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<p>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 <i>E</i>nhancing <i>S</i>tudent <i>R</i>epresentations in <i>C</i>ognitive <i>Diagnosis</i> (<i>ESR-CD</i>), 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.</p>

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Breaking student-concept sparsity barrier for cognitive diagnosis

  • 期刊论文

  • 发表日期：

    2025年4月3日

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

摘要

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.