GAT-LS: A Graph Attention Network for Learning Style Detection in Online Learning Environment

Abstract

In the context of the information age, the rapid growth and increasing diversity of learning resources underscore the urgency of personalized learning, while learning style is the most crucial factor to consider in personalized learning as it significantly influences students’ academic achievements and learning experiences. Traditional methods of assessing learning styles, such as completing questionnaires, have many drawbacks, including subjectivity and time costs. Therefore, in recent years, researchers have been exploring automatic methods to identify learning styles by analyzing students’ interactive behaviors. Motivated by these limitations, we propose a learning style detection method using a graph attention network (GAT), named GAT-LS. We originally constructed a bipartite graph between learners and learning materials, utilizing node features to represent the students’ behavior. Subsequently, we employ GAT to obtain hidden vectors for the graph nodes. These hidden vectors encapsulate both the overall graph information and the importance of neighboring nodes. We employ a multi-head attention network to process student nodes and combine a dropout mechanism with a single-layer attention network to process learning material nodes. Finally, we map the obtained hidden node features to the Felder-Silverman learning style model (FSLSM) and use K-means clustering to detect learning styles. The proposed method can be integrated into various types of educational systems or online learning platforms, providing a better educational experience and learning resource recommendations for both teachers and students. Experiments on the real-world dataset, KDD CUP 2015, demonstrated the superiority of our method. Our proposed approach achieved outstanding results with average values of 0.9647 accuracy, 0.9478 precision, 0.9171 recall, and 0.9346 F1 score.