Context-aware Nonlinear and Neural Attentive Knowledge-based Models for Grade Prediction
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Published
Jun 27, 2020
Sara Morsy
George Karypis
https://orcid.org/0000-0002-4007-2143
Abstract
Grade prediction can help students and their advisers select courses and design personalized degree programs based on predicted future course performance. One of the successful approaches for accu- rately predicting a student's grades in future courses is Cumulative Knowledge-based Regression Models (CKRM). CKRM learns shallow linear models that predict a student's grades as the similarity between his/her knowledge state and the target course. However, there can be more complex interactions among prior courses taken by a student, which cannot be captured by the current linear CKRM model. More- over, CKRM and other grade prediction methods ignore the effect of concurrently-taken courses on a student's performance in a target course. In this paper, we propose context-aware nonlinear and neural attentive models that can potentially better estimate a student's knowledge state from his/her prior course information, as well as model the interactions between a target course and concurrent courses. Compared to the competing methods, our experiments on a large real-world dataset consisting of more than 1.5 million grades show the effectiveness of the proposed models in accurately predicting students' grades. Moreover, the attention weights learned by the neural attentive model can be helpful in better designing their degree plans.
How to Cite
Morsy, S., & Karypis, G. (2020). Context-aware Nonlinear and Neural Attentive Knowledge-based Models for Grade Prediction. Journal of Educational Data Mining, 12(1), 1–18. https://doi.org/10.5281/zenodo.3911795
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Keywords
grade prediction, neural attentive models, knowledge-based models, degree plans, nonlinear models, undergraduate education
References
BAHDANAU, D., CHO, K., AND BENGIO, Y. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.
BRAXTON, J. M., HIRSCHY, A. S., AND MCCLENDON, S. A. 2011. Understanding and Reducing College Student Departure: ASHE-ERIC Higher Education Report, Volume 30, Number 3. Vol. 16. John Wiley & Sons.
CHEN, J., ZHANG, H., HE, X., NIE, L., LIU, W., AND CHUA, T.-S. 2017. Attentive collaborative filtering: Multimedia recommendation with item-and component-level attention. In Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 335–344.
DUCHI, J., HAZAN, E., AND SINGER, Y. 2011. Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research 12, Jul, 2121–2159.
ELBADRAWY, A. AND KARYPIS, G. 2016. Domain-aware grade prediction and top-n course recommendation. In Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 183–190.
HE, X. AND CHUA, T.-S. 2017. Neural factorization machines for sparse predictive analytics. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 355–364.
HE, X., HE, Z., SONG, J., LIU, Z., JIANG, Y.-G., AND CHUA, T.-S. 2018. NAIS: Neural attentive item similarity model for recommendation. IEEE Transactions on Knowledge and Data Engineering 30, 12, 2354–2366.
HU, Q. AND RANGWALA, H. 2018. Course-specific Markovian models for grade prediction. In Pacific Asia Conference on Knowledge Discovery and Data Mining. Springer, 29–41.
KENA, G., HUSSAR, W., MC FARLAND, J., DE BREY, C., MUSU -GILLETTE, L., WANG, X., ZHANG, J., RATHBUN, A., WILKINSON -FLICKER, S., DILIBERTI, M., ET AL . 2016. The condition of education 2016. NCES 2016-144. National Center for Education Statistics.
LAHA, A., CHEMMENGATH, S. A., AGRAWAL, P., KHAPRA, M., SANKARANARAYANAN, K., AND RAMASWAMY, H. G. 2018. On controllable sparse alternatives to softmax. In Advances in Neural Information Processing Systems. 6423–6433.
MARTINS, A. AND ASTUDILLO, R. 2016. From softmax to sparsemax: A sparse model of attention and multi-label classification. In International Conference on Machine Learning. 1614–1623.
MEI, L., REN, P., CHEN, Z., NIE, L., MA, J., AND NIE, J.-Y. 2018. An attentive interaction network for context-aware recommendations. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM, 157–166.
MORSY, S. AND KARYPIS, G. 2017. Cumulative knowledge-based regression models for next-term grade prediction. In Proceedings of the 2017 SIAM International Conference on Data Mining. SIAM, 552–560.
PARIKH, A., TÄCKSTRÖM, O., DAS, D., AND USZKOREIT, J. 2016. A decomposable attention model for natural language inference. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. 2249–2255.
POLYZOU, A. AND KARYPIS, G. 2016. Grade prediction with course and student specific models. In Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer. 89-101.
REN, Z., NING, X., AND RANGWALA, H. 2017. Grade prediction with temporal course-wise influence. In Proceedings of the 10th International Conference on Educational Data Mining. 48–55.
REN, Z., NING, X., AND RANGWALA, H. 2018. ALE: Additive latent effect models for grade prediction. In Proceedings of the 2018 SIAM International Conference on Data Mining. SIAM, 477–485.
