{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-irt-make-deep-learning-based-knowledge","title":"Deep-IRT: Make Deep Learning Based Knowledge Tracing Explainable Using Item Response Theory","arxiv_id":"1904.11738","date":"2019-04-26","proceeding":null,"authors":["Chun-kit Yeung"],"abstract":"Deep learning based knowledge tracing model has been shown to outperform\ntraditional knowledge tracing model without the need for human-engineered\nfeatures, yet its parameters and representations have long been criticized for\nnot being explainable. In this paper, we propose Deep-IRT which is a synthesis\nof the item response theory (IRT) model and a knowledge tracing model that is\nbased on the deep neural network architecture called dynamic key-value memory\nnetwork (DKVMN) to make deep learning based knowledge tracing explainable.\nSpecifically, we use the DKVMN model to process the student's learning\ntrajectory and estimate the student ability level and the item difficulty level\nover time. Then, we use the IRT model to estimate the probability that a\nstudent will answer an item correctly using the estimated student ability and\nthe item difficulty. Experiments show that the Deep-IRT model retains the\nperformance of the DKVMN model, while it provides a direct psychological\ninterpretation of both students and items.","url_abs":"http://arxiv.org/abs/1904.11738v1","url_pdf":"http://arxiv.org/pdf/1904.11738v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-irt-make-deep-learning-based-knowledge","repo_url":"https://github.com/ckyeungac/DeepIRT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"deep-irt-make-deep-learning-based-knowledge","repo_url":"https://github.com/jdxyw/deepKT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"knowledge-tracing","task_name":"Knowledge Tracing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1904.11738","atlas_url":"https://app.syntology.ai/?focus=1904.11738","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.11738"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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