Combining supervised and unsupervised machine learning algorithms to predict the learners’ learning styles

TLDR

Adaptive e‑learning systems rely on student models that capture characteristics such as learning style, which determines how students prefer to learn. This study proposes an automatic learning‑style identification method that leverages learners’ web‑usage behavior and machine‑learning techniques. The method preprocesses e‑learning logs to extract learner sequences, clusters them into 16 Felder‑Silverman style combinations using K‑modes, and then applies a real‑time naive‑Bayes classifier, with performance assessed via a confusion matrix on a real dataset. The results show that the proposed approach achieves excellent accuracy in predicting learners’ styles.

Abstract

The implementation of an efficient adaptive e-learning system requires the construction of an effective student model that represents the student’s characteristics, among those characteristics, there is the learning style that refers to the way in which a student prefers to learn. Knowing learning styles helps adaptive E-learning systems to improve the learning process by providing customized materials to students. In this work, we have proposed an approach to identify the learning style automatically based on the existing learners’ behaviors and using web usage mining techniques and machine learning algorithms. The web usage mining techniques were used to pre-process the log file extracted from the E-learning environment and capture the learners’ sequences. The captured learners’ sequences were given as an input to the K-modes clustering algorithm to group them into 16 learning style combinations based on the Felder and Silverman learning style model. Then the naive Bayes classifier was used to predict the learning style of a student in real time. To perform our approach, we used a real dataset extracted from an e-learning system’s log file, and in order to evaluate the performance of the used classifier, the confusion matrix method was used. The obtained results demonstrate that our approach yields excellent results.