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Breaking student-concept sparsity barrier for cognitive diagnosis

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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.

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Acknowledgements

This work has been supported in part by grants from the National Science and Technology Major Project (2021ZD0111802), the New Cornerstone Science Foundation through the XPLORER PRIZE, the National Natural Science Foundation of China (Grant Nos. 72188101, 62376086), and the Joint Funds of the National Natural Science Foundation of China (U22A2094).

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Authors and Affiliations

  1. Key Laboratory of Knowledge Engineering with Big Data, Hefei University of Technology, Hefei, 230601, China

    Pengyang Shao, Kun Zhang, Miaomiao Cai, Le Wu & Meng Wang

  2. Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China

    Chen Gao, Lei Chen & Yong Li

Authors
  1. Pengyang Shao
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Correspondence to Kun Zhang or Chen Gao.

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Additional information

Pengyang SHAO is currently pursuing a PhD degree at Hefei University of Technology (HFUT), China. He received his Bachelor’s degree in 2019 from the same university. His research interest lies on data mining, and large language models. He has published several papers in leading conferences and journals, including KDD, WWW, ACM TOIS and SCIS.

Kun ZHANG received a PhD degree in computer science and technology from the University of Science and Technology of China, China in 2019. He is currently a faculty member at the Hefei University of Technology (HFUT), China. His research interests include Natural Language Understanding and Recommender Systems. He has published several papers in referred journals and conferences, such as IEEE TSMC:S, IEEE TKDE, ACM TKDD, AAAI, KDD, ACL, and ICDM. He received the KDD 2018 Best Student Paper Award.

Chen GAO is now a Faculty Member (Research-track AP) of BNRist, Tsinghua University, China. He obtained his PhD degree (advised by Prof. Yong Li and Prof. Depeng Jin) and bachelor’s degree from the Department of Electronic Engineering, Tsinghua University in 2021 and 2016, respectively. His research primarily focuses on data mining (recommender system and spatio-temporal data mining), large language model, embodied agent, etc., with over 60 papers in top-tier venues (50+ CCF-A), attracting over 4,000 citations.

Lei CHEN is currently a postdoctoral researcher at Tsinghua University, China. He received his PhD from Hefei University of Technology, China in 2022. His research primarily focuses on fairness-aware recommender systems and large language model applications. He has published several papers in leading conferences and journals, including WWW, SIGIR, IEEE TKDE, and ACM TOIS.

Miaomiao CAI is a PhD candidate at Hefei University of Technology, China, where she also earned her Bachelor’s degree in Engineering in 2020. Her research primarily focuses on debiasing techniques for recommender systems. She has published several papers in leading conferences and journals, including ACM KDD, ACM MM, and ACM TIST.

Le WU is currently a professor at Hefei University of Technology (HFUT), China. She received her PhD degree from the University of Science and Technology of China (USTC), China. Her general area of research interests are data mining, recommender systems, and responsible user modeling. She has published more than 60 papers in referred journals and conferences, such as IEEE TKDE, NIPS, SIGIR, WWW, and AAAI. Dr. Le Wu is the recipient of the Best of SDM 2015 Award, and the Distinguished Dissertation Award from the China Association for Artificial Intelligence (CAAI) 2017.

Yong LI received the BS degree from Huazhong University of Science and Technology, China in 2007, and the MS and the PhD degrees in electrical engineering from Tsinghua University, China in 2009 and 2012, respectively. Currently, he is a Faculty Member of the Department of Electronic Engineering, Tsinghua University, China. His research interests are in the areas of wireless networking, mobile computing and urban computing. Dr. Li has served as General Chair, TPC Chair, TPC Member for several international workshops and conferences, and he is on the editorial board of four international journals.

Meng WANG is a professor at Hefei University of Technology, China. He received his BE degree and PhD degrees in the Special Class for the Gifted Young and the Department of Electronic Engineering and Information Science from the University of Science and Technology of China (USTC), China in 2003 and 2008, respectively. His current research interests include multimedia content analysis, computer vision, and pattern recognition. He is an associate editor of IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), IEEE Transactions on Circuits and Systems for Video Technology (IEEE TCSVT), IEEE Transactions on Multimedia (IEEE TMM), and IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS).

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Shao, P., Zhang, K., Gao, C. et al. Breaking student-concept sparsity barrier for cognitive diagnosis. Front. Comput. Sci. 19, 1911363 (2025). https://doi.org/10.1007/s11704-025-40591-2

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