Abstract
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.
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Roll, I., Baker, R.S., Aleven, V., McLaren, B.M., Koedinger, K.R. (2005). Modeling Students’ Metacognitive Errors in Two Intelligent Tutoring Systems. In: Ardissono, L., Brna, P., Mitrovic, A. (eds) User Modeling 2005. UM 2005. Lecture Notes in Computer Science(), vol 3538. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527886_48
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DOI: https://doi.org/10.1007/11527886_48
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