Modified Literature Based Approach to Identify Learning Styles in Adaptive E-Learning
To effectively understand the adaptation approaches in content delivery on E-learning, learner’s learning styles need to be identified first. There are two main approaches that detect the learning styles: Questionnaire based and Literature based. The main challenge of Adaptive E-learning is to capture the learner’s learning styles while using E-learning portal and provide adaptive user interface which includes adaptive contents and recommendations in learning environment to improve the efficiency and adaptability of E-learning. To address this challenge the literature based approach requires to be modified according to learner’s usage of e-learning portal and should generate learner’s profile according to standardized learning style model. The study focuses on engineering students and the learning style model considered is Felder-Silverman Learning Style Model. The paper presents the analysis of log data which is captured in log files and database. Analysis of obtained results show that the captured usage data is useful to identify the learning styles of the learners and the types of contents is proved important factor in literature based approach.
KeywordsAdaptive E-learning Felder-Silverman Learning Style Model Web Logs Behavioral Model
Unable to display preview. Download preview PDF.
- 1.Popescu, E., Trigano, P., Badica, C.: Relations between learning style and learner behaviour in as educational hypermedia system: An exploratory study. In: Proceedings of 8th IEEE International Conference on Advanced Learning Technologies, ICALT 2008 (2008)Google Scholar
- 2.Grapf, S., Kishnuk, L.T.C.: Identifying learning styles in learning management system by using indications from student’s behaviour. In: Proceedings of the 8th IEEE International Conference on Advanced Learning Technologies, ICALT 2008 (2008)Google Scholar
- 3.Dung, P.Q. and Florea, A.M.: A literature-based method to automatically detect learning styles in learning management systems. In: Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics (2012)Google Scholar
- 4.Salehi, M., Kmalabadi, I.N.: A hybrid attribute-based recommender system for e-learning material recommendation. In: International Conference on Future Computer Supported Education (2012)Google Scholar
- 5.Popescu, E., Badina, C., Moraret, L.: Accomodating learning styles in an adaptive educational system. Informatica 34 (2010)Google Scholar
- 6.Liu, H., Keselj, V.: Combined mining of web server logs and web contents for classifying user navigation patterns and predicting user’s future requests. In: Data and Knowledge Engineering, Elsevier (2007)Google Scholar
- 7.Simi, V. and Vojinovi, O. and Milentijevia, I.: E-learning: Let’s look around. In: Scientific Publications of the State University of Novi Pazar Series A: Applied Mathematics, Informatics and Mechanics (2011)Google Scholar
- 8.Felder, R.M., Silverman, L.K.: Learning and teaching styles in engineering education 78(7), 674–681 (2011)Google Scholar
- 9.Felder, R.M., Spurlin, J.: Applications, reliability and validity of the index of learning styles. International Journal of Engineering 21, 103–112 (2005)Google Scholar
- 10.Son, W.M., Kwek, S.W., Liu, F.: Implicit detection of learning styles. In: Proceedings of the 9th International CDIO Conference (2013)Google Scholar
- 11.Abraham, G., Balasubramanian, V., Saravanaguru, R.K.: Adaptive e-learning environment using learning style recognition. International Journal of Evaluation and Research in Education, IJERE (2013)Google Scholar