Learning style difference between gifted and nongifted sixth and seventh grade students. (The scholarship of teaching and learning).

Abstract

The purpose of this study was to determine whether the independent variables academic status (gifted and nongifted) and learning style preferences separated and combined, influence the academic performance of sixth- and seventh-grade students on the dependent variable of reading and mathematics. The instrument used to test the independent variable was the Learning Style Inventory (LSI) and the instrument used to test the dependent variable was the Texas Assessment of Academic Skills (TAAS).

Introduction

Many teachers are continually frustrated with the so-called "underachieving" mathematics and reading achievement. The teacher recognizes that such students exist in their classroom, but continues to instruct as usual, using the same lesson delivery over and over again. Consequently, students continue to underachieve (George, 1997). George further states that one reason for this problem could lie in the teachers' lack of knowledge about learning styles and how to organize experiences accordingly. Another reason could simply be that the teachers, due to their own preference, are unwilling to vary their instructional style to accommodate student learning style.

Purpose of the Study

This study determined the effect that, academic status (gifted and nongifted) and learning style preferences have on the academic performance of sixth- and seventh-grade students on the reading and mathematics portion of the TAAS.

Research by Parent, Mickler, and Rich (1978) revealed that varied teaching techniques, which met the instructional preferences of as many students as possible, positively affected student attitudes toward learning. Therefore, it is possible that many students could benefit from accommodation of their instructional preferences within the classroom.

George (1997) further stated that not all styles suit all subjects, but many teachers have a narrow repertoire. Teachers are as prone as anyone in any other profession to getting into a rut. He also asserted that there are certain conditions that must hold if an activity is to be classed as teaching. Primarily, the students must be learning. The teacher may be undertaking an activity with the intention that the students should learn, but if they are not learning, it is either because they are "switched off" or because they have already mastered the skill or item or knowledge. Thus, no teaching is strictly involved. Research shows that this is often the case with the gifted and talented students.

Learning Style: Reading and Mathematics Achievement

Some of the more significant studies involving learning style focused on reading and/or mathematics achievement. These studies revealed interesting findings regarding the effects of lighting, physical setting, and instructional scheduling as well as grouping on students' achievement.

In the Krimsky (1982) study, student reading performance was assessed through the scores obtained on the Gates-MacGinitie Test for Reading Speed and Accuracy. A statistical analysis was developed using 2 4-way ANOVAs for (a) the main effects of learning style preference for light and illuminated instructional environments, and (b) the interaction effect of learning style preference and the illuminated instructional environment. The data revealed two significant findings. Scores on both reading speed and accuracy consistently were significantly higher when the illuminated environment was congruent with each student's identified learning style preference for light.

Another study concentrated on the effects of physical setting in student reading performance. Shea (1983) tested all 410 ninth graders in a New York junior high for their preferences for either a formal or an informal instructional environment when concentrating. The students revealing strong design preferences on the Learning Style Inventory (Dunn, Dunn, & Price, 1978) were randomly assigned to either matched or mismatched designs. There were other studies concerned the effect of scheduling with regard to student time preferences on both reading and mathematics performance. First, Virostko (1983) monitored the reading and mathematics achievement of 296 elementary students over a two-year period. Students' schedules were designed to offer one subject at their preferred time of day and the other at their nonpreferred time. During the second year of that study, when each student's schedule was reversed, 98% of the students achieved statistically higher scores in the opposite subject (p <.001).

A study in 1985, Lemmon administered the Iowa Basic Skills Test in the morning and in the afternoon and scheduled 194 elementary school students so that they took their test at their best time of day. Students made significant gains over each of their previous-two-years' scores on the same standardized tests in both reading and mathematics. In another study in North Carolina, Andrews (1990) identified the time preferences of his underachieving elementary population. In one year, his school moved to the 40th percentile; by the second year, the scores were between the 74th and 78th percentile; and by the third year, they had risen to the 83rd percentile.

When we combine the concepts of instructional preferences and underachievement, the research literature appears to be nonexistent (Parent, Mickler, & Rich, 1978). Research which attempts in some way to explore the instructional preferences of nongifted students and compare those preferences to the instructional preferences of gifted students could be valuable in expanding the research concerning learning styles theory. Therefore, a study such as this one could initiate thinking about the instructional preferences of gifted and nongifted students and stimulate discussion concerning differences between gifted and nongifted academic achievement and instructional preference. Two hypotheses were developed. There were:

Hypothesis One. There is no significant difference in student performance on the reading section of the TAAS by academic status, learning styles, ethnicity, gender, and the interaction effect of academic status and learning styles.

