{"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/option-tracing-beyond-binary-knowledge","title":"Option Tracing: Beyond Binary Knowledge Tracing","arxiv_id":null,"date":"2020-12-11","proceeding":null,"authors":["Aritra Ghosh","Andrew S. Lan"],"abstract":"This paper details our solutions to Tasks 1&2 of the NeurIPS 2020 Education Challenge.1 Knowledge tracing, a family of methods to estimate each student’s mastery\r\nlevels on skills/knowledge components from their past responses to assessment\r\nquestions, is useful for progress monitoring, personalization, and helping teachers\r\nto deliver personalized and targeted feedback to students to improve their learning\r\noutcomes. One key limitation of current knowledge tracing methods is that they\r\ncan only estimate an overall knowledge level of a student since they analyze only\r\nthe binary-valued correctness of student responses. We adapt a series of popular\r\nknowledge tracing methods to the task of option prediction in multiple choice\r\nquestions. Experimental results show that our method performs well on both option\r\nprediction and correctness prediction.","url_abs":"https://dqanonymousdata.blob.core.windows.net/neurips-public/papers/arigosh/task_1_2_edu_neurips_ghosh_lan.pdf","url_pdf":"https://dqanonymousdata.blob.core.windows.net/neurips-public/papers/arigosh/task_1_2_edu_neurips_ghosh_lan.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":"option-tracing-beyond-binary-knowledge","repo_url":"https://github.com/arghosh/NeurIPSEducation2020","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"knowledge-tracing","task_name":"Knowledge Tracing"},{"task_slug":"multiple-choice","task_name":"Multiple-choice"},{"task_slug":"prediction","task_name":"Prediction"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}