{"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/incorporating-features-learned-by-an-enhanced","title":"Incorporating Features Learned by an Enhanced Deep Knowledge Tracing Model for STEM/Non-STEM Job Prediction","arxiv_id":"1806.03256","date":"2018-06-06","proceeding":null,"authors":["Chun-kit Yeung","Zizheng Lin","Kai Yang","Dit-yan Yeung"],"abstract":"The 2017 ASSISTments Data Mining competition aims to use data from a\nlongitudinal study for predicting a brand-new outcome of students which had\nnever been studied before by the educational data mining research community.\nSpecifically, it facilitates research in developing predictive models that\npredict whether the first job of a student out of college belongs to a STEM\n(the acronym for science, technology, engineering, and mathematics) field. This\nis based on the student's learning history on the ASSISTments blended learning\nplatform in the form of extensive clickstream data gathered during the middle\nschool years. To tackle this challenge, we first estimate the expected\nknowledge state of students with respect to different mathematical skills using\na deep knowledge tracing (DKT) model and an enhanced DKT (DKT+) model. We then\ncombine the features corresponding to the DKT/DKT+ expected knowledge state\nwith other features extracted directly from the student profile in the dataset\nto train several machine learning models for the STEM/non-STEM job prediction.\nOur experiments show that models trained with the combined features generally\nperform better than the models trained with the student profile alone. Detailed\nanalysis of the student's knowledge state reveals that, when compared with\nnon-STEM students, STEM students generally show a higher mastery level and a\nhigher learning gain in mathematics.","url_abs":"http://arxiv.org/abs/1806.03256v1","url_pdf":"http://arxiv.org/pdf/1806.03256v1.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":"incorporating-features-learned-by-an-enhanced","repo_url":"https://github.com/ckyeungac/ADM2017","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"job-prediction","task_name":"Job Prediction"},{"task_slug":"knowledge-tracing","task_name":"Knowledge Tracing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}