Introduction to the special issue on the fusion of domain knowledge with data for decision support
The Journal of Machine Learning Research
Providing support for adaptive scripting in an on-line collaborative learning environment
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
MArCo: Building an Artificial Conflict Mediator to Support Group Planning Interactions
International Journal of Artificial Intelligence in Education - "Caring for the Learner" in honour of John Self
Using Knowledge Tracing in a Noisy Environment to Measure Student Reading Proficiencies
International Journal of Artificial Intelligence in Education
Supporting Collaborative Learning With An Intelligent Web-Based System
International Journal of Artificial Intelligence in Education
Tutorial Dialogue as Adaptive Collaborative Learning Support
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Context Based Classification for Automatic Collaborative Learning Process Analysis
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
New challenges in CSCL: towards adaptive script support
ICLS'08 Proceedings of the 8th international conference on International conference for the learning sciences - Volume 3
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Using automated dialog analysis to assess peer tutoring and trigger effective support
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
A review of recent advances in learner and skill modeling in intelligent learning environments
User Modeling and User-Adapted Interaction
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Collaborative activities, like peer tutoring, can be beneficial for student learning, but only when students are supported in interacting effectively. Constructing intelligent tutors for collaborating students may be an improvement over fixed forms of support that do not adapt to student behaviors. We have developed an intelligent tutor to improve the help that peer tutors give to peer tutees by encouraging them to explain tutee errors and to provide more conceptual help. The intelligent tutor must be able to classify the type of peer tutor utterance (is it next step help, error feedback, both, or neither?) and the quality (does it contain conceptual content?). We use two techniques to improve automated classification of student utterances: incorporating domain context, and incorporating students' self-classifications of their chat actions. The domain context and self-classifications together significantly improve classification of student dialogue over a baseline classifier for help type. Using domain features alone significantly improves classification over baseline for conceptual content.