Classification of Learning Styles in Multimedia Learning Using Eye-Tracking and Machine Learning

The existence of a multimedia learning system still presents the same material for every student. Educational theory suggests that learning content ideally should be adaptive by considering each student’s learning style. To make learning more optimal, it is necessary to detect learning styles. Several learning detection approaches have been implemented. Conventional methods such as student assessment tests and interviews tend to be more subjective. An objective method of eye-tracking has been researched but limited as a validation tool for differentiating learning styles. To overcome the above mentioned problems, this study proposes a new approach using machine learning and eye-tracking techniques. The experiment and analysis involved 68 students. There were 23 male participants and 45 female participants. In the experiment, participants were assigned to interact with learning content and their eye movements were recorded using an eye-tracker sensor. From the experimental results using three classification algorithms — SVM, Naïve Bayes, and Logistic Regression — and using SVM-RFE as a feature selection method, the best model was achieved by Naïve Bayes algorithm through three features selected from SVM-RFE method. The model yielded 71% of accuracy, 60% of sensitivity, and 75% of specificity. This empirical study provides an opportunity for machine learning and eye-tracking approaches to automatically classify learning styles. These results can be used as guidelines for developing an adaptive multimedia learning system by considering students’ learning styles.