience

can change the physical structure of the brain and influence learning. Plasticity can

explain learning. Kovalik and Olsen (2001) stated that an enriched environment is a

learning environment rich in sensory stimuli. Students have more sensory experiences

which may lead to more brain growth. Erlauer (2003) and Jensen believe that teachers

can create an enriched environment to enhance student learning.

Left-Brain and Right-Brain Teaching Strategies

Many educators believe that most contemporary schools are dominated by a

left-brain curriculum, and generally, teaching strategies and learning activities are based

primarily on a linear, sequential, and analytic way of thinking (Chapman, 1998;

Chudzinski, 1988; Cooke & Haipt, 1986; Grady, 1984; Lewallen, 1985; Marxer, 1988;

Rubenzer, 1982; Turner, 1999; Vitale, 1982). Vitale stated that each child has his own

learning style that is unique. Rubenzer pointed out that there is a need to close the gap

between left-brain teaching strategies and right-brain learning styles because the

left-brain educational system handicaps many children who prefer right-brain processing

37

strategies. Bruggenman and Wehrman (1987) claimed that in order to reach all students,

teachers should expand their teaching strategies to include activities for the left-, right-,

and whole-brained students. Reviewing the literature, Saleh (2001) mentioned that

matching teaching strategies to hemispheric styles will help students gain higher test

scores. Beck (2001) reviewed the literature and stated that students will increase

achievement when teaching strategies are compatible with their hemispheric preferences.

Lewallen reviewed research and stated that the two hemispheres of the brain could

function both simultaneously and independently. When they worked together and

complemented each other, people reached their highest achievement. According to

Williams (1983), in order for all students to have the maximum opportunity to learn,

educators must incorporate left-brain and right-brain teaching strategies. Beck suggested

that teachers should select appropriate strategies to meet diverse students’ hemispheric

preferences as well as stimulate both hemispheres to develop the whole brain.

Jackson (1998) claimed that there are particular teaching strategies for teaching to

the right hemisphere and left hemisphere. Reviewing the literature shows that the

left-brain strategies seem to be writing, reading, computation, and multiple-choice tests or

choosing only one solution (Jackson; Lewallen; 1985). The following right-brain

strategies were derived from a review of the literature: physical relaxation; visual

thinking, for example, ideas expressed via pictures, maps, diagrams, charts, mind maps,

illustrations, flow charts, and graphs; brainstorming; fantasy; direct experiences such as

field trips, manipulation of materials, primary sources, and real objects; simulation; role

playing; multisensory learning requiring visual, auditory, tactile, and kinesthetic senses;

music and rhythm; color discrimination; spatial imagery; creativity; art and design;

38

analogy and metaphor; poetry; meditation; and open-ended questions or allowing many

possible solutions (Bishop, 1978; Jackson; Lewallen; Marxer, 1988; Williams, 1983).

Beck (2001) analyzed three learning styles inventories—the 4MAT System, Dunn’s

Learning Style Inventory, and Renzulli and Smith’s Learning Style Inventory—and

classified approximately 80 teaching strategies into 8 categories which he linked to

learning styles and brain hemispheric preferences. After matching teaching strategies to

brain hemispheric preferences, he identified the following left-brain teaching strategies:

lecture, recitation, textual readings, oral or written reports, inquiry, experimenting,

problem solving, independent study, mastery learning, programmed learning, internet

search, word processing, convergent questions, and prompting questions. Right-brain

teaching strategies included round table, magic circle, fish bowl, brainstorming,

cooperative learning groups, gaming, dramatic and role play, video conferencing, creative

software, divergent questions, and probing questions. Although most of the teaching

strategies appear to be more compatible with either the left or right hemisphere,

technological teaching can be adaptable and bilateral. For example, knowledge-based

computer software would be more suitable for the left hemisphere, whereas creative

computer software would be more appropriate for the right hemisphere.

McCarthy (1987) presented a more refined definition for experimenting. That is,

experimenting is classified into left-brain and right-brain modes. Doing experiments

systematically with teacher control is a left-brain teaching strategy, whereas doing

experiments randomly with student control is a right-brain teaching strategy.

39

Table 2.3 was derived from an extensive review of the literature to provide a

comprehensive list of teaching strategies aligned with either left- or right- hemispheric

processing.

Left-Brain Teaching Strategies Right-Brain Teaching Strategies▪ Lecture ▪ Recitation ▪ Textual readings ▪ Oral reports ▪ Written reports ▪ Computation ▪ Multiple-choice tests ▪ Choosing only one solution ▪ Inquiry ▪ Systematic experiments with teacher control ▪ Problem solving ▪ Independent study ▪ Mastery learning ▪ Programmed learning ▪ Internet search ▪ Word processing ▪ Convergent questions ▪ Prompting questions

▪ Physical relaxation ▪ Visual thinking (e.g., pictures, maps, diagrams, charts, mind maps, illustrations, flow charts, and graphs) ▪ Brainstorming ▪ Fantasy ▪ Direct experiences (e.g., field trips, manipulation of materials, primary sources, and real objects) ▪ Random experiments with student control ▪ Simulation ▪ Multisensory learning requiring visual, auditory, tactile, and/or kinesthetic senses ▪ Music and rhythm ▪ Color discrimination ▪ Spatial imagery ▪ Creativity ▪ Art and design ▪ Analogy and metaphor ▪ Poetry ▪ Meditation ▪ Open-ended questions ▪ Allowing many possible solutions ▪ Round table ▪ Magic circle ▪ Fish bowl ▪ Cooperative learning groups ▪ Dramatic and role play ▪ Video conferencing ▪ Creative software ▪ Divergent questions ▪ Probing questions

Table 2.3: Left-brain and right-brain teaching strategies.

40

Hemisphericity and Achievement

Sonnier and Kemp (1980) conducted a study to investigate the effect of different

teaching strategies—left-brain, right-brain, and integrated-brain—on students’

achievement in science. The study included 130 fifth-grade students enrolled in five

sections of science. The students were randomly assigned to four groups: (a) instruction

emphasizing a textbook oriented, verbal, and a left-brain approach; (b) instruction

emphasizing demonstrations, activities, visual aids, and a right-brain approach; (c)

instruction involving both left-brain and right-brain methods, and an integrated-brain

approach; and (d) the control group that followed the procedures outlined in the textbook.

The results showed that there was no significant difference in science achievement for the

four groups.

Hider and Rice (1986) conducted a study to investigate the effect of varied

instructional strategies—left-brain, right-brain, and integrated-brain—on fifth- and

sixth-grade students’ achievement and attitude in science. The 65 students were randomly

assigned to four groups: (a) science instruction oriented toward a left-brain, textbook

approach; (b) science instruction oriented toward a right-brain, hands-on approach; (c)

science instruction oriented toward an integrated-brain approach, involving both left- and

right-brain methods; and (d) a control group with no instruction related to the science unit.

Pretest and posttest data were collected using the science achievement test and School

Subjects Attitude Scale scores. The results showed that significant gains in science

achievement were greatest for the left-brain, textbook approach group but this group

scored the lowest gains in attitude. In addition, results indicated that the significant gains

in attitude were greatest for the integrated-brain approach and this group also showed

41

significant gains in science achievement. It was concluded that elementary school science

teachers should alter their instruction to combine left-brain and right-brain activities. In

this way, both brain processing modes can be involved in a complementary manner in the

learning process to improve both the achievement and attitude of students in the

elementary school science classroom.

Van Giesen et al. (1987) conducted a study to determine if there was a difference in

the reading achievement scores of remedial reading students who were identified as

having different hemispheric preferences. The study included 64 fourth- and fifth-grade

students in a remedial reading program. The Gates-MacGinitie Reading Test, Level D

was used to measure students’ reading achievement. The Zenhausern Hemispheric

Cognitive Style survey was used to determine students’ hemispheric preferences. The

results showed that there was a significant difference in the scores on the reading

vocabulary achievement test between the left-brain group and the right-brain group and

the left-brain group and the integrated-brain group. That is, left-brain students achieved

greater on the reading vocabulary achievement test than did right-brain students or

integrated-brain students. In addition, the results indicated that there was no significant

difference in the scores on the reading comprehension achievement test scores among the

left-brain students, right-brain students, and the integrated-brain students. Finally, the

results showed that female students achieved significantly greater scores on both the

vocabulary and comprehension tests compared to male students.

Jarsonbeck (1984) conducted a study to examine the effects of right-brain

mathematics instruction on low achieving fourth-grade students. The 147 fourth-grade

students were randomly assigned either to the control group, left-brain instruction, or to

42

the experimental group, right-brain instruction. The results showed that more right-brain

preference students were in the lower achieving group, whereas more left-brain

preference students were in the higher achieving group. In addition, it showed that

students in the control group, left-brain instruction, achieved higher scores if they were

left-brain preference, whereas students in the experimental group, right-brain instruction,

achieved higher scores if they were right-brain preference. The study indicated the

importance of matching teaching strategies to student hemispheric preference.

Shannon and Rice (1983) conducted a study to examine if there was a difference in

hemispheric preference between high ability and low ability elementary school students.

The study included 70 students ranging from first grade to sixth grade. Thirty-seven

students were identified as high ability students and 33 were identified as low ability

students based on their WISC-RIQ scores. The Your Style of Learning and Thinking

(SOLAT), Form C-A was administered to measure students’ hemispheric preferences.

The results showed that there was a significant difference between the two groups for the

integrated-brain responses, with the high ability group having a higher preference for

integrated-brain responses. In addition, it showed that there was a significant difference

between the two groups for both the left- and right-brain responses, with the low ability

group having a higher preference for either left- or right-brain responses. In conclusion,

hemispheric preferences were related to student ability level. Low ability students tended

to have either a left-brain or right-brain preference while high ability students tended to

have an integrated-brain preference.

43

Fountain and Fillmer (1987) conducted a study to investigate the relationship

between hemispheric preference and academic achievement by studying 131 average and

above average fourth- and seventh-grade students. The Metropolitan Achievement Test

was used to measure students’ academic achievement and the Your Style of Learning and

Thinking (SOLAT) was used to assess students’ hemispheric preferences. The results

showed that there were no significant differences between fourth- and seventh-grade

students in hemispheric preferences. The results also indicated that a significantly greater

number of females compared to males had an integrated-brain preference. The

fourth-grade students with an integrated-brain preference scored significantly higher on

all four tests—reading, mathematics, language, and a basic battery of tests—compared to

fourth-grade students with either right-brain or left-brain preference. The seventh-grade

males with an integrated-brain preference scored significantly higher on both

mathematics and the basic battery of tests compared to seventh-grade females with an

integrated-brain preference. It was concluded that hemispheric preferences affect learning

and that some significant differences are related to gender.

Douglass (1979) used three methods to teach biology, and then conducted a study to

examine the effects of these instructional methods on students’ achievement in biology.

The study included 627 high school students, approximately half female and

approximately half male. The students were randomly assigned to three instructional

groups: inductive group, deductive group, and control group. The students in the two

experimental groups studied the same units on genetics and probability but were taught

by different teaching strategies—an inductive sequence or a deductive sequence. The

students in the control group studied units on mitosis, meiosis, and chromosomal

44

abnormalities; however, they were tested on the material on probability and genetics. The

Embedded Figures Test was given to the students to determine their analytical abilities,

the Otis-Lennon Mental Ability Test was given to the students to assess their general

intelligence, and pretest and posttest data were collected from achievement tests in

biology. The results showed that highly analytical students do better with inductive

methods and highly global students do better with deductive methods. It was concluded

that students are more academically successful in learning biology when the instructional

materials they use complement their style of thinking.

Brennan (1984) conducted a study to investigate the effects of hemispheric

preference (left/right), cognitive style (analytic/global), instructional method (congruent

with analytic cognitive style/congruent with global cognitive style), and gender

(female/male) upon mathematics achievement test scores. The study included 56

tenth-grade geometry students. The students were randomly assigned to four groups: two

matched groups (analytic cognitive style/analytic instructional method, global cognitive

style/global instructional method) and two mismatched groups (analytic cognitive

style/global instructional method, global cognitive style/analytic instructional method).

Zenhausern’s Differential Hemispheric Activation Test was administered to determine

students’ hemispheric preferences; Sigel’s Conceptual Styles Test was administered to

measure students’ cognitive style; two sets of instructional materials were developed; and

pretest, posttest, and delayed posttest data were collected using mathematics achievement

tests. The results showed that there were no significant differences between the pretest,

posttest, or delayed posttest mathematics achievement test mean scores for the

hemispheric preference groups (left/right), the cognitive style groups (analytic/global), or

45

female and male groups. The variable of matching and mismatching instructional method

with cognitive style did produce higher mathematics achievement test scores for the

matched groups, although not at a significant level.

Dunn et al. (1990) conducted a study to investigate the (a) effects of hemispheric

preference (successive/simultaneous) and instructional strategies (analytic/global) on

developmental college minority students’ mathematics achievement and attitudes and (b)

the relationships between students’ hemispheric preferences and their learning-style

preferences. Zenhausern’s Differential Hemispheric Activation Test was used to

determine students’ dominant hemispheres, a Semantic Differential Instrument was used

to measure students’ attitudes and perceptions related to instructional strategies, the

Productivity Environmental Preference Survey was used to measure students’

learning-style preferences, and pretest and posttest data were collected using mathematics

achievement tests. The results showed that the simultaneous students achieved

significantly higher with global instructional strategies that were congruent with their

hemispheric preferences than with analytical instructional strategies that were

incongruent with their hemispheric preferences. In addition, results indicated that the

successive students achieved higher with analytical instructional strategies that were

congruent with their hemispheric preferences than with global instructional strategies that

were incongruent with their hemispheric preferences, although not at a significant level.

Furthermore, results indicated that there were no significant attitudinal differences

between successive and simultaneous students when they were taught with instructional

strategies congruent or incongruent with their hemispheric preferences. Finally, the

relationships between hemispheric preferences and learning styles showed that

46

simultaneous students preferred sound, tactile learning, kinesthetic learning, intake, and

frequent mobility while studying, whereas successive students preferred bright light and a

formal design.

Gadzella (1995) conducted a study to assess the effects of hemispheric preferences

upon course grades of undergraduate psychology students. The Human Information

Processing Survey was given to the students to determine their hemispheric preferences:

left-dominant information processor, right-dominant information processor,

integrated-information processor, and mixed-information processor. In this study, there

were 49 subjects identified as having a mixed strategy and they were deleted from the

analysis. The final sample was 55 undergraduates enrolled in the psychology classes. The

class grades were based on five 40-item multiple-choice tests and one written discussion

paper. The results showed that there were significant differences between left-brain

preference and right-brain preference groups with the left-brain preference group

obtaining higher course grades. The course grades did not differ significantly for the

integrated-brain group compared to left-brain and right-brain groups.

Summary

Research has shown that there are sex differences in the brain structure (Gur et al.,

1999; Halpern, 2000; Moir & Jessel, 1992; Sax, 2005); brain development (Berlin, 1978;

Gurian et al., 2001; James, 2007; Levy & Heller, 1992; Lindelow, 1983; Rose, 1982; Sax,

2005; Sonnier, 1982); and brain performance (Hyde, 1981, 1990; Linn & Petersen, 1985;

Voyer et al., 1995). But more research is needed to find more consistent differences in

these aspects of the brain. Research also has provided evidence to demonstrate the age for

the emergence of sex differences in some specific cognitive tasks; however, some of

47

these results related to age and gender differences are inconsistent. Thus, there is the need

for much more research exploring the maturational differences and gender differences for

specific cognitive tasks.

The brain is continually developing from the prenatal period through life. The

process of brain development is not fixed but it is dynamic—exhibiting great plasticity

(Diamond, 1988). Experiences and an enriched environment can change the physical

structure and growth of the brain and influence learning. Teachers can use many

enrichment practices to enhance student learning (Erlauer, 2003; Jensen, 1998).

Studies have revealed the effects of varied instructional practices related to

hemispheric preferences on student achievement and student attitude (Brennan, 1984;

Dunn et al., 1990; Fountain & Fillmer, 1987; Gadzella, 1995; Jarsonbeck, 1984; Van

Giesen et al., 1987); however, some of these results appear to be inconsistent (Brennan;

Sonnier & Kemp, 1980). Thus, more research is needed to explore the effects of

instructional practices related to hemispheric preferences on student achievement and

student attitude.

Most people have a preferred (or dominant) hemisphere, and this hemispheric

preference affects personality, abilities, and learning style (Sousa, 2006). Each child is

unique. However, most contemporary schools are dominated by a left-brain curriculum,

left-brain teaching strategies, and left-brain learning activities (Chapman, 1998;

Chudzinski, 1988; Cooke & Haipt, 1986; Grady, 1984; Lewallen, 1985; Marxer, 1988;

Rubenzer, 1982; Turner, 1999; Vitale, 1982). Teachers can greatly influence the learning

48

environment for students to impact and improve student learning. Thus, exploring the

relationships between hemispheric preferences, teaching practices, and learning may

provide greater insight to improve teaching and learning.

