MLKT4Rec: Enhancing Exercise Recommendation Through Multitask Learning With Knowledge Tracing

Personalized exercise recommendation is an important task in educational data mining, aiming to recommend exercises that match students’ intentions and abilities. However, existing recommendation methods often ignore the dynamic changes and individual differences in students’ knowledge levels and face serious data sparsity problems. To address these limitations, we employ graph neural networks (GNNs) to learn node representations in exercise recommendation contexts and propose a new knowledge tracing-enhanced multitask exercise recommendation framework, called MLKT4Rec. Unlike previous graph-based approaches that focus on explicitly observed relationships in the data, we use implicit edges to augment the graph structure and incorporate exercise difficulty attributes, relative time intervals, and location coding to enrich the exercise representation. Based on this, we construct a knowledge tracing model to capture students’ knowledge levels and integrate it into the exercise sequential recommendation process for joint multitask training. Extensive experiments on four real datasets validate the effectiveness of the proposed model.