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Yet existing models struggle to integrate the multidimensional and heterogeneous signals generated during learning\u2014such as exercise attributes, response behaviors, temporal factors, and hierarchical knowledge structure. Many methods rely on naive feature concatenation or fixed weighting, limiting their ability to capture synergistic interactions among features. We propose Gated full\u2010features Transformer Cognitive Knowledge Tracing (GCKT), a Transformer\u2010based model with a gated fusion mechanism that dynamically integrates multiple inputs. The model first embeds exercise, response correctness, response time, and hierarchical knowledge features (topics and concepts). Topic and concept embeddings are linearly projected into a unified knowledge representation. The exercise, time, correctness, and unified knowledge embeddings are then concatenated and passed through a learnable gating network (linear layer with sigmoid) to produce context\u2010aware importance weights. These weights are applied element\u2010wise to adaptively scale each feature before projection into a fused representation for the sequence encoder, enabling the Transformer to more accurately model the evolution of students\u2019 cognitive states. Extensive experiments on public datasets, including MOOCRadar and Math, show that GCKT consistently outperforms strong baselines\u2014such as DKT, AKT, and SAINT+\u2014on key metrics (AUC and F1), delivering robust gains across settings. The results demonstrate that dynamic, fine\u2010grained feature fusion substantially improves KT performance and that GCKT offers a general, effective approach for modeling complex learning scenarios.<\/jats:p>","DOI":"10.1155\/int\/3037960","type":"journal-article","created":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T15:42:20Z","timestamp":1772984540000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GCKT: Context\u2010Aware Gating of Heterogeneous Learning Features With Transformer for Cognitive Knowledge Tracing in Intelligent Tutoring Systems"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6960-509X","authenticated-orcid":false,"given":"Zhifeng","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4940-6172","authenticated-orcid":false,"given":"Jinyu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5799-6692","authenticated-orcid":false,"given":"Chunyan","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,3,3]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"publisher","DOI":"10.1080\/00461520.2011.611369"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-14363-2"},{"key":"e_1_2_11_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/bf01099821"},{"key":"e_1_2_11_4_2","first-page":"515","article-title":"Bayesian Knowledge Tracing, Its Extensions, and Its Competitors","volume":"10","author":"Pel\u00e1nek R.","year":"2017","journal-title":"IEEE Transactions on Learning Technologies"},{"key":"e_1_2_11_5_2","article-title":"A Survey of Deep Learning for Knowledge Tracing","author":"Liu Q.","year":"2021","journal-title":"arXiv preprint arXiv:2105.15106"},{"key":"e_1_2_11_6_2","first-page":"1","article-title":"A Deep Dive into Deep Learning for Knowledge Tracing: a Survey","volume":"55","author":"Abdessemed Y.","year":"2022","journal-title":"ACM Computing Surveys"},{"key":"e_1_2_11_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-39112-5_18"},{"key":"e_1_2_11_8_2","doi-asserted-by":"crossref","unstructured":"ZhangJ. 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