A robust classification to predict learning styles in adaptive E-learning systems

Abstract

In E-Learning Systems, the automatic detection of the learners’ learning styles provides a concrete way for instructors to personalize the learning to be made available to learners. The classification techniques are the most used techniques to automatically detect the learning styles by processing data coming from learner interactions with the system. By using these classification techniques, considerable results are obtained by several approaches with various learning style models. The performance of these approaches varies from one approach to another depending on the data used. However, these approaches have some limitations related to robustness. Indeed, a common feature of these approaches is that they consider only a single course in their trials. Whereas to construct a robust classifier, a representative set of data is crucial. Subsequently, a robust approach for automatically detecting learning styles must take into account the wealth of information to be processed. Therefore, it must consider that the data have to be gathered from learners’ learning behaviors that correspond to several courses. In this paper, we propose a robust classifier which can be able to identify the learning style of the learner in E Learning System. The learning behavior of the learner is captured on varied contexts typically on varied courses appertaining to a specific subject matter. The web usage mining is used for capturing the learners’ behaviors and then, the learning styles are mapped to Felder-Silverman Learning Style Model (FSLSM) categories. Fuzzy C Means (FCM) algorithm is used to cluster the captured learning behavioral data into FSLSM categories. The experiment results show the performance of our approach although the captured data are gathered from the learners’ learning behaviors corresponding to several courses.