Item Ordering Effects with Qualitative Explanations using Online Adaptive Tutoring Data

Abstract

Online computer adaptive learning is increasingly being used in classrooms as a way to provide guided learning for students. Such tutors have the potential to provide tailored feedback based on specific student needs and misunderstandings. Bayesian knowledge tracing (BKT) is used to model student knowledge when knowledge is assumed to be changing throughout a single assessment period; in contrast, traditional Item Response Theory (IRT) models assume student knowledge to be constant within an assessment period. The basic BKT model assumes that the chance a student transitions from "not knowing" to "knowing" after each item is the same, and problems are considered learning opportunities. It could be the case, however, that learning is actually context sensitive, where students' learning might be improved when the items and their associated tutoring content are delivered to the student in a particular order. In this paper, we use BKT models to find such context sensitive transition probabilities from real data delivered by an online tutoring system, ASSISTments. After empirically deriving orderings that lead to better learning, we qualitatively analyze the items and their tutoring content to uncover any mechanisms that might explain why such orderings are modeled to have higher learning potential.