Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Using Dialogue Features to Predict Trouble During Collaborative Learning
User Modeling and User-Adapted Interaction
Supporting CSCL with automatic corpus analysis technology
CSCL '05 Proceedings of th 2005 conference on Computer support for collaborative learning: learning 2005: the next 10 years!
YALE: rapid prototyping for complex data mining tasks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
The class imbalance problem: A systematic study
Intelligent Data Analysis
What's in a Cluster? Automatically Detecting Interesting Interactions in Student E-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
Tutorial Dialogue as Adaptive Collaborative Learning Support
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
CSCL'07 Proceedings of the 8th iternational conference on Computer supported collaborative learning
What's in a Cluster? Automatically Detecting Interesting Interactions in Student E-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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E-discussion tools provide students with the opportunity not only to learn about the topic under discussion but to acquire argumentation and collaboration skills and to engage in analytic thinking. However, too often, e-discussions are not fruitful and moderation is needed. We describe our approach, which employs intelligent data analysis techniques, to support teachers as they moderate multiple simultaneous discussions. We have generated six machine-learned classifiers for detecting potentially important discussion characteristics, such as a "reasoned claim" and an "argument-counterargument" sequence. All of our classifiers have achieved satisfactory Kappa values and are integrated in an online classification system. We hypothesize how a teacher might use this information by means of two authentic e-discussion examples. Finally, we discuss ways to bootstrap from these fine-grained classifications to the analysis of more complex patterns of interaction.