Learning Style Classification Based on Student's Behavior in Moodle Learning Management System

  • Manal Abdulaziz Abdullah Faculty of Computing and Information Technology FCIT, King Abdulaziz University KAU


In learning field, each student has his own learning style that affects his way of get, process, understand and percept information. Determining the learning style of students enhances the performance of learning process. Two methods are commonly used to acquire student's learning style: static by questionnaire and dynamic by tracing student's navigation on e-learning environment.
In this paper, a new approach to classify students dynamically depending on their learning style was proposed. This approach was experimented on 35 students for Data Structure online course created using Moodle. By extracting students' behavior, data from Moodle log, the learning style for each student was identified according to Felder and Silverman model. Also, learning style based on the behavior have been compared with a quiz results conducted at the end of the course.
Receiver operating characteristic (ROC) curve have been used to evaluate the quality of classification results comparing with quiz results. Good results with average accuracy of 76% are achieved. Students' data have been divided into four training and testing sets with different splitting ratio. Different testing accuracy values are obtained for the different ratios using each dimension of Felder-Silverman learning style model (FSLSM).

Author Biography

Manal Abdulaziz Abdullah, Faculty of Computing and Information Technology FCIT, King Abdulaziz University KAU
Dr Manal Abdullah is currently an assisst professor at Computer Science Department, Faculty of Computing and Information technology FCIT, King Abdulaziz University, KAU. Her research interests include pattern recognition, Machine Learning, Data classification, Big Data Analysis, and Network Administration.


]. Bainbridge, William Sims, and Mihail C. Roco, Converging Technologies for Improving Human Performance. 2003.

]. Felder, R., Brent, R, Understanding students differences. Journal of Engineering Education, 2005, 94(1), 57- 72

]. Nor Bahiah Hj Ahmad, Siti Mariyam Shamsuddin, and Ajith Abraham, Granular Mining of Student’s Learning Behavior in Learning Management System Using Rough Set Technique. Computational Intelligence for Tech Enhanced Learning, 2010, SCI 273, pp. 99–124.

]. Petchboonmee, Phanthipha, DuangkamolPhonak, and MonchaiTiantong. A Comparative Data Mining Technique for David Kolb's Experiential Learning Style Classification. International Journal of Information and Education Technology 5.9, 2015.

]. E-LEARNING, Textbook, website. 2013, [online]. available : http://www.talentlms.com/elearning/elearning-101- jan2014-v1.1.pdf

]. eLearnity Ltd, Shilling House, AmpneyCrucis, Cirencester, e-Learning the future of learning. 2000. [online]. Available. http://www.elearnity.com/A555F3/research/research.nsf/91b8b7752e3051cf80256ac6005d5c4 e/b9a8fda8bfbd6f1780256ae7006032ae/$FILE/whitepaper%20v1.0a.pdf

]. Felder, R., Silverman, L., Learning And Teaching Styles In Engineering Education. Engineering Education 1988, 78(7), 674–681

]. Felder, R.M., Solomon, B.A. Index of Learning Styles Questionnaire. 2009, http://www.engr.ncsu.edu/learningstyles/ilsweb.html.

]. Kerdprasop, N., Muenrat, N., Kerdprasop, K., Decision Rule Induction in a Learning Content Management

. Moodle. 2009, http://moodle.org/ Last accessed 22/12/2014

. Chris Banman, Supervised and Unsupervised Land Use Classification. Advanced Image Processing Class at Emporia State University, 2002, [online]. available : http://academic.emporia.edu/aberjame/student/banman5/perry3.html

. Amatriain, Xavier, et al. Data mining methods for recommender systems. Recommender Systems Handbook, Springer US, 2011. 39-71.

. Idrisi, Clark Labs I., IDRISI Focus Paper on Classification Tree Analysis. 2008. [Online]. Available: http://www.clarklabs.org/applications/upload/classification-tree-analysis-idrisi-focus-paper.pdf

. Popular Decision Tree: Classification and Regression Trees (C&RT). Textbook, 2014. [Online]. Available: http://www.statsoft.com/Textbook/Classification-and-Regression-Trees

. S. Graf, Kinshuk, and T.-C. Liu, Identifying Learning Styles in Learning Management Systems by Using Indications from Students’ Behaviour. IEEE International Conference on Advanced Learning Technologies (ICALT 2008), 2008, pp. 482-486.

. Mohamed Koutheaïr KHRIBI, Mohamed JEMNI, Olfa NASRAOUI , Sabine GRAF, and Kinshuk, Toward a fully automatic learnermodeling based on web usage mining with respect to educational preferences and learning styles. IEEE 13th International Conference on Advanced Learning Technologies, 2013.

. Pham Quang Dung, Adina Magda Florea. An approach for detecting learning styles in learning management systems based on learners’ behaviours. in International Conference on Education and Management Innovation, 2012.

. Heba A. Fasihuddin, Geoff D. Skinner, Rukshan I. Athauda, Personalizing Open Learning Environments through the adaptation to Learning Styles.

. Lakshmi Sreenivasa Reddy. D, M.Rao Batchanaboyina, D.V.V.S.Phanikumar, Dr B.Ravindrababu, Learning Styles Vs Suitable Courses. IEEE International Conference in MOOC, Innovation and Technology in Education (MITE), 2013.

. Tom Fawcett, An introduction to ROC analysis. Pattern Recognition Letters 27, 2006, p. 861–874.

How to Cite
Abdullah, M. A. (2015). Learning Style Classification Based on Student’s Behavior in Moodle Learning Management System. Transactions on Machine Learning and Artificial Intelligence, 3(1), 28. https://doi.org/10.14738/tmlai.31.868