In conclusion, understanding the relationships between teacher variables

(hemispheric preferences and teaching strategies) and student variables (gender, grade

level, hemispheric preferences, spatial ability, verbal ability, science achievement, and

attitudes toward science class) may provide valuable implications for education. The next

chapter will describe the methodology used in this study to explore these relationships.

49

CHAPTER 3

METHODOLOGY

This chapter is divided into two parts: the construction and validation of teacher and

student instruments and the investigation of the variables of interest. Various instruments

were acquired from commercial vendors or developed by the researcher, translated into

Chinese, and analyzed for validity and reliability for use with elementary school science

teachers and students in Taiwan, and then these validated instruments were employed in

the subsequent investigation. The description of the construction and validation of the

teacher and student instruments includes participants, context, teacher data sources, and

student data sources. Only the instruments developed by the researcher are available in

the Appendices. All commercial instruments are copyrighted and permission to use them

was provided by the publishers. The next section, the main study, explores the

relationships among the variables of interest. Participants, research design, data collection

procedures, and data analysis procedures are described. Pseudonyms are used to identify

all teachers and schools.

Construction and Validation of Instruments

Subjects

The participants for the construction and validation of instruments included 35

undergraduate students, 35 graduate students, 39 elementary school science teachers, and

50

199 elementary school students in Taiwan. The 35 undergraduate and 35 graduate

students were from Wen University in Taiwan. The 35 undergraduate students were from

the Department of Life Science and the 35 graduate students were from the Graduate

Institute of Statistics. The 39 elementary school science teachers were from three large

urban elementary schools in Taiwan. Fourteen of the 39 science teachers were from Liang

elementary school, 12 of the 39 science teachers were from Gong elementary school, and

13 of the 39 science teachers were from Jean elementary school. The 199 elementary

school students were from Liang elementary school, 98 fourth-grade students and 101

fifth-grade students. The elementary school students were 9- to 11-years-old.

Context

Wen University was founded in 1915 in China, and then was re-established in

Taiwan in 1962. The school is located in a suburb area in Jhongli city in Taiwan. Wen

University has developed into a comprehensive university and has gained an international

reputation for research and academic excellence. Wen University has now become

Taiwan’s leading school in the fields of geophysics and space science, and was selected

as one of the major research-oriented universities in 2001.

Liang elementary school was founded in 1899, the oldest elementary school in

Taiwan. This school is located in a commercial and cultural city in Taiwan, surrounded by

various kinds of stores and cultural heritage buildings. Currently, there are 98 classes and

3,040 students. Most parents are of a high socioeconomic status. The school is recognized

for the science performance of its students. The school has played a leading role in

sharing good practices and teaching resources in Taiwan.

51

Gong elementary school was founded in 1976. The school set a record for having

224 classes and 12,000 students, the largest elementary school in the world. Although the

school is very large, the school is very well organized. The school is located in a

commercial and residential city in Taiwan. Currently, there are 133 classes and 4,415

students. Most parents are of a high socioeconomic status. The school is noted for

outstanding student achievement in the fields of music education and physical education.

Jean elementary school was founded in 1954. The school is located in a commercial

and residential city in Taiwan. Currently, there are 96 classes and 3,226 students. Most

parents are blue collar workers with a low socioeconomic status. The school is

recognized for outstanding student achievement in the field of physical education.

Table 3.1 provides a summary of participants and context for the construction and

validation of instruments.

52

Subjects Level n Location

Department of Life Science students

Undergraduate 35 Wen University

Graduate Institute of Statistics students

Graduate 35 Wen University

Science teachers Elementary (Grades 1-6)

14 Liang Elementary School


12 Gong Elementary School


13 Jean Elementary School

Grade 4 98Elementary school students

Grade 5 101

Liang Elementary School

Table 3.1: A summary of subjects and context for the construction and validation of teacher and student instruments.

Teacher Data Sources

Human Information Processing (HIP) Survey

The Human Information Processing (HIP) Survey was developed by Torrance,

Taggart, and Taggart (1984a) to measure hemispheric preference. The survey was

developed for use with adults. The administration of the survey is not timed. It consists of

40 items with three forced-choice selections for each item. Respondents choose one of

three statements. Each item has a left, right, and integrated alternative response.

53

The following is an example from the HIP Survey:

A solve problems logically, rationally

B solve problems intuitively

C equally skilled in solving problems intuitively and logically

The choice of the first statement is scored on the left-brain scale, the choice of the second

statement is scored on the right-brain scale, and the choice of the third statement is scored

on the integrated-brain scale. According to the manual (Taggart & Torrance, 1984), the

scoring procedure involves counting the number of left-brain, right-brain, and

integrated-brain responses, recording the raw scores on profile sheets, and then

converting them to standard scores and percentiles. According to the strategy and tactics

profiles booklet (Torrance, Taggart & Taggart, 1984b), the standard score shows the

person’s brain processing strategy—left, right, integrated, or mixed. If the standard score

equals or exceeds 120 for left, right, or integrated, then the person is described as having

that processing strategy. The left-dominant information processor prefers to deal with

problems in an active, verbal, and logical manner. The right-dominant information

processor prefers to deal with problems in a receptive, spatial, and intuitive manner. The

integrated-information processor operates simultaneously in the left and right mode of

processing without a clear preference for either. If the standard score is less than 120 for

all three, then the person is described as having a mixed strategy. The mixed-information

processor uses either a left-dominant or a right-dominant strategy depending on the

situation.

Reliability and validity statistics for the HIP Survey have been reported in the

Human Information Processing Survey Administrator’s Manual (Taggart & Torrance,

54

1984). An alternate forms reliability study incorporating a 1-week interval between the

alternate forms, HIP Survey and Form A of SOLAT for adults, resulted in reliability

coefficients of .84 for the right-brain scale, .86 for the left-brain scale, and .82 for the

integrated-brain scale. Based upon a review of a large body of literature related to the

validity of the HIP Survey, Taggart and Torrance concluded that the HIP Survey met the

criteria for construct validity, concurrent validity, and predictive validity.

The HIP Survey used in the current study was translated into Chinese. The

researcher translated the English version of the survey into Chinese and then it was

back-translated by a bilingual professional translator. This translation and

back-translation process was repeated to develop the final form of the instrument. In

addition, content experts evaluated how well each item translated into Chinese

represented what it was intended to measure based upon the original English item. The

bilingual Chinese-English panel was composed of two faculty members and one doctoral

student in science education as well as one fourth-grade teacher and one fifth-grade

teacher. The panel members suggested some specific Chinese words to ensure the

meaning of the original English item. The panel members’ feedback was collected and

then used to revise the Chinese translation of the items.

The Chinese version of the HIP Survey was administered to 35 undergraduate

students and 35 graduate students at Wen University in northern Taiwan and 39 science

teachers from Liang, Gong, and Jean elementary schools in northern Taiwan during late

September to early October 2007. Cronbach’s alpha (n = 109) for the Chinese version of

the Human Information Processing Survey was 0.81 for the left-brain scale, 0.78 for the

right-brain scale, and 0.86 for the integrated-brain scale.

55

Table 3.2 is provided to summarize the Cronbach’s alpha coefficients for the Human

Information Processing (HIP) Survey.

Instrument No. of Items

No. of Subjects

Scales Cronbach’s Alpha

Left-Brain Scale 0.81

Right-Brain Scale 0.78

Human Information Processing (HIP) Survey

40 109

Integrated-Brain Scale 0.86

Table 3.2: Cronbach’s alpha coefficients for the Human Information Processing (HIP) Survey scales.

Student Data Sources

Style of Learning and Thinking (SOLAT, Elementary Form)

The Style of Learning and Thinking (SOLAT, elementary form) developed by

Torrance in 1988 is a self-report inventory consisting of 25 items designed to determine

hemispheric preference. For each item, there are two statements. There are four possible

ways to respond—choose one of two statements, both statements, or neither statement.

The following is an example:

I like to learn from pictures.

I like to learn from words.

The choice of the first statement is scored on the right-brain scale; the choice of the

second statement is scored on the left-brain scale; choosing both statements is scored on

the whole-brain scale. According to the manual (Torrance, 1988), the scoring procedure

56

involves counting the number of left-, right-, and whole-brain responses, recording the

raw scores on profile sheets, and then converting them to standard scores and percentiles.

A standard score of 120 is considered a dominant pattern.

The SOLAT elementary form was developed for use with grades K through 5 and it

is appropriate for group administration in grades 1 through 5. Administration time is

approximately 30-40 minutes. It can be administered to kindergarteners, but this is not

recommended because they have barely begun to lateralize.

Reliability and validity statistics for the SOLAT elementary form have been reported

in the Style of Learning and Thinking Administrator’s Manual (Torrance, 1988).

Cronbach’s alpha was 0.77 for the left-brain scale and 0.73 for the right-brain scale. No

reliability data was reported for the whole-brain scale. Not much has been found in the

literature regarding the validity of the SOLAT elementary form. However, as Torrance

pointed out, its validity primarily rests on the validity evidence accumulated from a few

older versions of the SOLAT.

The SOLAT elementary form used in the current study was translated into Chinese.

The researcher translated the English version of the inventory into the Chinese version

and then it was back-translated by a bilingual professional translator. This translation and

back-translation process was repeated to develop the final form of the instrument. In

addition, content experts evaluated how well each item represented what it was intended

to measure based upon the original English item. The bilingual Chinese-English panel

was composed of two faculty members and one doctoral student in science education as

well as one fourth-grade teacher and one fifth-grade teacher. The panel members

suggested some specific Chinese words to ensure the meaning of the original English

item. The panel members’ feedback was collected and then used to revise the Chinese

translation of the items.

The Chinese version of the SOLAT was administered to 98 fourth-grade students

and 101 fifth-grade students from Liang elementary school in northern Taiwan during late

October to late November in 2007. Cronbach’s alpha (n = 199) for the Chinese version of

the SOLAT was 0.75 for the left-brain scale, 0.74 for the right-brain scale, and 0.83 for

the whole-brain scale.

PMA Spatial Relations Test (Grades 4-6)

The Spatial Relations Test of the Primary Mental Abilities (PMA) tests (Thurstone,

1962) , appropriate for grades 4-6, is designed to determine students’ ability to visualize

how parts of objects or figures fit together, what their relationships are, and what they

look like when rotated in space. The subject is presented with one figure and four choices.

For example, the subject is presented with a triangle and is directed to choose one figure

that would complete a square (see the following example).

( ) ( ) ( ) ( )

57

58

The test consists of 25 items. There is a 6-minute time limit on this test. The choice

of a right answer is scored 1 point for each item. The scores are added to give a total

spatial ability score for each subject. These scores are added together to give an overall

score ranging from 0 to 25. Higher total scores indicate better spatial ability.

Reliability and validity statistics for the PMA Spatial Relations Test have been

reported in the PMA technical report (Thurstone, 1965). The test was standardized on

4,266 subjects ranging in age from 8- to 15-years-old. The test-retest reliability

coefficient for the Spatial Relations Test after a 1-week interval was 0.75 for fourth-grade

students, 0.87 for fifth-grade students, and .68 for sixth-grade students. After a 4-week

interval, the test-retest reliability coefficient was 0.65 for fourth-grade students, 0.72 for

fifth-grade students, and 0.67 for sixth-grade students.

The directions for the PMA Spatial Relations Test used in the current study were

translated into Chinese. The researcher translated the English version of the directions for

the PMA Spatial Relations Test into Chinese and then it was back-translated by a

bilingual professional translator. This translation and back-translation process was

repeated to develop the final form of the instrument. In addition, content experts

evaluated how well the Chinese translation of the directions represented the original

English directions. The bilingual Chinese-English panel was composed of two faculty

members and one doctoral student in science education as well as one fourth-grade

teacher and one fifth-grade teacher. The panel members suggested some specific Chinese

words to ensure the meaning of the original English directions. The panel members’

feedback was collected and then used to revise the Chinese translation of the directions.

59

The Chinese version of the PMA Spatial Relations Test was administered to 98

fourth-grade students and 101 fifth-grade students from Liang elementary school in

northern Taiwan during late October to late November in 2007. Cronbach’s alpha

(n = 199) for the Chinese version of the PMA Spatial Relations Test was 0.64 for

fourth-grade students and 0.79 for fifth-grade students.

Student Attitudes Toward Science Class Survey

The definition of student attitudes toward science class was modified from Dhindsa

and Chung’s (2003) definition of student attitudes toward science. They identified six

attitudinal constructs related to student attitudes toward science: (a) science

enjoyment—the extent to which a student enjoys a science lesson, (b) science

anxiety—the extent to which a student is anxious about a science lesson, (c) science

interest—the extent to which a student develops interest in science and its related

activities, (d) science confidence—the extent to which a student is confident and

successful doing science, (e) science motivation—the extent to which a student is

motivated to learn and pursue science in the future, and (f) importance of science—the

extent to which a student perceives science to be important in everyday life and a

worthwhile activity. The current study defined student attitudes toward science class in

terms of three constructs: (a) science enjoyment—the extent to which a student enjoys a

science class, (b) science confidence—the extent to which a student is confident and feels

successful doing science, and (c) importance of science—the extent to which a student

perceives science to be important in everyday life and a worthwhile activity.

The Student Attitudes Toward Science Class Survey is designed to measure student

attitudes toward science class. The 32 items were derived from other available surveys

60

(Fahey, 1992; Henry, 1996; Lee, 2005; Lewis, 2001; Monhardt, 1998; Moore, 2001;

Sager, 1996; Smith, 1991) and modified to make them suitable for elementary school

students in Taiwan. Table 3.3 provides a description and sample item for each construct in

the Student Attitudes Toward Science Class Survey.

Construct Description Sample Item

Enjoyment Extent to which a student enjoys a science class.

My science class is interesting.

Confidence Extent to which a student is confident and feels successful doing science.

I am afraid to answer questions in science class.

Importance Extent to which a student perceives science to be important in everyday life and a worthwhile activity.

Science class is a waste of time.

Table 3.3: Description and sample item for each construct in the Student Attitudes Toward Science Class Survey.

In addition, some commonly used teaching materials and teaching strategies for

science teaching in Taiwan were embedded in the items for each of the three constructs.

These teaching materials included materials such as the textbook, workbook, DVD film,

and poster. Teaching strategies included the use of experiments, lectures, learning groups,

and informal environments (e.g., field trip, playground). A balance of positive (n = 16)

and negative (n = 16) statements were generated and distributed among the teaching

materials, teaching strategies, and the three constructs: science enjoyment, science

61

confidence, and importance of science. Each item was assigned a unique number through

a random number generator. Table 3.4 provides a distribution of positive (+) and negative

(-) items among the teaching materials, teaching strategies, and the three constructs.

Teaching Materials/ Strategies

Enjoyment

Confidence

Importance

General Item 17+

Item 32+

Item 25-

Item 13-

Item 21+ Item 20- Item 22+

Item 4+

Item 18-

Item 15-

Science textbook Item 9+ Item 26+ Item 12-

Science workbook Item 10- Item 27- Item 7+

DVD film Item 19- Item 28+

Poster Item 16+ Item 23-

Experiment Item 24+ Item 1- Item 5+ Item 2-

Lecture Item 3+ Item 6- Item 31+

Learning groups Item 11- Item 8+

Informal environments

Item 14+ Item 29- Item 30-

Subtotal 7+ 7- 5+ 5- 4+ 4-

Table 3.4: Distribution of positive (+) and negative (-) items among the teaching materials, teaching strategies, and the three constructs of the Student Attitudes Toward Science Class Survey.

62

The following are some examples from the Student Attitudes Toward Science Class

Survey:

During science class, I like to read science posters.

In science class, science posters do not help me to learn science.

In science class, doing experiments is boring.

I like to do experiments in science class.

The Student Attitudes Toward Science Class Survey is a 5-point Likert-type

instrument designed to measure the strength of a student’s attitudes toward science class

(see Appendix A). The instrument contains 16 positive and 16 negative statements, for a

total of 32 items. The values recorded for the positive items are Strongly Disagree = 1,

Disagree = 2, Undecided = 3, Agree = 4, and Strongly Agree = 5. The values recorded for

the negative items are Strongly Agree = 1, Agree = 2, Undecided = 3, Disagree = 4, and

Strongly Disagree = 5. The scores for the recorded positive and negative statements were

added together to give a total attitude score for each participant, ranging from 32 to 160.

The higher the total score, the more positive is the attitude of the respondent toward

science class and the lower the total score, the less positive is the attitude of the

respondent toward science class.

