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Intell. Syst. Technol."],"published-print":{"date-parts":[[2026,10,31]]},"abstract":"<jats:p>\n                    <jats:bold>Knowledge Tracing (KT)<\/jats:bold>\n                    aims to trace changes in students\u2019 knowledge states throughout their entire learning process by analyzing their historical learning data and predicting their future learning performance. Existing forgetting curve theory\u2013based knowledge tracing models only consider the general forgetting caused by time intervals, ignoring the specific influences in KT task. First, the discriminative information in forgetting curve is personalized due to the difference of students. Second, the relationship between knowledge concepts could contribute to the generalized features in the forgetting process. Considering these two aspects, we propose a\n                    <jats:bold>Concept-Driven Personalized Forgetting (CPF)<\/jats:bold>\n                    knowledge tracing model, which integrates the relationships between knowledge concepts and the personalization of students in cognitive abilities. First, personalized cognitive abilities are integrated into the learning and forgetting processes. Individual cognitive differences are modeled to dynamically adjust learning gains and forgetting rates based on students\u2019 knowledge mastery and learning strategies, which enables a more personalized learning experience. Second, the hierarchical relationships among knowledge concepts are considered by designing a precursor-successor knowledge concept matrix. In this way, the potential impact of forgetting prior knowledge concepts on subsequent ones is also integrated in KT task. Furthermore, the proposed personalized forgetting mechanism not only could be applied into the learning of specific knowledge concepts but also in the forgetting-review mechanism of life-long learning process. Extensive experimental results on several public datasets show that our CPF outperforms current forgetting curve theory\u2013based methods in predicting student performance, demonstrating CPF can better simulate changes in students\u2019 knowledge state through the personalized forgetting mechanism. Our code is publicly available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/lqr-1169\/CPF\">https:\/\/github.com\/lqr-1169\/CPF<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3810940","type":"journal-article","created":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T15:38:26Z","timestamp":1777390706000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Personalized Forgetting Mechanism with Concept-Driven Knowledge Tracing"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3824-687X","authenticated-orcid":false,"given":"Shanshan","family":"Wang","sequence":"first","affiliation":[{"name":"The State Key Laboratory of Opto-Electronic Information Acquisition and Protection Technology, Hefei, China and Institutes of Physical Science and Information Technology, Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7322-0547","authenticated-orcid":false,"given":"Ying","family":"Hu","sequence":"additional","affiliation":[{"name":"Institutes of Physical Science and Information Technology, Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8484-955X","authenticated-orcid":false,"given":"Qianru","family":"Li","sequence":"additional","affiliation":[{"name":"Institutes of Physical Science and Information Technology, Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0201-1638","authenticated-orcid":false,"given":"Xun","family":"Yang","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9455-4214","authenticated-orcid":false,"given":"Zhongzhou","family":"Zhang","sequence":"additional","affiliation":[{"name":"Norla Institute of Technical Physics, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-1889-3541","authenticated-orcid":false,"given":"Keyang","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhejiang Dahua Technology Co., Ltd., Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5052-000X","authenticated-orcid":false,"given":"Xingyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,6,16]]},"reference":[{"issue":"11","key":"e_1_3_2_2_2","article-title":"Knowledge tracing: A survey","volume":"55","author":"Abdelrahman Ghodai","year":"2023","unstructured":"Ghodai Abdelrahman, Qing Wang, and Bernardo Nunes. 2023. 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