Generalizing models of student affect in game-based learning environments

Abstract

Evidence of the strong relationship between learning and emotion has fueled recent work in modeling affective states in intelligent tutoring systems. Many of these models are designed in ways that limit their ability to be deployed to a large audience of students by using expensive sensors or subject-dependent machine learning techniques. This paper presents work that investigates empirically derived Bayesian networks for prediction of student affect. Predictive models are empirically learned from data acquired from 260 students interacting with the game-based learning environment, Crystal Island. These models are then tested on data from a second identical study involving 140 students to examine issues of generalizability of learned predictive models of student affect. The findings suggest that predictive models of affect that are learned from empirical data may have significant dependencies on the populations on which they are trained, even when the populations themselves are very similar. © 2011 Springer-Verlag.