Detecting when students game the system, across tutor subjects and classroom cohorts

Authors:
Ryan Shaun Baker;Albert T. Corbett;Kenneth R. Koedinger;Ido Roll
Affiliations:
Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA;Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA;Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA;Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA
Venue:
UM'05 Proceedings of the 10th international conference on User Modeling
Year:
2005

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Abstract

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.