{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T04:18:58Z","timestamp":1778905138088,"version":"3.51.4"},"reference-count":34,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1016\/j.knosys.2026.115332","type":"journal-article","created":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T00:04:04Z","timestamp":1768953844000},"page":"115332","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["A temporal interaction-enhanced multi-view framework for knowledge tracing via self-supervised contrastive learning"],"prefix":"10.1016","volume":"337","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9184-2742","authenticated-orcid":false,"given":"Liqing","family":"Qiu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9025-1484","authenticated-orcid":false,"given":"Qingyun","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.knosys.2026.115332_bib0001","doi-asserted-by":"crossref","first-page":"1661","DOI":"10.1109\/TLT.2024.3403135","article-title":"Design and evaluation of trustworthy knowledge tracing model for intelligent tutoring system","volume":"17","author":"Lu","year":"2024","journal-title":"IEEE Trans. Learn. Technol."},{"key":"10.1016\/j.knosys.2026.115332_bib0002","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.117680","article-title":"Ensemble knowledge tracing: modeling interactions in learning process","volume":"207","author":"Sun","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.knosys.2026.115332_bib0003","series-title":"Artificial Intelligence in Education: 16th International Conference, AIED 2013, Memphis, TN, USA, July 9\u201313, 2013. Proceedings 16","first-page":"171","article-title":"Individualized bayesian knowledge tracing models","author":"Yudelson","year":"2013"},{"key":"10.1016\/j.knosys.2026.115332_bib0004","first-page":"505","article-title":"Deep knowledge tracing","volume":"28","author":"Piech","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.115332_bib0005","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/978-3-642-24797-2_4","article-title":"Long short-term memory","author":"Graves","year":"2012","journal-title":"Supervised sequence labelling with recurrent neural networks"},{"key":"10.1016\/j.knosys.2026.115332_bib0006","series-title":"A self-attentive model for knowledge tracing","author":"Pandey","year":"2019"},{"issue":"1","key":"10.1016\/j.knosys.2026.115332_bib0007","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","article-title":"The graph neural network model","volume":"20","author":"Scarselli","year":"2008","journal-title":"IEEE Trans. Neural Netw."},{"key":"10.1016\/j.knosys.2026.115332_bib0008","series-title":"IEEE\/WIC\/aCM International Conference on Web Intelligence","first-page":"156","article-title":"Graph-based knowledge tracing: modeling student proficiency using graph neural network","author":"Nakagawa","year":"2019"},{"key":"10.1016\/j.knosys.2026.115332_bib0009","series-title":"Improving knowledge tracing via pre-training question embeddings","author":"Liu","year":"2020"},{"key":"10.1016\/j.knosys.2026.115332_bib0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.120853","article-title":"Improving knowledge tracing via a heterogeneous information network enhanced by student interactions","volume":"232","author":"Xu","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.knosys.2026.115332_bib0011","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122249","article-title":"Heterogeneous graph-based knowledge tracing with spatiotemporal evolution","volume":"238","author":"Yang","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.knosys.2026.115332_bib0012","series-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","first-page":"2478","article-title":"Metapath-guided heterogeneous graph neural network for intent recommendation","author":"Fan","year":"2019"},{"key":"10.1016\/j.knosys.2026.115332_bib0013","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.ins.2022.12.075","article-title":"Self-supervised heterogeneous hypergraph network for knowledge tracing","volume":"624","author":"Wu","year":"2023","journal-title":"Inf. Sci."},{"key":"10.1016\/j.knosys.2026.115332_bib0014","series-title":"Proceedings of the 12th International Conference on Educational Data Mining","article-title":"Deep hierarchical knowledge tracing","author":"Wang","year":"2019"},{"key":"10.1016\/j.knosys.2026.115332_bib0015","series-title":"Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2020, Ghent, Belgium, September 14\u201318, 2020, Proceedings, Part I","first-page":"299","article-title":"GIKT: A graph-based interaction model for knowledge tracing","author":"Yang","year":"2021"},{"key":"10.1016\/j.knosys.2026.115332_bib0016","series-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2016"},{"key":"10.1016\/j.knosys.2026.115332_bib0017","series-title":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"409","article-title":"DyGKT: dynamic graph learning for knowledge tracing","author":"Cheng","year":"2024"},{"key":"10.1016\/j.knosys.2026.115332_bib0018","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.117681","article-title":"SGKT: Session graph-based knowledge tracing for student performance prediction","volume":"206","author":"Wu","year":"2022","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"10.1016\/j.knosys.2026.115332_bib0019","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3688570","article-title":"Federated recommender system based on diffusion augmentation and guided denoising","volume":"43","author":"Di","year":"2025","journal-title":"ACM Trans. Inf. Syst."