Hypothesis Two. There is no significant difference in student performance on the mathematics section of the TAAS by academic status, learning styles, ethnicity, gender, and the interaction effect of academic status and learning styles.

Research Questions

This study investigates the following two research questions.

Research Question One. Is there a significant difference in student performance on the reading section of the TAAS by academic status, learning styles, ethnicity, gender, and the interaction effect of academic status and learning styles?

Research Question Two. Is there a significant difference in student performance on the mathematics section of the TAAS by academic status, learning styles, ethnicity, gender, and the interaction effect of academic status and learning styles?

Operational Definitions of Terms

Following are operational definitions of terms employed in this study:

1. Academic status. Academic status refers to the classification of the student participants in this study -- gifted and nongifted students. Gifted students are identified by the school district, using a minimum of five criteria, as required by the Texas Education Agency (TEA) for participants in the school district's gifted program, Nongifted students are the regular education students, as identified by the school district using TEA criteria.

2. Learning Style Inventory (LSI). The Learning Style Inventory of Dunn, Dunn, and Price (1997), is the data collection instrument used to identify learning preferences. The LSI, surveys individual preferences in each of 22 components. It is used to identify how students prefer to function, learn, concentrate, and perform during educational activities in the 22 components.

3. Academic achievement. Academic achievement is determined by the Texas Assessment of Academic Skills (TAAS). Academic recognition is given for any student scoring at the 95% level or above. A score of 70 means 70% of the items were correct on the subject area being tested.

Instrument

Two instruments were used to collect the data for this study. The Learning Style Inventory (LSI) and the Texas Assessment of Academic Skills (TAAS). All students participating in this study .will also be administered the Learning Style Inventory. These students will have taken the mathematics and reading sections of the Texas Assessment of Academic Skills test (TAAS).

Learning Style Inventory

Specifically, the LSI, developed through content and analysis, surveys individual preferences in each of 22 different areas. It is used as a comprehensive approach to the identification of how students prefer to function, learn, concentrate, and perform during educational activities. Responses to questions concerning each of these areas tend to reveal highly personalized preferences that are said to represent the way in which the individuals prefer to study or concentrate. A listing and explanation of the 22 areas include the following:

 1. Noise level (quiet and sound).
 2. Light (low or bright).
 3. Temperature (cool or warm).
 4. Design (informal or formal).
 5. Unmotivated/motivated.
 6. Not persistent/persistent.
 7. Irresponsible/responsible.
 8. Structure (wants or does not want
    structure).
 9. Learning alone/peer-oriented learner.
10. Authority figures present.
11. Prefers learning in several ways.
12. Auditory preferences.
13. Visual preferences.
14. Tactile preferences.
15. Kinesthetic preferences.
16. Requires intake.
17. Functions best in evening/morning.
18. Functions best in late morning.
19. Functions best in afternoon.
20. Mobility.
21. Parent figure motivated.
22. Teacher motivated.

Academic Achievement Test

The Texas Assessment of Academic Skills (TAAS) is given to students in the third through eighth grades, and again in the tenth grade. It is the primary tool for measuring the success of Texas public school.

Mathematical Section of TAAS

The mathematical section of the TAAS contains 13 objectives designed to match this curriculum: (a) Number Concepts, (b) Algebraic/Mathematics Relationships and Functions, (c) Geometric Properties and Relationships, (e) Measurement Concepts, (f) Probability and Statistics, (g) Use of Addition to Solve Problems, (h) Use of Subtraction to Solve Problems, (i) Use of Multiplication to Solve Problems, (j) Use of Division to Solve problems, (k) Problem Solving Using Estimation, (l) Problem Solving Using Solution Strategies, (m) Problem Solving Using Mathematical Representation, (n) Evaluation of the Reasonableness of a Solution. Reading Section of TAAS contains six objectives designed to match this curriculum: (a) Word Meaning, (b) Supporting Ideas, (c) Summarization, (d) Relationship and Generalization, (e) Inferences and Generalizations, (f) Point of View, Propaganda, Fact and Nonfact.

