Breaking student-concept sparsity barrier for cognitive diagnosis

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.

Graphical abstract

Keywords

cognitive diagnosis / student modeling / educational data mining

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Higher Education Press