},{"issue":"1","key":"10.1016\/j.knosys.2026.115332_bib0020","first-page":"1","article-title":"Fedrl: a reinforcement learning federated recommender system for efficient communication using reinforcement selector and hypernet generator","volume":"4","author":"Di","year":"2025","journal-title":"ACM Trans. Recommender Syst."},{"key":"10.1016\/j.knosys.2026.115332_bib0021","first-page":"1","article-title":"Personalized consumer federated recommender system using fine-grained transformation and hybrid information sharing","author":"Di","year":"2025","journal-title":"IEEE Trans. Consum. Electron."},{"issue":"8","key":"10.1016\/j.knosys.2026.115332_bib0022","doi-asserted-by":"crossref","first-page":"6611","DOI":"10.1007\/s10115-025-02433-2","article-title":"Efficient federated recommender system based on slimify module and feature sharpening module","volume":"67","author":"Di","year":"2025","journal-title":"Knowl. Inf. Syst."},{"key":"10.1016\/j.knosys.2026.115332_bib0023","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.108274","article-title":"Bi-CLKT: bi-graph contrastive learning based knowledge tracing","volume":"241","author":"Song","year":"2022","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.knosys.2026.115332_bib0024","series-title":"2023 International Joint Conference on Neural Networks (IJCNN)","first-page":"1","article-title":"Cakt: coupling contrastive learning with attention networks for interpretable knowledge tracing","author":"Zu","year":"2023"},{"key":"10.1016\/j.knosys.2026.115332_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2023.120032","article-title":"Question-response representation with dual-level contrastive learning for improving knowledge tracing","volume":"658","author":"Zhao","year":"2024","journal-title":"Inf. Sci."},{"issue":"1","key":"10.1016\/j.knosys.2026.115332_bib0026","article-title":"Transformer-based convolutional forgetting knowledge tracking","volume":"13","author":"Liu","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.knosys.2026.115332_bib0027","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"156","article-title":"Temporal convolutional networks for action segmentation and detection","author":"Lea","year":"2017"},{"key":"10.1016\/j.knosys.2026.115332_bib0028","series-title":"2023 IEEE International Conference on Control, Electronics and Computer Technology (ICCECT)","first-page":"200","article-title":"A temporal convolutional knowledge tracing model integrating forgetting factors and item response theory","author":"Changsheng","year":"2023"},{"key":"10.1016\/j.knosys.2026.115332_bib0029","series-title":"Directed graph convolutional network","author":"Tong","year":"2020"},{"key":"10.1016\/j.knosys.2026.115332_bib0030","article-title":"Sequence to sequence learning with neural networks","volume":"27","author":"Sutskever","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.115332_bib0031","series-title":"Learning phrase representations using RNN encoder-decoder for statistical machine translation","author":"Cho","year":"2014"},{"key":"10.1016\/j.knosys.2026.115332_bib0032","series-title":"Proceedings of the 26th International Conference on World Wide Web","first-page":"765","article-title":"Dynamic key-value memory networks for knowledge tracing","author":"Zhang","year":"2017"},{"key":"10.1016\/j.knosys.2026.115332_bib0033","series-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","first-page":"2330","article-title":"Context-aware attentive knowledge tracing","author":"Ghosh","year":"2020"},{"issue":"3","key":"10.1016\/j.knosys.2026.115332_bib0034","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3638350","article-title":"DGEKT: A dual graph ensemble learning method for knowledge tracing","volume":"42","author":"Cui","year":"2024","journal-title":"ACM Trans. Inf. Syst."}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126000754?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126000754?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T07:42:27Z","timestamp":1772091747000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705126000754"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":34,"alternative-id":["S0950705126000754"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.115332","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A temporal interaction-enhanced multi-view framework for knowledge tracing via self-supervised contrastive learning","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.115332","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"115332"}}