Data Analysis

The sample population for this study consisted of 405 sixth- and seventh-grade students from a large school district located in southeast Texas. Testing of the hypotheses was accomplished through the application of a 4-way Analysis of Variance and Schheffe' post hoc test. The hypotheses were tested at the .05 level or better.

Sample

For the purposes of this study all sixth- (N = 646) and all seventh-grade (N = 630) students were invited to participate in the study. There was a total of 203 sixth-grade students participating in the study and a total of 202 seventh-grade students participating in the study.

Demographic Data Regarding the Participants in the Study

There were four hundred five (405) sixth- and seventh-grade students who participated in this study. Descriptive data was computed by gender, academic status, ethnicity, and learning style preferences. Regarding the variable gender, 172 or 42.5% of the respondents were male. In comparison, there were 233 or 57.5% female participants. In reference to the variable academic status, 204 of the participants or 50.4% were identified as nongifted. Moreover, 201 or 49.6% of the students were identified as gifted. Sixty nine or 17% of the students expressed their ethnicity as Asian American. Thirty-one or 7.7% indicated their ethnic status as Hispanic American, and 38 or 9.4% of the participants reported they were African American Americans. Finally, 267 or 65.9% of the respondents indicated they were Caucasian Americans. Table 1 illustrates ethnic makeup of the gifted and talented population. See issue's website <http://rapidintellect.com/AEQweb/spri02.htm> Learning Style Preferences Regarding the variable learning style preference, 151 or 37.3% of the respondents were identified as being environmental. Of these respondents, 82 or 54.3% were gifted and 69 or 45.7% were nongifted. Forty-seven or 11.6% of the students' learning style preference were identified as emotionality. Of these respondents, 11 or 23.4% were gifted and 36 or 76.6% were nongifted. Additionally, 141 or 34.8% of the students were identified as having a sociological learning preference. Of these respondents, 78 or 55.3% were gifted and 63 or 44.7% were nongifted. Furthermore, 66 or 16.3% of the participants were identified as having a physical learning style preference. Of these respondents, 24 or 36.3% were gifted and 42 or 63.7% were nongifted.

Results

Two 4-way ANOVAs was used to examine differences by gender, academic status, ethnicity, and learning on TAAS mathematics score. The results indicated that there were significant differences by academic status (F = 251.189, df = 3/374, p < .001), but there were no gender differences (F = 1.956, df = 3/374, p < .05); ethnicity (F = 2.319, df = 3/374, p < .05); and learning style preference (F = .108, df = 3/374, p < .05). Table 2 reports the means and standard deviations by academic status. See issue's website <http://rapidintellect.com/AEQweb/spri02.htm>

Additionally, there was no significant interaction between academic status and learning style preference (F = .350, df = 3/374, p < .05). The main effect differences regarding the variable academic status indicated that gifted students (M = 88.90) scored significantly higher on the mathematics section of the TAAS than their nongifted counterparts (M = 76.67). The results of the 4-way ANOVA examining students' gender, academic status, ethnicity, and learning styles on their TAAS reading scores are as follows. There were significant differences found between the reading scores of the two gender groups (F = 4.113, df = 1/374, p < .05); academic status (F = 247.678, df = 1/374, p < .001); and ethnicity (F = 4.629, df = 3/374, p < .01). However, there were no significant differences in the reading scores with respect to learning style (F = 2.149, df = 3/374, p < .05).

Additionally, there was no significant interactions found between academic status and learning styles preferences (F = 1.556, df = 3/374, p < .05) on reading scores of the TAAS. The mean values that indicated that female students (M = 86.71) had higher reading score than male students (M = 84.80). In addition, gifted students (M = 92.73) scored significantly higher in reading than their nongifted peers (M = 79.17). An analysis of gender, academic status, and ethnicity indicated that Caucasian students (M = 87.52) had higher reading score on the TAAS than African American students (M = 80.24), Hispanic students (M = 80.40), and Asian students (M = 84.99).

Summary of Hypotheses

There were two major statistical (null) hypotheses formulated and tested in this study. In hypotheses one and two, differences by academic status were found to be statistically significant. Gifted students scored significantly higher than nongifted students on reading and mathematics sections of the TAAS. There were significant differences by gender and ethnicity. In reading female students, regardless of academic status, scored significantly higher than males. Caucasian students scores significantly higher than Asian, African American, and Hispanic students on reading TAAS scores.