The Student Attitudes Toward Science Class Survey used in the current study was

translated into Chinese. The researcher translated the English version of the survey into

Chinese and then it was back-translated by a bilingual professional translator. This

translation and back-translation process was repeated to develop the final form of the

instrument. In addition, content experts evaluated how well each item represented what it

was intended to measure based upon the definition of the specific constructs of science

63

enjoyment, science confidence, and importance of science. The bilingual Chinese-English

panel was composed of two faculty members and one doctoral student in science

education as well as one fourth-grade teacher and one fifth-grade teacher. The panel

members suggested some specific Chinese words to ensure the meaning of the original

English item. The panel members’ feedback was collected and then used to revise the

Chinese translation of the items.

The Chinese version of the Student Attitudes Toward Science Class Survey was

administered to 98 fourth-grade students and 101 fifth-grade students from Liang

elementary school in northern Taiwan from late October to late November in 2007. In the

principal components analysis of the 32 items, factors generating eigenvalues 1.00 or

higher were rotated by varimax rotation and revealed one major factor—the final version

of the survey consisting of all 32 items in one scale. Cronbach’s alpha (n = 199) for the

Chinese version of the Student Attitudes Toward Science Class Survey was 0.90.

64

Table 3.5 is provided to summarize the Cronbach’s alpha coefficients for the student

instruments.

Instrument No. of Items

No. of Subjects

Scale/Group Cronbach’s Alpha

Left-Brain Scale 0.75

Right-Brain Scale 0.74

Style of Learning and Thinking (SOLAT) Inventory

25 199

Whole-Brain Scale 0.83

98 Grade 4 0.64 PMA Spatial Relations Test 25

101 Grade 5 0.79


32 199 0.90

Table 3.5: Cronbach’s alpha coefficients for student instruments.

Main Study

Participants

The main study included 4 elementary school science teachers and 133 fourth- and

fifth-grade students, 65 fourth graders from two classes and 68 fifth graders from two

classes during the 2007 fall semester. The four classes were taught by the four science

teachers respectively. All the teachers and students were from Liang elementary school

located in northern Taiwan. The elementary school students were 9- to 11-years-old.

Table 3.6 shows the demographic information for the elementary school science

teachers and students for the 4 classes, including teacher name, teacher gender, grade

65

level, number of male and female students for each class, marginal totals, and grand total.

One male teacher and one female teacher taught each grade level. Approximately 50% of

the students were males and approximately 50% of the students were females in each

class.

Students Teacher Name

Teacher Gender

Grade Level

Males n

Females n

Total

John Male 4 17 15 32

Betty Female 4 17 16 33

Subtotal 34 31 65

Peter Male 5 17 18 35

Lucy Female 5 17 16 33

Subtotal 34 34 68

Total 68 65 133

Table 3.6: Demographic information for Liang elementary school science teachers (n = 4) and elementary school students (n = 133).

Research Design




science class using student and teacher data from multiple instruments. The second

66


data and matching these teaching strategies to teacher and student hemispheric

preferences.

The basic assumption for a mixed methods design is that the combination of

quantitative and qualitative data provides a better understanding of the research questions

than either quantitative or qualitative data by itself (Creswell, 2008). In the current study,

quantitative data, such as scores on surveys, yield specific numbers that can be

statistically analyzed and can produce results to describe the relationships among

variables. In addition, qualitative data, such as classroom observations can explore

teaching strategies in further depth. The rationale for the study is that a mixed methods

study provides a better understanding of the research questions by converging on both

broad numeric trends from quantitative research and the detail of qualitative research.

Data Collection Procedures

In the main study, instruments were used to obtain teacher demographic information

and to develop teacher hemispheric preference profiles. In addition, classroom

observations were used to identify the teaching strategies employed by each teacher.

Furthermore, instruments were used to explore student hemispheric preference, assess

spatial ability, measure verbal ability, measure science achievement, and measure student

attitudes toward science class.

Teacher Data Collection

Data collection: Teacher Demographics Questionnaire. The Teacher Demographics

Questionnaire was developed by the researcher to obtain teacher background information

including name, class, gender, age, type of degree, major, current teaching grade(s),

67

current teaching subject(s), number of years teaching science, and number of years

teaching. See Appendix B for a copy of the Teacher Demographics Questionnaire. During

early October 2007, the Teacher Demographics Questionnaire was administered to 4

elementary school science teachers in Liang elementary school to obtain background

information.

Data collection: Human Information Processing Survey. During early October 2007,

the Human Information Processing (HIP) Survey was administered to 4 elementary

school science teachers in Liang elementary school to determine their hemispheric

preferences. The science teachers were identified as left-brain, right-brain,

integrated-brain, or mixed-brain preference according to the HIP results.

Data collection: Classroom observations. Classroom observations were conducted

for the main study. Classroom observations focused on science teaching strategies.

Before beginning the classroom observations, a list of science teaching strategies was

generated based upon an extensive literature review (see Table 2.3). During classroom

observations, the preliminary list was used as a reference to identify science teaching

strategies. A timer was also used during classroom observations because the current study

focused upon the specific time for each teaching strategy. A classroom observation

schedule for observing the 4 elementary school science teachers was established (see

Table 3.7).

68

Monday Tuesday Wednesday Thursday Friday

8:40- 9:20 John Betty

9:30-10:10 John Betty

10:30-11:10 Lucy Betty John

11:20-12:00 Peter

1:20- 2:00 Peter Lucy

2:10- 2:50 Peter Lucy

3:10- 3:50

Table 3.7: Classroom observation schedule for the 4 elementary school science teachers.

Lesson topics and total number of class sessions for observing the 4 elementary

school science teachers were identified (see Table 3.8). The total number of

observations for each teacher was 8 class sessions from early October to the middle of

November in 2007. Each class session was 40 minutes.

69

Teacher Grade Level

Lesson Topics Class Sessions

2-1 Investigation of aquatic organisms

2-3 Secrets of aquatic animals

2-4 Secrets of aquatic plants

2-5 The magic carpet over water

2-6 Decorating your aquarium

2-7 I love nature

3-1 Does salt disappear in water?

3-2 Is salt invisible?

John

Betty

4

3-3 How much salt can dissolve in water?

8

2-1 Functions of root, stem, and leaf

2-2 Functions of flower, fruit, and seed

3-3 Liquid

Peter

Lucy

5

3-4 The degree of acidity or alkalinity of a liquid

8

Table 3.8: Lesson topics and total class sessions for observations of the 4 elementary school science teachers.

Field notes were taken during classroom observations. The start time and end time

for each period of time were documented in the first column of the classroom

observation field notes. Total time for each period of time was computed and

documented in the second column of the classroom observation field notes. The

classroom observation field notes included descriptions of classroom teaching in the

70

middle column. Teaching Strategy Code, Teaching Strategy Category, and Category

Abbreviation appear in the final columns of the classroom observation field notes and

were completed after all classroom observations were completed and analyzed. Table

3.9 provides an example of the classroom observation field notes.

Session 1: 34 minutes 50 seconds Grade: 4 Teacher: Betty Gender: Female Activity: 2-1 Investigation of aquatic organisms Date: Oct-12-2007 (Friday) Time: 8:40~9:20

Start time End time

Subtotal Time

min sec min sec

Description Teaching Strategy Code

Teaching Strategy Category

Category Abbreviation

0: 1:

00 00

1: 00 Students read textbooks

1: 2:

00 22

1: 22 Lecture

2: 2:

22 58

0: 36 Lecture (explain science posters on the back board)

2: 3:

58 16

0: 18 Lecture (explain science pictures on the back board)

3: 8:

16 46


8: 8:

46 57

0: 11 Teacher demonstration by real objects

8: 27:

57 23

18: 26 Lecture (explain the content of science posters) one by one

27: 27:

23 59

0: 36 Students read textbooks aloud

27: 31:

59 50

3: 51 Lecture

31: 34:

50 50

3: 00 DVD film on TV

Table 3.9: An example of the classroom observation field notes before coding and categorizing.

71

Student Data Collection

Data collection: Style of Learning and Thinking. During late November in 2007,

the Style of Learning and Thinking (SOLAT, Elementary Form) was administered to

133 students in Liang elementary school, 65 in fourth grade and 68 in fifth grade, to

determine student hemispheric preference. The students were identified as left-brain,

right-brain, or whole-brain preference according to their responses to the SOLAT.

Data collection: PMA Spatial Relations Test. During late November in 2007, the

PMA Spatial Relations Test was administered to 133 students in Liang elementary school,

65 in fourth grade and 68 in fifth grade, to measure student spatial ability as an indicator

of right-hemispheric processing. A total spatial score indicated a student’s ability to

visualize how parts of objects or figures fit together, what their relationships are, and

what they look like when rotated in space.

Data collection: Chinese language midterm and final exams. In each semester, a

midterm and a final exam are administered in each public elementary school in Taiwan

to assess student Chinese language ability. All students in a school complete the test on

a specific day and at a specific time; students in each grade take the same test at the

same time. The Chinese language exam in elementary schools in Taiwan usually

includes different types of questions such as identifying words and phrases, making

sentences, applying grammar, and reading comprehension. In the current study, the sum

of the scores on the midterm and final exams were used to determine verbal ability as an

indicator of left-hemispheric processing. With regard to grade 4, a total of 27 questions

on the midterm exam were worth 100 points and a total of 43 questions on the final

exam were worth 100 points. With regard to grade 5, a total of 60 questions on the

72

midterm exam were worth 100 points and a total of 55 questions on the final exam were

worth 100 points. The Chinese language midterm and final exams were administered to

133 elementary school students in Liang elementary school early in November of 2007

and early in January of 2008, respectively.

Data collection: Science midterm and final exams. In each semester, a midterm and

a final exam are administered in each public elementary school in Taiwan to assess

student science achievement. All students in a school complete the test on a specific day

and at a specific time; students in each grade take the same test at the same time. The

sum of the scores on the midterm and final exams measures science achievement. With

regard to grade 4, a total of 77 questions on the midterm exam were worth 100 points

and a total of 61 questions on the final exam were worth 100 points. With regard to

grade 5, a total of 25 questions on the midterm exam were worth 100 points and a total

of 25 questions on the final exam were worth 100 points. The Science midterm and final

exams were administered to 133 elementary school students in Liang elementary school

early in November of 2007 and early in January of 2008, respectively.

Data collection: Student Attitudes Toward Science Class Survey. Late in November

of 2007, the Student Attitudes Toward Science Class Survey was administered to 133

students in Liang elementary school, 65 in fourth grade and 68 in fifth grade, to measure

their attitudes toward science class. A total attitude score reflected the strength of a

student’s attitudes toward science class in terms of three constructs: science enjoyment,

science confidence, and importance of science.

73

Data Analysis Procedures

This section is organized into three parts. Part one describes the data analyses for

teacher data including descriptive statistics related to the Teacher Demographics

Questionnaire and the Human Information Processing Survey. In addition, coding and

categorizing data and descriptive statistics for the classroom observations are described.

Part two describes the data analyses for student data including descriptive statistics for

the Style of Learning and Thinking (SOLAT). Also, Pearson Product Moment

Correlations, Multivariate Analysis of Variance (MANOVA), and Analysis of Variance

(ANOVA) for student data from multiple instruments are described. Part three describes

the data analyses for teacher data and student data including Confidence Intervals for

Proportion tests for teaching strategies from the classroom observations and student

hemispheric preferences from the Style of Learning and Thinking (SOLAT).

Teacher Data Analyses

Data analysis: Teacher Demographics Questionnaire. For the 4 elementary school

science teachers’ responses to the Teacher Demographics Questionnaire, descriptive

statistics were computed. A table was developed to display teacher background

information including teacher pseudonym, gender, current teaching grade, age, major,

number of years teaching science, and total number of years teaching. English names

were chosen as pseudonyms to easily identify a male or female teacher.

Data analysis: Human Information Processing Survey. For the 4 elementary school

science teachers’ responses to the Human Information Processing (HIP) Survey,

74

descriptive statistics were computed. A table was developed to display teachers’ standard

scores on the three scales (left-brain, right-brain, and integrated-brain) and teachers’

hemispheric preferences.

Data analysis: Classroom observations. Observations were conducted in the

classrooms of the 4 elementary school science teachers. The field notes from the

classroom observations were recorded in Chinese and then translated into English. The

preliminary list derived from an extensive literature review included 18 types of left-brain

teaching strategies and 27 types of right-brain teaching strategies (see Table 2.3). The

preliminary teaching strategies were used as guidelines to code and categorize the

classroom observation field notes as a left-brain teaching strategy, a right-brain teaching

strategy, or a whole-brain teaching strategy. If a left-brain teaching strategy and a

right-brain teaching strategy were used simultaneously, this combined teaching strategy

was identified as a whole-brain teaching strategy. All teaching strategies were categorized

and grouped as left-brain, right-brain, or whole-brain teaching strategies. A list of

abbreviations was generated for all teaching strategies. Based on the coding results and

total time, a percent of instructional time for each of the three types of teaching

strategies—left-brain, right-brain, and whole-brain—was calculated for each teacher. The

percentage for each of the three types of teaching strategies for the 4 elementary school

science teachers was presented in tables and bar graphs. Table 3.10 provides an example

of the classroom observation field notes, including Teaching Strategy Code, Teaching

Strategy Category, and Category Abbreviation.

75

Session 1: 34 minutes 50 seconds Grade: 4 Teacher: Betty Gender: Female Activity: 2-1 Investigation of aquatic organisms Date: Oct-12-2007 (Friday) Time: 8:40~9:20

Start time End time

Subtotal Time

min sec min sec

Description Teaching Strategy Code

Teaching Strategy Category


0: 1:

00 00

1: 00 Students read textbooks Textual readings

Left Brain L2

1: 2:

00 22

1: 22 Lecture Lecture Left Brain L1

2: 2:

22 58


Lecture Visual aids

Whole Brain W2

2: 3:

58 16

0: 18 Lecture (explain science pictures on the back board)

Lecture Visual aids

Whole Brain W2

3: 8:

16 46


Lecture Visual aids

Whole Brain W2

8: 8:

46 57

0: 11 Teacher demonstration by real objects

Demonstration

Right Brain R1

8: 27:

57 23

18: 26 Lecture (explain the content of science posters) one by one

Lecture Visual aids

Whole Brain W2

27: 27:

23 59

0: 36 Students read textbooks aloud

Textual readings

Left Brain L2

27: 31:

59 50

3: 51 Lecture Lecture Left Brain L1

31: 34:

50 50

3: 00 DVD film on TV Film projection

Whole Brain W1

Table 3.10: An example of the classroom observation field notes after coding and categorizing. Student Data Analyses

Data analysis: Style of Learning and Thinking. For student responses to the Style

of Learning and Thinking, descriptive statistics (n = 133) were computed. The

percentage for each student hemispheric preference—left-brain preference, right-brain

preference, whole-brain preference, and no hemispheric preference—was presented in

tables according to class, grade level, and gender.

Data analysis: Student multiple instruments. Based upon the scores of 133

elementary school students on the Style of Learning and Thinking, PMA Spatial

Relations Test, Chinese language exams, Science exams, and Student Attitudes Toward

Science Class Survey, descriptive statistics were first computed—means and standard

deviations were presented in tables according to gender and grade level to describe

general tendencies and the spread of scores on the surveys.

Next, Pearson Product Moment Correlations were computed to examine the

relationships among gender (male, female), grade level (fourth grade, fifth grade), student

hemispheric preference, spatial ability, verbal ability, science achievement, and student

attitudes toward science class. The correlation coefficients for the selected variables were

presented in matrices. Pearson product moment correlation coefficients determine the

strength and direction of the linear relationship between two variables. Relationships

between variables were tested at the .05p < level of significance.

Lastly, a two-way MANOVA was computed to determine gender and grade level

differences for left-brain scale scores, right-brain scale scores, whole-brain scale scores,

PMA Spatial Relations Test scores, Chinese language exam scores, Science exam scores,

and Student Attitudes Toward Science Class Survey scores. The follow-up ANOVA was

computed to further understand the main effects.

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77

Teacher and Student Data Analyses

Data analysis: Classroom observations and Style of Learning and Thinking. To

match teaching strategies to student hemispheric preferences, Confidence Intervals for

Proportion tests were computed based upon teacher data from classroom observations

and student data from the Style of Learning and Thinking (SOLAT). In the main study,

confidence intervals for the proportions of students’ hemispheric preferences—left-brain

preference, right-brain preference, and whole-brain preference—for each class were first

computed, and then the proportions for the three types of teaching strategies for each

teacher were matched to these confidence intervals.

Summary

This chapter consists of two parts: (a) the construction and validation of teacher and

student instruments and (b) the main study to explore the relationships among variables

of interest. In the first part, the Human Information Processing (HIP) Survey was

translated and validated for use with elementary school science teachers in Taiwan. In

addition, the Style of Learning and Thinking (SOLAT) and PMA Spatial Relations Test

were translated and validated for use with elementary school students in Taiwan. In

addition, the Student Attitudes Toward Science Class Survey was developed, translated,

and validated for use with elementary school students in Taiwan. The results showed that

these instruments translated into Chinese are valid and reliable for use in Taiwan. These

validated instruments were used in the main study.

