{"pk":31342,"title":"Using Theory Revision to Model Students and Acquire Stereotypical Errors","subtitle":null,"abstract":"Student modeling has been identified as an important component to the long term development of Intelligent Computer-Aided Instruction (ICAI) systems. Two basic approaches have evolved to model student misconceptions. One uses a static, predefined library of user bugs which contains the misconceptions modeled by the system. The other uses induction to learn student misconceptions from scratch. Here, we present a third approach that uses a machine learning technique called theory revision. Using theory revision allows the system to automatically construct a bug library for use in modeling while retaining the flexibility to address novel errors.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[],"section":"Talks","is_remote":true,"remote_url":"https://escholarship.org/uc/item/7pj183nd","frozenauthors":[{"first_name":"Paul","middle_name":"T.","last_name":"Baffes","name_suffix":"","institution":"University of Texas at Austin","department":""},{"first_name":"Raymond","middle_name":"J.","last_name":"Mooney","name_suffix":"","institution":"University of Texas at Austin","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"1992-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/31342/galley/22411/download/"}]}