Discussion of Results

The major purpose of this investigation was to determine whether there were differences by, academic status (gifted and nongifted) and learning style preferences categories (environmental, sociological, perceptual and physical), the academic performance of sixth- and seventh-grade students on the reading and mathematics portion of the TAAS. In addition, this study examined the influence on the students' gender and ethnicity of their reading and mathematics scores on the TAAS. Two 4-way (Analysis of Variance) factorial design was employed to analyze the data for this study. Four hundred and five sixth- and seventh-grade students (201 nongifted and 204 gifted) from a school district in a metropolitan area in southeast Texas participated in this investigation. Two instruments entitled "Learning Style Preferences Inventory" and the "Texas Assessment of Academic Skills" were used to gather the data. The findings of the study are as follows:

1. Gifted sixth- and seventh-grade students out performed their nongifted peers on the mathematics and reading sections of the TAAS.

2. There are no learning style preference differences on the academic achievement of sixth- and seventh-grade students on the reading and mathematics sections of the TAAS.

3. Together, academic status and learning style preferences do not differentially affect the academic performance of sixth- and seventh-grade students on the reading and mathematics sections of the TAAS.

4. Female sixth- and seventh-grade students out-performed male students on the reading sections of the TAAS.

5. Caucasian sixth- and seventh-grade students did better academically on the reading sections of the TAAS than did Asian, Hispanic and African American students.

Significance of the Study

This study provides two significant findings for administrators, teachers, and parents with pertinent information regarding the learning styles of identified gifted and nongifted students. First, gifted students out-performed regular students on both reading and mathematics sections of the TAAS. Second, female students, regardless of academic status out-performed male students in the mathematics and reading sections of the TAAS. An understanding of how identified gifted and nongifted students learn can go a long way in developing strategies to assist in the learning process.

Finally, a study of this nature is useful to educators, who work with identified gifted and nongifted students. An empirical analysis of the learning styles of identified gifted and nongifted students provides data to school districts. This kind of analysis can impact special programs, traditional programs, and academic performance of all students.

References

Alvino, J.J., & Richert, E. S., & McDonnel, R.C. (1982). National report on identification: Assessment and recommendations for comprehensive identification of gifted youth. Washington, DC: Educational Information Resource Center, U.S. Department of Education.

Dunn, R., Dunn, K., & Price, G.E. (1978). Learning style inventory. Lawrence, KS: Price Systems.

Dunn, R., Dunn, K., & Price, G.E. (1997). Learning style inventory. Lawrence, KS: Price Systems.

George, D. (1997). The challenge of the able child. (2nd ed). London: David Fulton.

Krimsky, J. (1982). A comparative analysis of the effects of matching and mismatching students with their learning style preference for the environmental element of light and their subsequent reading speed and accuracy scores (Doctoral dissertation, St. John's University) Dissertation Abstracts International, 43, 66A. Lemmon, P. (1985). A school where learning styles makes a difference. Principal, 64(4), 26-29.

Missouri, R., & Cramp, H. I. (1990) Learning styles of elementary school students. American School Journal 17(1), 14-22. Parent, L. A., Mickler, M.L., & Rich, Y. (1978). Learning Style Inventory: Primary., St. John's University, New York: Learning Styles Network

Shea, T.C. (1983). An investigation of the relationship among preferences for learning style elements of design, selected instructional environments, and reading achievement with ninth-grade students to improve administrative determinations concerning effective educational facilities (Doctoral dissertation, St. John's University). Dissertation Abstracts International, 44, 2004A.

Virostko, J. (1983). An analysis of the relationships among academic achievement in mathematics and reading, assigned instructional schedules, and the learning style time preferences of third-, fourth-, fifth-, and sixth-grade students (Doctoral dissertation, St. John's University. Dissertation Abstracts International, 44, 1683A

William J. Heath, University of Houston, TX Theresa M. Monaco, University of Houston, TX

William, EdD, is a Research Associate and teacher at Fort Bend I.S.D. Theresa, PhD, is a Professor of Curriculum & Instruction, and Director Center for Gifted Education.