KT-Bi-GRU: Student Performance Prediction with a Bi-Directional Recurrent Knowledge Tracing Neural Network
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Published
Jun 21, 2023
Marina Delianidi
Konstantinos Diamantaras
https://orcid.org/0000-0001-7378-676X
Abstract
Student performance is affected by their knowledge which changes dynamically over time. Therefore,
employing recurrent neural networks (RNN), which are known to be very good in dynamic time series
prediction, can be a suitable approach for student performance prediction. We propose such a neural
network architecture containing two modules: (i) a dynamic sub-network including a recurrent Bi-GRU
layer used for knowledge state estimation, (ii) a non-dynamic, feed-forward sub-network for predicting
answer correctness based on the current question and current student knowledge state. The model
modifies our previously proposed architecture and is different from all other existing models because it
estimates the student’s knowledge state considering only their previous responses. Thus the dynamic
sub-network generates more stable knowledge state vector representations since they are independent of
the current question. We studied both single-skill and multi-skill question scenarios and employed embeddings
to represent questions and responses. In the multi-skill case the initialization of the question
embedding matrix with pretrained word-embeddings is found to improve model performance. The experimental
results showed that our current KT-Bi-GRU model and the previous one have similar performance
while both surpassed the performance of previous state-of-the-art knowledge tracing models for five out
of seven datasets where in some cases, the difference is quite noticeable.
How to Cite
Delianidi, M., & Diamantaras, K. (2023). KT-Bi-GRU: Student Performance Prediction with a Bi-Directional Recurrent Knowledge Tracing Neural Network. Journal of Educational Data Mining, 15(2), 1–21. https://doi.org/10.5281/zenodo.7808087
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Keywords
knowledge tracing, student performance prediction, dynamic neural networks, knowledge state
References
ABDELRAHMAN, G. AND WANG, Q. 2019. Knowledge tracing with sequential key-value memory networks. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, 175–184. https://doi.org/10.1145/3331184.3331195.
ASSISTMENTSDATA. 2015. Assistments data. https://sites.google.com/site/assistmentsdata/.
CEN, H., KOEDINGER, K., AND JUNKER, B. 2006. Learning factors analysis – a general method for cognitive model evaluation and improvement. In Intelligent Tutoring Systems, M. Ikeda, K. D. Ashley, and T.-W. Chan, Eds. Springer Berlin Heidelberg, 164–175.
CHO, K., VAN MERRIËNBOER, B., GULCEHRE, C., BAHDANAU, D., BOUGARES, F., SCHWENK, H., AND BENGIO, Y. 2014. Learning phrase representations using RNN encoder–decoder for statistical machine translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, 1724–1734.
CORBETT, A. T. AND ANDERSON, J. R. 1994. Knowledge tracing: Modeling the acquisition of procedural knowledge. User modeling and user-adapted interaction 4, 4, 253–278.
DELIANIDI, M., DIAMANTARAS, K., CHRYSOGONIDIS, G., AND NIKIFORIDIS, V. 2021. Student performance prediction using dynamic neural models. In Proceedings of the Fourteenth International Conference on Educational Data Mining (EDM 2021), S. Hsiao, S. Sahebi, F. Bouchet, and J.-J. Vie, Eds. Educational Data Mining Society, 46–54.
GHOSH, A., HEFFERNAN, N., AND LAN, A. S. 2020. Context-aware attentive knowledge tracing. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining. Association for Computing Machinery, 2330–2339.
HAMBLETON, R. K., SWAMINATHAN, H., AND ROGERS, H. J. 1991. Fundamentals of item response theory. Vol. 2. Sage Publications.
HE, Z., LI, W., AND YAN, Y. 2021. Modeling knowledge proficiency using multi-hierarchical capsule graph neural network. Applied Intelligence 52, 7230–7247. https://doi.org/10.1007/s10489-021- 02765-w.
HOCHREITER, S. AND SCHMIDHUBER, J. 1997. Long short-term memory. Neural computation 9, 8, 1735–1780.
