Abstract
Cognitive diagnosis, which aims to diagnose students’ knowledge proficiency, is crucial for numerous online education applications, such as personalized exercise recommendation. Existing methods in this area mainly exploit students’ exercising records, which ignores students’ full learning process in online education systems. Besides, the latent relation of exercises with course structure and texts is still underexplored. In this paper, a learning behavior-aware cognitive diagnosis (LCD) framework is proposed for students’ cognitive modeling with both learning behavior records and exercising records. The concept of LCD was first introduced to characterize students’ knowledge proficiency more completely. Second, a course graph was designed to explore rich information existed in course texts and structures. Third, an interaction function was put forward to explore complex relationships between students, exercises and videos. Extensive experiments on a real-world dataset prove that LCD predicts student performance more effectively, the output of LCD is also interpretable.
Keywords
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Acknowledgement
This work is supported by the National Key Research and Development Program of China (2018YFB1005100). It also got partial support from National Engineering Laboratory for Cyberlearning and Intelligent Technology, and Beijing Key Lab of Networked Multimedia.
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Mao, Y. et al. (2021). Learning Behavior-Aware Cognitive Diagnosis for Online Education Systems. In: Zeng, J., Qin, P., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2021. Communications in Computer and Information Science, vol 1452. Springer, Singapore. https://doi.org/10.1007/978-981-16-5943-0_31
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