I’ve Got Style…What’s Your Style?

Matching Students With Their Learning Style

Seminar in Applied Theory and Research II

By: Peta-Gaye Grey

Table of Contents

• Introduction

Statement of the Problem Review of Related Literature Statement of the Hypothesis

• Method

Participants (N) Instrument (s) Experimental Design Procedure

• Results

• Discussion

• Implications

• References

• Appendix (ces) Consent Forms Student Surveys Pre-test Posttest Correlation Charts/Tables

Statement of the Problem

• Educators, Practitioners and Researchers agree that all children can learn, but not all children learn in the same way. Even though we believe this, many of us are not embracing the idea or implementing a change.

Theorist

• Howard Gardner

According to multiple intelligence theory, there are nine basic types of intelligence.

• Rita & Kenneth Dunn

Identification of key learning styles of each student and matching instruction and learning activities with each student's styles.

Review of Related Literature (Pros)

• We know teaching to the students’ learning style will improve scores (Dunn & Dunn, 1992; Searson & Dunn, 2001)

• Teachers deliver content in ways that better match students’ strengths. This leads to increase academic performance and improved attitudes towards school (Beglane, 2001; Dunn et al. 2009; )

Review of Related Literature (Cons)

• Ivie (2009) states “most of the supporting research cited by the Dunns comes from their own in-house studies of faculty who finished their doctoral work at St. John’s University. Few of this studies meet the standard of having been published in peer-reviewed journals.

• The application of learning style theory encompasses three pervasive problems: confusion in definitions, weakness in measurement reliability and validity and identification of relevant learner characteristics (Curry, 1990).

Dunn and Dunn Learning Style Model

Statement of the Hypothesis

• HR1: Integrating preferred learning styles over a six week period to 21 kindergarten students at PS X during math times will increase on-task behavior and increase content knowledge.

Method

• Participants (N)-The sample for this action research paper was conducted with twenty-one kindergarten students in Brooklyn, New York. The students are from an intact class from a school with middle-to-high income families. The class of consists of 11 boys and 10 girls. Of all the students 50%- White, 10%- Black and 40%- Bi-Racial.

MethodInstruments

• Consent forms administered to: Principal Parents

• Likert Scale Survey administered to: Students prior to

intervention

• Math Assessment 1 Pretest (TERC math

program) 1 Posttest (created by

the research)

Research Design

Pre-Experiment Design: One Group Pretest- Posttest Design

• Single Group: Single group is Pre-tested (O), exposed to a treatment (X), and Posttested (O).

• Symbolic Design: OX1O

MethodProcedure

• The research was conducted over a four month period from September 2010 to December 2010:

Consent forms were administered to the principal and 21 parents in September 2010.

Pretest was administered to the single group of 21 students in October 2010

Survey was administered to the 21 students in October 2010.

Students were group according to learning preference in November 2010.

Posttest was administered to the 21 students according to the learning preferences.

Threats to Internal and External Validity

History: Attendance, poor weather conditions, assemblies, fire drills, school tours and students misbehavior.

Maturation: May not be valid, however one student is older due to the fact that he repeated pre-school.

Testing: The pre-test and posttest is very similar, familiarity could improve scores.

Instrumentation: Researcher created student surveys based on the Dunn and Dunn Learning Style Inventory which was presented by the teacher.

Selection: Non-random student group.

Mortality: Failure to pay tuition, school/parent decision that the school is not the right fit or relocation for a family.

Selection: Students vary in age and gender.

Generalizable Conditions: Same instructor for multiple subjects allows students to adjust to instructors teaching style.

Pre-Test Treatment: The exposure to pre-test questions are likely to have an impact on post-test scores.

Selection-Treatment Interaction: The teacher and student participants were chosen, rather than selected randomly.

Experimenter Effects: The actions of the researcher through observation, whether conscious or unconscious, may affect the performance and responses of the participants.

Data of Pretest without Learning Preference

Students Scores without Preference

1 100%

2 80%

3 90%

4 80%

5 90%

6 80%

7 30%

8 60%

9 100%

10 40%

11 100%

12 40%

13 70%

14 100%

15 80%

16 30%

17 70%

18 90%

19 90%

20 100%

21 80%

Pre-test Without Learning Preference

0%

20%

40%

60%

80%

100%

120%

Student 1Student 3Student 5Student 7Student 9Student 11Student 13Student 15Student 17Student 19Student 21

Test Scores

Pre-test

Class Average- 76%

Results Student Pretest Posttest

2 80% 90%

3 90% 90%

4 80% 90%

6 80% 80%

7 30% 50%

14 100% 100%

15 80% 90%

16 30% 60%

17 70% 70%

18 90% 90%

19 90% 100%

21 80% 90%

Global LearnersStudent Pretest Posttest

1 100% 90%

5 90% 100%

8 60% 70%

9 100% 100%

10 40% 60%

11 100% 100%

12

13

20

40%

70%

100%

60%

90%

90%

Analytical Learners

Pretest Posttest

Global 75% 83%

Analytical 78% 84%

Averages

Analytical Learners Pre-test/Posttest

0%

20%

40%

60%

80%

100%

120%

Student 1Student 5Student 8Student 9Student 10Student 11Student 12Student 13Student 20

Test Scores

Pre-test

PosttestGlobal Learners Pre-test/Posttest

0%

20%

40%

60%

80%

100%

120%

Student 2Student 3Student 4Student 6Student 7Student 14Student 15Student 16Student 17Student 18Student 19Student 21

Test Scores

Pre-test

Posttest

Correlation

• Correlation of students on-task behavior in mathematics when paired with their learning preferences.

• Correlation between students you prefer to work on the floor and test scores. Question 7- I like to lay on the

floor when I am learning new things.

• There was a negative correlation between laying on the floor and test scores -0.404

• Correlation between students who like to sit at a desk and test scores. Question 8- I like to sit at a desk

when I am learning new things.• There was a weak correlation

between sitting at a table and test scores 0.125

Global Learners/Laying on the Floor

0

20

40

60

80

100

120

-1 0 1 2 3

Students Response to Survey Question

Posttest Scores

y

Analytical Learners/ Sitting at a Desk

0

20

40

60

80

100

120

0 0.5 1 1.5 2 2.5

Students Responses to Survey Question

Posttest Scores

y

Implications

• Further research needs to be done• More participants are needed• A longer study needs to be done• A different population needs to be

studied

Discussions

• The findings supports previous research

• Test scores improved• Correlation was weak and negative• Class average increased• Researchers uncertainty