JOULIN, A., GRAVE, E., BOJANOWSKI, P., DOUZE, M., JÉGOU, H., AND MIKOLOV, T. Dec 2016. Fasttext.zip: Compressing text classification models. arXiv, arXiv:1612.03651. https://doi.org/10.48550/arXiv.1612.03651.
KÄSER, T., KLINGLER, S., SCHWING, A. G., AND GROSS, M. H. 2017. Dynamic bayesian networks for student modeling. IEEE Transactions on Learning Technologies 10, 450–462.
KHAYI, N. A. AND RUS, V. 2019. Clustering students based on their prior knowledge. In Proceedings of the 12th International Conference on Educational Data Mining (EDM 2019), C. F. Lynch, A. Merceron, M. Desmarais, and R. Nkambou, Eds. Educational Data Mining Society, 246–251.
KINGMA, D. P. AND BA, J. 2015. Adam: A method for stochastic optimization. In Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, Y. Bengio and Y. LeCun, Eds. https://doi.org/10.48550/arXiv.1412.6980.
KOEDINGER, K. R., BAKER, R. S., CUNNINGHAM, K., SKOGSHOLM, A., LEBER, B., AND STAMPER, J. 2010. A data repository for the edm community: The pslc datashop. In Handbook of educational data mining, C. Romero, S. Ventura, M. Pechenizkiy, and R. S. Baker, Eds. Vol. 43. CRC Press, Boca Raton, FL, 43–56. https://doi.org/10.1201/b10274.
LI, P., LUO, A., LIU, J., WANG, Y., ZHU, J., DENG, Y., AND ZHANG, J. 2020. Bidirectional gated recurrent unit neural network for chinese address element segmentation. ISPRS International Journal of Geo-Information 9, 11. https://doi.org/10.3390/ijgi9110635.
LING, C. X., HUANG, J., AND ZHANG, H. 2003. Auc: a statistically consistent and more discriminating measure than accuracy. In Proceedings of the 18th International Joint Conference on Artificial Intelligence. IJCAI’03, vol. 3. Morgan Kaufmann Publishers Inc., 519–524.
LIU, Q., HUANG, Z., YIN, Y., CHEN, E., XIONG, H., SU, Y., AND HU, G. 2019. Ekt: Exercise-aware knowledge tracing for student performance prediction. IEEE Transactions on Knowledge and Data Engineering 33, 1, 100–115.
LIU, Q., SHEN, S., HUANG, Z., CHEN, E., AND ZHENG, Y. 2021. A survey of knowledge tracing. CoRR abs/2105.15106. https://doi.org/10.48550/arXiv.2105.15106.
LIU, Y., YANG, Y., CHEN, X., SHEN, J., ZHANG, H., AND YU, Y. 2021. Improving knowledge tracing via pre-training question embeddings. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, C. Bessiere, Ed. IJCAI’20. International Joint Conferences on Artificial Intelligence, 1577–1583.
MA, R., ZHANG, L., LI, J., MEI, B., MA, Y., AND ZHANG, H. 2021. Dtkt: An improved deep temporal convolutional network for knowledge tracing. In Proceedings of the 2021 16th International Conference on Computer Science & Education (ICCSE). IEEE, 794–799.
MIKOLOV, T., CHEN, K., CORRADO, G., AND DEAN, J. 2013. Efficient estimation of word representations in vector space. In Proceedings of the 1st International Conference on Learning Representations, Workshop Track Proceedings, Y. Bengio and Y. LeCun, Eds. ICLR 2013. Scottsdale, Arizona, USA. https://doi.org/10.48550/arXiv.1301.3781.
MILLER, A., FISCH, A., DODGE, J., KARIMI, A.-H., BORDES, A., AND WESTON, J. 2016. Keyvalue memory networks for directly reading documents. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, J. Su, K. Duh, and X. Carreras, Eds. Association for Computational Linguistics, 1400–1409.
OMAR, T., ALZAHRANI, A., AND ZOHDY, M. 2020. Clustering approach for analyzing the student’s efficiency and performance based on data. Journal of Data Analysis and Information Processing 8, 03, 171–182. https://doi.org/10.4236/jdaip.2020.83010.
