{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T16:28:59Z","timestamp":1779294539235,"version":"3.51.4"},"reference-count":44,"publisher":"Association for Computing Machinery (ACM)","issue":"3","funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2022YFC3303600"],"award-info":[{"award-number":["2022YFC3303600"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key Laboratory of Smart Education of Guangdong Higher Education Institutes, Jinan University","award":["2022LSYS003"],"award-info":[{"award-number":["2022LSYS003"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2026,6,30]]},"abstract":"<jats:p>\n                    Knowledge tracing (KT) is a problem of modeling students\u2019 knowledge states to predict their future performance by observing their historical learning interactions. The collection of educational data presents significant challenges, as students\u2019 limited learning engagement restricts the generation of large-scale interaction data, while stringent privacy regulations further limit the availability of student learning sequences from online platforms. Hence, it is crucial to enhance the capabilities of deep learning-based KT (DLKT) models by constructing large-scale datasets through the integration of student interaction data across multiple subjects and sources. The success of ChatGPT demonstrates that the decoder-only Transformer architecture is highly effective in capturing complex information from large-scale sequential data. Against this background, we propose a novel decoder-only Transformer architecture-based model, named Unified DLKT (\n                    <jats:italic toggle=\"yes\">UniKT<\/jats:italic>\n                    ), to learn coherent and unified representations across a wide range of data sources. Specifically, we combine student learning sequences from six educational scenarios and utilize a multi-source encoding to learn unified representations of interactions from mixed data.\n                    <jats:italic toggle=\"yes\">UniKT<\/jats:italic>\n                    is a stack of Transformer decoder layers for handling long-term dependencies among students\u2019 historical interactions and future performance. We evaluate\n                    <jats:italic toggle=\"yes\">UniKT<\/jats:italic>\n                    on six publicly available real-world educational datasets, and experimental results demonstrate that our method outperforms the majority of existing DLKT models in terms of AUC and accuracy. Furthermore, the empirical analysis shows the strong transferability and adaptability of\n                    <jats:italic toggle=\"yes\">UniKT<\/jats:italic>\n                    in learning from multiple sources. To encourage reproducible research, we make our data and code publicly available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/pykt.org\/\">https:\/\/pykt.org\/<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3735652","type":"journal-article","created":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T09:05:16Z","timestamp":1747731916000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Improving Knowledge Tracing through Multi-Source Scaling with Decoder-Only Transformers"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6604-475X","authenticated-orcid":false,"given":"Teng","family":"Guo","sequence":"first","affiliation":[{"name":"Guangdong Institute of Smart Education, Jinan University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2372-257X","authenticated-orcid":false,"given":"Bojun","family":"Zhan","sequence":"additional","affiliation":[{"name":"Guangdong Institute of Smart Education, Jinan University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0217-7494","authenticated-orcid":false,"given":"Shuyan","family":"Huang","sequence":"additional","affiliation":[{"name":"TAL Education Group, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6095-1041","authenticated-orcid":false,"given":"Jiahao","family":"Chen","sequence":"additional","affiliation":[{"name":"TAL Education Group, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2926-4416","authenticated-orcid":false,"given":"Xiangyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Data Science, City University of Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5225-2195","authenticated-orcid":false,"given":"Mingliang","family":"Hou","sequence":"additional","affiliation":[{"name":"TAL Education Group, Beijing, China and Guangdong Institute of Smart Education, Jinan University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0491-307X","authenticated-orcid":false,"given":"Zitao","family":"Liu","sequence":"additional","affiliation":[{"name":"Guangdong Institute of Smart Education, Jinan University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,3,20]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331195"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3206447"},{"key":"e_1_3_2_4_2","unstructured":"Josh Achiam Steven Adler Sandhini Agarwal Lama Ahmad Ilge Akkaya Florencia Leoni Aleman Diogo Almeida Janko Altenschmidt Sam Altman Shyamal Anadkat et al. 2023. 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