Discovering student learning styles in engineering mathematic at Politeknik Merlimau using neural network techniques

Discovering student learning styles in engineering mathematic at Politeknik Merlimau using neural network techniques

The identification of students’ learning style in learning mathematics is important for educators in choosing an effective teaching approach/methodology. Students from different field of studies to complete were asked the Index Learning Styles questionnaire to identify the student’s learning style o...

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Main Author: Mat Esa, Asmarizan
Format: Thesis
Language:English
English
Published: 2015
Subjects:
QA Mathematics
QA76 Computer software
Online Access:http://eprints.utem.edu.my/id/eprint/15877/1/DISCOVERING%20STUDENT%20LEARNING%20STYLES%20IN%20ENGINEERING%20MATHEMATICS%20AT%20POLITEKNIK%20MERLIMAU%20USING%20NEURAL%20NETWORK%20TECHNIQUES%20%2824%20pgs%29.pdf
http://eprints.utem.edu.my/id/eprint/15877/2/Discovering%20student%20learning%20styles%20in%20engineering%20mathematic%20at%20Politeknik%20Merlimau%20using%20neural%20network%20techniques.pdf
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record_format uketd_dc
institution Universiti Teknikal Malaysia Melaka
collection UTeM Repository
language English
English
topic QA Mathematics
QA76 Computer software
spellingShingle QA Mathematics
QA76 Computer software
Mat Esa, Asmarizan
Discovering student learning styles in engineering mathematic at Politeknik Merlimau using neural network techniques
description The identification of students’ learning style in learning mathematics is important for educators in choosing an effective teaching approach/methodology. Students from different field of studies to complete were asked the Index Learning Styles questionnaire to identify the student’s learning style of learning DBM1013 - Engineering Mathematics. This technique is used to consider their learning styles and how to improve students’ performance in learning DBM1013 – Engineering, Mathematics, the questionnaires were evaluated to identify the best learning styles used by students in learning Engineering Mathematics. However, the problem with this method is the time spent by students in answering questions and the accuracy of the results obtained. If questionnaires are too long, students tend to choose both answers arbitrarily instead of thinking about the result of the student’s learning style observed through analysis. This research identified the classification of students learning styles based on the Felder Silverman Learning dimension. Four learning dimension has been classified by using backpropagation neural networks. The algorithm has been run on training, validation and testing, training process data and 20 neurons. The result shows that the neural network is able to identify the students' learning styles according to the dimension with satisfying result.
format Thesis
qualification_name Master of Philosophy (M.Phil.)
qualification_level Master's degree
author Mat Esa, Asmarizan
author_facet Mat Esa, Asmarizan
author_sort Mat Esa, Asmarizan
title Discovering student learning styles in engineering mathematic at Politeknik Merlimau using neural network techniques
title_short Discovering student learning styles in engineering mathematic at Politeknik Merlimau using neural network techniques
title_full Discovering student learning styles in engineering mathematic at Politeknik Merlimau using neural network techniques
title_fullStr Discovering student learning styles in engineering mathematic at Politeknik Merlimau using neural network techniques
title_full_unstemmed Discovering student learning styles in engineering mathematic at Politeknik Merlimau using neural network techniques
title_sort discovering student learning styles in engineering mathematic at politeknik merlimau using neural network techniques
granting_institution Universiti Teknikal Malaysia Melaka
granting_department Faculty of Information and Communication Technology
publishDate 2015
url http://eprints.utem.edu.my/id/eprint/15877/1/DISCOVERING%20STUDENT%20LEARNING%20STYLES%20IN%20ENGINEERING%20MATHEMATICS%20AT%20POLITEKNIK%20MERLIMAU%20USING%20NEURAL%20NETWORK%20TECHNIQUES%20%2824%20pgs%29.pdf
http://eprints.utem.edu.my/id/eprint/15877/2/Discovering%20student%20learning%20styles%20in%20engineering%20mathematic%20at%20Politeknik%20Merlimau%20using%20neural%20network%20techniques.pdf
_version_ 1776103096048418816
spelling my-utem-ep.158772022-10-06T09:23:13Z Discovering student learning styles in engineering mathematic at Politeknik Merlimau using neural network techniques 2015 Mat Esa, Asmarizan QA Mathematics QA76 Computer software The identification of students’ learning style in learning mathematics is important for educators in choosing an effective teaching approach/methodology. Students from different field of studies to complete were asked the Index Learning Styles questionnaire to identify the student’s learning style of learning DBM1013 - Engineering Mathematics. This technique is used to consider their learning styles and how to improve students’ performance in learning DBM1013 – Engineering, Mathematics, the questionnaires were evaluated to identify the best learning styles used by students in learning Engineering Mathematics. However, the problem with this method is the time spent by students in answering questions and the accuracy of the results obtained. If questionnaires are too long, students tend to choose both answers arbitrarily instead of thinking about the result of the student’s learning style observed through analysis. This research identified the classification of students learning styles based on the Felder Silverman Learning dimension. Four learning dimension has been classified by using backpropagation neural networks. The algorithm has been run on training, validation and testing, training process data and 20 neurons. The result shows that the neural network is able to identify the students' learning styles according to the dimension with satisfying result. 