Data-driven misconception discovery in constraint-based intelligent tutoring systems (2012)
Type of ContentConference Contributions - Published
PublisherUniversity of Canterbury. Computer Science and Software Engineering
Students often have misconceptions in the domain they are studying. Misconception identification is a difficult task but allows teachers to create strategies to appropriately address misconceptions held by students. This project investigates a data-driven technique to discover students' misconceptions in interactions with constraint-based Intelligent Tutoring Systems(ITSs). This analysis has not previously been done. EER-Tutor is one such constraint-based ITS, which teaches conceptual database design using Enhanced Entity-Relationship (EER) data modelling. As with any ITS, a lot of data about each student's interaction within EER-Tutor are available: as individual student models, containing constraint histories, and logs, containing detailed information about each student action. This work can be extended to other ITSs and their relevant domains.
CitationElmadani, M., Mathews, M., Mitrovic, A. (2012) Data-driven misconception discovery in constraint-based intelligent tutoring systems. Singapore: 20th International Conference on Computers in Education (ICCE), 26-30 Nov 2012. 9-16.
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KeywordsMisconceptions; data mining; constraint-based modeling
ANZSRC Fields of Research08 - Information and Computing Sciences::0806 - Information Systems::080602 - Computer-Human Interaction
08 - Information and Computing Sciences::0801 - Artificial Intelligence and Image Processing::080105 - Expert Systems
39 - Education::3904 - Specialist studies in education::390405 - Educational technology and computing
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