STUDENTS’ LEARNING STYLES AND PREFERENCES IN LEARNING SELECTED STATISTICAL TOPICS BASED ON INTERVIEW AND MULTIDIMENSIONAL SCALING TECHNIQUE
Abstract
Students very often perceive statistics as a difficult subject to learn and have generally shown very little interest in it much to the dismay of educationists and instructors alike. This study investigates the problems students face when learning certain statistical topics by firstly relating these learning difficulties with the effort put in when understanding the topics. This is followed by categorizing students’ learning styles and preferences based on Kolb’s Learning Model. Phase 1 explores these levels of learning difficulty, while Phase 2, the case study of 22 students, further investigates the students’ learning styles. The analysis is based on a content matrix of students’ learning of statistical topics using Kolb’s Learning Model, Qualitative Data Analysis (QDA) and Multidimensional Scaling. The study results indicate that learning difficulties can be overcome by employing different learning styles. The majority of the students fall into the ‘Converging’ category as they prefer to do practice exercises and study on their own. A small number of students fall into the ‘Accommodating’ and ‘Diverging’ group, as they prefer to study in small groups and consult lecturers when the need arises. Finally, an even smaller number comes under the ‘Assimilating’ group as they prefer to read textbooks and look up for notes from other sources from the internet. Other factors such as prior knowledge in statistics, ability to work with statistics and personal effort are also found to support students’ interest in learning statistics.
Keywords: Learning Difficulties, Learning Styles, Kolb’s Learning Model, Qualitative Data Analysis (QDA) and Multidimensional Scaling (MDS).
Keywords
Learning Difficulties Learning Styles Kolb‟s Learning Model Qualitative Data Analysis (QDA)
References
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- Chapman, A. (2006). Diagrams of Kolb’s Learning Styles. In Kolb learning styles. Retrieved from http://www.businessballs.com/kolblearningstyles.htm
- Christou, N. and Dinov, I. D. (2010). A Study of Students’ Learning Style, Discipline Attitudes and Knowledge Acquisition in Technology-Enhanced Probability and Statistics Education. Merlot Journal of Online Learning and Teaching, 6(3), 546-572.
- Creswell, J. W., Klassen, A. C., Clark, V. L. P. and Smith, K. C. (2010). Best Practices for Mixed Methods Research in the Health Sciences.
- Ding, C. S. (2006). Multidimensional scaling modelling approach to latent profile analysis in psychological research. International Journal of Psychology, 41(3), 226-238.
- Garfield, J. (1995). How Students Learn Statistics. International Statistical Review, 63(1), 25-34.
- Garfield, J., & Ben-Zvi, D. (2004). Research on statistical literacy, reasoning, and thinking: issues, challenges, and implications. In D. Ben-Zvi & J.Garfield (Eds.),The Challenge of Developing Statistical Literacy, Reasoning, and Thinking (pp. 397-409). Dordrecht, The Netherlands: Kluwer Academic Publishers (Springer).
- Hair, J., Black, B. Babin, B., Anderson, R. and Tatham, R. (2010). Multivariate Data Analysis (7th edition). Upper Saddle River, NJ: Prentice-Hall.
- Johnson, R. A., & Wichern, D. W. (2002). Applied multivariate statistical analysis. Upper Saddle River, NJ: Prentice Hall.
- Kolb, D.A. (1984). Experiential learning: Experience as the source of learning and development. Englewood Cliffs: Prentice-Hall Inc.
- Kruskal, J. B., & Wish, M. (1978). Multidimensional scaling. Beverly Hills: Sage.
- Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook. Sage.
- Ministry of Educations. (2006). Integrated Curriculum for Secondary Schools: Syllabus Additional Mathematics. Kuala Lumpur, Curriculum Development Centre.
- Nooriafshar, M. (2003). Factors contributing to making the learning of statistics an enjoyable experience. International Journal for Mathematics Teaching and Learning, (May 14), 1-12.
- Quinn, G. P., & Keough, M. J. (2002). Experimental design and data analysis for biologists. Cambridge University Press.
- Reid, G. (2005). Learning Style and Inclusion. London, Paul Chapman Publishing.
- Robbin, S. (1998) Organization Behavior. NJ: Prentice Hall.
- Romanelli, F., Bird, E. and Ryan, M. (2009). Learning Styles: A Review of Theory, Application, and Best Practices.
- Schau, C., Millar, M., & Petocz, P. (2012). Research On Attitudes Towards Statistics, Statistics Education Research Journal, 11(2), 2-5.
- Seiler, D. (2011). Age and learning style in the adult learner. J. Hum. Res. Adult Learn, 7(2), 133-138.
- Tanner, K. & Allen, D. (2004) Approaches to biology teaching and learning: learning styles and the problem of instructional selection–engaging all students in science courses, Cell Biology Education, 3, pp.97–201.
