Object-oriented modeling and design
Object-oriented modeling and design
Monitoring computer-based collaborative problem solving
Journal of Artificial Intelligence in Education
International Journal of Human-Computer Studies - Special issue: group support systems
Statistical Language Learning
A Coached Collaborative Learning Environment for Entity-Relationship Modeling
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
The reliability of a dialogue structure coding scheme
Computational Linguistics
Computer supported interaction analysis of group problem solving
CSCL '99 Proceedings of the 1999 conference on Computer support for collaborative learning
Using Dialogue Features to Predict Trouble During Collaborative Learning
User Modeling and User-Adapted Interaction
Designing collaborative learning systems: current trends & future research agenda
CSCL '05 Proceedings of th 2005 conference on Computer support for collaborative learning: learning 2005: the next 10 years!
A Method to Classify Collaboration in CSCL Systems
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
CSCL'07 Proceedings of the 8th iternational conference on Computer supported collaborative learning
Identifying the interaction context in CSCLE
CONTEXT'05 Proceedings of the 5th international conference on Modeling and Using Context
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
The big five and visualisations of team work activity
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Dynamical user networking and profiling based on activity streams for enhanced social learning
ICWL'11 Proceedings of the 10th international conference on Advances in Web-Based Learning
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Students bring to a collaborative learning situation a great deal of specialized knowledge and experiences that undoubtedly shape the collaboration and learning processes. How effectively this unique knowledge is shared and assimilated by the group affects both the process and the product of the collaboration. In this paper, we describe a machine learning approach, Hidden Markov Modeling, to analyzing and assessing on-line knowledge sharing conversations. We show that this approach can determine the effectiveness of knowledge sharing episodes with 93% accuracy, performing 43% over the baseline. Understanding how members of collaborative learning groups share, assimilate, and build knowledge together may help us identify situations in which facilitation may increase the effectiveness of the group interaction.