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Inf. Syst."],"published-print":{"date-parts":[[2024,5,31]]},"abstract":"Knowledge tracing aims to trace students\u2019 evolving knowledge states by predicting their future performance on concept-related exercises. Recently, some graph-based models have been developed to incorporate the relationships between exercises to improve knowledge tracing, but only a single type of relationship information is generally explored. In this article, we present a novel Dual Graph Ensemble learning method for Knowledge Tracing (DGEKT), which establishes a dual graph structure of students\u2019 learning interactions to capture the heterogeneous exercise\u2013concept associations and interaction transitions by hypergraph modeling and directed graph modeling, respectively. To combine the dual graph models, we introduce the technique of online knowledge distillation. This choice arises from the observation that, while the knowledge tracing model is designed to predict students\u2019 responses to the exercises related to different concepts, it is optimized merely with respect to the prediction accuracy on a single exercise at each step. With online knowledge distillation, the dual graph models are adaptively combined to form a stronger ensemble teacher model, which provides its predictions on all exercises as extra supervision for better modeling ability. In the experiments, we compare DGEKT against eight knowledge tracing baselines on three benchmark datasets, and the results demonstrate that DGEKT achieves state-of-the-art performance.<\/jats:p>","DOI":"10.1145\/3638350","type":"journal-article","created":{"date-parts":[[2023,12,22]],"date-time":"2023-12-22T11:31:30Z","timestamp":1703244690000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":22,"title":["DGEKT: A Dual Graph Ensemble Learning Method for Knowledge Tracing"],"prefix":"10.1145","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3332-1348","authenticated-orcid":false,"given":"Chaoran","family":"Cui","sequence":"first","affiliation":[{"name":"Shandong University of Finance and Economics, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6289-8918","authenticated-orcid":false,"given":"Yumo","family":"Yao","sequence":"additional","affiliation":[{"name":"Shandong University of Finance and Economics, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3156-754X","authenticated-orcid":false,"given":"Chunyun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shandong University of Finance and Economics, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5859-1152","authenticated-orcid":false,"given":"Hebo","family":"Ma","sequence":"additional","affiliation":[{"name":"Shandong University of Finance and Economics, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3306-9317","authenticated-orcid":false,"given":"Yuling","family":"Ma","sequence":"additional","affiliation":[{"name":"Shandong Jianzhu University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9076-6565","authenticated-orcid":false,"given":"Zhaochun","family":"Ren","sequence":"additional","affiliation":[{"name":"Leiden University, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3942-4671","authenticated-orcid":false,"given":"Chen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hong Kong Polytechnic University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7576-2286","authenticated-orcid":false,"given":"James","family":"Ko","sequence":"additional","affiliation":[{"name":"Education University of Hong Kong, China"}]}],"member":"320","published-online":{"date-parts":[[2024,1,22]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331195"},{"key":"e_1_3_2_3_2","unstructured":"Rohan Anil Gabriel Pereyra Alexandre Passos Robert Ormandi George E Dahl and Geoffrey E Hinton. 2018. 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