{"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/knowledge-tracing-machines-factorization","title":"Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing","arxiv_id":"1811.03388","date":"2018-11-08","proceeding":null,"authors":["Jill-Jênn Vie","Hisashi Kashima"],"abstract":"Knowledge tracing is a sequence prediction problem where the goal is to\npredict the outcomes of students over questions as they are interacting with a\nlearning platform. By tracking the evolution of the knowledge of some student,\none can optimize instruction. Existing methods are either based on temporal\nlatent variable models, or factor analysis with temporal features. We here show\nthat factorization machines (FMs), a model for regression or classification,\nencompasses several existing models in the educational literature as special\ncases, notably additive factor model, performance factor model, and\nmultidimensional item response theory. We show, using several real datasets of\ntens of thousands of users and items, that FMs can estimate student knowledge\naccurately and fast even when student data is sparsely observed, and handle\nside information such as multiple knowledge components and number of attempts\nat item or skill level. Our approach allows to fit student models of higher\ndimension than existing models, and provides a testbed to try new combinations\nof features in order to improve existing models.","url_abs":"http://arxiv.org/abs/1811.03388v2","url_pdf":"http://arxiv.org/pdf/1811.03388v2.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":"knowledge-tracing-machines-factorization","repo_url":"https://github.com/jilljenn/ktm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"knowledge-tracing-machines-factorization","repo_url":"https://github.com/jilljenn/slam2018","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"knowledge-tracing","task_name":"Knowledge Tracing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.03388","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}