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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cen, H., Koedinger, K., Junker, B.: Learning factors analysis – a general method for cognitive model evaluation and improvement. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 164–175. Springer, Heidelberg (2006). https://doi.org/10.1007/11774303_17
De La Torre, J.: Dina model and parameter estimation: a didactic. J. Educ. Behav. Stat. 34(1), 115–130 (2009)
DiBello, L.V., Roussos, L.A., Stout, W.: 31a review of cognitively diagnostic assessment and a summary of psychometric models. Handb. Stat. 26, 979–1030 (2006)
Embretson, S.E., Reise, S.P.: Item Response Theory. Psychology Press (2013)
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249–256 (2010)
Hornik, K., Stinchcombe, M., White, H., et al.: Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359–366 (1989)
Hou, L., la Torre, J.D., Nandakumar, R.: Differential item functioning assessment in cognitive diagnostic modeling: application of the Wald test to investigate DIF in the DINA model. J. Educ. Measur. 51(1), 98–125 (2014)
Huang, Z., Yin, Y., Chen, E., Xiong, H., Su, Y., Hu, G., et al.: EKT: exercise-aware knowledge tracing for student performance prediction. IEEE Trans. Knowl. Data Eng. 30, 100–105 (2019)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Liu, Q., et al.: Fuzzy cognitive diagnosis for modelling examinee performance. ACM Trans. Intelli. Syst. Technol. (TIST) 9(4), 1–26 (2018)
Liu, Y., et al.: Collaborative learning team formation: a cognitive modeling perspective. In: Navathe, S.B., Wu, W., Shekhar, S., Du, X., Wang, X.S., Xiong, H. (eds.) DASFAA 2016. LNCS, vol. 9643, pp. 383–400. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32049-6_24
Martin, A.J., Lazendic, G.: Computer-adaptive testing: implications for students’ achievement, motivation, engagement, and subjective test experience. J. Educ. Psychol. 110(1), 27 (2018)
Nakagawa, H., Iwasawa, Y., Matsuo, Y.: Graph-based knowledge tracing: modeling student proficiency using graph neural network. In: 2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pp. 156–163. IEEE (2019)
Pan, L., Li, C., Li, J., Tang, J.: Prerequisite relation learning for concepts in MOOCs. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1447–1456 (2017)
Peng, H., et al.: Fine-grained event categorization with heterogeneous graph convolutional networks. arXiv preprint arXiv:1906.04580 (2019)
Piech, C., et al.: Deep knowledge tracing. In: Advances in Neural Information Processing Systems, pp. 505–513 (2015)
Reckase, M.D.: Multidimensional Item Response Theory Models, pp. 79–112. Springer, New York (2009). https://doi.org/10.1007/978-0-387-89976-3
Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61–80 (2008)
Thai-Nghe, N., Schmidt-Thieme, L.: Multi-relational factorization models for student modeling in intelligent tutoring systems. In: 2015 Seventh International Conference on Knowledge and Systems Engineering (KSE), pp. 61–66. IEEE (2015)
Wang, F., et al.: Neural cognitive diagnosis for intelligent education systems. arXiv preprint arXiv:1908.08733 (2019)
Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W.L., Leskovec, J.: Graph convolutional neural networks for web-scale recommender systems. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 974–983 (2018)
Yu, J., et al.: MOOCCube: a large-scale data repository for NLP applications in MOOCs. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3135–3142 (2020)
Zhang, J., Shi, X., King, I., Yeung, D.Y.: Dynamic key-value memory networks for knowledge tracing. In: Proceedings of the 26th International Conference on World Wide Web, pp. 765–774 (2017)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-981-16-5943-0_31
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-5942-3
Online ISBN: 978-981-16-5943-0
eBook Packages: Computer ScienceComputer Science (R0)