Square it up!: How to model step duration when predicting student performance

Abstract

In this paper, we explore how we can model students' response times to predict student performance in Intelligent Tutoring Systems. Related research suggests that response time can provide information with respect to correctness. However, time is not consistently used when modeling students' performance. Here, we build on previous work that indicated that the relationship between response time and student performance is non-linear. Based on this concept, we compare three models: a standard Additive Factors Analysis Model (AFM), an AFM model enhanced with a linear step duration parameter and an AFM model enhanced with a quadratic, step duration parameter. The results of this comparison show that the AFM model that is enhanced with the quadratic step duration parameter outperforms the other models over four different datasets and for most of the metrics we used to evaluate the models in cross validation and prediction.