Desirable Difficulty and Other Predictors of Effective Item Orderings.

Online adaptive tutoring systems are 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. The basic BKT model assumes that the chance a student transitions from ”not knowing”to ”knowing”after each item is the same, with each item in the tutor considered a learning opportunity. It could be the case, however, that learning is actually context sensitive; context in our analysis is the order in which the items were administered. In this paper, we use BKT models to find such context sensitive transition probabilities in a mathematics tutoring system and offer a methodology to test the significance of our model based findings. We employ cross validation techniques to find models where including item ordering context improves predictive capability compared to the base BKT models. We then use regression testing to try to find features that may predict the effectiveness of an item ordering.