- Tsai, C., & Kuo, P., (2008). Cram School Students’ Conceptions of Learning and Learning Science in Taiwan. International Journal of Science Education, 30 (3), 353-375.
- Zamalia, M., and Rosli, A. R. (2009). ” Visualizing patterns of interview conversations regarding students’ learning difficulties in statistical concepts via QMD Scaling techniques. Wseas Transactions on Information Science and Applications.
- Zamalia, M. and Nur Hasmaniza, O. (2010). Statistical competency and attitude towards learning elementary statistics: A case of SMK Bandar Baru Sg Buloh. In Proceedings of the Regional Conference on Statistical Sciences (pp. 335-348).
Abstract
References
- Borg, I., Groenen, P. J., & Mair, P. (2012). Applied multidimensional scaling. Springer.
- Chapman, A. (2006). Diagrams of Kolb’s Learning Styles. In Kolb learning styles. Retrieved from http://www.businessballs.com/kolblearningstyles.htm
- Christou, N. and Dinov, I. D. (2010). A Study of Students’ Learning Style, Discipline Attitudes and Knowledge Acquisition in Technology-Enhanced Probability and Statistics Education. Merlot Journal of Online Learning and Teaching, 6(3), 546-572.
- Creswell, J. W., Klassen, A. C., Clark, V. L. P. and Smith, K. C. (2010). Best Practices for Mixed Methods Research in the Health Sciences.
- Ding, C. S. (2006). Multidimensional scaling modelling approach to latent profile analysis in psychological research. International Journal of Psychology, 41(3), 226-238.
- Garfield, J. (1995). How Students Learn Statistics. International Statistical Review, 63(1), 25-34.
- Garfield, J., & Ben-Zvi, D. (2004). Research on statistical literacy, reasoning, and thinking: issues, challenges, and implications. In D. Ben-Zvi & J.Garfield (Eds.),The Challenge of Developing Statistical Literacy, Reasoning, and Thinking (pp. 397-409). Dordrecht, The Netherlands: Kluwer Academic Publishers (Springer).
- Hair, J., Black, B. Babin, B., Anderson, R. and Tatham, R. (2010). Multivariate Data Analysis (7th edition). Upper Saddle River, NJ: Prentice-Hall.
- Johnson, R. A., & Wichern, D. W. (2002). Applied multivariate statistical analysis. Upper Saddle River, NJ: Prentice Hall.
- Kolb, D.A. (1984). Experiential learning: Experience as the source of learning and development. Englewood Cliffs: Prentice-Hall Inc.
- Kruskal, J. B., & Wish, M. (1978). Multidimensional scaling. Beverly Hills: Sage.
- Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook. Sage.
- Ministry of Educations. (2006). Integrated Curriculum for Secondary Schools: Syllabus Additional Mathematics. Kuala Lumpur, Curriculum Development Centre.
- Nooriafshar, M. (2003). Factors contributing to making the learning of statistics an enjoyable experience. International Journal for Mathematics Teaching and Learning, (May 14), 1-12.
- Quinn, G. P., & Keough, M. J. (2002). Experimental design and data analysis for biologists. Cambridge University Press.
- Reid, G. (2005). Learning Style and Inclusion. London, Paul Chapman Publishing.
- Robbin, S. (1998) Organization Behavior. NJ: Prentice Hall.
- Romanelli, F., Bird, E. and Ryan, M. (2009). Learning Styles: A Review of Theory, Application, and Best Practices.
- Schau, C., Millar, M., & Petocz, P. (2012). Research On Attitudes Towards Statistics, Statistics Education Research Journal, 11(2), 2-5.
- Seiler, D. (2011). Age and learning style in the adult learner. J. Hum. Res. Adult Learn, 7(2), 133-138.
- Tanner, K. & Allen, D. (2004) Approaches to biology teaching and learning: learning styles and the problem of instructional selection–engaging all students in science courses, Cell Biology Education, 3, pp.97–201.
- Tsai, C., & Kuo, P., (2008). Cram School Students’ Conceptions of Learning and Learning Science in Taiwan. International Journal of Science Education, 30 (3), 353-375.
- Zamalia, M., and Rosli, A. R. (2009). ” Visualizing patterns of interview conversations regarding students’ learning difficulties in statistical concepts via QMD Scaling techniques. Wseas Transactions on Information Science and Applications.
- Zamalia, M. and Nur Hasmaniza, O. (2010). Statistical competency and attitude towards learning elementary statistics: A case of SMK Bandar Baru Sg Buloh. In Proceedings of the Regional Conference on Statistical Sciences (pp. 335-348).
Details
| Journal Section | Articles |
|---|---|
| Authors | |
| Publication Date | April 22, 2016 |
| Submission Date | April 17, 2016 |
| Published in Issue | Year 2016Volume: 2 Issue: 4 |
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