Help seeking, learning and contingent tutoring
Computers & Education
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
Developing a generalizable detector of when students game the system
User Modeling and User-Adapted Interaction
Automatic Generation of Fine-Grained Representations of Learner Response Semantics
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Toward Meta-cognitive Tutoring: A Model of Help Seeking with a Cognitive Tutor
International Journal of Artificial Intelligence in Education
Can Help Seeking Be Tutored? Searching for the Secret Sauce of Metacognitive Tutoring
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Classification errors in a domain-independent assessment system
EANL '08 Proceedings of the Third Workshop on Innovative Use of NLP for Building Educational Applications
A corpus of fine-grained entailment relations
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Generalizing detection of gaming the system across a tutoring curriculum
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Towards teaching metacognition: supporting spontaneous self-assessment
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
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Intelligent tutoring systems help students acquire cognitive skills by tracing students' knowledge and providing relevant feedback. However, feedback that focuses only on the cognitive level might not be optimal – errors are often the result of inappropriate metacognitive decisions. We have developed two models which detect aspects of student faulty metacognitive behavior: A prescriptive rational model aimed at improving help-seeking behavior, and a descriptive machine-learned model aimed at eliminating attempts to “game” the tutor. In a comparison between the two models we found that while both successfully identify gaming behavior, one is better at characterizing the types of problems students game in, and the other captures a larger variety of faulty behaviors. An analysis of students' actions in two different tutors suggests that the help-seeking model is domain independent, and that students' behavior is fairly consistent across classrooms, age groups, domains, and task elements.