Breaking student-concept sparsity barrier for cognitive diagnosis

Pengyang Shao, Kun Zhang, Chen Gao, Lei Chen, Miaomiao Cai, Le Wu, Yong Li, Meng Wang

Abstract: Educational Cognitive Diagnosis (CD) aims to provide students’ mastery levels on different concepts. One common observation is that students often conduct many exercises but engage with a small subset of concepts, leading to a sparsity barrier. Current CD models mostly adopt mastery levels on all concepts as student modeling, overlooking the sparsity barrier. If a student does not interact with all concepts, we can not ensure that each dimension of mastery levels on concepts can be well-trained. In this paper, we propose a novel Enhancing Student Representations in Cognitive Diagnosis (ESR-CD), which combines application abilities and comprehension degrees for mastery levels on concepts. To model application ability, we propose a sparsity-based mask module that solely depends on the dense student-concept entries. Simultaneously, to further enhance comprehension degrees, we propose two layers: a matrix factorization layer and a relation refinement layer. Extensive experiments on two real-world datasets demonstrate the effectiveness of ESR-CD.

External IDs:dblp:journals/fcsc/ShaoZGCCWLW25