PANDEY, S. AND KARYPIS, G. 2019. A self-attentive model for knowledge tracing. In Proceedings of the 12th International Conference on Educational Data Mining, EDM 2019, C. F. Lynch, A. Merceron, M. Desmarais, and R. Nkambou, Eds. International Educational Data Mining Society, 384–389.
PAVLIK, P. I., CEN, H., AND KOEDINGER, K. R. 2009. Performance factors analysis –a new alternative to knowledge tracing. In Proceedings of the 2009 Conference on Artificial Intelligence in Education: Building Learning Systems That Care: From Knowledge Representation to Affective Modelling, V. Dimitrova, R. Mizoguchi, B. du Boulay, and A. Graesser, Eds. IOS Press, 531–538.
PIECH, C., BASSEN, J., HUANG, J., GANGULI, S., SAHAMI, M., GUIBAS, L., AND SOHL-DICKSTEIN, J. 2015. Deep knowledge tracing. In Advances in Neural Information Processing Systems, C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, and R. Garnett, Eds. Vol. 28. Curran Associates, Inc., 505–513.
PU, S., CONVERSE, G., AND HUANG, Y. 2021. Deep performance factors analysis for knowledge tracing. In Proceedings of the Artificial Intelligence in Education: 22nd International Conference, AIED 2021. Springer-Verlag, Berlin, Heidelberg, 331–341.
SCHUSTER, M. AND PALIWAL, K. K. 1997. Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45, 11, 2673–2681.
SHEN, S., LIU, Q., CHEN, E., WU, H., HUANG, Z., ZHAO, W., SU, Y., MA, H., AND WANG, S. Convolutional knowledge tracing: Modeling individualization in student learning process. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR ’20. Association for Computing Machinery, 1857–1860. https://doi.org/10.1145/3397271.3401288.
SONG, X., LI, J., TANG, Y., ZHAO, T., CHEN, Y., AND GUAN, Z. 2021. Jkt: A joint graph convolutional network based deep knowledge tracing. Information Sciences 580, 510–523.
SRIVASTAVA, N., HINTON, G., KRIZHEVSKY, A., SUTSKEVER, I., AND SALAKHUTDINOV, R. 2014. Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15, 1, 1929–1958.
VASWANI, A., SHAZEER, N., PARMAR, N., USZKOREIT, J., JONES, L., GOMEZ, A. N., KAISER, L., AND POLOSUKHIN, I. 2017. Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems, U. von Luxburg, I. Guyon, S. Bengio, H.Wallach, and R. Fergus, Eds. NIPS’17. Curran Associates Inc., 6000–6010.
WANG, W., LIU, T., CHANG, L., GU, T., AND ZHAO, X. 2020. Convolutional recurrent neural networks for knowledge tracing. In Proceedings of the 2020 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). IEEE, 287–290.
WANG, Z., LAMB, A., SAVELIEV, E., CAMERON, P., ZAYKOV, Y., HERNÁNDEZ-LOBATO, J. M., TURNER, R. E., BARANIUK, R. G., BARTON, C., JONES, S. P., WOODHEAD, S., AND ZHANG, C. 2020. Diagnostic questions: The neurips 2020 education challenge. arXiv preprint arXiv:2007.12061.
YAMADA, I., ASAI, A., SAKUMA, J., SHINDO, H., TAKEDA, H., TAKEFUJI, Y., AND MATSUMOTO, Y. 2020. Wikipedia2Vec: An efficient toolkit for learning and visualizing the embeddings of words and entities from Wikipedia. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Association for Computational Linguistics, 23–30. https://wikipedia2vec.github.io/wikipedia2vec/.
YANG, S., ZHU, M., HOU, J., AND LU, X. Deep knowledge tracing with convolutions. In 12th International Conference on Educational Data Mining, EDM 2019, C. F. Lynch, A. Merceron, M. Desmarais, and R. Nkambou, Eds. International Educational Data Mining Society, 683–686.
