Modeling Students' Metacognitive Errors in Two Intelligent Tutoring Systems
posted on 2005-01-01, 00:00 authored by Ido Roll, Ryan S. Baker, Vincent Aleven, Bruce M. McLaren, Kenneth R. KoedingerIntelligent 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.