Informing the Detection of the Students' Motivational State: An Empirical Study
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Off-task behavior in the cognitive tutor classroom: when students "game the system"
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
An architecture to combine meta-cognitive and cognitive tutoring: Pilot testing the Help Tutor
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Inferring learning and attitudes from a Bayesian Network of log file data
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Data-Driven refinement of a probabilistic model of user affect
UM'05 Proceedings of the 10th international conference on User Modeling
Detecting when students game the system, across tutor subjects and classroom cohorts
UM'05 Proceedings of the 10th international conference on User Modeling
Modeling students' metacognitive errors in two intelligent tutoring systems
UM'05 Proceedings of the 10th international conference on User Modeling
Towards Collaborative Intelligent Tutors: Automated Recognition of Users' Strategies
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Domain-Specific and Domain-Independent Interactive Behaviors in Andes
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
International Journal of Artificial Intelligence in Education
An analysis of students' gaming behaviors in an intelligent tutoring system: predictors and impacts
User Modeling and User-Adapted Interaction
An analysis of gaming behaviors in an intelligent tutoring system
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
STEMscopes: contextualizing learning analytics in a K-12 science curriculum
Proceedings of the Third International Conference on Learning Analytics and Knowledge
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In recent years, a number of systems have been developed to detect differences in how students choose to use intelligent tutoring systems, and the attitudes and goals which underlie these decisions. These systems, when trained using data from human observations and questionnaires, can detect specific behaviors and attitudes with high accuracy. However, such data is time-consuming to collect, especially across an entire tutor curriculum. Therefore, to deploy a detector of behaviors or attitudes across an entire tutor curriculum, the detector must be able to transfer to a new tutor lesson without being re-trained using data from that lesson. In this paper, we present evidence that detectors of gaming the system can transfer to new lessons without re-training, and that training detectors with data from multiple lessons improves generalization, beyond just the gains from training with additional data.