SWEENEY, M., LESTER, J., RANGWALA, H., AND JOHRI, A. 2016. Next-term student performance prediction: A recommender systems approach. Journal of Educational Data Mining 8, 1, 22–51.
VASWANI, A., SHAZEER, N., PARMAR, N., USZKOREIT, J., JONES, L., GOMEZ, A. N., KAISER, Ł., AND POLOSUKHIN, I. 2017. Attention is all you need. In Advances in Neural Information Processing Systems. 5998–6008.
XIAO, J., YE, H., HE, X., ZHANG, H., WU, F., AND CHUA, T.-S. 2017. Attentional factorization machines: learning the weight of feature interactions via attention networks. In Proceedings of the 26th International Joint Conference on Artificial Intelligence. AAAI Press, 3119–3125.
BRAXTON, J. M., HIRSCHY, A. S., AND MCCLENDON, S. A. 2011. Understanding and Reducing College Student Departure: ASHE-ERIC Higher Education Report, Volume 30, Number 3. Vol. 16. John Wiley & Sons.
CHEN, J., ZHANG, H., HE, X., NIE, L., LIU, W., AND CHUA, T.-S. 2017. Attentive collaborative filtering: Multimedia recommendation with item-and component-level attention. In Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 335–344.
DUCHI, J., HAZAN, E., AND SINGER, Y. 2011. Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research 12, Jul, 2121–2159.
ELBADRAWY, A. AND KARYPIS, G. 2016. Domain-aware grade prediction and top-n course recommendation. In Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 183–190.
HE, X. AND CHUA, T.-S. 2017. Neural factorization machines for sparse predictive analytics. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 355–364.
HE, X., HE, Z., SONG, J., LIU, Z., JIANG, Y.-G., AND CHUA, T.-S. 2018. NAIS: Neural attentive item similarity model for recommendation. IEEE Transactions on Knowledge and Data Engineering 30, 12, 2354–2366.
HU, Q. AND RANGWALA, H. 2018. Course-specific Markovian models for grade prediction. In Pacific Asia Conference on Knowledge Discovery and Data Mining. Springer, 29–41.
KENA, G., HUSSAR, W., MC FARLAND, J., DE BREY, C., MUSU -GILLETTE, L., WANG, X., ZHANG, J., RATHBUN, A., WILKINSON -FLICKER, S., DILIBERTI, M., ET AL . 2016. The condition of education 2016. NCES 2016-144. National Center for Education Statistics.
LAHA, A., CHEMMENGATH, S. A., AGRAWAL, P., KHAPRA, M., SANKARANARAYANAN, K., AND RAMASWAMY, H. G. 2018. On controllable sparse alternatives to softmax. In Advances in Neural Information Processing Systems. 6423–6433.
MARTINS, A. AND ASTUDILLO, R. 2016. From softmax to sparsemax: A sparse model of attention and multi-label classification. In International Conference on Machine Learning. 1614–1623.
MEI, L., REN, P., CHEN, Z., NIE, L., MA, J., AND NIE, J.-Y. 2018. An attentive interaction network for context-aware recommendations. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM, 157–166.
MORSY, S. AND KARYPIS, G. 2017. Cumulative knowledge-based regression models for next-term grade prediction. In Proceedings of the 2017 SIAM International Conference on Data Mining. SIAM, 552–560.
PARIKH, A., TÄCKSTRÖM, O., DAS, D., AND USZKOREIT, J. 2016. A decomposable attention model for natural language inference. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. 2249–2255.
POLYZOU, A. AND KARYPIS, G. 2016. Grade prediction with course and student specific models. In Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer. 89-101.
REN, Z., NING, X., AND RANGWALA, H. 2017. Grade prediction with temporal course-wise influence. In Proceedings of the 10th International Conference on Educational Data Mining. 48–55.
REN, Z., NING, X., AND RANGWALA, H. 2018. ALE: Additive latent effect models for grade prediction. In Proceedings of the 2018 SIAM International Conference on Data Mining. SIAM, 477–485.
SWEENEY, M., LESTER, J., RANGWALA, H., AND JOHRI, A. 2016. Next-term student performance prediction: A recommender systems approach. Journal of Educational Data Mining 8, 1, 22–51.
VASWANI, A., SHAZEER, N., PARMAR, N., USZKOREIT, J., JONES, L., GOMEZ, A. N., KAISER, Ł., AND POLOSUKHIN, I. 2017. Attention is all you need. In Advances in Neural Information Processing Systems. 5998–6008.
XIAO, J., YE, H., HE, X., ZHANG, H., WU, F., AND CHUA, T.-S. 2017. Attentional factorization machines: learning the weight of feature interactions via attention networks. In Proceedings of the 26th International Joint Conference on Artificial Intelligence. AAAI Press, 3119–3125.
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