Case-based reasoning
Automatic Learning and Recognition of Graphical Symbols in Engineering Drawings
Selected Papers from the First International Workshop on Graphics Recognition, Methods and Applications
Using Graph Search Techniques for Contextual Colour Retrieval
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Extensionally defining principles and cases in ethics: an AI model
Artificial Intelligence - Special issue on AI and law
Link analysis ranking: algorithms, theory, and experiments
ACM Transactions on Internet Technology (TOIT)
Helping Teachers Handle the Flood of Data in Online Student Discussions
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Using Machine Learning Techniques to Analyze and Support Mediation of Student E-Discussions
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Empowering researchers to detect interaction patterns in e-collaboration
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Computer supported moderation of e-discussions: the ARGUNAUT approach
CSCL'07 Proceedings of the 8th iternational conference on Computer supported collaborative learning
Query-by-example: a data base language
IBM Systems Journal
Helping Teachers Handle the Flood of Data in Online Student Discussions
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Supporting Collaborative Learning and E-Discussions Using Artificial Intelligence Techniques
International Journal of Artificial Intelligence in Education
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Students in classrooms are starting to use visual argumentation tools for e-discussions --- a form of debate in which contributions are written into graphical shapes and linked to one another according to whether they, for instance, support or oppose one another. In order to moderate several simultaneous e-discussions effectively, teachers must be alerted regarding events of interest. We focused on the identification of clustersof contributions representing interaction patterns that are of pedagogical interest (e.g., a student clarifies his or her opinion and then gets feedback from other students). We designed an algorithm that takes an example cluster as input and uses inexact graph matching, text analysis, and machine learning classifiers to search for similar patterns in a given corpus. The method was evaluated on an annotated dataset of real e-discussions and was able to detect almost 80% of the annotated clusters while providing acceptable precision performance.