{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T10:53:28Z","timestamp":1781607208893,"version":"3.54.5"},"reference-count":28,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T00:00:00Z","timestamp":1781049600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62266054"],"award-info":[{"award-number":["62266054"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["62266054"],"award-info":[{"award-number":["62266054"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Major Science and Technology Project of Yunnan Province","award":["202402AD080002"],"award-info":[{"award-number":["202402AD080002"]}]},{"name":"Yunnan Fundamental Research Projects","award":["202401AT070122"],"award-info":[{"award-number":["202401AT070122"]}]},{"name":"Open Fund for Chongqing Key Laboratory of Computational Intelligence","award":["2020FF02"],"award-info":[{"award-number":["2020FF02"]}]},{"DOI":"10.13039\/501100001459","name":"Ministry of Education","doi-asserted-by":"publisher","award":["23YJA870011"],"award-info":[{"award-number":["23YJA870011"]}],"id":[{"id":"10.13039\/501100001459","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Knowledge tracing aims to predict students\u2019 future performance based on their historical learning interactions, thereby supporting learner modeling in online learning platforms and intelligent tutoring systems. Recent deep learning-based knowledge tracing models have achieved strong predictive performance, but many of them rely on increasingly complex architectures or additional optimization objectives, which may increase training difficulty and computational cost. To examine whether recurrent models can remain competitive when equipped with more effective exercise representation, this paper proposes ExerCAKT, an Exercise Context-Aware Knowledge Tracing model based on gated recurrent units. Different from conventional GRU-based knowledge tracing models that mainly use interaction sequences to update students\u2019 knowledge states, ExerCAKT explicitly separates knowledge-state modeling and exercise-context modeling through two GRU-based feature extractors. The knowledge-state feature extractor captures students\u2019 evolving mastery patterns, while the exercise feature extractor models contextual exercise information to enhance question-level prediction without introducing extra optimization objectives. Experiments on four public knowledge tracing datasets show that ExerCAKT achieves the best AUC results in six out of seven evaluation settings and obtains similar advantages in ACC. At the question level, ExerCAKT improves AUC by 2.00\u20134.71% over DKT on AL2005, AS2009, and NIPS34. At the knowledge-concept level on AS2009, AL2005, and NIPS34, it improves AUC by 0.24\u20131.95% over AKT. These results suggest that recurrent knowledge tracing models can still achieve competitive performance when exercise contextualization is explicitly modeled.<\/jats:p>","DOI":"10.3390\/info17060578","type":"journal-article","created":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T02:59:31Z","timestamp":1781146771000},"page":"578","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ExerCAKT: A Knowledge Tracing Model Based on GRU Capturing Contextual Features of Exercises"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-5211-9536","authenticated-orcid":false,"given":"Zijie","family":"Li","sequence":"first","affiliation":[{"name":"Key Laboratory of Education Informatization for Nationalities, Ministry of Education, Yunnan Normal University, Kunming 650500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1287-857X","authenticated-orcid":false,"given":"Jianhou","family":"Gan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Education Informatization for Nationalities, Ministry of Education, Yunnan Normal University, Kunming 650500, China"},{"name":"Yunnan Key Laboratory of Smart Education, Yunnan Normal University, Kunming 650500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juxiang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Education Informatization for Nationalities, Ministry of Education, Yunnan Normal University, Kunming 650500, China"},{"name":"Yunnan Key Laboratory of Smart Education, Yunnan Normal University, Kunming 650500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Education Informatization for Nationalities, Ministry of Education, Yunnan Normal University, Kunming 650500, China"},{"name":"Yunnan Key Laboratory of Smart Education, Yunnan Normal University, Kunming 650500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"},{"name":"Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3569576","article-title":"Knowledge Tracing: A Survey","volume":"55","author":"Abdelrahman","year":"2023","journal-title":"Acm Comput. Surv."