In the second part, the main study was conducted to explore the relationships among

variables of interest. The participants included the 4 elementary school science teachers

and 133 fourth- and fifth-grade elementary school students who were purposively

78

selected from a large urban elementary school, Liang elementary school, in northern

Taiwan. Table 3.11 is provided to summarize the data sources and data analyses for the

main study.

Data Sources Data Analyses

Teacher Demographics Questionnaire ▪ Descriptive Statistics

Human Information Processing Survey ▪ Descriptive Statistics

Teacher

Classroom Observations ▪ Coding and Categorizing Data

▪ Descriptive Statistics

Style of Learning and Thinking

PMA Spatial Relations Test

Chinese language exams

Science exams

Student


▪ Descriptive Statistics

▪ Pearson Product Moment Correlations

▪ MANOVA

▪ ANOVA

Classroom Observations Teacher and

Student Style of Learning and Thinking

▪ Confidence Intervals for Proportion tests

Table 3.11: A summary of data sources and data analyses for the main study.

79

CHAPTER 4

RESULTS OF DATA ANALYSES This chapter reports the results of all data analyses organized into three sections. The

first section presents the results of the analyses of teacher data including descriptive

statistics related to the Teacher Demographics Questionnaire and the Human Information

Processing (HIP) Survey. Results from the teacher classroom observations were

qualitatively coded and categorized according to hemispheric preferences and then

analyzed using descriptive statistics. The second section presents the results of the

analyses of student data including descriptive statistics related to the Style of Learning

and Thinking (SOLAT). Results from Pearson Product Moment Correlations, multivariate

analysis of variance (MANOVA), and analysis of variance (ANOVA) are reported for all

student independent and dependent variables. The third section presents the results of

teacher and student data analyses. Results from Confidence Intervals for Proportion tests

from teacher classroom observations and student responses to the Style of Learning and

Thinking (SOLAT) are reported.

Results of Teacher Data Analyses

Data Results: Teacher Demographics Questionnaire

Based upon the responses of the 4 elementary school science teachers to the Teacher

Demographics Questionnaire, Table 4.1 provides the following demographic information:

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teacher pseudonym, gender, current teaching grade, age, major, science teaching years,

and total teaching years for each teacher. In the main study, the participants of the

purposeful sample were 4 elementary school science teachers from Liang elementary

school. Two males and two females were from grade 4 and grade 5, one male and one

female for each grade, respectively. The ages of the 4 elementary school science teachers

ranged from 38 to 46 years. The 4 elementary school science teachers had different

majors—ranging from three majors related to education but not specifically to science

education to Network Engineering. The number of science teaching years also varied

among the 4 teachers. The two males served as elementary school science teachers for 10

and 16 years but also taught other subjects for a total of 18 and 17 years, respectively.

The two females served as relatively new elementary school science teachers for 3 years

yet taught other subjects for a total of 26 and 17 years, respectively.

Teacher

Gender

Current Teaching Grade

Age

Major

Science Teaching Years

Total Teaching Years

John Male 4 38 Curriculum and Instruction

10 18

Betty Female 4 46 Special Education 3 26

Peter Male 5 38 Network Engineering 16 17

Lucy Female 5 39 Physical Education 3 17

Table 4.1: Demographic information for the 4 elementary school science teachers.

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Data Results: Human Information Processing Survey

This section presents the results of the analysis of the responses of the 4 elementary

school science teachers to the Human Information Processing (HIP) Survey. As described

in chapter 3, the standard score on the HIP Survey identifies the person’s processing

strategy—left, right, integrated, or mixed. If the standard score equals or exceeds 120 for

left, right, or integrated, then the person is described as having that processing strategy. If

the standard score is less than 120 for all three, then the person is described as having a

mixed-processing strategy. Table 4.2 is a profile of the HIP Survey results for the 4

elementary school science teachers. With regard to John, his standard score was 109 on

the left-brain scale, 59 on the right-brain scale, and 129 on the integrated-brain scale.

John was identified as exhibiting an integrated-brain preference. With regard to Betty, her

standard score was 97 on the left-brain scale, 88 on the right-brain scale, and 114 on the

integrated-brain scale. Betty was identified as exhibiting a mixed-brain preference. With

regard to Peter, his standard score was 67 on the left-brain scale, 92 on the right-brain

scale, and 136 on the integrated-brain scale. Peter was identified as exhibiting an

integrated-brain preference. With regard to Lucy, her standard score was 80 on the

left-brain scale, 88 on the right-brain scale, and 129 on the integrated-brain scale. Lucy

was identified as exhibiting an integrated-brain preference. In summary, 3 of the

elementary school science teachers were identified as exhibiting an integrated-brain

preference. The remaining elementary school science teacher was identified as exhibiting

a mixed-brain preference.

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Human Information Processing (HIP) Survey Teacher

Left-Brain Score

Right-Brain Score

Integrated-Brain

Score

Brain Hemispheric Preference

John 109 59 129 Integrated

Betty 97 88 114 Mixed

Peter 67 92 136 Integrated

Lucy 80 88 129 Integrated

Table 4.2: Standard scores on the HIP Survey left-brain, right-brain, and integrated-brain scales and brain hemispheric preference for the 4 elementary school science teachers.

Figure 4.1 provides a distribution of the standard scores on the left-brain, right-brain,

and integrated-brain scales for the 4 elementary school science teachers. The graph

suggests that John scored highest on the left-brain scale but scored lowest on the

right-brain scale compared to the other teachers. Betty scored similarly on each of the

three scales as no score reached 120. The distribution pattern of the scores on the three

scales for John was similar to that of Betty—they both scored highest on the

integrated-brain scale, next on the left-brain scale, and lowest on the right-brain scale.

Peter scored highest on the right-brain and the integrated-brain scales but scored lowest

on the left-brain scale compared to the other teachers, and each of Peter’s scores seemed

considerably different from each other. The distribution pattern of the standard scores on

each of the three scales for Peter and Lucy were similar—they scored highest on the

integrated-brain scale, next on the right-brain scale, and lowest on the left-brain scale.

0

20

40

60

80

100

120

140

160

John Betty Peter Lucy

Left-brain

Right-brain

Integrated-brain

Figure 4.1: Standard scores on the HIP Survey left-brain, right-brain, and integrated-brain scales for the 4 elementary school science teachers.

Data Results: Classroom Observations

This section provides the results of the classroom observations for the 4 elementary

school science teachers. The teaching strategies in all classroom observation field notes

were identified according to Table 2.3. Eventually, 11 teaching strategies emerged via an

analytic constant comparative process. Five of the teaching strategies were categorized as

left-brain processing or preference, four of the teaching strategies were categorized as

right-brain processing or preference, and two of the teaching strategies were categorized

as integrated-brain or whole-brain processing or preference. Table 4.3 lists the teaching

strategies for the left-brain, right-brain, and whole-brain processing or preference along

with the abbreviations recorded in the classroom observation field notes.

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Teaching Strategy Code Teaching Strategy Category


Lecture L1

Textual readings L2

Experiments with teacher control L3

Independent work L4

Oral report

Left Brain

L5

Demonstration R1

Small group discussion R2

Experiments with student control R3

Visual aids

Right Brain

R4

Film projection W1

Lecture with visual aids

Whole Brain

W2

Table 4.3: Teaching strategy code, teaching strategy category, and category abbreviation.

Based upon coding and categorizing the teaching strategies in the field notes for the

classroom observations of the 4 elementary school science teachers, the percentage of

instructional time for each teaching strategy related to each of the three hemispheric

preferences (left-brain, right-brain, and whole-brain) was computed. Table 4.4 profiles

the findings related to the teaching strategies employed by each teacher.

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Grade 4 Grade 5

Teaching Strategy Code

Category Abbreviation John Betty Peter Lucy

Lecture L1 39.46% 35.17% 13.81% 38.74%

Textual readings L2 6.42% 0.54%

Experiments with teacher control

L3 7.79% 21.59%

Independent work L4 6.73% 2.69% 19.67% 12.57%

Oral report L5 2.04% 6.64% 1.24%

Demonstration R1 11.54% 7.70% 9.44% 4.90%

Small group discussion R2 4.38% 5.95% 2.18% 0.58%

Experiments with student control

R3 12.36% 50.78%

Visual aids R4

Film projection W1 14.50% 5.51% 5.86%

Lecture with visual aids W2 9.00% 22.12% 4.12% 13.98%

Table 4.4: Percentage of instructional time for teaching strategies related to left-brain, right-brain, and whole-brain processing for the 4 elementary school science teachers.

John employed eight teaching strategies: three left-brain teaching strategies, three

right-brain teaching strategies, and two whole-brain teaching strategies. Among the

teaching strategies, the highest percentage was for lecture (39.46%), followed by film

projection (14.50%), experiments with student control (12.36%), and demonstration

(11.54%). The lowest percentages (< 10%) were for lecture with visual aids (9.00%),

independent work (6.73%), small group discussion (4.38%), and oral report (2.04%).

Betty employed nine teaching strategies: five left-brain teaching strategies, two


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teaching strategies, the highest percentage was for lecture (35.17%), followed by lecture

with visual aids (22.12%). The lowest percentages (< 10%) were for experiments with

teacher control (7.79%), demonstration (7.70%), oral report (6.64%), textual readings

(6.42%), small group discussion (5.95%), film projection (5.51%), and independent work

(2.69%).

Peter employed six teaching strategies: two left-brain teaching strategies, three

right-brain teaching strategies, and one whole-brain teaching strategy. Among the

teaching strategies, the highest percentage was for experiments with student control

(50.78%), followed by independent work (19.67%), and lecture (13.81%). The lowest

percentages (< 10%) were for demonstration (9.44%), lecture with visual aids (4.12%),

and small group discussion (2.18%).

Lucy employed nine teaching strategies: five left-brain teaching strategies, two


teaching strategies, the highest percentage was for lecture (38.74%); followed by

experiments with teacher control (21.59%), lecture with visual aids (13.98%), and

independent work (12.57%). The lowest percentages (< 10%) were for film projection

(5.86%), demonstration (4.90%), oral report (1.24%), small group discussion (0.58%),

and textual readings (0.54%).

Based upon the descriptive results, it appears that the two female teachers, Betty and

Lucy, employed more types of left-brain teaching strategies (n = 5) and more often

(> 50% of the time) compared to the two male teachers. The two male teachers, John and

Peter, seemed to employ more types of right-brain teaching strategies (n = 3) and Peter

employed these right-brain teaching strategies 62.40% of the instructional time. In

addition, John, Betty, and Lucy seemed to prefer lecture, whereas Peter seemed to prefer

experiments with student control.

Figure 4.2 provides a distribution of the percentage of instructional time for each

teaching strategy category abbreviation related to left-brain, right-brain, and whole-brain

processing for the 4 elementary school science teachers. The graph suggests that lecture

(L1) was employed for a similar percentage of time by John, Betty, and Lucy, whereas

experiments with student control (R3) was clearly used by Peter. It also suggests that the

two female teachers preferred experiments with teacher control (L3), whereas the two

male teachers preferred experiments with student control (R3).

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

L1 L2 L3 L4 L5 R1 R2 R3 R4 W1 W2

John

Betty

Peter

Lucy

Note. L1 = Lecture; L2 = Textual Readings; L3 = Experiments With Teacher Control; L4 = Independent Work; L5 = Oral Report; R1 = Demonstration; R2 = Small Group Discussion; R3 = Experiments With Student Control; R4 = Visual Aids; W1 = Film Projection; W2 = Lecture With Visual Aids. Figure 4.2: Percentage of instructional time for each teaching strategy category abbreviation related to left-brain, right-brain, and whole-brain processing for the 4 elementary school science teachers.

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Table 4.5 displays the total percentage of instructional time for teaching strategies

related to left-brain, right-brain, and whole-brain processing for the 4 elementary school

science teachers. With regard to John, the highest percentage was for left-brain teaching

strategies (48.23%), followed by right-brain teaching strategies (28.28%) and

whole-brain teaching strategies (23.49%). With regard to Betty, the highest percentage

was for left-brain teaching strategies (58.71%), followed by whole-brain teaching

strategies (27.64%) and right-brain teaching strategies (13.65%). With regard to Peter, the

highest percentage was for right-brain teaching strategies (62.40%), followed by

left-brain teaching strategies (33.48%) and whole-brain teaching strategies (4.12%). With

regard to Lucy, the highest percentage was for left-brain teaching strategies (74.68%),

followed by whole-brain teaching strategies (19.84%) and right-brain teaching strategies

(5.48%). Based upon these results, it appears that John, Betty, and Lucy preferred

left-brain teaching strategies, whereas Peter preferred right-brain teaching strategies.

Grade 4 Grade 5

Processing John Betty Peter Lucy

Left Brain 48.23% 58.71% 33.48% 74.68%

Right Brain 28.28% 13.65% 62.40% 5.48%

Whole Brain 23.49% 27.64% 4.12% 19.84%

Table 4.5: Total percentage of instructional time for teaching strategies related to left-brain, right-brain, and whole-brain processing for the 4 elementary school science teachers.

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Figure 4.3 illustrates the total percentage of instructional time for teaching strategies

related to left-brain, right-brain, and whole-brain preference for the 4 elementary school

science teachers. Comparing the fourth-grade teachers, it appears that both John and

Betty most often used left-brain teaching strategies (nearly 50% and nearly 60% of the

time, respectively). Also, the percentage of instructional time devoted to whole-brain

teaching strategies was similar for John and Betty (approximately 25%). However, the

distribution pattern of teaching strategies for right-brain teaching strategies for John was

different from that for Betty. John seemed to employ right-brain and whole-brain teaching

strategies for nearly the same percentage of time (approximately 25%). In contrast, Betty

seemed to employ whole-brain teaching strategies (approximately 25%) two times more

compared to right-brain teaching strategies. Comparing the fifth-grade teachers, it

appears that Peter clearly used right-brain teaching strategies (> 60%), whereas Lucy

clearly used left-brain teaching strategies (nearly 75%). Peter employed right-brain

teaching strategies most often and whole-brain teaching strategies least often, whereas

Lucy employed left-brain teaching strategies most often and right-brain teaching

strategies least often. The distribution pattern of left-, right-, and whole-brain teaching

strategies for Peter appears to be considerably different from that for Lucy.

Comparing the two male teachers, the distribution pattern of left-brain, right-brain,

and whole-brain teaching strategies for John was considerably different from that for

Peter. Peter clearly used right-brain teaching strategies most often (> 60%) followed by

left-brain teaching strategies (> 30%). Peter rarely used whole-brain teaching strategies

(< 5%). In contrast, John used left-brain teaching strategies most often (nearly 50%) and

right- and whole-brain teaching strategies for nearly the same percentage of time

(approximately 25%). Comparing the two female teachers, the distribution pattern of

left-brain, right-brain, and whole-brain teaching strategies for Betty was similar to that

for Lucy, the highest percentage was for left-brain teaching strategies (> 50%), followed

by whole-brain teaching strategies (< 30%), and right-brain teaching strategies (< 20%).

0.00%

10.00%20.00%30.00%40.00%50.00%60.00%70.00%80.00%

John Betty Pe te r Lucy

Left-bra in s tra tegies

Right-bra in s tra tegies

Whole -bra in s tra tegies

Figure 4.3: Total percentage of instructional time for teaching strategies related to left-brain, right-brain, and whole-brain processing for the 4 elementary school science teachers.

Results of Student Data Analyses

Data Results: Style of Learning and Thinking

This section provides the results of student (n = 133) responses to the Style of

Learning and Thinking (SOLAT). According to the manual (Torrance, 1988), scoring

procedures involve counting the number of left-brain, right-brain, and whole-brain

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91

responses, recording the raw scores on profile sheets, and then converting them to

standard scores and percentiles. A standard score of 120 is considered a hemispheric

preference. Some children may not exhibit a hemispheric preference.

Class

Grade Level

No. of Subjects

Gender

Left-Brain Preference

Right-Brain Preference

Whole-Brain Preference

No Hemispheric Preference

17 Male 5.88% 41.18% 5.88% 47.06% John

4

15 Female 0.00% 60.00% 13.33% 26.67%

17 Male 0.00% 29.41% 11.76% 58.82% Betty

4

16 Female 6.25% 18.75% 31.25% 43.75%

17 Male 13.33% 73.33% 13.33% 0.00% Peter

5

18 Female 18.75% 18.75% 12.50% 50.00%

17 Male 5.88% 47.06% 5.88% 41.18% Lucy

5

16 Female 18.75% 56.25% 0.00% 25.00%

Table 4.6: Percentage of male and female students for left-brain, right-brain, and whole-brain preferences and no hemispheric preference for the four science classes.

