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Knowl. Discov. Data"],"published-print":{"date-parts":[[2025,1,31]]},"abstract":"<jats:p>\n            Knowledge Tracing (KT) aims to predict students\u2019 future performance on answering questions based on their historical exercise sequences. To alleviate the problem of data sparsity in KT, recent works have introduced auxiliary information to mine question similarity, resulting in the enhancement of question embeddings. Nonetheless, there remains a gap in developing an approach that effectively incorporates various forms of auxiliary information, including relational information (e.g.,\n            <jats:italic>question\u2013student<\/jats:italic>\n            ,\n            <jats:italic>question\u2013skill<\/jats:italic>\n            relation), relationship attributes (e.g.,\n            <jats:italic>correctness<\/jats:italic>\n            indicating a student's performance on a question), and node attributes (e.g.,\n            <jats:italic>student ability<\/jats:italic>\n            ). To tackle this challenge, the Similarity-enhanced Question Embedding (SimQE) method for KT is proposed, with its central feature being the utilization of weighted and attributed meta-paths for extracting question similarity. To capture multi-dimensional question similarity semantics by integrating multiple relations, various meta-paths are constructed for learning question embeddings separately. These embeddings, each encoding different similarity semantics, are then fused to serve the task of KT. To capture finer-grained similarity by leveraging the relationship attributes and node attributes on the meta-paths, the\n            <jats:italic>biased random walk algorithm<\/jats:italic>\n            is designed. In addition, the\n            <jats:italic>auxiliary node generation method<\/jats:italic>\n            is proposed to capture high-order question similarity. Finally, extensive experiments conducted on six datasets demonstrate that SimQE performs the best among 10 representative question embedding methods. Furthermore, SimQE proves to be more effective in alleviating the problem of data sparsity.\n          <\/jats:p>","DOI":"10.1145\/3703158","type":"journal-article","created":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T14:29:17Z","timestamp":1730730557000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Question Embedding on Weighted Heterogeneous Information Network for Knowledge Tracing"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0951-1072","authenticated-orcid":false,"given":"Jianwen","family":"Sun","sequence":"first","affiliation":[{"name":"National Engineering Research Center of Educational Big Data, Central China Normal University, Wuhan, China and Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5792-631X","authenticated-orcid":false,"given":"Shangheng","family":"Du","sequence":"additional","affiliation":[{"name":"Shanghai Institute of Artificial Intelligence for Education, East China Normal University, Shanghai, China and School of Computer Science and Technology, East China Normal University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4570-3298","authenticated-orcid":false,"given":"Jianpeng","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3521-2837","authenticated-orcid":false,"given":"Xin","family":"Yuan","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Educational Big Data, Central China Normal University, Wuhan, China and Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6663-5821","authenticated-orcid":false,"given":"Xiaoxuan","family":"Shen","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Educational Big Data, Central China Normal University, Wuhan, China and Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8842-3079","authenticated-orcid":false,"given":"Ruxia","family":"Liang","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Educational Big Data, Central China Normal University, Wuhan, China and Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,12,10]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"175","volume-title":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR \u201919)","author":"Abdelrahman Ghodai","year":"2019","unstructured":"Ghodai Abdelrahman and Qing Wang. 2019. 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