Analyzing Student Gaming with Bayesian Networks

摘要

This paper examines the problem of modeling when students are engaged in "gaming the system." We propose and partially validate an approach that uses a hidden Markov model, as is used in knowledge tracing, to estimate whether the student is gaming on the basis of observable actions. By doing so, we provide a common modeling approach that is applicable to gaming, or other constructs such as off task behavior. We find that our initial approach gave promising results, with parameter estimates that are plausible, and also exposed some weaknesses in our initial attempt. Specifically, that relying solely on response time is probably insufficient to construct a strong model of gaming.