Table 4.6 presents the percentage of male and female students for left-brain,

right-brain, and whole-brain preferences and no hemispheric preference for the four

science classes. Inspection of the table reveals some extreme values, for example, 0.00%

of the female students in John’s class exhibited a left-brain preference, 0.00% of the male

students in Betty’s class exhibited a left-brain preference, 0.00% of the female students in

Lucy’s class exhibited a whole-brain preference, and 0.00% of the male students in

Peter’s class did not exhibit a hemispheric preference. In contrast, it showed that a

relatively high percentage (73.33%) of the male students in Peter’s class exhibited a

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right-brain preference. A relatively high percentage (60.00%) of the female students in

John’s class exhibited a right-brain preference. A relatively high percentage (58.82%) of

the male students in Betty’s class did not exhibit a hemispheric preference. A relatively

high percentage (56.25%) of the female students in Lucy’s class exhibited a right-brain

preference. A relatively high percentage (50.00%) of the female students in Peter’s class

did not exhibit a hemispheric preference.

With regard to John’s class, a higher percentage (although small) of the male

students (5.88%) compared to the female students (0.00%) exhibited a left-brain

preference and a higher percentage of the male students (47.06%) compared to the female

students (26.67%) did not exhibit a hemispheric preference. In contrast, a higher

percentage of the female students (60.00%) compared to the male students (41.18%)

exhibited a right-brain preference and a higher percentage of the female students (13.33%)

compared to the male students (5.88%) exhibited a whole-brain preference. Based upon

the results for John’s class, it appears that a relatively high percentage (> 40%) of the

male and female students exhibited a right-brain preference, whereas a relatively low

percentage (< 10%) of the male and female students exhibited a left-brain preference. Of

note, there were no female students who exhibited a left-brain preference but a large

percentage of the female students (60.00%) exhibited a right-brain preference. In addition,

it appears that a relatively high percentage (> 40%) of the male students did not exhibit a

hemispheric preference.

With regard to Betty’s class, a higher percentage of the male students (29.41%)

compared to the female students (18.75%) exhibited a right-brain preference and a higher

percentage of the male students (58.82%) compared to the female students (43.75%) did

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not exhibit a hemispheric preference. In contrast, a higher percentage of the female

students (6.25%) compared to the male students (0.00%) exhibited a left-brain preference

and a higher percentage of the female students (31.25%) compared to the male students

(11.76%) exhibited a whole-brain preference. Based upon the results for Betty’s class, it

appears that a relatively high percentage (> 40%) of the male and female students did not

exhibit a hemispheric preference, whereas a relatively low percentage (< 10%) of the

male and female students exhibited a left-brain preference. Of note, there were no male

students who exhibited a left-brain preference. In addition, it appears that the highest

percentage for the male students (58.82%) and for the female students (43.75%) was for

no hemispheric preference.

With regard to Peter’s class, a higher percentage of the male students (73.33%)

compared to the female students (18.75%) exhibited a right-brain preference and a

slightly higher percentage of the male students (13.33%) compared to the female students

(12.50%) exhibited a whole-brain preference. In contrast, a higher percentage of the

female students (18.75%) compared to the male students (13.33%) exhibited a left-brain

preference and a higher percentage of the female students (50.00%) compared to the male

students (0.00%) did not exhibit a hemispheric preference. Based upon the results for

Peter’s class, it appears that half of the female students (50.00%) did not exhibit a

hemispheric preference, whereas every male student exhibited a hemispheric preference.

Of note, the highest percentage (73.33%) for the male students in Peter’s class was for a

right-brain preference.

With regard to Lucy’s class, a higher percentage of the male students (5.88%)

compared to the female students (0.00%) exhibited a whole-brain preference and a higher

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percentage of the male students (41.18%) compared to the female students (25.00%) did

not exhibit a hemispheric preference. In contrast, a higher percentage of the female

students (18.75%) compared to the male students (5.88%) exhibited a left-brain

preference and a higher percentage of the female students (56.25%) compared to the male

students (47.06%) exhibited a right-brain preference. Based upon the results for Lucy’s

class, it appears that a relatively high percentage (> 40%) of the male and female students

exhibited a right-brain preference, whereas a relatively low percentage (< 10%) of the

male and female students exhibited a whole-brain preference. Of note, there were no

female students who exhibited a whole-brain preference but a high percentage of the

female students (> 50%) exhibited a right-brain preference. In addition, it appears that a

relatively high percentage (> 40%) of the male students did not exhibit a hemispheric

preference.

Grade Level

No. of Subjects

Gender

Left-Brain Preference

Right-Brain Preference

Whole-Brain Preference

No Hemispheric Preference

34 Male 2.94% 35.29% 8.82% 52.94% 4

31 Female 3.23% 38.71% 22.58% 35.48%

35 Male 9.38% 59.38% 9.38% 21.88% 5

33 Female 18.75% 37.50% 6.25% 37.50%

Table 4.7: Total percentage for male and female students for left-brain, right-brain, and whole-brain preferences and no hemispheric preference for the two grade levels.

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Table 4.7 presents the total percentage for male and female students for left-brain,

right-brain, and whole-brain preferences and no hemispheric preference for the fourth and

fifth grades. Comparing the fourth-grade and the fifth-grade male students, it appears that

although the percentage is small, approximately three times more fifth-grade male

students (9.38%) compared to the fourth-grade male students (2.94%) exhibited a

left-brain preference and a considerably higher percentage of the fifth-grade male

students (59.38%) compared to the fourth-grade male students (35.29%) exhibited a

right-brain preference. Nearly the same percentage of the fifth-grade male students

(9.38%) compared to the fourth-grade male students (8.82%) exhibited a whole-brain

preference. In contrast, a considerably higher percentage of the fourth-grade male

students (52.94%) compared to the fifth-grade male students (21.88%) did not exhibit a

hemispheric preference. Based upon the results, it appears that a higher percentage of the

fifth-grade male students exhibited a left-brain preference or a right-brain preference

compared to the fourth-grade male students. Both the fourth-grade and fifth-grade male

students exhibited a relatively high percentage of right-brain preference. In addition, it

appears that a similar percentage of the fifth-grade and fourth-grade male students

exhibited a similar, relatively small whole-brain preference.

Comparing the fourth-grade female and the fifth-grade female students, it appears

that a considerably higher percentage of the fifth-grade female students (18.75%)

compared to the fourth-grade female students (3.23%) exhibited a left-brain preference.

Nearly the same percentage of the fourth-grade female students (38.71%) compared to

the fifth-grade female students (37.50%) exhibited a right-brain preference. A

considerably higher percentage of the fourth-grade female students (22.58%) compared

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to the fifth-grade female students (6.25%) exhibited a whole-brain preference. Nearly the

same percentage of the fifth-grade female students (37.50%) compared to the

fourth-grade female students (35.48%) did not exhibit a hemispheric preference. Based

upon the results, it appears that a higher percentage of the fifth-grade female students

exhibited a left-brain preference compared to the fourth-grade female students. In

addition, it appears that a similar, relatively high percentage of the fifth-grade female and

the fourth-grade female students exhibited a right-brain preference. Furthermore, it

appears that a higher percentage of the fourth-grade female students exhibited a

whole-brain preference compared to the fifth-grade female students.

Data Results: Student Multiple Instruments

This section provides descriptive statistics and correlation analyses for the scores of

133 elementary school students on the Style of Learning and Thinking, PMA Spatial

Relations Test, Chinese language exams, Science exams, and Student Attitudes Toward

Science Class Survey. The results from the administration of these instruments to

measure students’ hemispheric preference, spatial ability, verbal ability, science

achievement, and attitudes toward science class are presented according to gender and

according to grade level. The correlation analyses included all independent variables

(gender and grade level) and all dependent variables (left-brain preference, right-brain

preference, whole-brain preference, spatial ability, verbal ability, science achievement,

and attitudes toward science class).

Descriptive Statistics

Table 4.8 presents means and standard deviations for the dependent variables for

male and female students. The mean for the left-brain scale on the SOLAT for the male

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students was 93.47 (SD = 18.86) and for the female students was 92.50 (SD = 21.24). The

mean for the right-brain scale on the SOLAT for the male students was 120.43

(SD = 20.39) and for the female students was 113.41 (SD = 23.03). The mean for the

whole-brain scale on the SOLAT for the male students was 92.40 (SD = 16.84) and for

the female students was 99.67 (SD = 19.85). The mean on the PMA Spatial Relations Test

for the male students was 131.96 (SD = 14.94) and for the female students was 125.03

(SD = 15.68). The mean on the Chinese language exams for the male students was 186.66

(SD = 12.37) and for the female students was 191.12 (SD = 11.22). The mean on the

Science exams for the male students was 184.59 (SD = 16.23) and for the female students

was 187.03 (SD = 15.32). Lastly, the mean on the Student Attitudes Toward Science Class

Survey for the male students was 131.19 (SD = 15.81) and for the female students was

129.63 (SD = 16.89). Based upon the results, it appears that the male students achieved a

higher mean score compared to the female students for right-brain preference and spatial

ability. In contrast, the female students achieved a higher mean score compared to the

male students for whole-brain preference and verbal ability.

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Males (n = 68) Females (n = 65)

Variables M SD M SD

Left-Brain Preference 93.47 18.86 92.50 21.24

Right-Brain Preference 120.43 20.39 113.41 23.03

Whole-Brain Preference 92.40 16.84 99.67 19.85

Spatial Ability 131.96 14.94 125.03 15.68

Verbal Ability 186.66 12.37 191.12 11.22

Science Achievement 184.59 16.23 187.03 15.32

Attitudes Toward Science Class 131.19 15.81 129.63 16.89

Table 4.8: Means and standard deviations for the dependent variables for the male and female students.

Table 4.9 presents means and standard deviations for the dependent variables for the

fourth-grade and fifth-grade students. The mean for the left-brain scale on the SOLAT for

the fourth-grade students was 88.46 (SD = 17.15) and for the fifth-grade students was

97.40 (SD = 21.61). The mean for the right-brain scale on the SOLAT for the

fourth-grade students was 112.55 (SD = 21.08) and for the fifth-grade students was

121.36 (SD = 21.99). The mean for the whole-brain scale on the SOLAT for the

fourth-grade students was 96.29 (SD = 19.30) and for the fifth-grade students was 95.57

(SD = 18.14). The mean on the PMA Spatial Relations Test for the fourth-grade students

was 127.69 (SD = 13.50) and for the fifth-grade students was 129.42 (SD = 17.50). The

mean on the Chinese exams for the fourth-grade students was 188.20 (SD = 11.46) and

for the fifth-grade students was 189.46 (SD = 12.53). The mean on the Science exams for

the fourth-grade students was 192.94 (SD = 7.02) and for the fifth-grade students was

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178.94 (SD = 18.60). Lastly, the mean on the Student Attitudes Toward Science Class

Survey for the fourth-grade students was 128.54 (SD = 19.28) and for the fifth-grade

students was 132.32 (SD = 12.56). Based upon the results, it appears that the fourth-grade

students achieved a higher mean score compared to the fifth-grade students for science

achievement, whereas the fifth-grade students achieved a higher mean score compared to

the fourth-grade students for left-brain preference and right-brain preference.

Grade 4 (n = 65) Grade 5 (n = 68) Variables M SD M SD

Left-Brain Preference 88.46 17.15 97.40 21.61

Right-Brain Preference 112.55 21.08 121.36 21.99

Whole-Brain Preference 96.29 19.30 95.57 18.14

Spatial Ability 127.69 13.50 129.42 17.50

Verbal Ability 188.20 11.46 189.46 12.53

Science Achievement 192.94 7.02 178.94 18.60

Attitudes Toward Science Class 128.54 19.28 132.32 12.56

Table 4.9: Means and standard deviations for the dependent variables for grade 4 and grade 5 students. Correlation Analyses

Five correlation analyses were computed. The first correlation analysis was used to

explore the relationships among variables of interest for all students. The second and

third correlation analyses were used to explore the relationships among variables of

interest for the students grouped by gender (i.e., male students and female students). The

fourth and fifth correlation analyses were used to explore the relationships among

variables of interest for the students grouped by grade level (i.e., fourth-grade students

and fifth-grade students).

Specifically, the first correlation analysis involved 133 elementary school students to

exam the relationships among gender, grade level, left-brain preference, right-brain

preference, and whole-brain preference, spatial ability, verbal ability, science

achievement, and attitudes toward science class. Table 4.10 presents the correlation

matrix generated from the results of the correlation analysis for all students.

Variables 1 2 3 4 5 6 7 8 9

1. Gender --

2. Grade .02 --

3. Left -.02 .23∗∗ --

4. Right -.16 .20∗ - .22∗ --

5. Whole .20∗ -.02 - .41∗∗ - .49∗∗ --

6. Spatial - .22 ∗ .06 -.12 .07 .05 --

7. Verbal .19∗ .00 .08 .02 .22∗ .20∗ --

8. Science .08 .00 .10 .04 .21∗ .24∗∗ .63∗∗ --

9. Attitudes -.05 .12 -.00 .11 .21∗ .10 .25∗∗ .34∗∗ --

Note. Grade = Grade Level; Left = Left-Brain Preference; Right = Right-Brain Preference; Whole = Whole-Brain Preference; Spatial = Spatial Ability; Verbal = Verbal Ability; Science = Science Achievement; Attitudes = Attitudes Toward Science Class.

.05p∗ < . . .01p∗∗ < Table 4.10: Pearson correlation coefficients (r) for scores on the independent and dependent variables for elementary school students (n = 133).

100

According to Table 4.10, there was a significant weak relationship between gender

and whole-brain preference ( .20r = , .05p < ). That is, female students tended to exhibit a

stronger whole-brain preference. There was a significant weak negative relationship

between gender and spatial ability ( .22r = − , .05p < ). That is, male students tended to

exhibit better spatial ability. There was a significant weak relationship between gender

and verbal ability ( ,.19r = .05p < ). That is, female students tended to exhibit better

verbal ability.

There was a significant weak relationship between grade level and left-brain

preference ( , ). That is, fifth-grade students tended to exhibit a stronger

left-brain preference. There was a significant weak relationship between grade level and

right-brain preference ( ,

.23r = .01p <

.20r = .05p < ). That is, fifth-grade students tended to exhibit

stronger a right-brain preference.

There was a significant weak relationship between whole-brain preference and

verbal ability ( , ). That is, students who exhibited a stronger whole-brain

preference tended to exhibit better verbal ability. There was a significant weak

relationship between whole-brain preference and science achievement ( ,

.22r = .05p <

.21r = .05p < ).

That is, students who exhibited a stronger whole-brain preference tended to exhibit better

science achievement. There was a significant weak relationship between whole-brain

preference and student attitudes toward science class ( .21r = , .05p < ). That is, students

who exhibited a stronger whole-brain preference tended to exhibit more positive attitudes

toward science class.

101

There was a significant weak relationship between spatial ability and verbal ability

( , ). That is, students who exhibited better spatial ability tended to exhibit

better verbal ability. There was a significant weak relationship between spatial ability and

science achievement ( ,

.20r = .05p <

.24r = .01p < ). That is, students who exhibited better spatial

ability tended to exhibit better science achievement.

There was a significant strong relationship between verbal ability and science

achievement ( , ). That is, students who exhibited better verbal ability

tended to exhibit better science achievement. There was a significant weak relationship

between verbal ability and student attitudes toward science class ( , ). That

is, students who exhibited better verbal ability tended to exhibit more positive attitudes


.63r = .01p <

.25r = .01p <

There was a significant moderate relationship between science achievement and

student attitudes toward science class ( .34r = , .01p < ). That is, students who exhibited

better science achievement tended to exhibit more positive attitudes toward science class.

Further, to investigate gender differences in the above relationships, the second and

third correlation analyses were computed on the 133 elementary school students by

gender. Table 4.11 presents the correlation matrix generated from the results of the

correlation analyses for 68 male students.

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Variables 1 2 3 4 5 6 7 8

1. Grade --

2. Left .13 --

3. Right .33∗ -.18 --

4. Whole -.06 - .44∗∗ - .47∗∗ --

5. Spatial .23 -.23 .03 .18 --

6. Verbal -.03 .10 -.02 .22 .18 --

7. Science -.08 -.03 -.10 .28∗ .19 .53∗∗ --

8. Attitudes -.04 -.07 .09 .24 .23 .30∗ .28∗ --


.05p∗ < . . .01p∗∗ < Table 4.11: Pearson correlation coefficients (r) for scores on the independent and dependent variables for elementary school male students (n = 68).

According to Table 4.11, there was a significant moderate relationship between

grade level and right-brain preference ( .33r = , .05p < ) for male students. That is,

fifth-grade male students tended to exhibit a stronger right-brain preference.


science achievement ( ,.28r = .05p < ) for male students. That is, male students who

exhibited a stronger whole-brain preference tended to exhibit better science achievement.

For male students, there was a significant strong relationship between verbal ability

and science achievement ( ,.53r = .01p < ).That is, male students who exhibited better

verbal ability tended to exhibit better science achievement. For male students, there was a

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significant moderate relationship between verbal ability and attitudes toward science

class ( , ).That is, male students who exhibited better verbal ability tended

to exhibit more positive attitudes toward science class.

