Detecting When Students Game the System, Across Tutor Subjects and Classroom Cohorts
Building a generalizable detector of student behavior within intelligent tutoring systems presents two challenges: transferring between different cohorts of students (who may develop idiosyncratic strategies of use), and transferring between different tutor lessons (which may have considerable variation in their interfaces, making cognitively equivalent behaviors appear quite different within log files). In this paper, we present a machine-learned detector which identifies students who are “gaming the system”, attempting to complete problems with minimal cognitive effort, and determine that the detector transfers successfully across student cohorts but less successfully across tutor lessons.
Unable to display preview. Download preview PDF.
- 1.Baker, R.S., Corbett, A.T., Koedinger, K.R.: Detecting Student Misuse of Intelligent Tutoring Systems. In: Proceedings of the 7th International Conference on Intelligent Tutoring Systems, pp. 531–540 (2004)Google Scholar
- 2.Baker, R.S., Corbett, A.T., Koedinger, K.R., Wagner, A.Z.: Off-Task Behavior in the Cognitive Tutor Classroom: When Students Game the System. In: Proceedings of ACM CHI 2004: Computer-Human Interaction, pp. 383–390 (2004)Google Scholar
- 6.Yu, L., Liu, H.: Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution. In: Proc. of the Intl. Conference on Machine Learning (ICML 2003), pp. 856–863 (2003)Google Scholar