YANG, Y., SHEN, J., QU, Y., LIU, Y., WANG, K., ZHU, Y., ZHANG, W., AND YU, Y. 2021. Gikt: a graph-based interaction model for knowledge tracing. In Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2020, F. Hutter, K. Kersting, J. Lijffijt, and I. Valera, Eds. Springer International Publishing, Cham, 299–315.
YEUNG, C.-K. 2019. Deep-irt: Make deep learning based knowledge tracing explainable using item response theory. In Proceedings of the 12th International Conference on Educational Data Mining, EDM 2019, C. F. Lynch, A. Merceron, M. Desmarais, and R. Nkambou, Eds. International Educational Data Mining Society, 683–686.
ZHANG, J., SHI, X., KING, I., AND YEUNG, D.-Y. 2017. Dynamic key-value memory networks for knowledge tracing. In Proceedings of the 26th International Conference on World Wide Web. WWW ’17. International World Wide Web Conferences Steering Committee, 765–774. https://doi.org/10.1145/3038912.3052580.
ASSISTMENTSDATA. 2015. Assistments data. https://sites.google.com/site/assistmentsdata/.
CEN, H., KOEDINGER, K., AND JUNKER, B. 2006. Learning factors analysis – a general method for cognitive model evaluation and improvement. In Intelligent Tutoring Systems, M. Ikeda, K. D. Ashley, and T.-W. Chan, Eds. Springer Berlin Heidelberg, 164–175.
CHO, K., VAN MERRIËNBOER, B., GULCEHRE, C., BAHDANAU, D., BOUGARES, F., SCHWENK, H., AND BENGIO, Y. 2014. Learning phrase representations using RNN encoder–decoder for statistical machine translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, 1724–1734.
CORBETT, A. T. AND ANDERSON, J. R. 1994. Knowledge tracing: Modeling the acquisition of procedural knowledge. User modeling and user-adapted interaction 4, 4, 253–278.
DELIANIDI, M., DIAMANTARAS, K., CHRYSOGONIDIS, G., AND NIKIFORIDIS, V. 2021. Student performance prediction using dynamic neural models. In Proceedings of the Fourteenth International Conference on Educational Data Mining (EDM 2021), S. Hsiao, S. Sahebi, F. Bouchet, and J.-J. Vie, Eds. Educational Data Mining Society, 46–54.
GHOSH, A., HEFFERNAN, N., AND LAN, A. S. 2020. Context-aware attentive knowledge tracing. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining. Association for Computing Machinery, 2330–2339.
HAMBLETON, R. K., SWAMINATHAN, H., AND ROGERS, H. J. 1991. Fundamentals of item response theory. Vol. 2. Sage Publications.
HE, Z., LI, W., AND YAN, Y. 2021. Modeling knowledge proficiency using multi-hierarchical capsule graph neural network. Applied Intelligence 52, 7230–7247. https://doi.org/10.1007/s10489-021- 02765-w.
HOCHREITER, S. AND SCHMIDHUBER, J. 1997. Long short-term memory. Neural computation 9, 8, 1735–1780.
JOULIN, A., GRAVE, E., BOJANOWSKI, P., DOUZE, M., JÉGOU, H., AND MIKOLOV, T. Dec 2016. Fasttext.zip: Compressing text classification models. arXiv, arXiv:1612.03651. https://doi.org/10.48550/arXiv.1612.03651.
KÄSER, T., KLINGLER, S., SCHWING, A. G., AND GROSS, M. H. 2017. Dynamic bayesian networks for student modeling. IEEE Transactions on Learning Technologies 10, 450–462.
KHAYI, N. A. AND RUS, V. 2019. Clustering students based on their prior knowledge. In Proceedings of the 12th International Conference on Educational Data Mining (EDM 2019), C. F. Lynch, A. Merceron, M. Desmarais, and R. Nkambou, Eds. Educational Data Mining Society, 246–251.
KINGMA, D. P. AND BA, J. 2015. Adam: A method for stochastic optimization. In Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, Y. Bengio and Y. LeCun, Eds. https://doi.org/10.48550/arXiv.1412.6980.
