DATA ANALYSIS AND INTERPRETATION

RESEARCH METHODOLOGY

3.1 Research design

            The study was descriptive (both qualitative and quantitative) in nature. A research questions was developed as a research instrument for the collection of data. 3.2 Population

            The research group of the study was composed of 39 from the Department of Computer Education and Instructional Technology. A pre and post-test was designed consists of 30 multiple choices questions to define effectiveness of the learning environments. The alpha reliability coefficient of the test was found as .54, validity of the test was found as .53.

3.3 Sample and Sampling Technique

            The sampling technique used was the random sampling method. The sample size was only 39 from the Department of Computer Education and Instructional Technology including 30 items concerning the behaviors to be gained in three different learning environments and Kolb’s Learning Style Inventory are used to collect data.

3.4 Instrumentation

              In this study pre-post-tests including 30 items concerning the behaviors to be gained in three different learning environments and Kolb’s Learning Style Inventory are used to collect data.

3.5 Validity and Reliability of Instrument

Validation and authentication of the research instrument is imperative to obtain exact and precise results. Therefore it is very important to remove the weakness, Ambiguities and misconceptions of the research instrument. The alpha reliability coefficient of the test was found as .54, validity of the test was found as .53.

3.6 Data Collection

              The researchers distributed questionnaires among the participants of the research study and data was collected.

3.7 Data Analysis

              The data collected in this study were analysed through repeated measures of one way ANOVA test.

CHAPTER 4

DATA ANALYSIS AND INTERPRETATION

4.1 Descriptive Statistics

            Analysis of variance (ANOVA) is a statistical technique that is used to check if the means of two or more groups are significantly different from each other. ANOVA checks the impact of one or more factors by comparing the means of different samples.

            Three different learning environments are designed within the scope of the study. The characteristics of learning styles and the details concerning the data collection process are as follows:

  • Text-based learning environment: Mayer’s (2001) principles of multimedia instructional design are taken into consideration to prepare the text to be used in this environment. The text including directions and questions was distributed among students; and the students were asked to progress at their own pace.
  • Narration-based learning environment.
  • Computer-mediated (narration + music + text + static picture) learning environment.

            The results of the study are presented in the order of aforementioned research questions:

  • What is the effect of learning styles on success in a text-based learning environment?

Table 4.1: Pre-test- post-test achievement score means and ANOVA results of learners who have different learning styles in a text-based learning environment

 Pre-testPost-test
NsdNsd
Assimilator194.161.8195.471.7
Converger204.251.5205.551.5
Source of the Variance Within SubjectsSum Squares(df) SdMean SquaresFp
Achievement score (pre-posttest)33,34133,3422,78.00
Learning style Achievement score,0011.001,001.98
Error54,15371,46  

            Table 4.1 shows the pretest-posttest achievement scores of assimilator and Converger learners in a text-based learning environment. From table 4.1 it is identified that, of 39 students in the research group who filled in Kolb’s Learning Style Inventory, 19 students have an assimilator learning style and 20 students have a Converger learning style.

            The pretest and posttest score means of students having assimilator and Converger learning styles differentiate in favor of posttest. The achievement of learners who have different learning styles in text-based learning environment does not show a statistically significant change (p = .98). The absence of a significant difference among the scores of students having different learning styles though the achievement increases may be due to the fact that students having assimilator and convergent learning styles have common ability to organize and use the information they acquire from the text.

            Rasmussen and Davidson-Shivers (1998) state that Converger and assimilator individuals are successful in similar learning environments. Bostrom, Olfman and Sein (1990) found out in their study that assimilator and Converger students were more successful in comprehension test compared to students having the other learning styles. Wu, Dale and Bethel (1998) point out that Converger and assimilator students have comparable success. Furthermore, students’ opportunity in text-based learning environment to study at their own pace, make use of their own studying strategies (highlighting, note-taking, etc.), re-read the points they have missed and be involved in the process actively with the help of questions and directions about the text may also contribute to this result.

  • What is the effect of learning styles on success in a narration-based learning environment?

Table 4.2 shows the pre-test-post-test achievement scores of assimilator and Converger learners in a narration-based learning environment.

Table 4.2: Pre-test-post-test achievement score means and ANOVA results of    learners who have different learning styles in a narration-based learning environment

 Pre-testPost-test
NsdNSd
Assimilator194.421.35195.421.57
Converger204.151.09205.451.23
Source of the Variance Within SubjectsSum Squares  (df) sdMean SquaresFP
Achievement score (pre-posttest)25.77  125.77  23.78.00
Learning style Achievement score.441.44.41.53
Error40.1371.08  

            From table 4.2 the pre-test and post-test score means of students having assimilator and Converger learning styles differentiate in favor of post-test. The achievement of learners who have different learning styles in narration-based learning environment does not show a statistically significant change (p = .53). The absence of a significant difference among the scores of students having different learning styles though the achievement increases may be due to the fact that students having assimilator and convergent learning styles have the opportunity in narration-based learning environment to make use of their own studying strategies (note-taking, etc.) and ask the points they have not understood and due to the style of communication between the course instructor and students through the methods and techniques used in the course. Currie (1995) indicates that assimilator and Converger students are more successful in classroom environments where narration and discussion methods are used. Furthermore, Sein and Robey (1991) observed that assimilator and Converger students had comparable successes in comparable learning environments.

