Automatic Detection of Learning Styles for An E-Learning System

Sep. 01, 2009

Source: Computers and Education, Volume 53, Issue 2, September 2009, Pages 355-367.
(Reviewed by the Portal Team)

A desirable characteristic for an e-learning system is to provide the learner the most appropriate information based on his requirements and preferences. This can be achieved by capturing and utilizing the learner model. Learner models can be extracted based on personality factors like learning styles, behavioral factors like user’s browsing history and knowledge factors like user’s prior knowledge.
In this article, the authors address the problem of extracting the learner model based on Felder–Silverman learning style model. The target learners in this problem are the ones studying basic science. Using NBTree classification algorithm in conjunction with Binary Relevance classifier, the learners are classified based on their interests. Then, learners’ learning styles are detected using these classification results. Experimental results are also conducted to evaluate the performance of the proposed automated learner modeling approach.
The results show that the match ratio between the obtained learner’s learning style using the proposed learner model and those obtained by the questionnaires traditionally used for learning style assessment is consistent for most of the dimensions of Felder–Silverman learning style

R.M. Felder and L.K. Silverman. "Learning Styles and Teaching Styles in Engineering Education." Engineering Education, 78 (7), 674-681 (1988).

R.M. Felder, "Reaching the Second Tier: Learning and Teaching Styles in College Science Education," J. Coll. Sci. Teaching, 23(5), 286--290 (1993).

Updated: Jul. 20, 2009