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"110036","DOI":"10.1016\/j.knosys.2022.110036","article-title":"A Survey on Deep Learning Based Knowledge Tracing","volume":"258","author":"Song","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1109\/TLT.2017.2689017","article-title":"Dynamic Bayesian Networks for Student Modeling","volume":"10","author":"Klingler","year":"2017","journal-title":"IEEE Trans. Learn. Technol."},{"key":"ref_4","unstructured":"Yamkovenko, B., Hogg, C., Miller-Vedam, M., Grimaldi, P., and Wells, W. (2025). Practical Evaluation of Deep Knowledge Tracing Models for Use in Learning Platforms. Proceedings of the 18th International Conference on Educational Data Mining, Palermo, Italy, 20\u201323 July 2025, International Educational Data Mining Society."},{"key":"ref_5","first-page":"18542","article-title":"pyKT: A Python Library to Benchmark Deep Learning Based Knowledge Tracing Models","volume":"Volume 35","author":"Koyejo","year":"2022","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"ref_6","unstructured":"Cortes, C., Lawrence, N., Lee, D., Sugiyama, M., and Garnett, R. (2015). Deep Knowledge Tracing. Proceedings of the Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, J., Shi, X., King, I., and Yeung, D.Y. (2017, January 3\u20137). Dynamic Key-Value Memory Networks for Knowledge Tracing. Proceedings of the WWW \u201917: 26th International Conference on World Wide Web, Perth, Australia.","DOI":"10.1145\/3038912.3052580"},{"key":"ref_8","unstructured":"Pandey, S., and Karypis, G. (2019). A Self-Attentive Model for Knowledge Tracing. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Choi, Y., Lee, Y., Cho, J., Baek, J., Kim, B., Cha, Y., Shin, D., Bae, C., and Heo, J. (2020, January 12\u201314). Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing. Proceedings of the L@S \u201920: Seventh ACM Conference on Learning @ Scale, Virtual Event.","DOI":"10.1145\/3386527.3405945"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ghosh, A., Heffernan, N., and Lan, A.S. (2020, January 6\u201310). Context-Aware Attentive Knowledge Tracing. Proceedings of the KDD \u201920: 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Virtual Event.","DOI":"10.1145\/3394486.3403282"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Nakagawa, H., Iwasawa, Y., and Matsuo, Y. (2019, January 14\u201317). Graph-Based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural Network. Proceedings of the WI \u201919: IEEE\/WIC\/ACM International Conference on Web Intelligence, Thessaloniki, Greece.","DOI":"10.1145\/3350546.3352513"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Guo, X., Huang, Z., Gao, J., Shang, M., Shu, M., and Sun, J. (2021, January 20\u201324). Enhancing Knowledge Tracing via Adversarial Training. Proceedings of the MM \u201921: 29th ACM International Conference on Multimedia, Virtual Event.","DOI":"10.1145\/3474085.3475554"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Lee, W., Chun, J., Lee, Y., Park, K., and Park, S. (2022, January 25\u201329). Contrastive Learning for Knowledge Tracing. Proceedings of the WWW \u201922: ACM Web Conference 2022, Virtual Event.","DOI":"10.1145\/3485447.3512105"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yeung, C.K., and Yeung, D.Y. (2018, January 26\u201328). Addressing Two Problems in Deep Knowledge Tracing via Prediction-Consistent Regularization. Proceedings of the L@S \u201918: Fifth Annual ACM Conference on Learning at Scale, London, UK.","DOI":"10.1145\/3231644.3231647"},{"key":"ref_15","unstructured":"Liu, Z., Liu, Q., Chen, J., Huang, S., and Luo, W. (2023). simpleKT: A Simple but Tough-to-Beat Baseline for Knowledge Tracing. arXiv."