Abstract
Planning course study is critical to facilitate strategic intervention in education. As a significant basis of planning course study, student performance prediction aims to utilize students existing relevant information to predict their future learning performance including course grades, course failure, grade point average, etc. We target course grade prediction for course study planning, provide reminders for students’ learning and teachers’ teaching. A student’s performance is usually influenced by a variety of factors, such as the difficulty of the test, personal performance, and learning conditions. Recent studies focus on the various factors that influence student performance. However, most of them ignore the differences in the impact of these factors on different students. Moreover, existing prediction models pay less attention to the impact of test complexity on students’ performance. To tackle these problems, we propose a novel Complexity-based Attentive Interactive Student Performance prediction model (CAISP) for personalized course study planning. In CAISP, the individual differences among students can be considered by assigning dynamically different importance using attention network to facilitate personalized planning. Especially, to effectively alleviate the student performance bias caused by test complexity, we fuse test complexity features to enhance the ability of feature representation via deep neural networks. Meanwhile, we combine factorization machines to develop a deep joint representation based on interaction learning and further boost the correlation between features, leading to optimal prediction results. Extensive experiments on two real-world datasets show that CAISP outperforms the state-of-the-art solutions.
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Notes
We use the terms “difficulty coefficient” and “complexity” interchangeably.
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References
Abu Amra, I. A., & Maghari, A. Y. A. (2017). Students performance prediction using knn and naive bayesian. 2017 8th International Conference on Information Technology (ICIT) (pp. 909–913). Jordan: Amman.
Adekitan, A. I., & Noma-Osaghae, E. (2019). Data mining approach to predicting the performance of first year student in a university using the admission requirements. Education and Information Technologies, 24, 1527–1543.
Agrawal, R., Christoforaki, M., Gollapudi, S., Kannan, A., Kenthapadi, K., Swaminathan, A. (2014). Mining videos from the web for electronic textbooks. In Proceedings of the Twelfth International Conference of Formal Concept Analysis, Cluj-Napoca, Romania, June 10-13 (pp. 219–234). Springer
Bakhshinategh, B., Zaïane, O.R., ElAtia, S., Ipperciel, D. (2018). Educational data mining applications and tasks: A survey of the last 10 years. Education and Information Technologies, 23, 537–553.
de Barros Costa, E., Fonseca, B., Santana, M. A., de Araújo, F. F., & Rego, J. B. A. (2017). Evaluating the effectiveness of educational data mining techniques for early prediction of students’ academic failure in introductory programming courses. Computers in Human Behavior, 73, 247–256.
Feng, W., Tang, J., Liu, T.X. (2019). Understanding dropouts in moocs. In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI Press). pp. 517–524.
Guo, H., Tang, R., Ye, Y., Li, Z., He, X., 2017. Deepfm: A factorizationmachine based neural network for CTR prediction. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI, Melbourne, Australia, August 19-25, ijcai.org, pp. 1725–1731
He, X., Chua, T. (2017). Neural factorization machines for sparse predictive analytics. In Proceedings of the 40th International Conference on Research and Development in Information Retrieval, Shinjuku, Tokyo, Japan, August 7-11, ACM, pp. 355–364
Hu, Q., Rangwala, H. (2018). Course-specific markovian models for grade prediction. In Proceedings of Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD, Melbourne, VIC, Australia, June 3-6, Part II, pp. 29–41.
Hwang, C. S., & Su, Y. C. (2015). Unified clustering locality preserving matrix factorization for student performance prediction. IAENG International Journal of Computer Science, 42, 245–253.
Injadat, M., Moubayed, A., Nassif, A. B., & Shami, A. (2020). Multi-split optimized bagging ensemble model selection for multi-class educational data mining. Applied Intelligence, 50, 4506–4528.
Khan, A., & Ghosh, S. K. (2021). Student performance analysis and prediction in classroom learning: A review of educational data mining studies. Education and Information Technologies, 26, 205–240.
