Explicit referencing in chat supports collaborative learning
CSCL '05 Proceedings of th 2005 conference on Computer support for collaborative learning: learning 2005: the next 10 years!
Online large-margin training of dependency parsers
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Humoroids: conversational agents that induce positive emotions with humor
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Engagement vs. Deceit: Virtual Humans with Human Autobiographies
IVA '09 Proceedings of the 9th International Conference on Intelligent Virtual Agents
Optimizing endpointing thresholds using dialogue features in a spoken dialogue system
SIGdial '08 Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Effects of conversational agents on human communication in thought-evoking multi-party dialogues
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Design for Off-task Interaction - Rethinking Pedagogy in Technology Enhanced Learning
ICALT '10 Proceedings of the 2010 10th IEEE International Conference on Advanced Learning Technologies
Engaging learning groups using social interaction strategies
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
Socially capable conversational tutors can be effective in collaborative learning situations
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
AutoTutor: an intelligent tutoring system with mixed-initiative dialogue
IEEE Transactions on Education
Triggering effective social support for online groups
ACM Transactions on Interactive Intelligent Systems (TiiS)
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Instructional efficacy of automated Conversational Agents designed to help small groups of students achieve higher learning outcomes can be improved by the use of social interaction strategies. These strategies help the tutor agent manage the attention of the students while delivering useful instructional content. Two technical challenges involving the use of social interaction strategies include determining the appropriate policy for triggering these strategies and regulating the amount of social behavior performed by the tutor. In this paper, a comparison of six different triggering policies is presented. We find that a triggering policy learnt from human behavior in combination with a filter that keeps the amount of social behavior comparable to that performed by human tutors offers the most effective solution to the these challenges.