Abstract
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.
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References
Beck, J.E., Woolf, B.P.: High Level Student Modeling with Machine Learning. In: Gauthier, G., VanLehn, K., Frasson, C. (eds.) ITS 2000. LNCS, vol. 1839, pp. 584–593. Springer, Heidelberg (2000)
Breese, J., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pp. 43–52. Morgan Kaufmann, San Francisco (1998)
Cetintas, S., Si, L., Xin, Y.P., Hord, C.: Predicting Correctness of Problem Solving from Low-level Log Data in Intelligent Tutoring Systems. In: Proceedings of the 2nd International Conference on Educational Data Mining, pp. 230–239 (2009)
Cho, K., Schunn, C.C., Wilson, R.W.: Validity and Reliability of Scaffolded Peer Assessment of Writing from Instructor and Student Perspectives. Journal of Educational Psychology 98(4), 891–901 (2006)
Ding, Y., Li, X.: Time Weight Collaborative Filtering. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management, pp. 485–492. ACM, New York (2005)
Koren, Y.: Collaborative Filtering with Temporal Dynamics. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 447–456. ACM, New York (2009)
Lathia, N., Hailes, S., Capra, L.: Temporal Collaborative Filtering with Adaptive Neighborhoods. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 796–797. ACM, New York (2009)
Loll, F., Pinkwart, N.: Using Collaborative Filtering Algorithms as eLearning Tools. In: Proceedings of the 42nd Hawaii International Conference on System Sciences, pp. 1–10. IEEE Computer Society, Washington (2009)
Maletsky, E.M., Andrews, A.G., Burton, G.M., Johnson, H.C., Luckie, L.A.: Harcourt Math, Indiana edn. Harcourt, Chicago (2004)
Pinkwart, N., Aleven, V., Ashley, K., Lynch, C.: Toward Legal Argument Instruction with Graph Grammars and Collaborative Filtering Techniques. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 227–236. Springer, Heidelberg (2006)
Robinet, V., Bisson, G., Gordon, M.B., Lemaire, B.: Inducing High-Level Behaviors from Problem-Solving Traces Using Machine-Learning Tools. IEEE Intelligent Systems 22(4), 22–30 (2007)
Tsymbal, A.: The Problem of Concept Drift: Definitions and Related Work. Technical report, TCD-CS-2004-15, Comp. Sci. Department, Trinity College Dublin, Ireland (2004)
Walker, A., Recker, M., Lawless, K., Wiley, D.: Collaborative Information Filtering: a Review and an Educational Application. International Journal of Artificial Intelligence in Education 14, 1–26 (2004)
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Cetintas, S., Si, L., Xin, Y.P., Hord, C. (2010). Predicting Correctness of Problem Solving in ITS with a Temporal Collaborative Filtering Approach. In: Aleven, V., Kay, J., Mostow, J. (eds) Intelligent Tutoring Systems. ITS 2010. Lecture Notes in Computer Science, vol 6094. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13388-6_6
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DOI: https://doi.org/10.1007/978-3-642-13388-6_6
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