2015 Thesis http://eprints.utem.edu.my/id/eprint/15877/ http://eprints.utem.edu.my/id/eprint/15877/1/DISCOVERING%20STUDENT%20LEARNING%20STYLES%20IN%20ENGINEERING%20MATHEMATICS%20AT%20POLITEKNIK%20MERLIMAU%20USING%20NEURAL%20NETWORK%20TECHNIQUES%20%2824%20pgs%29.pdf text en public http://eprints.utem.edu.my/id/eprint/15877/2/Discovering%20student%20learning%20styles%20in%20engineering%20mathematic%20at%20Politeknik%20Merlimau%20using%20neural%20network%20techniques.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=96223 mphil masters Universiti Teknikal Malaysia Melaka Faculty of Information and Communication Technology 1. Bahiah, N. et al., 2006. Modeling Learning Styles Based on the Student Behavior in Hypermedia Learning System Using Neural Network . 2. Carmona, C., Castillo, G. & Millán, E., 2008. Designing a Dynamic Bayesian Network for Modeling Students’ Learning Styles. 2008 Eighth IEEE International Conference on Advanced Learning Technologies, pp.346–350. Available at: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4561705 [Accessed October 28, 2014]. 3. Dobson, J.L., 2010. A comparison between learning style preferences and sex, status, and course performance. Advances in physiology education, 34(4), pp.197–204. Available at: http://www.ncbi.nlm.nih.gov/pubmed/21098387 [Accessed November 12, 2014]. 4. Dunn, K.E. & Mulvenon, S.W., 2009. A Critical Review of Research on Formative Assessment : The Limited Scientific Evidence of the Impact of Formative Assessment in Education. , 14(7). 5. Fleming, N.D., 1995. I ’ m different ; not dumb Modes of presentation ( V . A . R . K .) in the tertiary classroom. , pp.1–7. 6. Guskey, T.R., 2005. Formative Classroom Assessment and Benjamin S . Bloom : Theory , Research , and Implications. , (April). 7. Mohamad, F.S., Mumtazimah, M. & Fadzli, S.A., 2013. Integrating an e-learning model using IRT, Felder-Silverman and Neural Network approach. 2013 Second International Conference on Informatics & Applications (ICIA), pp.207–211. Available at: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6650257. 8. Moody, D., Associate, V. & Models, T., 2002. Empirical research methods. , pp.1–4. 9. Mustafa, H.M. & Badran, S.M., 2011. On assessment of brain function adaptability in Open Learning systems using Neural Networks modeling (cognitive styles approach). 2011 International Conference on Communications and Information Technology (ICCIT), pp.218–226. Available at: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5762686. 10. Nguyen, L., 2013. A New Approach for Modeling and Discovering Learning Styles byusing Hidden Markov Model. , 13(4). 11. Notes, T., 2010. How to select appropriate statistical test ? , 1(2), pp.1–3. 12. Role, T., 2009. Chapter 1. 13. Saglam, O. & Milanova, V., 2013. How do qualitative and quantitative research differ ? 14. Shin-ike, K. & Iima, H., 2009. A method for Development of collaborative learning by using a neural network and a genetic algorithm. 2009 International Symposium on Autonomous Decentralized Systems, pp.1–6. Available at: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5207353. 15. Flemming, N. (2011a). Aural Study Strategies. Retrieved from http://www.vark-learn.com/english/page.asp?p-aural 16. Flemming, N. (2011b). Kinesthetic Study Strategies. Retrieved from http://www.vark-learn.com/english/page.asp?p-kinesthetic 17. Flemming, N. (2011c). Visual Study Strategies. Retrieved from http://www.vark-learn.com/english/page.asp?p-visual 18. Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development (Vol. 1). Englewood Cliffs, NJ: Prentice-Hall. 19. Dunn R (2003). Rita Dunn, Kenneth Dunn. The Dunn and Dunn Learning Style Model and Its Theoretical Cornerstone. St John's University, New York, 2003 20. Felder, R.M., & Silverman, L.K. (1988). Learning and teaching styles in engineering education [Electronic Version]. Engr. Education, 78(7), 674-681 21. Pask (1976). G. Pask. Styles and Strategies of Learning. british Journal of Educational Psychology, 1976 22. Vermunt (1996). J.D. Vermunt. Meta-cognitive, Cognitive and Affective Aspects of Learning Styles aned Startegies: a Phenomenon graphic Analysis, Higher Education, 1996 23. Witkin Moore, Goodenough, Cox (1997). H.A. Witkin, C.A. Moore, D. R. Goodenough, P. W. Cox. Field-dependent and Field-independent Cognitive Styles and Their Educational Implications. Review of Educational Research, 1977 24. http://www.wiley.com/college/msci/callister320137/ils/ 25. http://research-advisors.com/ 26. Applications, I., 2008. Using R for Data Analysis and Graphics Introduction , Code and Commentary. Australian Journal of Zoology, 22(January), pp.1–99. Available at: http://www.mendeley.com/research/using-r-for-data-analysis-and-graphics-introduction-code-and-commentary-2/.

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