Lan, A.S., Studer, C., Baraniuk, R.G. (2014a). Time-varying learning and content analytics via sparse factor analysis. In: Proceedings of International Conference on Knowledge Discovery and Data Mining, KDD, New York, NY, USA - August 24 - 27 (pp. 452–461). ACM
Lan, A. S., Waters, A. E., Studer, C., & Baraniuk, R. G. (2014). Sparse factor analysis for learning and content analytics. Journal of Machine Learning Research, 15, 1959–2008.
Li, H., Lynch, C.F., Barnes, T. (2018). Early prediction of course grades:Models and feature selection. CoRR abs/1812.00843. http://arxiv.org/ abs/1812.00843.
Li, H., Wei, H., Wang, Y., Song, Y., Qu, H. (2020). Peer-inspired student performance prediction in interactive online question pools with graph neural network. In Proceedings of The 29th International Conference on Information and Knowledge Management, Virtual Event, Ireland, October 19-23 (pp. 2589–2596) ACM
Liu, Q., Huang, Z., Yin, Y., Chen, E., Xiong, H., Su, Y., & Hu, G. (2021). EKT: exercise-aware knowledge tracing for student performance prediction. IEEE Transactions on Knowledge and Data Engineering, 33, 100–115.
Lu, X., Zhu, Y., Xu, Y., & Yu, J. (2021). Learning from multiple dynamic graphs of student and course interactions for student grade predictions. Neurocomputing, 431, 23–33.
Lykourentzou, I., Giannoukos, I., Mpardis, G., Nikolopoulos, V., & Loumos, V. (2009). Early and dynamic student achievement prediction in e-learning courses using neural networks. Journal of the Association for Information Science and Technology, 60, 372–380.
Meier, Y., Xu, J., Atan, O., van der Schaar, M. (2015). Personalized grade prediction: A data mining approach. In Proceedings of International Conference on Data Mining, Atlantic City, NJ, USA, November 14-17 (pp. 907–912) IEEE Computer Society
Morsy, S., Karypis, G., 2017. Cumulative knowledge-based regression models for next-term grade prediction, in: Proceedings of the International Conference on Data Mining, Houston, Texas, USA, April 27-29, SIAM. pp. 552–560.
Nahar, K., Shova, B. I., Ria, T., Rashid, H. B., & Islam, A. H. M. S. (2021). Mining educational data to predict students performance. Education and Information Technologies, 26, 6051–6067.
Pardos, Z.A., Heffernan, N.T. (2010). Using hmms and bagged decision trees to leverage rich features of user and skill from an intelligent tutoring system dataset. Journal of Machine Learning Research W & CP 40
Rendle, S. (2010). Factorization machines. In Proceedings of the International Conference on Data Mining, Sydney, Australia, December 14-17 (pp. 995–1000) IEEE Computer Society
Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33, 135–146.
Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics. Part C (Applications and Reviews), 40, 601–618.
Thai-Nghe, N., Drumond, L., Horváth, T., Schmidt-Thieme, L., 2012. Using factorization machines for student modeling, in: Workshop and Poster Proceedings of the 20th Conference on User Modeling, Adaptation, and Personalization, Montreal, Canada, July 16-20, CEUR-WS.org.
Wen, P., Yuan, W., Qin, Q., Sang, S., & Zhang, Z. (2021). Neural attention model for recommendation based on factorization machines. Applied Intelligence, 51, 1829–1844.
Xiao, J., Ye, H., He, X., Zhang, H., Wu, F., Chua, T. (2017). Attentional factorization machines: Learning the weight of feature interactions via attention networks. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI, Melbourne, Australia, August 19-25, ijcai.org. pp. 3119–3125.
Xin, X., Chen, B., He, X., Wang, D., Ding, Y., Jose, J. (2019). CFM: convolutional factorization machines for context-aware recommendation. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI, Macao, China, August 10-16, ijcai.org. pp. 3926–3932.
Xu, Z., Yuan, H., & Liu, Q. (2021). Student performance prediction based on blended learning. IEEE Transactions on Education, 64, 66–73.
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Wang, P., Li, L., Wang, R. et al. Complexity-based attentive interactive student performance prediction for personalized course study planning. Educ Inf Technol 27, 5471–5493 (2022). https://doi.org/10.1007/s10639-021-10842-3
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DOI: https://doi.org/10.1007/s10639-021-10842-3