KOEDINGER, K. R., BAKER, R. S., CUNNINGHAM, K., SKOGSHOLM, A., LEBER, B., AND STAMPER, J. 2010. A data repository for the edm community: The pslc datashop. In Handbook of educational data mining, C. Romero, S. Ventura, M. Pechenizkiy, and R. S. Baker, Eds. Vol. 43. CRC Press, Boca Raton, FL, 43–56. https://doi.org/10.1201/b10274.
LI, P., LUO, A., LIU, J., WANG, Y., ZHU, J., DENG, Y., AND ZHANG, J. 2020. Bidirectional gated recurrent unit neural network for chinese address element segmentation. ISPRS International Journal of Geo-Information 9, 11. https://doi.org/10.3390/ijgi9110635.
LING, C. X., HUANG, J., AND ZHANG, H. 2003. Auc: a statistically consistent and more discriminating measure than accuracy. In Proceedings of the 18th International Joint Conference on Artificial Intelligence. IJCAI’03, vol. 3. Morgan Kaufmann Publishers Inc., 519–524.
LIU, Q., HUANG, Z., YIN, Y., CHEN, E., XIONG, H., SU, Y., AND HU, G. 2019. Ekt: Exercise-aware knowledge tracing for student performance prediction. IEEE Transactions on Knowledge and Data Engineering 33, 1, 100–115.
LIU, Q., SHEN, S., HUANG, Z., CHEN, E., AND ZHENG, Y. 2021. A survey of knowledge tracing. CoRR abs/2105.15106. https://doi.org/10.48550/arXiv.2105.15106.
LIU, Y., YANG, Y., CHEN, X., SHEN, J., ZHANG, H., AND YU, Y. 2021. Improving knowledge tracing via pre-training question embeddings. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, C. Bessiere, Ed. IJCAI’20. International Joint Conferences on Artificial Intelligence, 1577–1583.
MA, R., ZHANG, L., LI, J., MEI, B., MA, Y., AND ZHANG, H. 2021. Dtkt: An improved deep temporal convolutional network for knowledge tracing. In Proceedings of the 2021 16th International Conference on Computer Science & Education (ICCSE). IEEE, 794–799.
MIKOLOV, T., CHEN, K., CORRADO, G., AND DEAN, J. 2013. Efficient estimation of word representations in vector space. In Proceedings of the 1st International Conference on Learning Representations, Workshop Track Proceedings, Y. Bengio and Y. LeCun, Eds. ICLR 2013. Scottsdale, Arizona, USA. https://doi.org/10.48550/arXiv.1301.3781.
MILLER, A., FISCH, A., DODGE, J., KARIMI, A.-H., BORDES, A., AND WESTON, J. 2016. Keyvalue memory networks for directly reading documents. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, J. Su, K. Duh, and X. Carreras, Eds. Association for Computational Linguistics, 1400–1409.
OMAR, T., ALZAHRANI, A., AND ZOHDY, M. 2020. Clustering approach for analyzing the student’s efficiency and performance based on data. Journal of Data Analysis and Information Processing 8, 03, 171–182. https://doi.org/10.4236/jdaip.2020.83010.
PANDEY, S. AND KARYPIS, G. 2019. A self-attentive model for knowledge tracing. In Proceedings of the 12th International Conference on Educational Data Mining, EDM 2019, C. F. Lynch, A. Merceron, M. Desmarais, and R. Nkambou, Eds. International Educational Data Mining Society, 384–389.
PAVLIK, P. I., CEN, H., AND KOEDINGER, K. R. 2009. Performance factors analysis –a new alternative to knowledge tracing. In Proceedings of the 2009 Conference on Artificial Intelligence in Education: Building Learning Systems That Care: From Knowledge Representation to Affective Modelling, V. Dimitrova, R. Mizoguchi, B. du Boulay, and A. Graesser, Eds. IOS Press, 531–538.