.30r = .05p <

For male students, there was a significant weak relationship between science

achievement and attitudes toward science class ( .28r = , .05p < ).That is, male students

who exhibited better science achievement tended to exhibit more positive attitudes


Table 4.12 presents the correlation matrix generated from the results of the

correlation analyses for 65 female students.

Variables 1 2 3 4 5 6 7 8

1. Grade --

2. Left .31∗ --

3. Right .09 -.27 --

4. Whole .01 - .39∗∗ - .48∗∗ --

5. Spatial -.10 -.04 .04 .02 --

6. Verbal .03 .08 .13 .16 .38∗∗ --

7. Science .07 .21 .17 .13 .37∗∗ .74∗∗ --

8. Attitudes .27 .06 .11 .22 -.06 .22 .41∗∗ --


.05p∗ < . . .01p∗∗ < Table 4.12: Pearson correlation coefficients (r) for scores on the independent and dependent variables for elementary school female students (n = 65). 104

According to Table 4.12, there was a significant moderate relationship between

grade level and left-brain preference ( .31r = , .05p < ) for female students. That is,

fifth-grade female students tended to exhibit a stronger left-brain preference.

For female students, there was a significant moderate relationship between spatial

ability and verbal ability ( ,.38r = .01p < ). That is, female students who exhibited better

spatial ability tended to exhibit better verbal ability. For female students, there was a

significant moderate relationship between spatial ability and science achievement

( , ).That is, female students who exhibited better spatial ability tended to

exhibit better science achievement.

.37r = .01p <

For female students, there was a significant strong relationship between verbal

ability and science achievement ( .74r = , .01p < ).That is, female students who exhibited

better verbal ability tended to exhibit better science achievement. For female students,

there was a significant moderate relationship between science achievement and attitudes

toward science class ( ,.41r = .01p < ). That is, female students who exhibited better

science achievement tended to exhibit more positive attitudes toward science class.

Lastly, to investigate grade level differences in the above relationships, the fourth

and fifth correlation analyses were computed on the 133 elementary school students by

grade level. Table 4.13 presents the correlation matrix generated from the results of the

correlation analyses for 65 fourth-grade students.

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Variables 1 2 3 4 5 6 7 8

1. Gender --

2. Left -.15 --

3. Right -.06 -.09 --

4. Whole .16 - .41∗∗ - .41∗∗ --

5. Spatial -.07 -.06 .02 -.01 --

6. Verbal .16 .11 .24 .18 .29∗ --

7. Science .00 .19 .28∗ .20 .14 .54∗∗ --

8. Attitudes -.18 -.04 .17 .29∗ .23 .30∗ .37∗∗ --

Note. Left = Left-Brain Preference; Right = Right-Brain Preference; Whole = Whole-Brain Preference; Spatial = Spatial Ability; Verbal = Verbal Ability; Science = Science Achievement; Attitudes = Attitudes Toward Science Class.

.05p∗ < . . .01p∗∗ < Table 4.13: Pearson correlation coefficients (r) for scores on the independent and dependent variables for elementary school fourth-grade students (n = 65). According to Table 4.13, there was a significant weak relationship between

right-brain preference and science achievement ( .28r = , .05p < ) for fourth-grade

students. That is, fourth-grade students who exhibited a stronger right-brain preference

tended to exhibit better science achievement.


attitudes toward science class ( .29r = , .05p < ) for fourth-grade students. That is,

fourth-grade students who exhibited a stronger whole-brain preference tended to exhibit

more positive attitudes toward science class. There was a significant weak relationship

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between spatial ability and verbal ability ( .29r = , .05p < ) for fourth-grade students. That

is, fourth-grade students who exhibited better spatial ability tended to exhibit better

verbal ability.

For fourth-grade students, there was a significant strong relationship between verbal

ability and science achievement ( .54r = , .01p < ), and significant moderate relationship

between verbal ability and attitudes toward science class ( .30r = , ). That is,

fourth-grade students who exhibited better verbal ability tended to exhibit better science

achievement and more positive attitudes toward science class.

.05p <


attitudes toward science class for fourth-grade students ( .37r = , ). That is, for

fourth-grade students who exhibit better science achievement tend to exhibit more

positive attitudes toward science class.

.01p <

Table 4.14 presents the correlation matrix generated from the results of the

correlation analyses for 68 fifth-grade students.

107

Variables 1 2 3 4 5 6 7 8

1. Gender --

2. Left .07 --

3. Right -.27 - .43∗∗ --

4. Whole .23 - .42∗∗ - .58∗∗ --

5. Spatial - .34 ∗∗ -.19 .08 .10 --

6. Verbal .21 .07 -.18 .26 .15 --

7. Science .15 .04 -.17 .21 .31∗ .72∗∗ --

8. Attitudes .14 -.02 -.04 .11 -.07 .19 .33∗ --

Note. Left = Left-Brain Preference; Right = Right-Brain Preference; Whole = Whole-Brain Preference; Spatial = Spatial Ability; Verbal = Verbal Ability; Science = Science Achievement; Attitudes = Attitudes Toward Science Class.

.05p∗ < . . .01p∗∗ < Table 4.14: Pearson correlation coefficients (r) for scores on the independent and dependent variables for elementary school fifth-grade students (n = 68).

According to Table 4.14, for fifth-grade students, there was a significant moderate

negative relationship between gender and spatial ability ( .34r = − , ). That is,

fifth-grade male students tended to exhibit a stronger right-brain preference and better

spatial ability.

.01p <

There was a significant moderate relationship between spatial ability and science

achievement ( , ) for fifth-grade students. That is, fifth-grade students who

exhibited better spatial ability tended to exhibit better science achievement. There was a

.31r = .05p <

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significant strong relationship between verbal ability and science achievement

( , ) for fifth-grade students. That is, fifth-grade students who exhibited

better verbal ability tended to exhibit better science achievement.

.72r = .01p <


attitudes toward science class for fifth-grade students ( .33r = , .05p < ). That is, for

fifth-grade students who exhibited better science achievement, they tended to exhibit

more positive attitudes toward science class.

Two-Way Multivariate Analysis of Variance (MANOVA)

To determine gender and grade level differences for left-brain scale scores,

right-brain scale scores, whole-brain scale scores, PMA Spatial Relations Test scores,

Chinese language exam scores, Science exam scores, and Student Attitudes Toward

Science Class Survey scores, a two-way MANOVA was computed involving 133

elementary school students. The dependent variables included left-brain preference,

right-brain preference, whole-brain preference, spatial ability, verbal ability, science

achievement, and attitudes toward science class. The independent variables included

gender and grade level. The .05 level of significance was used for all analyses.

Table 4.15 contains a summary of the multivariate analysis for all dependent

variables by gender and grade level. As can be seen from Table 4.15, there were no

significant interaction effects between gender and grade level for the combination of

dependent variables. With regard to gender, there was a significant difference between the

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male and female students for the combination of dependent variables, Wilk’s Lambda

= .85, F (7,119) = 3.12, p < .01. With regard to grade level, there was a significant

difference between the fourth-grade and fifth-grade students for the combination of

dependent variables, Wilk’s Lambda = .70, F (7,119) = 7.35, p < .001.

Variable Wilk’s Lambda

F

df

p

Gender .85 3.12∗∗ 7, 119 .005

Grade Level .70 7.35∗∗∗ 7, 119 .000

Gender × Grade Level .93 1.33 7, 119 .242

.05p∗ < . . . .01p∗∗ < .001p∗∗∗ < Table 4.15: Results of multivariate analyses of variance for all dependent variables by gender and grade level.

To further understand the main effects, follow-up analyses of variance (ANOVA)

were computed. Table 4.16 presents the results of the follow-up univariate analyses.

Results of follow-up univariate analyses for the gender main effect indicated that three

dependent variables, whole-brain preference, spatial ability, and verbal ability, were

found to be significant (p < .05). That is, there was a significant difference between the

male students and the female students with regard to whole-brain preference,

F (1,125) = 5.77, p = .018; spatial ability, F (1,125) = 5.94, p = .016; and verbal ability,

F (1,125) = 5.63, p = .019. Inspection of the mean scores indicated that female students

110

111

exhibited a stronger whole-brain preference (M = 99.67, SD = 19.85) compared to male

students (M = 92.40, SD = 16.84). In addition, female students exhibited better verbal

ability (M = 191.12, SD = 11.22) compared to male students (M = 186.66, SD = 12.37). In

contrast, male students exhibited better spatial ability (M = 131.96, SD = 14.94)

compared to female students (M = 125.03, SD = 15.68).

For the grade level main effect (see Table 4.16), results of univariate analyses

indicated that two dependent variables, left-brain preference and right-brain preference,

were found to be significant (p < .05). That is, there was a significant difference between

the fourth-grade students and the fifth-grade students with regard to left-brain preference,

F (1,125) = 5.23, p = .024 and right-brain preference, F (1,125) = 5.70, p = .018. Inspection of

the mean scores indicated that the fifth-grade students (M = 97.40, SD = 21.61) exhibited

a stronger left-brain preference compared to fourth-grade students (M = 88.46, SD =

17.15). In addition, the fifth-grade students exhibited a stronger right-brain preference (M

= 121.36, SD = 21.99) compared to fourth-grade students (M = 112.55, SD = 21.08).

Source Dependent Variable F df p

Left-Brain Preference .16 1, 125 .694 Gender

Right-Brain Preference 2.38 1, 125 .125

Whole-Brain Preference 1, 125 .018 5.77∗

Spatial Ability 1, 125 .016 5.94∗

Verbal Ability 1, 125 .019 5.63∗

Science Achievement 1.64 1, 125 .203

Attitudes Toward Science Class .19 1, 125 .668

Grade Level Left-Brain Preference 1, 125 .024 5.23∗

Right-Brain Preference 1, 125 .018 5.70∗

Whole-Brain Preference .10 1, 125 .756

Spatial Ability .70 1, 125 .405

Verbal Ability .11 1, 125 .738

Science Achievement .30 1, 125 .588

Attitudes Toward Science Class 1.59 1, 125 .209

Gender × Grade Level Left-Brain Preference .80 1, 125 .374

Right-Brain Preference 1.75 1, 125 .188

Whole-Brain Preference .09 1, 125 .767

Spatial Ability 3.12 1, 125 .080

Verbal Ability .06 1, 125 .802

Science Achievement .04 1, 125 .834

Attitudes Toward Science Class 2.72 1, 125 .102

.05p∗ < . . .01p∗∗ < Table 4.16: Results of univariate analyses of variance for all dependent variables by gender and grade level.

112

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Results of Teacher and Student Data Analyses

Data Results: Classroom Observations and Style of Learning and Thinking

In this section, teacher and student data were used to investigate the relationship

between the percentage of instructional time for left-brain, right-brain, and whole-brain

science teaching strategies and the percentage of student left-, right-, and whole-brain

preferences in each of the 4 classes. Confidence Intervals for Proportion tests were used

to determine the alignment between the percentage of instructional time for teaching

strategies and the percentage of student hemispheric preferences related to left-brain,

right-brain, and whole-brain preferences.

A confidence interval (CI) is an interval estimate of a population parameter. The

95% confidence interval for a proportion is calculated from the sample and contains the

population proportion with the probability of 95%. The lower and upper limits of the 95%

confidence interval for a proportion were calculated for the percentage of instructional

time for teacher left-brain, right-brain, and whole-brain teaching strategies in relationship

to the percentage of student left-brain, right-brain, and whole-brain preferences. The

corresponding confidence limits define the boundaries of the interval.

Table 4.17 presents the results of the Confidence Intervals for Proportion tests for

John’s teaching strategies and student left-brain, right-brain, and whole-brain preferences.

According to Table 4.17, John employed left-brain teaching strategies 48.23% of the

instructional time, right-brain teaching strategies 28.28% of the instructional time, and

whole-brain teaching strategies 23.49% of the instructional time. According to Table 4.17,

the 95% confidence interval for the proportion for student left-brain preference was

0.00% to 9.15%. Since the proportion of John’s left-brain teaching strategies was 48.23%,

http://en.wikipedia.org/wiki/Interval_estimation

http://en.wikipedia.org/wiki/Population_parameter

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the confidence interval did not contain the population proportion of 48.23%. The 95%

confidence interval for the proportion for student right-brain preference was 32.65% to

67.32%. Since the proportion of John’s right-brain teaching strategies was 28.28%, the

confidence interval did not contain the population proportion of 28.28%. Lastly, the 95%

confidence interval for the proportion for student whole-brain preference was 0.00% to

19.47%. Since the proportion of John’s whole-brain teaching strategies was 23.49%, the

confidence interval did not contain the population proportion of 23.49%. In conclusion,

there were no alignments between John’s teaching strategies and student hemispheric

preferences.

95% Confidence Interval Processing

Student Hemispheric Preference

Teaching Strategy

Lower

Upper

Left Brain 3.13% 48.23% 0.00% 9.15%

Right Brain 50.00% 28.28% 32.65% 67.32%

Whole Brain 9.38% 23.49% 0.00% 19.47%

Table 4.17: Confidence intervals for proportion tests for teaching strategies and student left-brain, right-brain, and whole-brain preferences in John’s class.


Betty’s teaching strategies and student left-brain, right-brain, and whole-brain preferences.

According to Table 4.18, Betty employed left-brain teaching strategies 58.71% of the


115



0.00% to 8.88%. Since the proportion of Betty’s left-brain teaching strategies was

58.71%, the confidence interval did not contain the population proportion of 58.71%. The

95% confidence interval for the proportion for student right-brain preference was 9.62%

to 38.86%. Since the proportion of Betty’s right-brain teaching strategies was 13.65%, the

confidence interval contained the population proportion of 13.65%. Lastly, the 95%


35.16%. Since the proportion of Betty’s whole-brain teaching strategies was 27.64%, the

confidence interval contained the population proportion of 27.64%. In conclusion, there

was an alignment between Betty’s right-brain teaching strategies and student right-brain

preference. In addition, there was an alignment between Betty’s whole-brain teaching

strategies and student whole-brain preference.



Teaching Strategy

Lower

Upper

Left Brain 3.03% 58.71% 0.00% 8.88%

Right Brain 24.24% 13.65% 9.62% 38.86%

Whole Brain 21.21% 27.64% 7.26% 35.16%

Table 4.18: Confidence intervals for proportion tests for teaching strategies and student left-brain, right-brain, and whole-brain preferences in Betty’s class.

116


Peter’s teaching strategies and student left-brain, right-brain, and whole-brain preferences.

According to Table 4.19, Peter employed left-brain teaching strategies 33.48% of the




3.10% to 29.08%. Since the proportion of Peter’s left-brain teaching strategies was



to 62.68%. Since the proportion of Peter’s right-brain teaching strategies was 62.40%, the

confidence interval contained the population proportion of 62.40%. Lastly, the 95%


24.70%. Since the proportion of Peter’s whole-brain teaching strategies was 4.12%, the


there was an alignment between Peter’s right-brain teaching strategies and student right

hemispheric preference.

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Teaching Strategy

Lower

Upper

Left Brain 16.13% 33.48% 3.10% 29.08%

Right Brain 45.16% 62.40% 27.64% 62.68%

Whole Brain 12.90% 4.12% 11.00% 24.70%

Table 4.19: Confidence intervals for proportion tests for teaching strategies and student left-brain, right-brain, and whole-brain preferences in Peter's class.


Lucy’s teaching strategies and student left-brain, right-brain, and whole-brain preferences.

According to Table 4.20, Lucy employed left-brain teaching strategies 74.68% of the




0.99% to 23.26%. Since the proportion of Lucy’s left-brain teaching strategies was



to 68.57%. Since the proportion of Lucy’s right-brain teaching strategies was 5.48%, the

confidence interval did not contain the population proportion of 5.48%. Lastly, the 95%


8.88%. Since the proportion of Lucy’s whole-brain teaching strategies was 19.84%, the

118


there were no alignments between Lucy’s teaching strategies and student hemispheric

preferences.



Teaching Strategy

Lower

Upper

Left Brain 12.12% 74.68% 0.99% 23.26%

Right Brain 51.52% 5.48% 34.48% 68.57%

Whole Brain 3.03% 19.84% 0.00% 8.88%

Table 4.20: Confidence intervals for proportion tests for teaching strategies and student left-brain, right-brain, and whole-brain preferences in Lucy’s class.

Summary

In this chapter, results of data analyses using coding and categorizing data,

descriptive statistics, Pearson Product Moment Correlations, MANOVA, ANOVA, and

Confidence Intervals for Proportion tests were presented. Table 4.21 provides a summary

of data sources, data analysis techniques, and results of data analyses for the main study.

