Data-driven misconception discovery in constraint-based intelligent tutoring systems
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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.
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Field of Research::08 - Information and Computing Sciences::0801 - Artificial Intelligence and Image Processing::080105 - Expert Systems
Fields of Research::39 - Education::3904 - Specialist studies in education::390405 - Educational technology and computing