  • What is the effect of learning styles on success in a computer-mediated (narration + music + text + static picture) learning environment?

Table 4.3 shows the pre-test-post-test achievement scores of assimilator and Converger learners in a computer mediated (narration + music + text + static picture) learning environment.

Table 4.3: Pre-test-post-test achievement score means and ANOVA results of learners who have different learning styles in a computer-mediated (narration + music + text + static picture)

 Pre-testPost-test
NsdNSd
Assimilator193.841.26195.261.28
Converger204.101.77205.901.48
Source of the Variance Within SubjectsSum Squares  (df) sdMean SquaresFP
Achievement score (pre-posttest)50.55  150.55  24.64.00
Learning style Achievement score.701.70.34.56
Error75.91372.05  

            From table 4.3, the mean pre-test and post-test scores of students having assimilator and Converger learning styles differentiate in favor of post-test. The achievement of learners who have different learning styles learners in a computer-mediated (narration + music + text + static picture) does not show a statistically significant change (p = .56).

            McWilliams (2001) observed student performances in computer-assisted learning environments and did not find a significant difference in terms of learning styles. In a similar study, Corman (1986) did not find a significant difference between learning styles and performance.

            There are similar findings in the literature. Melara (1996) designed two computerized learning environments and found out that success of students did not vary according to learning styles. Dalkir (1998) also states that learning acquired in computer-assisted learning environments do not vary according to learning styles.

            The lack of a significant difference among the scores of students having different learning styles learners in a computer-mediated (narration + music + text + static picture) may be explained due to the fact that students progress at their own pace and make use of their own studying strategies. Furthermore, students having both learning styles prefer individual study because they have concrete conceptualization and learn through thinking and logical thought analysis, which may have resulted in a differentiation of achievement in this environment.

CHAPTER 5

SUMMARY, FINDINGS, CONCLUSION AND RECOMMENDATIONS

5.1 SUMMARY

            As a result, many students with non-Converger learning styles such as the dominant Assimilator learning style many not be performing as well as Converger students. This state of affair is unacceptable. To cope with this situation, courses and programs in the university should be designed with a consideration of multiple student learning styles. Accommodation to learning styles of students could likely facilitate student learning. In this regard, we believe, contrary to the summary judgment of Pashler et al. (2008), that there is continued merit to investigate the interaction of instructional methods and learning styles with the goal of improving the academic achievement of all students including undergraduate students.

5.2 FINDINGS

            The finding demonstrates that audiovisual materials used in well-designed learning environments do not affect the achievement of students who have different learning styles. This result shows that the time and place of using a certain type of media is more important than the type of media used for the design of learning environments. The major findings of the study are that the dominant learning style was Assimilator and that learning style and gender influenced academic achievement. Learning styles are characteristic ways of perceiving and processing information.

5.3 DISCUSSION

            Werner (2003) studies the effect of self-awareness about learning styles on the selection of learning strategies and the development of comprehension process. Kolb Learning Styles Inventory was used to identify the learning styles of forty-one adult learners who were observed for six months. The subjects tackled strategies and techniques on the basis of time, keeping in the memory, reading, note-taking and decision-making. The data concerning the learning preferences of subjects were collected through the compositions they wrote. The findings of the study show that the learning types (strategies) preferred according to the learning styles of the subjects were not the appropriate strategies. Daniel, Price and Merrifield (2002) studied the effect of learning styles and learning environments on the distance education of students in the department of physiotherapy. They made use of synchronous (interactive TV) and asynchronous (computer-assisted teaching) learning environments as well as Kolb’s Learning Styles Inventory. The data show that neither of these variables affected the success of students.

5.4 CONCLUSION

            Consequently, it seems that learning styles do not have effects on the achievement of students in different learning environments. Studies on various learning environments in the literature also support this finding. Rouke and Lysynchuk (2000) studied the effect of learning styles on success in web-based learning environments. Students whose learning styles were determined by Kolb Learning Styles Inventory were divided into two groups and took place in two different learning environments. The first group studied in a web-based learning environment, and the other group studied in a learning environment composed of printed materials. Then, both groups took an exam. The exam results showed that Diverger students received high scores in both learning environments and assimilator students received low scores in both environments. These results indicate that web-based learning environments affect the success of learners having different learning styles.

5.5 RECOMMENDATIONS

1. Programs should be designed to improve students’ learning styles and learning strategies for all levels to make the teaching and learning process more effective. 

2. It is also recommended that course design should be flexible enough to reach a variety of learning styles.

3. The students should be properly guided and given incentives to select individual learning styles that are appropriate and applicable in their environment for them to achieve their personal academic objective. The students should adopt a suitable learning style that would be beneficial to them.

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