PIECH, C., BASSEN, J., HUANG, J., GANGULI, S., SAHAMI, M., GUIBAS, L., AND SOHL-DICKSTEIN, J. 2015. Deep knowledge tracing. In Advances in Neural Information Processing Systems, C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, and R. Garnett, Eds. Vol. 28. Curran Associates, Inc., 505–513.
PU, S., CONVERSE, G., AND HUANG, Y. 2021. Deep performance factors analysis for knowledge tracing. In Proceedings of the Artificial Intelligence in Education: 22nd International Conference, AIED 2021. Springer-Verlag, Berlin, Heidelberg, 331–341.
SCHUSTER, M. AND PALIWAL, K. K. 1997. Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45, 11, 2673–2681.
SHEN, S., LIU, Q., CHEN, E., WU, H., HUANG, Z., ZHAO, W., SU, Y., MA, H., AND WANG, S. Convolutional knowledge tracing: Modeling individualization in student learning process. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR ’20. Association for Computing Machinery, 1857–1860. https://doi.org/10.1145/3397271.3401288.
SONG, X., LI, J., TANG, Y., ZHAO, T., CHEN, Y., AND GUAN, Z. 2021. Jkt: A joint graph convolutional network based deep knowledge tracing. Information Sciences 580, 510–523.
SRIVASTAVA, N., HINTON, G., KRIZHEVSKY, A., SUTSKEVER, I., AND SALAKHUTDINOV, R. 2014. Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15, 1, 1929–1958.
VASWANI, A., SHAZEER, N., PARMAR, N., USZKOREIT, J., JONES, L., GOMEZ, A. N., KAISER, L., AND POLOSUKHIN, I. 2017. Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems, U. von Luxburg, I. Guyon, S. Bengio, H.Wallach, and R. Fergus, Eds. NIPS’17. Curran Associates Inc., 6000–6010.
WANG, W., LIU, T., CHANG, L., GU, T., AND ZHAO, X. 2020. Convolutional recurrent neural networks for knowledge tracing. In Proceedings of the 2020 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). IEEE, 287–290.
WANG, Z., LAMB, A., SAVELIEV, E., CAMERON, P., ZAYKOV, Y., HERNÁNDEZ-LOBATO, J. M., TURNER, R. E., BARANIUK, R. G., BARTON, C., JONES, S. P., WOODHEAD, S., AND ZHANG, C. 2020. Diagnostic questions: The neurips 2020 education challenge. arXiv preprint arXiv:2007.12061.
YAMADA, I., ASAI, A., SAKUMA, J., SHINDO, H., TAKEDA, H., TAKEFUJI, Y., AND MATSUMOTO, Y. 2020. Wikipedia2Vec: An efficient toolkit for learning and visualizing the embeddings of words and entities from Wikipedia. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Association for Computational Linguistics, 23–30. https://wikipedia2vec.github.io/wikipedia2vec/.
YANG, S., ZHU, M., HOU, J., AND LU, X. Deep knowledge tracing with convolutions. In 12th International Conference on Educational Data Mining, EDM 2019, C. F. Lynch, A. Merceron, M. Desmarais, and R. Nkambou, Eds. International Educational Data Mining Society, 683–686.
YANG, Y., SHEN, J., QU, Y., LIU, Y., WANG, K., ZHU, Y., ZHANG, W., AND YU, Y. 2021. Gikt: a graph-based interaction model for knowledge tracing. In Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2020, F. Hutter, K. Kersting, J. Lijffijt, and I. Valera, Eds. Springer International Publishing, Cham, 299–315.
YEUNG, C.-K. 2019. Deep-irt: Make deep learning based knowledge tracing explainable using item response theory. In Proceedings of the 12th International Conference on Educational Data Mining, EDM 2019, C. F. Lynch, A. Merceron, M. Desmarais, and R. Nkambou, Eds. International Educational Data Mining Society, 683–686.
ZHANG, J., SHI, X., KING, I., AND YEUNG, D.-Y. 2017. Dynamic key-value memory networks for knowledge tracing. In Proceedings of the 26th International Conference on World Wide Web. WWW ’17. International World Wide Web Conferences Steering Committee, 765–774. https://doi.org/10.1145/3038912.3052580.
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