A Hybrid Machine Learning Approach to Predict Learning Styles in Adaptive E-Learning System

The increasing use of E-learning environments by learners makes it indispensable to implement adaptive e-learning systems (AeS). The AeS have to take into account the learners’ learning styles to provide convenient contents and enhance the learning process. Learning styles refer to the preferred way in which an individual learns best. The traditional methods detecting learning styles (using questionnaires) present many limits, as: (1) the time-consuming process of filling in the questionnaire and (2) producing inaccurate results because students aren’t always aware of their own learning preferences. Thus in this paper we have proposed an approach for detecting learning styles automatically, based on Felder and Silverman learning style model (FSLSM) and using machine learning algorithms. The proposed approach is composed of two parts: The first part aims to extract the learners’ sequences from the log file, and then using an unsupervised algorithm (K-means) in order to group them into sixteen clusters according to the FSLSM, and the second part consists in using a supervised algorithm (Naive Bayes) to predict the learning style for a new sequence or a new learner. To perform our approach, we used a real dataset extracted from an e-learning system’s log file. In order to evaluate the performance, we used the confusion matrix technique. The obtained results demonstrate that our approach yields excellent results.