ASCD: An Adaptive Framework for Long-Tailed Cognitive Diagnosis

Cognitive Diagnosis Modeling is a fundamental task in intelligent education, intending to assess students’ mastery levels on knowledge concepts through interactions. Previous methodologies prioritized enhancing average diagnostic accuracy. However, they often neglected the Long-Tailed issue in interactions. To address this issue, we propose an Adaptive Self-Supervised Graph Learning for Cognitive Diagnosis (ASCD) framework that leverages relatively balanced sparse views to forcing the graph network to focus on long-tailed nodes, aiming to tackle the long-tailed problem in graph-based cognitive diagnosis. Additionally, we employ self-supervised manners to mitigate the impact of dropped information on other nodes. Our approach leverages the adaptive graph confusion method to create sparse views of the original student-exercise interaction graph. In these sparse views, both long-tailed and head students carry similar weights, pushing the graph network to allocate attention impartially across all students. We integrate two different graph confusion techniques into adaptive graph confusion to accommodate varying degrees of data sparsity: edge dropout and feature masking. ASCD can serve as a plug-and-play module integrated into any graph-based cognitive diagnosis model, enhancing its performance regarding long-tailed scenarios. Extensive experiments on real-world datasets show the effectiveness of our approach, especially on the students with much sparser interaction records.