},{"key":"ref_16","unstructured":"Liu, Y., Yang, Y., Chen, X., Shen, J., Zhang, H., and Yu, Y. (2021, January 7\u201315). Improving knowledge tracing via pre-training question embeddings. Proceedings of the IJCAI\u201920: Twenty-Ninth International Joint Conference on Artificial Intelligence, Yokohama, Japan."},{"key":"ref_17","unstructured":"Huang, T., Liang, M., Yang, H., Li, Z., Yu, T., and Hu, S. (July, January 29). Context-Aware Knowledge Tracing Integrated with the Exercise Representation and Association in Mathematics. Proceedings of the 14th International Conference on Educational Data Mining, Virtual Event."},{"key":"ref_18","unstructured":"Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lee, J., and Yeung, D.Y. (2019, January 4\u20138). Knowledge Query Network for Knowledge Tracing: How Knowledge Interacts with Skills. Proceedings of the LAK19: 9th International Conference on Learning Analytics & Knowledge, Tempe, AZ, USA.","DOI":"10.1145\/3303772.3303786"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"21395","DOI":"10.1038\/s41598-025-07422-7","article-title":"Deep Learning Based Knowledge Tracing in Intelligent Tutoring Systems","volume":"15","author":"Zhou","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yin, Y., Dai, L., Huang, Z., Shen, S., Wang, F., Liu, Q., Chen, E., and Li, X. (May, January 30). Tracing Knowledge Instead of Patterns: Stable Knowledge Tracing with Diagnostic Transformer. Proceedings of the WWW \u201923: ACM Web Conference 2023, Austin, TX, USA.","DOI":"10.1145\/3543507.3583255"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Im, Y., Choi, E., Kook, H., and Lee, J. (2023, January 21\u201325). Forgetting-aware Linear Bias for Attentive Knowledge Tracing. Proceedings of the CIKM \u201923: 32nd ACM International Conference on Information and Knowledge Management, Birmingham, UK.","DOI":"10.1145\/3583780.3615191"},{"key":"ref_23","unstructured":"Li, X., Bai, Y., Guo, T., Liu, Z., Huang, Y., Zhao, X., Xia, F., Luo, W., and Weng, J. (2024, January 3\u20139). Enhancing length generalization for attention based knowledge tracing models with linear biases. Proceedings of the IJCAI \u201924: Thirty-Third International Joint Conference on Artificial Intelligence, Jeju, Republic of Korea."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"108274","DOI":"10.1016\/j.knosys.2022.108274","article-title":"Bi-CLKT: Bi-graph Contrastive Learning Based Knowledge Tracing","volume":"241","author":"Song","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1109\/TLT.2019.2922902","article-title":"Modeling Engagement in Self-Directed Learning Systems Using Principal Component Analysis","volume":"13","author":"Hershcovits","year":"2020","journal-title":"IEEE Trans. Learn. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Nagatani, K., Zhang, Q., Sato, M., Chen, Y.Y., Chen, F., and Ohkuma, T. (2019, January 13\u201317). Augmenting Knowledge Tracing by Considering Forgetting Behavior. Proceedings of the WWW \u201919: The World Wide Web Conference, San Francisco, CA, USA.","DOI":"10.1145\/3308558.3313565"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Shen, S., Liu, Q., Chen, E., Huang, Z., Huang, W., Yin, Y., Su, Y., and Wang, S. (2021, January 14\u201318). Learning Process-Consistent Knowledge Tracing. Proceedings of the KDD \u201921: 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, Virtual Event.","DOI":"10.1145\/3447548.3467237"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Liu, Z., Liu, Q., Chen, J., Huang, S., Gao, B., Luo, W., and Weng, J. (May, January 30). Enhancing Deep Knowledge Tracing with Auxiliary Tasks. Proceedings of the WWW \u201923: ACM Web Conference 2023, Austin, TX, USA.","DOI":"10.1145\/3543507.3583866"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/17\/6\/578\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T10:05:18Z","timestamp":1781604318000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/17\/6\/578"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,10]]},"references-count":28,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["info17060578"],"URL":"https:\/\/doi.org\/10.3390\/info17060578","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,10]]}}}