Predicting and Understanding Student Learning Performance Using Multi-Source Sparse Attention Convolutional Neural Networks

Predicting and understanding student learning performance has been a long-standing task in learning science, which can benefit personalized teaching and learning. This study shows that the progress towards this task can be accelerated by using learning record data to feed a deep learning model that considers the intrinsic course association and the structured features. We proposed a multi-source sparse attention convolutional neural network (MsaCNN) to predict the course grades in a general formulation. MsaCNN adopts multi-scale convolution kernels on student grade records to capture structured features, a global attention strategy to discover the relationship between courses, and multiple input-heads to integrate multi-source features. All achieved features are then poured into a softmax classifier towards an end-to-end supervised deep learning model. Conducting insights into higher education on real-world university datasets, the results show that MsaCNN achieves better performance than traditional methods and delivers an interpretation of student performance by virtue of the resulted course relationships. Inspired by this interpretation, we created an association map for all mentioned courses, followed by evaluating the map with a questionnaire survey. This study provides computer-aided system tools and discovers the course-space map from the educational data, potentially facilitating the personalized learning progress.