119

Part 1: Teacher Data Analyses

Sources: Human Information Processing Survey, Teacher Classroom Observations

Analyses: Coding and Categorizing Data, Descriptive Statistics

Variables Results

Teacher Hemispheric Preference & Teaching Strategies —

Teacher Hemispheric Preference & Teacher Gender —

Teaching Strategies & Teacher Gender Males > Right-Brain Teaching Strategies

Females > Left-Brain Teaching Strategies

Part 2.1 : Student Data Analyses

Sources: Style of Learning and Thinking, PMA Spatial Relations Test, Chinese Language Exams, Science Exams, Student Attitudes Toward Science Class Survey

Analyses: Pearson Product Moment Correlations

Variables Results

Hemispheric Preference & Spatial Ability —

Hemispheric Preference & Spatial Ability (by gender) —

Hemispheric Preference & Spatial Ability (by grade level) —

Whole-Brain Preference & Verbal Ability Weak, Positive r

Whole-Brain Preference & Verbal Ability (by gender) —

Whole-Brain Preference & Science Achievement Weak, Positive r

Whole-Brain Preference & Science Achievement (by gender)

Weak, Positive r

Males

Continued Table 4.21: A summary of data sources, data analyses, and results for the main study.

120

Table 4.21 continued

Whole-Brain Preference & Attitudes Toward Science Class

Weak, Positive r

Whole-Brain Preference & Attitudes Toward Science Class (by gender)

—

Whole-Brain Preference & Attitudes Toward Science Class (by grade level)

Weak, Positive r Grade 4

Part 2.2: Student Data Analyses

Sources: Style of Learning and Thinking, PMA Spatial Relations Test, Chinese Language Exams, Science Exams, Student Attitudes Toward Science Class Survey

Analyses: MANOVA and ANOVA

Variables Results

Left-Brain Preference & Gender —

Right-Brain Preference & Gender —

Whole-Brain Preference & Gender Females > Males

Left-Brain Preference & Grade Level Grade 5 > Grade 4

Right-Brain Preference & Grade Level Grade 5 > Grade 4

Whole-Brain Preference & Grade Level —

Spatial Ability & Gender Males > Females

Spatial Ability & Grade Level —

Verbal Ability & Gender Females > Males

Science Achievement & Gender —

Attitudes Toward Science Class & Gender —

Attitudes Toward Science Class & Grade Level —

Continued

121

Table 4.21 continued Part 2.3 : Student Data Analyses

Sources: Style of Learning and Thinking

Analyses: Descriptive Statistics

Student Hemispheric Preference & Gender ▪ No Hemispheric Preference

Males > Females

▪ Left Hemisphere

Females > Males

▪ Right Hemisphere

Males > Females

Student Hemispheric Preference & Gender & Grade Level ▪ Left Hemisphere: Females

Grade 5 > Grade 4

▪ Right Hemisphere: Males

Grade 5 > Grade 4

Part 3.1: Teacher and Student Data Analyses

Sources: Human Information Processing Survey, Style of Learning and Thinking

Analyses: Descriptive Statistics

Variables Results

Teacher Hemispheric Preference & Student Hemispheric Preference

—

Teacher Hemispheric Preference & Student Hemispheric Preference (by gender)

—

Teacher Hemispheric Preference & Student Hemispheric Preference (by grade level)

—

Continued

122

Table 4.21 continued

Part 3.2: Teacher and Student Data Analyses

Sources: Teacher Classroom Observations, Style of Learning and Thinking

Analyses: Confidence Intervals for Proportion Tests

Variables Results

Teaching Strategies & Student Hemispheric Preference Partially Related for 2 Teachers

123

CHAPTER 5

CONCLUSIONS AND DISCUSSION

This chapter consists of five sections. The first section of the chapter presents a

summary of the main study. The second section of this chapter discusses the overall

findings of the main study in terms of the research questions and comparisons with other

research studies in the literature as described in chapter 2. A list of the major conclusions

is found in the third section of this chapter. The fourth section of this chapter provides the

implications for teacher education programs and curriculum developers. The last section

of this chapter provides suggestions for future research in science education.

Summary of the Main Study

Many educators believe that most contemporary schools are dominated by a

left-brain curriculum, and generally, instructional strategies and teaching materials stress

a linear, sequential mode of reasoning (Chapman, 1998; Chudzinski, 1988; Cooke &

Haipt, 1986; Grady, 1984; Lewallen, 1985; Marxer, 1988; Rubenzer, 1982; Turner, 1999;

Vitale, 1982). Rubenzer pointed out that there is a need to close the gap between

left-brain teaching strategies and right-brain learning styles because the left-brain

educational system has handicapped many children who prefer right-brain teaching

strategies. Research has shown that teaching strategies and student hemispheric

preferences do affect learning (Brennan, 1984; Dunn et al., 1990; Fountain & Fillmer,

124

1987; Gadzella, 1995; Jarsonbeck, 1984; Van Giesen et al., 1987). Ellis (2001) and Van

Giesen et al. claimed that research on hemisphericity and learning is inconclusive. Thus,

exploring the relationships among teacher variables (hemispheric preference and science

teaching strategies) and student variables (gender, grade level, hemispheric preference,

spatial ability, verbal ability, science achievement, and attitudes toward science class)

may offer new insight into ways to help children learn science.




science class using teacher and student data from multiple instruments. The second


data and match these teaching strategies to teacher and student hemispheric preferences.

In order to answer the following research questions, quantitative and qualitative analyses

involving 4 elementary school science teachers and 133 elementary school students were

conducted.

1. What is the relationship between science teacher hemispheric preference and

science teaching strategies? Is there a gender difference in the relationship?

2. What is the relationship between student hemispheric preference and spatial

ability? Is there a gender and/or grade level difference in the relationship?

3. What is the relationship between student hemispheric preference and verbal

ability? Is there a gender difference in the relationship?

4. What is the relationship between student hemispheric preference and science

achievement? Is there a gender difference in the relationship?

125

5. What is the relationship between student hemispheric preference and attitudes

toward science class? Is there a gender and/or grade level difference in the

relationship?

6. What is the relationship between teacher and student hemispheric preference? Is

there a gender and/or grade level difference in the relationship?

7. Are there any differences in student hemispheric preference by gender and grade

level?

8. Are there any differences in student spatial ability by gender and grade level?

9. Are there any differences in student verbal ability by gender?

10. Are there any differences in student science achievement by gender?

11. Are there any differences in student attitudes toward science class by gender and

grade level?

12. What is the relationship between science teaching strategies and student

hemispheric preference?

Four elementary school science teachers and 133 fourth- and fifth-grade students, 65

fourth graders from two classes and 68 fifth graders from two classes, were purposively

selected for the main study. The four classes were taught by four science teachers. All the

teachers and students were from Liang elementary school located in northern Taiwan.

The data collection process took approximately 2 months. Teacher data sources included

the Teacher Demographics Questionnaire, Human Information Processing Survey, and

classroom observations, whereas student data sources included the Style of Learning and

Thinking, PMA Spatial Relations Test, Chinese language midterm and final exams,

Science midterm and final exams, and Student Attitudes Toward Science Class Survey.

126

Teacher data were collected, organized, and analyzed using descriptive statistics. Teacher

classroom observations were coded and categorized to identify left-, right-, and

whole-brain science teaching strategies. For each teacher, the percentage of instructional

time devoted to each hemispheric category was computed. Student data were collected,

organized, and analyzed using four procedures: descriptive statistics, Pearson Product

Moment Correlations, MANOVA, and ANOVA. In addition, teacher and student data

were collected, organized, and analyzed using confidence intervals for proportion tests. In

the next section, the findings of the study will be discussed in relationship to the research

questions and previous literature.

Discussion of the Research Questions and the Literature

Research Question 1

Research Question 1 is “What is the relationship between science teacher

hemispheric preference and science teaching strategies? Is there a gender difference in

the relationship? ” According to the analysis of teacher responses to the Human

Information Processing Survey and classroom observations, science teacher hemispheric

preference was not related to science teaching strategies. John, Peter, and Lucy

demonstrated an integrated-brain preference—meaning they were using both the left and

right hemispheres simultaneously—but they seemed to prefer left-brain, right-brain, and

left-brain teaching strategies, respectively. No clear relationships between John’s, Peter’s,

and Lucy’s hemispheric preferences and teaching strategies were found. In contrast, Betty

demonstrated a mixed-brain preference—meaning she was using either the left or right

hemisphere depending upon the situation. However, Betty seemed to prefer left-brain

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teaching strategies. Thus, there did not appear to be any relationship between Betty’s

hemispheric preference and her teaching strategies. Based upon the results, it appears that

science teacher hemispheric preference was not related to science teaching strategies.

In addition, teacher hemispheric preference did not appear to be related to gender.

The two male teachers, John and Peter, exhibited an integrated-brain preference, whereas

the two female teachers, Betty and Lucy, exhibited a mixed-brain and an integrated-brain

preference, respectively. There did not appear to be any relationship between teacher

hemispheric preference and gender.

However, gender seemed to be related to overall left-brain teaching strategies and

overall right-brain teaching strategies. In particular, the two male teachers employed

more types of right-brain teaching strategies for a greater percentage of instructional time

compared to the two female teachers. In contrast, the two female teachers employed more

types of left-brain teaching strategies for a greater percentage of instructional time

compared to the two male teachers. Lastly, the findings suggest that gender seemed to be

related to the specific teaching strategy of using experiments. In particular, the two male

teachers preferred to use experiments with student control—a right-brain teaching

strategy—whereas the two female teachers preferred to use experiments with teacher

control—a left-brain teaching strategy. Based upon the results, the male teachers seemed

to prefer right-brain teaching strategies and the female teachers seemed to prefer

left-brain teaching strategies and this gender difference was particularly evident with

regard to student or teacher control of experiments. Thus, gender was related to science

teaching strategies.

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Another factor that may contribute to teaching strategy preference is teaching

experience. For example, the females who tended to use the left-brain teaching strategies

had extensive teaching experience but only 3 years were spent teaching science. Perhaps,

they were less confident in their science teaching ability and therefore tended to use more

left-brain teaching strategies (e.g., lecture, teacher control of experiments, etc.). In

contrast, Peter preferred right-brain teaching strategies. Perhaps, the fact that Peter had 17

years of teaching experience and 16 of these years were teaching science may have

contributed to his confidence in teaching science and his extensive use of right-brain

teaching strategies such as experiments with student control. Although John preferred

left-brain teaching strategies, he employed more types of right-brain teaching strategies

for a greater percentage of instructional time compared to the two female teachers.

Perhaps, the fact that John had 18 years of teaching experience and 10 of these years

were teaching science also may have contributed to his confidence in teaching science

and the use of right-brain teaching strategies such as experiments with student control.

Research Question 2

Research Question 2 is “What is the relationship between student hemispheric

preference and spatial ability? Is there a gender and/or grade level difference in the

relationship?” According to the analysis of student responses to the Style of Learning and

Thinking and the PMA Spatial Relations Test, student right-brain preference was slightly

positively related to spatial ability but did not reach the significance level. According to a

number of researchers (Botkin et al., 1980; Cherry et al., 1989; Richards, 1984; Vitale,

1982), the right hemisphere is specialized for spatial, concrete, and visual processing.

Spatial ability is generally attributed to the right hemisphere but the relationship between

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student right-brain preference and spatial ability was not found in the current study. A

possible explanation that may account for the lack of a relationship between student

right-brain preference and spatial ability is that a right-brain preference may not have

developed in many of the fourth-grade and fifth-grade students in the current study. The

results of the current study indicate that a high percentage of the students in grades 4 and

5 exhibited no hemispheric preference. There was no significant relationship between

student hemispheric preference and spatial ability by gender or grade level.

Research Question 3


preference and verbal ability? Is there a gender difference in the relationship?” According

to the analysis of student responses to the Style of Learning and Thinking and the

Chinese language exams, student hemispheric preference was significantly and positively

related to verbal ability. In particular, students with a stronger whole-brain preference

tended to exhibit better verbal ability, whereas the left-brain and right-brain preferences

showed little relationship with verbal ability. This finding appears to be consistent with

Fountain and Fillmer’s (1987) research. They found that fourth-grade students with an

integrated-brain preference achieved significantly higher on reading, mathematics,

language, and a basic battery of tests compared to fourth-grade students with either

left-brain or right-brain preference. However, this finding is not consistent with the

research of Van Giesen et al. (1987). They found that students with a left-brain preference

achieved better on a reading vocabulary achievement test compared to students with a

right-brain preference or an integrated-brain preference. In addition, they found no

significant difference in the scores on a reading comprehension achievement test for

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students with a left-brain preference, right-brain preference, or an integrated-brain

preference. Moreover, when male students were considered separately from female

students in the current study, there were no significant relationships between left-brain,

right-brain, or whole-brain preference and verbal ability.

Research Question 4


preference and science achievement?” Is there a gender difference in the relationship?

According to the analysis of student responses to the Style of Learning and Thinking and

the Science exams, student hemispheric preference was significantly and positively

related to science achievement. Students with a stronger whole-brain preference tended to

exhibit better science achievement. However, there was little relationship between

left-brain or right-brain preference and science achievement. Moreover, there was a

gender difference in this relationship. Male students exhibited a positive relationship

between whole-brain preference and science achievement. However, there was no

significant relationship between hemispheric preference and science achievement for

female students.

Research Question 5


preference and attitudes toward science class? Is there a gender and/or grade level

difference in the relationship? According to the analysis of student responses to the Style

of Learning and Thinking and Student Attitudes Toward Science Class Survey, student

hemispheric preference was significantly and positively related to student attitudes

toward science class. Students with a stronger whole-brain preference tended to exhibit

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more positive attitudes toward science class. Moreover, there was no gender difference

with regard to this relationship. However, there was a grade level difference with regard

to this relationship. Only the fourth-grade students exhibited a positive relationship

between whole-brain preference and attitudes toward science class.

Research Question 6

Research Question 6 is “What is the relationship between teacher and student

hemispheric preference? Is there a gender and/or grade level difference in the

relationship?” According to the analysis of teacher responses to the Human Information

Processing Survey and student responses to the Style of Learning and Thinking, teacher

hemispheric preferences were not related to student hemispheric preferences. In addition,

the findings indicate that there were no relationships between teacher and student

hemispheric preference for male students or female students. Furthermore, the findings

indicate that there were no relationships between teacher and student hemispheric

preference for fourth graders or fifth graders.

Research Question 7

Research Question 7 is “Are there any differences in student hemispheric preference

by gender and grade level?” According to the analysis of student responses to the Style of

Learning and Thinking, there were significant gender and grade level differences for

student hemispheric preference. Based upon the results, female students tended to exhibit

a stronger whole-brain preference compared to male students. This finding confirms the

research of Fountain and Fillmer (1987) who found that a significantly greater number of

female students compared to male students had an integrated-brain preference. In

addition, the findings of the current study indicate that fifth-grade students tended to

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exhibit a stronger right-brain preference compared to fourth-grade students, and

fifth-grade students tended to exhibit a stronger left-brain preference compared to

fourth-grade students. These findings are not consistent with the research of Fountain and

Fillmer who found no significant difference between fourth- and seventh-grade students

with regard to hemispheric preference.

Evidence indicates that student hemispheric preference was related to gender and

grade level. Overall, a higher percentage of the fourth-grade and fifth-grade students

exhibited a right-brain preference. This may suggest that the right hemisphere develops

earlier in both male and female students. Also, the percentage of fourth-grade male

students with a left-brain preference was similar to that of fourth-grade female students

with a left-brain preference and the percentage of fourth-grade male students with a

right-brain preference was similar to that of fourth-grade female students with a

right-brain preference. However, the percentage of fifth-grade female students with a

left-brain preference was much higher compared to fifth-grade male students with a

left-brain preference. Similarly, the percentage of fifth-grade male students with a

right-brain preference was much higher compared to fifth-grade female students with a

right-brain preference. This may suggest that the left hemisphere develops earlier in

female students and the right hemisphere develops earlier in male students. These

findings appear to be consistent with the conclusions of Levy and Heller (1992) who

suggested that the right hemisphere matures earlier in males and the left hemisphere

matures earlier in females. Moreover, the percentage of fifth-grade male students with a

right-brain preference was much higher compared to fourth-grade male students with a

right-brain preference. Similarly, the percentage of fifth-grade female students with a

133

left-brain preference was much higher compared to fourth-grade female students with a

left-brain preference. This may suggest that fifth-grade male students exhibited a stronger

right-brain preference compared to fourth-grade male students, and fifth-grade female

students exhibited a stronger left-brain preference compared to fourth-grade female

students.

Other evidence related to the lack of hemispheric preference provides insight into

the development of brain lateralization in males and females. For fourth-grade students,

the percentage of female students with no hemispheric preference was much lower

compared to male students with no hemispheric preference. This may suggest that brain

lateralization in female students occurs earlier compared to male students. This finding is

consistent with the suggestion made by Berlin (1978) and James (2007) that female

brains lateralize earlier compared to male brains. However, for the fifth-grade, the

percentage of male students with no hemispheric preference was considerably less

compared to fourth-grade male students with no hemispheric preference and much lower

compared to fifth-grade female students with no hemispheric preference. Consequently, a

reasonable conjecture may be that fifth-grade is a sensitive time for brain lateralization

for male students. This conjecture warrants further investigation with a larger sample of

fourth- and fifth-grade students.

