Collaborative filtering (CF) is a technique that utilizes how users are associated with items in a target application and predicts the utility of items for a particular user. Temporal collaborative filtering (temporal CF) is a time-sensitive CF approach that considers the change in user-item interactions over time. Despite its capability to deal with dynamic educational applications with rapidly changing user-item interactions, there is no prior research of temporal CF on educational tasks. This paper proposes a temporal CF approach to automatically predict the correctness of students' problem solving in an intelligent math tutoring system. Unlike traditional user-item interactions, a student may work on the same problem multiple times, and there are usually multiple interactions for a student-problem pair. The proposed temporal CF approach effectively utilizes information coming from multiple interactions and is compared to i) a traditional CF approach, ii) a temporal CF approach that uses a sliding-time-window but ignores old data and multiple interactions and iii) a combined temporal CF approach that uses a sliding-time-window together with multiple interactions. An extensive set of experiment results show that using multiple-interactions significantly improves the prediction accuracy while using sliding-time-windows doesn't make a significant difference. © Springer-Verlag Berlin Heidelberg 2010.
CITATION STYLE
Cetintas, S., Si, L., Xin, Y. P., & Hord, C. (2010). Predicting correctness of problem solving in ITS with a temporal collaborative filtering approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6094 LNCS, pp. 15–24). https://doi.org/10.1007/978-3-642-13388-6_6
Mendeley helps you to discover research relevant for your work.