Identifying Learning Styles in MOOCs Environment through Machine Learning Predictive Modeling

With the advancements in technology and the growing demand for online education, Virtual Learning Environments (VLEs) have experienced rapid development in recent years. This demand was especially evident during the COVID-19 pandemic. The incorporation of new technologies in VLEs provides new opportunities to better understand the behaviors of learners. Identifying the learning styles (LSs) of learners can greatly impact the learning process and enhance the effectiveness and satisfaction of both learners and teachers in Massive Online Open Courses (MOOCs). In this study, an approach to automatically recognize the LSs of learners based on the amount of data interactions generated in MOOC is presented. The Felder Silverman Learning Style Model (FSLSM) is used as the basis for prediction, as it is one of the most widely used models in VLEs. The data collected from the learning activities in the XuetanX platform from 08-2016 to 08-2017 was prepared using the Get and Structure Data (GSD) algorithm and then clustered using the K-means algorithm based on the Felder & Silverman Model. The learner's degree performance in each learning style was analyzed using the confusion matrix, learning curves, and performance metrics (accuracy, precision, recall, and macro/micro-averaged precision) for the neural network (NN), decision tree (DT), Random Forests (RF), Naive Bayes (NB), and K-nearest neighbors (KNN) algorithms. The evaluation results showed that the DT achieved a high accuracy rate of over 99% in predicting learners' learning styles.