{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T14:06:33Z","timestamp":1773842793268,"version":"3.50.1"},"reference-count":79,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2020,2,20]],"date-time":"2020-02-20T00:00:00Z","timestamp":1582156800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"the National Key Research and Development Program of China","award":["2016YFB1000904"],"award-info":[{"award-number":["2016YFB1000904"]}]},{"name":"the Science Foundation of Ministry of Education of China & China Mobile","award":["MCM20170507"],"award-info":[{"award-number":["MCM20170507"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61922073,U19A2079"],"award-info":[{"award-number":["61922073,U19A2079"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the Foundation of State Key Laboratory of Cognitive Intelligence, iFLYTEK, P.R. 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To address these problems, in this article, we present a focused study on diagnosing the knowledge proficiency of students, where the goal is to\n            <jats:italic>track<\/jats:italic>\n            and\n            <jats:italic>explain<\/jats:italic>\n            their evolutions simultaneously. Specifically, we first devise an explanatory probabilistic matrix factorization model,\n            <jats:italic>Knowledge Proficiency Tracing<\/jats:italic>\n            (KPT), by leveraging educational priors. KPT model first associates each exercise with a knowledge vector in which each element represents a specific knowledge concept with the help of\n            <jats:italic>Q<\/jats:italic>\n            -matrix. Correspondingly, at each time, each student can be represented as a proficiency vector in the same knowledge space. Then, our KPT model jointly applies two classical educational theories (i.e.,\n            <jats:italic>learning curve<\/jats:italic>\n            and\n            <jats:italic>forgetting curve<\/jats:italic>\n            ) to capture the change of students\u2019 proficiency level on concepts over time. Furthermore, for improving the predictive performance, we develop an improved version of KPT, named\n            <jats:italic>Exercise-correlated Knowledge Proficiency Tracing<\/jats:italic>\n            (EKPT), by considering the connectivity among exercises with the same knowledge concepts. Finally, we apply our KPT and EKPT models to three important diagnostic tasks, including knowledge estimation, score prediction, and diagnosis result visualization. Extensive experiments on four real-world datasets demonstrate that both of our models could track the knowledge proficiency of students effectively and interpretatively.\n          <\/jats:p>","DOI":"10.1145\/3379507","type":"journal-article","created":{"date-parts":[[2020,2,20]],"date-time":"2020-02-20T11:46:14Z","timestamp":1582199174000},"page":"1-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":96,"title":["Learning or Forgetting? A Dynamic Approach for Tracking the Knowledge Proficiency of Students"],"prefix":"10.1145","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1661-0420","authenticated-orcid":false,"given":"Zhenya","family":"Huang","sequence":"first","affiliation":[{"name":"University of Science and Technology of China, Hefei, Anhui, China"}]},{"given":"Qi","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, Anhui, China"}]},{"given":"Yuying","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, Anhui, China"}]},{"given":"Le","family":"Wu","sequence":"additional","affiliation":[{"name":"Hefei University of Technology, China and iFLYTEK Co., Ltd, China"}]},{"given":"Keli","family":"Xiao","sequence":"additional","affiliation":[{"name":"Stony Brook University, NY, USA"}]},{"given":"Enhong","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, Anhui, China"}]},{"given":"Haiping","family":"Ma","sequence":"additional","affiliation":[{"name":"Anhui University, Hefei, Anhui, China"}]},{"given":"Guoping","family":"Hu","sequence":"additional","affiliation":[{"name":"iFLYTEK Research, Hefei, Anhui, China"}]}],"member":"320","published-online":{"date-parts":[[2020,2,20]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2566486.2568042"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ergon.2011.05.001"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmp.2010.08.009"},{"key":"e_1_2_1_4_1","volume-title":"Proceedings of the American Association for Artificial Intelligence Educational Data Mining Workshop. 1--8.","author":"Barnes Tiffany","year":"2005"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3030024.3038262"},{"key":"e_1_2_1_6_1","volume-title":"Proceedings of the International Workshop on 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