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Inf. Syst."],"published-print":{"date-parts":[[2023,10,31]]},"abstract":"<jats:p>Knowledge tracing, the goal of which is predicting students\u2019 future performance given their past question response sequences to trace their knowledge states, is pivotal for computer-aided education and intelligent tutoring systems. Although many technical efforts have been devoted to modeling students based on their question-response sequences, fine-grained interaction modeling between question-response pairs within each sequence is underexplored. This causes question-response representations less contextualized and further limits student modeling. To address this issue, we first conduct a data analysis and reveal the existence of complex cross effects between different question-response pairs within a sequence. Consequently, we propose MRT-KT, a multi-relational transformer for knowledge tracing, to enable fine-grained interaction modeling between question-response pairs. It introduces a novel relation encoding scheme based on knowledge concepts and student performance. Comprehensive experimental results show that MRT-KT outperforms state-of-the-art knowledge tracing methods on four widely-used datasets, validating the effectiveness of considering fine-grained interaction for knowledge tracing.<\/jats:p>","DOI":"10.1145\/3580595","type":"journal-article","created":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T13:18:45Z","timestamp":1674134325000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":37,"title":["Fine-Grained Interaction Modeling with Multi-Relational Transformer for Knowledge Tracing"],"prefix":"10.1145","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5900-7643","authenticated-orcid":false,"given":"Jiajun","family":"Cui","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, East China Normal University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0286-6196","authenticated-orcid":false,"given":"Zeyuan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, East China Normal University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4768-5946","authenticated-orcid":false,"given":"Aimin","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Shanghai Institute for AI Education, East China Normal University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7555-170X","authenticated-orcid":false,"given":"Jianyong","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6763-8146","authenticated-orcid":false,"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Shanghai Institute for AI Education, East China Normal University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,3,23]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"1881","volume-title":"Proceedings of the IEEE International Conference on Computer and Communications","author":"Cai Dejun","year":"2019","unstructured":"Dejun Cai, Yuan Zhang, and Bintao Dai. 2019. 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