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