Research Question 8

Research Question 8 is “Are there any differences in student spatial ability by

gender and grade level?” According to the analysis of student responses to the PMA

Spatial Relations Test, there were significant gender differences related to student spatial

ability; however, there were no grade level differences related to spatial ability. In

134

particular, male students tended to perform better related to spatial ability compared to

female students. This finding confirms the conclusions of Maccoby and Jacklin (1974)

and Hyde (1981) who suggested that males had an advantage related to mathematical and

spatial ability.

Moreover, the male advantage related to spatial ability found in the current study is

consistent with the work of Linn and Petersen (1985) who suggested that sex differences

in spatial ability may be detected prior to adolescence for spatial perception and mental

rotation. In contrast, Maccoby and Jacklin (1974) suggested that sex differences in spatial

ability emerge during adolescence. According to Berlin (1978) and Voyer et al. (1995),

the age of emergence of sex differences is related to the test and the aspects of the

construct embedded in the test. For example, a greater male advantage related to the

generic mental rotation task and the PMA Spatial Relations Test appears around

10-years-old. The age of the fourth- to fifth-grade elementary school students in the

current study were 9- to 11-years-old. Thus, the findings in the current study seem be to

consistent with the conclusions of Berlin and Voyer et al. that the emergence of sex

differences in spatial ability depend upon the instruments used to measure spatial ability.

There were no differences between fourth-grade students and fifth-grade students related

to spatial ability in the current study.

Research Question 9

Research Question 9 is “Are there any differences in student verbal ability by

gender?” According to the analysis of student responses to the Chinese language exams,

there were significant gender differences related to student verbal ability. Female students

tended to perform better related to verbal ability.

135

The gender differences in the current study are consistent with the conclusions of

Maccoby and Jacklin (1974) and Hyde (1981) who suggested that females had an

advantage for verbal ability. Similarly, Van Giesen et al. (1987) found that female

students achieved significantly greater scores on both vocabulary and comprehension

tests compared to male students. Moreover, Maccoby and Jacklin suggested that females

had an advantage for verbal ability and that this advantage begins to appear around the

age of 11-years-old. However, in contrast, Hyde and Linn (1988) concluded that there

were no sex differences related to verbal ability at all ages from childhood to adulthood.

Research Question 10

Research Question 10 is “Are there any differences in student science achievement

by gender?” According to the analysis of student responses to the Science exams, there

were no gender differences in student science achievement. This finding is consistent

with the research of Hyde and McKinley (1997) who suggested that any gender

differences in science achievement are small.


Research Question 11 is “Are there any differences in student attitudes toward

science class by gender and grade level?” According to the analysis of student responses

to the Student Attitudes Toward Science Class Survey, there were no gender differences

nor grade level differences for student attitudes toward science class. That is, there were

no differences between male students and female students related to attitudes toward

science class. In addition, there were no differences between fourth-grade students and

fifth-grade students related to attitudes toward science class.

136


Research Question 12 is “What is the relationship between teacher science teaching

strategies and student hemispheric preference?” According to the analysis of classroom

observations and student responses to the Style of Learning and Thinking, Generally,

science teaching strategies were not related to student hemispheric preferences. However,

there was an alignment between Betty’s right-brain teaching strategies and student

right-brain preference as well as Betty’s whole-brain teaching strategies and student

whole-brain preference. In addition, there was an alignment between Peter’s right-brain

teaching strategies and student right-brain preference. Thus, some teachers’ science

teaching strategies were partially related to student hemispheric preferences.

Table 5.1 is provided to summarize the findings for the main study for each of the 12

research questions.

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Question Relationships Results

▪ Science teacher hemispheric preference was not related to science teaching strategies.

▪ Gender was not related to science teacher hemispheric preference.

1

▪ Gender was related to science teaching strategies.

▪ Male teachers employed more types of right-brain teaching strategies for a greater percentage of instructional time.

▪ Female teachers employed more types of left-brain teaching strategies for a greater percentage of instructional time.

▪ Student hemispheric preference was not related to spatial ability.

2

▪ There was no significant relationship between student hemispheric preference and spatial ability by gender or grade level.

▪ Student hemispheric preference was significantly and positively related to verbal ability.

▪ Students with a stronger whole-brain preference tended to exhibit better verbal ability.

3

▪ When male students were considered separately from female students, there were no significant relationships between hemispheric preferences and verbal ability.

Continued

Table 5.1: A summary of the findings for the 12 research questions for the main study.

138

Table 5.1 continued

▪ Student hemispheric preference was significantly and positively related to science achievement.

▪ Students with a stronger whole-brain preference tended to exhibit better science achievement.

4

▪ There was a gender difference in the relationship between student hemispheric preference and science achievement.

▪ Male students exhibited a positive relationship between whole-brain preference and science achievement.

▪ Student hemispheric preference was significantly and positively related to student attitudes toward science class.

▪ Students with a stronger whole-brain preference tended to exhibit more positive attitudes toward science class.

▪ There were no gender differences in the relationships between student hemispheric preferences and attitudes toward science class.

5

▪ There was a grade level difference in the relationship between student hemispheric preference and attitudes toward science class.

▪ Fourth-grade students exhibited a positive relationship between whole-brain preference and attitudes toward science class.

▪ Teacher hemispheric preferences were not related to student hemispheric preferences.

▪ There were no relationships between teacher and student hemispheric preferences for male students or female students.

6

▪ There were no relationships between teacher and student hemispheric preferences for fourth graders or fifth graders.

Continued

139

Table 5.1 continued

▪ There was a gender difference for student hemispheric preference.

▪ Female students tended to exhibit a stronger whole-brain preference compared to male students.

▪ There were grade level differences for student hemispheric preferences.

▪ Fifth-grade students tended to exhibit a stronger right-brain preference compared to fourth-grade students.

▪ Fifth-grade students tended to exhibit a stronger left-brain preference compared to fourth-grade students.

▪ Student hemispheric preferences were related to gender.

▪ Right hemisphere develops earlier in male students.

▪ Left hemisphere develops earlier in female students.

▪ Brain lateralization in female students occurs earlier compared to male students.

7

▪ Student hemispheric preferences were related to gender and grade level

▪ Fifth-grade male students exhibited a stronger right-brain preference compared to fourth-grade male students.

▪ Fifth-grade female students exhibited a stronger left-brain preference compared to fourth-grade female students.

▪ There was a gender difference related to student spatial ability.

▪ Male students tended to perform better related to spatial ability compared to female students.

8

▪ There were no grade level differences related to student spatial ability.

Continued

140

Table 5.1 continued

9 ▪ There were gender differences related to student verbal ability.

▪ Female students tended to perform better related to verbal ability compared to male students.

10 ▪ There were no gender differences in student science achievement.

▪ There were no gender differences for student attitudes toward science class.

11

▪ There were no grade level differences for student attitudes toward science class.

12 ▪ Generally, science teaching strategies were not related to student hemispheric preferences.

▪ Science teaching strategies for some teachers were partially related to student hemispheric preferences.

▪ There was alignment between science teaching strategies and student hemispheric preference for Betty and Peter.

Figure 5.1 provides an overview of the findings for the main study. Dashed lines

present partially significant results, whereas solid lines present significant results in the

current study.

141

Mixed-brain preference

Spatial ability

Science achievement

Right-brain preference

Attitudes toward science class Verbal

ability

Whole-brain teaching strategies

Right-brain teaching strategies

Male

Hemispheric preference

Left-brain preference

Female

Female

Hemispheric

Right-brain preference

Left-brain preference

Whole-brain preference

Fifth grade

Integrated-brain preference

Teaching strategies

Left-brain teaching strategies

Fourth grade

Male

preference

Student

Teacher

Figure 5.1: An overview of the findings for the main study.

142

Major Conclusions

The major conclusions from the current study are as follows:

1. Science teaching strategies seem to be related to gender differences. The results

suggest that male teachers preferred right-brain teaching strategies, whereas

female teachers preferred left-brain teaching strategies.

2. Verbal ability, science achievement, and attitudes toward science class seem to

be related to student hemispheric preferences. The results suggest that students

with a stronger whole-brain preference tended to exhibit better verbal ability,

better science achievement, and more positive attitudes toward science class.

Specifically, male students with a stronger whole-brain preference tended to

exhibit better science achievement. In addition, fourth-grade students with a

stronger whole-brain preference tended to exhibit more positive attitudes toward

science class.

3. Student hemispheric preferences seem to be related to gender and grade level

differences. The results suggest that the right hemisphere may develop earlier in

male students and the left hemisphere may develop earlier in female students.

For female students, brain lateralization seems to develop earlier and they tended

to exhibit a stronger whole-brain preference compared to male students.

Fifth-grade students tended to exhibit stronger right-brain and left-brain

preferences compared to fourth-grade students, which may suggest greater brain

lateralization in older students. Fifth-grade male students tended to exhibit a

143

stronger right-brain preference compared to fourth-grade male students;

fifth-grade female students tended to exhibit a stronger left-brain preference

compared to fourth-grade female students.

4. Spatial and verbal ability seem to be related to gender differences. The results

suggest that male students tended to perform better related to spatial ability

compared to female students, whereas female students tended to perform better

related to verbal ability compared to male students.

5. Generally, science teaching strategies were not related to student hemispheric

preference. However, science teaching strategies for 2 teachers were partially

related to student hemispheric preference.

Implications

Teacher Education Programs

The results of this study suggest that male brains and female brains do not mature at

the same time in terms of the development of brain lateralization and brain hemispheric

preference. Also, male students tended to perform better in spatial ability, whereas female

students tended to perform better in verbal ability. Programs should be developed to help

current and future science teachers to understand student differences in brain maturation,

brain lateralization, hemispheric preference, gender differences in cognitive ability, and

hemispheric processing differences related to science teaching strategies. Teachers should

be prepared and encouraged to plan and implement differentiating instruction to

accommodate these differences, providing effective instructional opportunities for all. In

particular, science teachers should be encouraged to use experiments with student control

rather than step-by-step experiments with teacher control.

144

The results of this study suggest that students with a stronger whole-brain preference

tended to exhibit better verbal ability, better science achievement, and more positive

attitudes toward science class. Programs should be developed to help current and future

science teachers understand the importance of incorporating left-brain and right-brain

teaching strategies to stimulate both hemispheres and develop the whole brain. Teacher

preparation and enhancement should help teachers to expand their instructional repertoire

and encourage teachers to design and implement complementary science instruction to

activate both hemispheres.

Curriculum Developers

The results of this study suggest that male teachers preferred right-brain teaching

strategies, whereas female teachers preferred left-brain teaching strategies. Many

educators believe that most contemporary schools are dominated by a left-brain

curriculum, and generally, instructional strategies and teaching materials are based on the

linear, sequential, and analytic functions of the left hemisphere of the brain. The left-brain

educational system may handicap many children who prefer right-brain teaching

strategies. Unfortunately, an unbalanced curriculum often mismatches teaching materials

and teaching strategies with the thinking and learning processes of students. Curriculum

designers should combine left-hemisphere and right-hemisphere teaching materials and

teaching strategies and adapt the curriculum to actively employ both hemispheres. A

balanced curriculum is one that stimulates both hemispheres and is beneficial to all

students, not just the left-brain preference students.

145

Suggestions for Future Research

Based upon the results of the current study, some suggestions for future research

have emerged. Future research could include varied samples and different methodologies.

The results suggest that teacher hemispheric preference was not related to science

teaching strategies. In addition, the results suggest that there were gender differences

related to science teaching strategies. An intensive review of literature has pointed out

that most contemporary schools are dominated by a left-brain curriculum, and generally,

instructional strategies and teaching materials stress a linear, sequential mode of

processing. Possible factors affecting the choice of teaching strategies may be a

biological force (e.g., gender), an educational force (e.g., curriculum), or a social force

(e.g., parental expectation). However, this study did not provide insight into the specific

factors that guided teachers in their choice of science teaching strategies. Therefore, an

exploratory case study could be conducted to explore in detail how and why teaching

strategies are employed by male and female teachers in science classrooms. Further

research may use more qualitative research methods to gather data through multiple ways:

surveys, interviews, classroom observations, and classroom documents.

It would be beneficial to include a larger sample of elementary school science

teachers and students and expand the research to include science teachers and students at

the middle and high school levels. Similar instruments and quantitative research method

could be employed to confirm results and expand the results to other populations.

The current study did not examine the effects of a match between teaching strategies

and student hemispheric preference and a mismatch between teaching strategies and

student hemispheric preference on student science achievement and attitudes toward

146

science class. Therefore, an experimental study could be conducted using several sets of

both matched and mismatched instructional strategies to teach science. Each student may

receive teaching strategies both matched and mismatched to his/her hemispheric

preference, permitting the researcher to determine the effects of matching and

mismatching on each student.

The results of the current study suggest that males tended to perform better in spatial

ability compared to females, whereas females tended to perform better in verbal ability

compared to males. Since the results of the current study did not show the age of

emergence of sex differences in spatial ability and verbal ability, a longitudinal study

could be conducted to determine the age of emergence of sex differences related to spatial

ability and verbal ability. The current research was conducted with fourth-grade and

fifth-grade students; future research could be conducted with first-grade students to

sixth-grade students.

The development of brain lateralization and hemispheric preference may be an

interesting topic for future work. The results from the current study suggest that from

fourth grade to fifth grade, the percentage of left-brain preference and right-brain

preference increases regardless of gender. A longitudinal study could be conducted to

provide insight into how brain lateralization and left-brain, right-brain, and whole-brain

preferences develop for male students and female students from first grade to sixth grade.

Future research could include surveys, interviews, classroom observations, and classroom

documents related to teachers and students. In addition, some technological imaging tools,

such as fMRI or PET, could be used to identify teacher and student hemispheric

preferences.

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APPENDIX A

STUDENT ATTITUDES TOWARD SCIENCE CLASS

Student Name: Date:

School: Grade Level: Class:

ATTITUDES TOWARD SCIENCE CLASS

Directions: Each of the statements expresses a feeling that a person has about science class.

Please indicate whether you disagree or agree with the statement by circling one of the

following for each statement:

SD Strongly Disagree U Undecided S Strongly Agree

D Disagree A Agree

1 In science class, doing experiments is boring. SD D U A SA

2 In science class, experiments are difficult. SD D U A SA

3 In science class, listening to lectures from the teacher is interesting. SD D U A SA

4 I usually understand what is taught in my science class. SD D U A SA

5 The experiments I do in science class are useful. SD D U A SA

6 In science class, the teacher’s lectures do not help me to learn science. SD D U A SA

7 The questions in the science workbook are easy for me. SD D U A SA

8 In science class, I learn more science when I work in a group. SD D U A SA

9 I enjoy reading the science textbook. SD D U A SA

10 I do not like answering the questions in my science workbook. SD D U A SA

11 I do not like group work in science class. SD D U A SA

12 The material in the science textbook is hard for me. SD D U A SA

13 I would enjoy school more if there were no science class. SD D U A SA

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14 I like when the teacher teaches our science class outside. SD D U A SA

15 I am afraid to answer questions in science class. SD D U A SA

16 During science class, I like to read science posters. SD D U A SA

17 My science class is interesting. SD D U A SA

18 Science class is hard for me. SD D U A SA

19 In science class, watching a science film on TV is boring. SD D U A SA

20 Science class is a waste of time. SD D U A SA

21 Science class provides me with knowledge to use in my daily life. SD D U A SA

22 I usually get good scores in science class. SD D U A SA

23 In science class, science posters do not help me to learn science. SD D U A SA

24 I like to do experiments in science class. SD D U A SA

25 I don’t like science class. SD D U A SA

26 The material in the science textbook helps me to learn science. SD D U A SA

27 The questions in the science workbook do not help me to learn science. SD D U A SA

28 In science class, watching a science film on TV helps me to learn science. SD D U A SA

29 I do not like field trips in my science class. SD D U A SA

30 In my science class, field trips do not help me to learn science. SD D U A SA

31 It is easy for me to understand the teacher’s lectures in science class. SD D U A SA

32 I look forward to science class. SD D U A SA

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APPENDIX B

TEACHER DEMOGRAPHICS QUESTIONNAIRE

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TEACHER DEMOGRAPHICS QUESTIONNAIRE

Name:

Class:

Gender: □ Male □ Female

Age:

Type of Degree: □ Bachelor’s degree □ Master’s degree □ Ph.D.

Major:

Current teaching:

Grade(s):

Subject(s):

Number of years science teaching:

Number of years teaching:

brain hemispheric preferences of fourth- and fifth-grade

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