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Interaction Networks: Generating High Level Hints Based on Network Community Clustering.

ERIC Descriptors: Data Analysis; Interaction; Network Analysis; Problem Solving; Feedback (Response); Intelligent Tutoring Systems; Graphs

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We introduce a novel data structure, the Interaction Network, for representing interaction-data from open problem solving environment tutors. We show how using network community detecting techniques are used to identify sub-goals in problems in a logic tutor. We then use those community structures to generate high level hints between sub-goals. The preliminary results show that using network analysis techniques are promising for exploring and understanding user data from open problem solving environments. (Contains 3 figures.) [For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012)," see ED537074.]

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Author:Eagle, Michael; Johnson, Matthew; Barnes, Tiffany
Publication:ERIC: Reports
Article Type:Abstract
Geographic Code:1USA
Date:Jun 19, 2012
Words:185
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