Modeling Students' Metacognitive Errors in Two Intelligent Tutoring Systems Ido Roll Ryan S. Baker Vincent Aleven Bruce M. McLaren Kenneth R. Koedinger 10.1184/R1/6470261.v1 https://kilthub.cmu.edu/articles/journal_contribution/Modeling_Students_Metacognitive_Errors_in_Two_Intelligent_Tutoring_Systems/6470261 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. 2005-01-01 00:00:00 Human Computer Interaction