Abstract
In intelligent education, cognitive diagnosis is a fundamental but important task, which aims to discover students’ mastery of different knowledge concepts. Plenty of methods have been proposed to exploit student-exercise interactions, especially graph-based methods. However, most of them treat student behaviors to exercises as a binary interaction (i.e., interacted or not), neglecting diverse behavior patterns (i.e., correct and incorrect interactions). Moreover, the number of concepts is much smaller than exercises, presenting a challenge for measuring student proficiency. Therefore, in this paper, we propose a novel Multi-Relational Cognitive Diagnosis (MRCD) framework. Specifically, we first divide students’ answer behaviors into correct and incorrect interactions with exercises, and form the corresponding two student-exercise relation graphs. We then leverage Graph Convolutional Network to learn exercise-level representations of students and exercises based on different relation graphs. Since dividing operation exacerbate the data sparsity problem, we employ graph contrastive learning to enhance MRCD on representation learning. Moreover, considering the relatively small number of concepts, we directly employ attention mechanism to generate student and exercise representations based on relevant concepts. After that, we fuse exercise-level and concept-level representations, and send them to a cognitive diagnosis model to predict student performance. Extensive experiments over two real-world datasets demonstrate the effectiveness of our proposed model.
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References
Adams, R.J., Wilson, M., Wang, W.C.: The multidimensional random coefficients multinomial logit model. Appl. Psychol. Meas. 21(1), 1–23 (1997)
Allen, M.J., Yen, W.M.: Introduction to Measurement Theory. Waveland Press, Long Grove (2001)
Anderson, A., Huttenlocher, D., Kleinberg, J., Leskovec, J.: Engaging with massive online courses. In: WWW, pp. 687–698 (2014)
Anderson, J.R., Boyle, C.F., Reiser, B.J.: Intelligent tutoring systems. Science 228(4698), 456–462 (1985)
Bradley, A.P.: The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recogn. 30(7), 1145–1159 (1997)
Chai, T., Draxler, R.R.: Root mean square error (RMSE) or mean absolute error (MAE)? - arguments against avoiding RMSE in the literature. Geosci. Model Dev. 7(3), 1247–1250 (2014)
Chen, L., Wu, L., Hong, R., Zhang, K., Wang, M.: Revisiting graph based collaborative filtering: a linear residual graph convolutional network approach. In: AAAI, vol. 34, pp. 27–34 (2020)
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: ICML, pp. 1597–1607. PMLR (2020)
Chen, Y., Li, X., Liu, J., Ying, Z.: Recommendation system for adaptive learning. Appl. Psychol. Meas. 42(1), 24–41 (2018)
Cheng, S., et al.: Dirt: deep learning enhanced item response theory for cognitive diagnosis. In: CIKM, pp. 2397–2400 (2019)
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, Hove (2013)
Gao, W., et al.: RCD: relation map driven cognitive diagnosis for intelligent education systems. In: SIGIR, pp. 501–510 (2021)
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. JMLR Workshop and Conference Proceedings (2010)
He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., Wang, M.: LightGCN: simplifying and powering graph convolution network for recommendation. In: SIGIR, pp. 639–648 (2020)
Khosla, P., et al.: Supervised contrastive learning. In: NeurIPS, vol. 33, pp. 18661–18673 (2020)
Lin, Z., Tian, C., Hou, Y., Zhao, W.X.: Improving graph collaborative filtering with neighborhood-enriched contrastive learning. In: WWW, pp. 2320–2329 (2022)
Liu, M., Shao, P., Zhang, K.: Graph-based exercise-and knowledge-aware learning network for student performance prediction. In: Fang, L., Chen, Y., Zhai, G., Wang, J., Wang, R., Dong, W. (eds.) CICAI 2021. LNCS, vol. 13069, pp. 27–38. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-93046-2_3
Liu, Q., et al.: EKT: exercise-aware knowledge tracing for student performance prediction. IEEE Trans. Knowl. Data Eng. 33(1), 100–115 (2019)
Liu, Q., et al.: Exploiting cognitive structure for adaptive learning. In: KDD, pp. 627–635 (2019)
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)
Van den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv e-prints arXiv-1807 (2018)
Qiu, J., et al.: GCC: graph contrastive coding for graph neural network pre-training. In: KDD, pp. 1150–1160 (2020)
Rasch, G.: On general laws and the meaning of measurement in psychology. In: Berkeley Symposium on Mathematical Statistics, vol. 4, pp. 321–333 (1961)
Reckase, M.D.: Multidimensional item response theory models. In: Reckase, M.D. (ed.) Multidimensional Item Response Theory. SSBS, pp. 79–112. Springer, New York (2009). https://doi.org/10.1007/978-0-387-89976-3_4
Schlichtkrull, M., Kipf, T.N., Bloem, P., van den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 593–607. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93417-4_38
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)
Tong, S., et al.: Structure-based knowledge tracing: an influence propagation view. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 541–550. IEEE (2020)
Wang, F., et al.: Neural cognitive diagnosis for intelligent education systems. In: AAAI, vol. 34, pp. 6153–6161 (2020)
Wang, T., Isola, P.: Understanding contrastive representation learning through alignment and uniformity on the hypersphere. In: ICML, pp. 9929–9939. PMLR (2020)
Wang, X., He, X., Wang, M., Feng, F., Chua, T.S.: Neural graph collaborative filtering. In: SIGIR, pp. 165–174 (2019)
Wu, J., et al.: Self-supervised graph learning for recommendation. In: SIGIR, pp. 726–735 (2021)
Wu, L., He, X., Wang, X., Zhang, K., Wang, M.: A survey on accuracy-oriented neural recommendation: from collaborative filtering to information-rich recommendation. IEEE Trans. Knowl. Data Eng. (2022)
Wu, R., et al.: Cognitive modelling for predicting examinee performance. In: IJCAI (2015)
Yang, Y., et al.: GIKT: a graph-based interaction model for knowledge tracing. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds.) ECML PKDD 2020. LNCS (LNAI), vol. 12457, pp. 299–315. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67658-2_18
Zhou, Y., et al.: Modeling context-aware features for cognitive diagnosis in student learning. In: KDD, pp. 2420–2428 (2021)
Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., Wang, L.: Graph contrastive learning with adaptive augmentation. In: WWW, pp. 2069–2080 (2021)
Acknowledgments
This work was supported in part by grants from the National Key Research and Development Program of China (Grant No. 2021ZD0111802), the Fundamental Research Funds for the Central Universities (JZ2021HGTB0075), and the Open Project Program of the National Laboratory of Pattern Recognition (NLPR).
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Wu, K., Yang, Y., Zhang, K., Wu, L., Liu, J., Li, X. (2022). Multi-Relational Cognitive Diagnosis for Intelligent Education. In: Fang, L., Povey, D., Zhai, G., Mei, T., Wang, R. (eds) Artificial Intelligence. CICAI 2022. Lecture Notes in Computer Science(), vol 13605. Springer, Cham. https://doi.org/10.1007/978-3-031-20500-2_35
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