{"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/dynamic-key-value-memory-networks-for","title":"Dynamic Key-Value Memory Networks for Knowledge Tracing","arxiv_id":"1611.08108","date":"2016-11-24","proceeding":null,"authors":["Jiani Zhang","Xingjian Shi","Irwin King","Dit-yan Yeung"],"abstract":"Knowledge Tracing (KT) is a task of tracing evolving knowledge state of\nstudents with respect to one or more concepts as they engage in a sequence of\nlearning activities. One important purpose of KT is to personalize the practice\nsequence to help students learn knowledge concepts efficiently. However,\nexisting methods such as Bayesian Knowledge Tracing and Deep Knowledge Tracing\neither model knowledge state for each predefined concept separately or fail to\npinpoint exactly which concepts a student is good at or unfamiliar with. To\nsolve these problems, this work introduces a new model called Dynamic Key-Value\nMemory Networks (DKVMN) that can exploit the relationships between underlying\nconcepts and directly output a student's mastery level of each concept. Unlike\nstandard memory-augmented neural networks that facilitate a single memory\nmatrix or two static memory matrices, our model has one static matrix called\nkey, which stores the knowledge concepts and the other dynamic matrix called\nvalue, which stores and updates the mastery levels of corresponding concepts.\nExperiments show that our model consistently outperforms the state-of-the-art\nmodel in a range of KT datasets. Moreover, the DKVMN model can automatically\ndiscover underlying concepts of exercises typically performed by human\nannotations and depict the changing knowledge state of a student.","url_abs":"http://arxiv.org/abs/1611.08108v2","url_pdf":"http://arxiv.org/pdf/1611.08108v2.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":"dynamic-key-value-memory-networks-for","repo_url":"https://github.com/lucky7-code/DKVMN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"dynamic-key-value-memory-networks-for","repo_url":"https://github.com/ZhijieXiong/pyedmine","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"knowledge-tracing","task_name":"Knowledge Tracing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1611.08108","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}