LEARNING STYLE DETECTION USING K-MEANS CLUSTERING
Abstract
Learning theorist established the fact that learners are characterized according to their distinct learning styles. Investigating learners' learning style is important in the educational system in order to provide adaptivity and improve learning experience. Past researches have proposed various approaches to detect learning styles. Among unsupervised learning methods, the K-means clustering has emerged as a widely used method to predict patterns in data because of its simplicity. This paper evaluates the performance of K-means clustering in automatically detecting learners’ learning style in an online learning environment. The experimental results prove differences in learning thus characterizing learners based on learning style.
References
References
Battaglia, M. (2008). Convenience Sampling. In P. J. Lavrakas, Encyclopedia of Survey Research Methods (pp. 149-150). Thousand Oaks, CA: Sage Publications.
Cha, H. J., Kim, Y. S., Park, S. H., Yoon, T. B., Jung, Y. M., & Lee, J. H. (2006). Learning Style Diagnosis Based on User Interface Behavior for the Customization of Learning Interfaces in an Intelligent Tutoring System. In M. Ikeda, K. D. Ashley, & T. W. Chan, Intelligent Tutoring Systems, Springer, Lecture Notes in Computer Science (pp. 513-524).
Chang, Y., Kao, W., Chu, C., & Chiu, C. (2009). A learning style classification mechanism for e-learning . Computers & Education, 53(2), 273–285.
Felder, R., & Silverman, L. (1988). Learning and teaching styles in engineering education. Eng. Educ, 78, 674–681.
GarcÃa, P., Amandi, A., Schiaffino, S., & Campo, M. (2007). Evaluating Bayesian Networks’ Precision for Detecting Students’ Learning Styles. Computers & Education, 49(3), 794-808.
Graf, S. (2007). Adaptivity in Learning Management Systems Focussing on Learning Styles. PhD thesis.Vienna University of Tecnology.
Graf, S., Kinshuk, & Liu, T. (2008). Identifying learning styles in learning management systems by using indications from students’ behaviour. In: ICALT, IEEE. 8th IEEE International Conference on Advanced Learning Technologies, (pp. 482-486).
Hao, Z., Tao, H., Liu, S., Hao, Y., Jia, L., Huali, Y., & Yu, X. (2020). A learning style classification approach based on deep belief network for large-scale online education. J Cloud Comp, 9(26).
Jain, A., & Dubes, R. (1988). Algorithms for Clustering Data. New Jersey: Prentice Hall.
Learn Moodle. (2016, August). Retrieved from Moodle: http://research.moodle.net/158/
Li, L., & Abdul Rahman, S. (2018a, July 25). Students' learning style detection using tree augmented naive Bayes. doi:10.1098/rsos.172108
Li, L., & Abdul Rahman, S. (2018b). Supplementary material from "Students' learning style detection using tree augmented naive Bayes". The Royal Society. Available: https://doi.org/10.6084/m9.figshare.c.4156019.v2.
Morissette, L., & Chartier, S. (2013). The k-means clustering technique: General considerations and implementation in Mathematica. Tutorials in Quantitative Methods for Psychology . 9, pp. 15-24. doi:10.20982/tqmp.09.1.p015
Petchboonmee, P., Phonak, D., & Tiantong, M. (2015, September). A Comparative data mining technique for David Kolb's experiential learning style classification. International Journal of Information and Education Technology, 5(9).
Prabhani Pitigala Liyanage, M., Lasith Gunawardena, K. S., & Hirakawa, M. (2013). International Conference on Advances in ICT for Emerging Regions (ICTer). 261-265.
Copyright (c) 2020 FUDMA JOURNAL OF SCIENCES
This work is licensed under a Creative Commons Attribution 4.0 International License.
FUDMA Journal of Sciences
