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Intell. Syst. Technol."],"published-print":{"date-parts":[[2026,4,30]]},"abstract":"<jats:p>\n                    Cognitive modeling, as an emerging technology in the field of computer-aided education, aims to explore students\u2019 knowledge levels and learning abilities to achieve various intelligent educational applications. Although some existing work focuses on addressing the problem of student forgetting, it is still a less explored area how to naturally integrate the forgetting effect caused by the time interval between answering exercises into student knowledge state modeling. Additionally, traditional cognitive modeling methods mostly assume that students answer exercises one by one, which often does not align with real answering behavior and cannot be directly extended to diverse learning scenarios. Therefore, in this article, we propose a\n                    <jats:bold>Continuous Time-based Neural Cognitive (CT-NC)<\/jats:bold>\n                    framework and several implemented models (CT-NCM and two extensions) to effectively integrate the dynamic and continuous characteristics of knowledge forgetting into student learning process modeling, making it more natural. Specifically, we adopt a specially designed learning event encoding method to adjust the neural Hawkes process to capture the relationship between knowledge learning and forgetting over continuous time. Furthermore, we propose a customizable learning function to jointly model the changes in different knowledge states and their interaction with each practice moment. In the end, we demonstrate an extension CT-NCM+ that can adapt well to diverse learning scenarios, indicating that CT-NCM can solve real-world problems by flexibly adjusting its structure. Extensive experimental results on real datasets clearly demonstrate that CT-NCM and CT-NCM+ outperform the current state-of-the-art KT methods in student performance prediction, while our work points out a realistic research direction for KT and demonstrates its interpretability in knowledge learning visualization.\n                  <\/jats:p>","DOI":"10.1145\/3785501","type":"journal-article","created":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T14:15:28Z","timestamp":1766153728000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Reconciling Cognitive Modeling with Knowledge Forgetting: A Continuous Time-aware General Neural Network Framework"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-1552-3976","authenticated-orcid":false,"given":"Ziwen","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Anhui University, Hefei, China, Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei, China, and Institutes of Physical Science and Information Technology, Anhui University, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7666-8179","authenticated-orcid":false,"given":"Jingyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Institutes of Physical Science and Information Technology, Anhui University, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3115-6855","authenticated-orcid":false,"given":"Haiping","family":"Ma","sequence":"additional","affiliation":[{"name":"Institutes of Physical Science and Information Technology, Anhui University, Hefei, China and State Key Laboratory of Opto-Electronic Information Acquisition and Protection Technology, Anhui University, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4570-643X","authenticated-orcid":false,"given":"Hengshu","family":"Zhu","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0837-5424","authenticated-orcid":false,"given":"Shangshang","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Anhui University, Hefei, China and Anhui Province Key Laboratory of Intelligent Computing and Applications, Huaibei Normal University, Huaibei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3728-6914","authenticated-orcid":false,"given":"Xiaoshan","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Anhui University, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7666-5796","authenticated-orcid":false,"given":"Shuhuan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Big Data and Statistics, Anhui University, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7094-669X","authenticated-orcid":false,"given":"Haifeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mathematical Science, Anhui University, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"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":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,2,19]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331195"},{"key":"e_1_3_1_3_2","first-page":"3","article-title":"Defining educational quality","volume":"1","author":"Adams Don","year":"1993","unstructured":"Don Adams. 1993. 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