Developing a generalizable detector of when students game the system

Some students, when working in interactive learning environments, attempt to "game the system", attempting to succeed in the environment by exploiting properties of the system rather than by learning the material and trying to use that knowledge to answer correctly. In this paper, we present a system that can accurately detect whether a student is gaming the system, within a Cognitive Tutor mathematics curricula. Our detector also distinguishes between two distinct types of gaming which are associated with different learning outcomes. We explore this detector's generalizability, and find that it transfers successfully to both new students and new tutor lessons. Key words: Gaming the System, Latent Response models, Cognitive Tutors, behavior detection, machine learning, generalizable models, student modeling, interactive learning environments

• Type: Journal
• Year: 2008
• Where: UMUAI
• Authors: Ryan Shaun Joazeiro de Baker, Albert T. Corbett, Ido Roll, Kenneth R. Koedinger
• Keywords: Cognitive Tutors, Interactive Learning Environments, Latent Response Models, UMUAI 2008