Informing the Detection of the Students' Motivational State: An Empirical Study
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Diagnosing and acting on student affect: the tutor's perspective
User Modeling and User-Adapted Interaction
Developing a generalizable detector of when students game the system
User Modeling and User-Adapted Interaction
What Are You Feeling? Investigating Student Affective States During Expert Human Tutoring Sessions
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Engagement tracing: using response times to model student disengagement
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Effects of Dissuading Unnecessary Help Requests While Providing Proactive Help
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Tools for Authoring a Dialogue Agent that Participates in Learning Studies
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Repairing Disengagement With Non-Invasive Interventions
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Empirically building and evaluating a probabilistic model of user affect
User Modeling and User-Adapted Interaction
Prevention of off-task gaming behavior in intelligent tutoring systems
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Gaze tutor: A gaze-reactive intelligent tutoring system
International Journal of Human-Computer Studies
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Adapting to multiple affective states in spoken dialogue
SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Learner characteristics and dialogue: recognising effective and student-adaptive tutorial strategies
International Journal of Learning Technology
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We investigate whether an overall student disengagement label and six different labels of disengagement type are predictive of learning in a spoken dialog computer tutoring corpus. Our results show first that although students' percentage of overall disengaged turns negatively correlates with the amount they learn, the individual types of disengagement correlate differently with learning: some negatively correlate with learning, while others don't correlate with learning at all. Second, we show that these relationships change somewhat depending on student prerequisite knowledge level. Third, we show that using multiple disengagement types to predict learning improves predictive power. Overall, our results suggest that although adapting to disengagement should improve learning, maximizing learning requires different system interventions depending on disengagement type.