SNMCF: A Scalable Non-negative Matrix Co-Factorization for Student Cognitive Modeling

Shenbao Yu, Yifeng Zeng, Yinghui Pan, Fan Yang

Research output: Contribution to journalArticlepeer-review


Student cognitive modeling plays an important role in the rapid development of educational data mining research. It aims to discover students' proficiency in knowledge concepts as well as to predict students' performance in conducting exercises. Studies in the past few years have been mainly centered around two types of techniques: cognitive diagnosis models and data mining approaches. Cognitive diagnosis models focus on students' cognitive states and assess their knowledge concept proficiency through handcrafted features. The subjective features may trigger cascading errors in the students' performance prediction. On the other hand, data mining techniques, e.g., matrix factorization methods, achieve high prediction accuracy by directly modeling the students' exercising process. It lacks measuring the students' knowledge concept proficiency. To address the dilemma of the aforementioned methods, in this paper, we propose a scalable non-negative matrix co-factorization (SNMCF) model by jointly modeling the students' knowledge states and their exercising process. SNMCF can achieve high accuracy in predicting students' exercise performance while modeling their states of knowledge concepts in a given domain. We conduct extensive experiments on several real-world datasets, including large sparse ones, and demonstrate the effectiveness of our new approach in terms of prediction accuracy, cognitive diagnostic ability, and scalability
Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Early online date30 Oct 2023
Publication statusE-pub ahead of print - 30 Oct 2023

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