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Knowl. Discov. Data"],"published-print":{"date-parts":[[2025,9,30]]},"abstract":"<jats:p>\n            Knowledge Tracing (KT) is the task of predicting students\u2019 future performance based on their past interactions with educational resources. A key aspect of KT is representation learning, which aims to capture meaningful features from students\u2019 learning behaviors to improve prediction performance. Recently, contrastive learning methods have shown great promise in representation learning. As a result, KT models based on contrastive learning have been introduced to enhance representation learning for KT. However, these models have posed several challenges. Firstly, most of these models adopt the contrastive learning approach used in other fields, which involves data augmentation followed by contrastive learning, yet effectively applying data augmentation in KT remains an open challenge. Secondly, these models typically apply contrastive learning to only one of the fundamental components of KT: questions, interactions, or knowledge states, thereby limiting their overall performance. To address these issues, this article proposes a Multi-level Contrastive learning model for Knowledge Tracing (MCKT). MCKT (The code can be found at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/lilstrawberry\/MCKT\">https:\/\/github.com\/lilstrawberry\/MCKT<\/jats:ext-link>\n            .) does not rely on data augmentation strategies; instead, it deeply integrates domain knowledge and performs contrastive learning at three levels: questions, interactions, and knowledge states. Experimental results on four publicly available datasets, compared against a total of 20 state-of-the-art KT models, demonstrate that MCKT consistently outperforms other models. Subsequent experiments further validate the effectiveness of the multi-level contrastive learning approach.\n          <\/jats:p>","DOI":"10.1145\/3759920","type":"journal-article","created":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T20:41:46Z","timestamp":1755031306000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Multi-level Contrastive Learning for Knowledge Tracing"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6663-5821","authenticated-orcid":false,"given":"Xiaoxuan","family":"Shen","sequence":"first","affiliation":[{"name":"National Engineering Research Center of\u00a0Educational Big Data, Central China Normal University, Wuhan, China and Faculty of Artificial Intelligence\u00a0in Education, Central China Normal University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1488-265X","authenticated-orcid":false,"given":"Fenghua","family":"Yu","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of\u00a0Educational Big Data, Central China Normal University, Wuhan, China and Faculty of Artificial Intelligence\u00a0in Education, Central China Normal University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4504-3912","authenticated-orcid":false,"given":"Qian","family":"Wan","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of\u00a0Educational Big Data, Central China Normal University, Wuhan, China and Faculty of Artificial Intelligence\u00a0in Education, Central China Normal University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3262-3088","authenticated-orcid":false,"given":"Ruxia","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Central China Normal University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0951-1072","authenticated-orcid":false,"given":"Jianwen","family":"Sun","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of\u00a0Educational Big Data, Central China Normal\u00a0University, Wuhan, China and Faculty of Artificial Intelligence\u00a0in Education, Central China Normal\u00a0University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,9,17]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331195"},{"key":"e_1_3_2_3_2","first-page":"7844","article-title":"Deep graph memory networks for forgetting-robust knowledge tracing","author":"Abdelrahman Ghodai","year":"2022","unstructured":"Ghodai Abdelrahman and Qing Wang. 2022. 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