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Inf. Syst."],"published-print":{"date-parts":[[2024,1,31]]},"abstract":"<jats:p>Knowledge Tracing (KT) is the task of modeling a learner\u2019s knowledge state to predict future performance in e-learning systems based on past performance. Deep learning-based methods, such as recurrent neural networks, memory-augmented neural networks, and attention-based neural networks, have recently been used in KT. Such methods have demonstrated excellent performance in capturing the latent dependencies of a learner\u2019s knowledge state on recent exercises. However, these methods have limitations when it comes to dealing with the so-called Skill Switching Phenomenon (SSP), i.e., when learners respond to exercises in an e-learning system, the latent skills in the exercises typically switch irregularly. SSP will deteriorate the performance of deep learning-based approaches for simulating the learner\u2019s knowledge state during skill switching, particularly when the association between the switching skills and the previously learned skills is weak. To address this problem, we propose the Memory-augmented Attentive Network (MAN), which combines the advantages of memory-augmented neural networks and attention-based neural networks. Specifically, in MAN, memory-augmented neural networks are used to model learners\u2019 longer term memory knowledge, while attention-based neural networks are used to model learners\u2019 recent term knowledge. In addition, we design a context-aware attention mechanism that automatically weighs the tradeoff between these two types of knowledge. With extensive experiments on several e-learning datasets, we show that MAN effectively improve predictive accuracies of existing state-of-the-art DLKT methods.<\/jats:p>","DOI":"10.1145\/3589340","type":"journal-article","created":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T12:53:29Z","timestamp":1692363209000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["MAN: Memory-augmented Attentive Networks for Deep Learning-based Knowledge Tracing"],"prefix":"10.1145","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3554-912X","authenticated-orcid":false,"given":"Liangliang","family":"He","sequence":"first","affiliation":[{"name":"College of Computer, National University of Defense Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8613-4779","authenticated-orcid":false,"given":"Xiao","family":"Li","sequence":"additional","affiliation":[{"name":"Information Center, National University of Defense Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4450-8848","authenticated-orcid":false,"given":"Pancheng","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer, National University of Defense Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8802-3906","authenticated-orcid":false,"given":"Jintao","family":"Tang","sequence":"additional","affiliation":[{"name":"College of Computer, National University of Defense Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7780-2330","authenticated-orcid":false,"given":"Ting","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer, National University of Defense Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,8,18]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/11774303_17"},{"key":"e_1_3_2_3_2","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1145\/3386527.3405945","volume-title":"Proceedings of the 7th ACM Conference on Learning@Scale","author":"Choi Youngduck","year":"2020","unstructured":"Youngduck Choi, Youngnam Lee, Junghyun Cho, Jineon Baek, Byungsoo Kim, Yeongmin Cha, Dongmin Shin, Chan Bae, and Jaewe Heo. 2020. 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