Abstract
For decades, intelligent tutoring systems researchers have been developing various methods of student modeling. Most of the models, including two of the most popular approaches: Knowledge Tracing model and Performance Factor Analysis, all have similar assumption: the information needed to model the student is the student’s performance. However, there are other sources of information that are not utilized, such as the performance on other students in same class. This paper extends the Student-Skill extension of Knowledge Tracing, to take into account the class information, and learns four parameters: prior knowledge, learn, guess and slip for each class of students enrolled in the system. The paper then compares the accuracy using the four parameters for each class versus the four parameters for each student to find out which parameter set works better in predicting student performance. The result shows that modeling at coarser grain sizes can actually result in higher predictive accuracy, and data about classmates’ performance is results in a higher predictive accuracy on unseen test data.
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References
Corbett, A., Anderson, J.: Knowledge Tracing: Modeling the Acquisition of Procedural Knowledge. User Modeling and User-Adapted Interaction 4, 253–278 (1995)
Pavlik, P.I., Cen, H., Koedinger, K.: Performance Factors Analysis – A New Alternative to Knowledge. In: Proceedings of the 14th International Conference on Artificial Intelligence in Education, pp. 531–538 (2009)
Gong, Y., Beck, J.E., Ruiz, C.: Modeling Multiple Distributions of Student Performances to Improve Predictive Accuracy. In: Masthoff, J., Mobasher, B., Desmarais, M.C., Nkambou, R. (eds.) UMAP 2012. LNCS, vol. 7379, pp. 102–113. Springer, Heidelberg (2012)
Wang, Y., Heffernan, N.T.: The Student Skill Model. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds.) ITS 2012. LNCS, vol. 7315, pp. 399–404. Springer, Heidelberg (2012)
Pardos, Z.A., Heffernan, N.T.: Modeling Individualization in a Bayesian Networks Implementation of Knowledge Tracing. In: De Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 255–266. Springer, Heidelberg (2010)
Murphy, K.P.: The Bayes Net Toolbox for Matlab, Computing Science and Statistics. Proceedings of Interface 33 (2001)
Xiong, X., Beck, J.E., Li, S.: Class distinctions: Leveraging class-level features to predict student retention performance. In: Chad Lane, H., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS (LNAI), vol. 7926, pp. 820–823. Springer, Heidelberg (2013)
Trivedi, S., Pardos, Z.A., Heffernan, N.T.: The Utility of Clustering in Prediction Tasks. In: Proceedings of the 17th Conference on Knowledge Discovery and Data Mining (2011)
Song, F., Sarkozy, G.N., Trivedi, S., Wang, Y., Heffernan, N.T.: Applying Clustering to the Problem of Predicting Retention within an ITS: Comparing Regularity Clustering with Traditional Methods. Accepted by the 24th FLAIRS
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Wang, Y., Beck, J. (2013). Class vs. Student in a Bayesian Network Student Model. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds) Artificial Intelligence in Education. AIED 2013. Lecture Notes in Computer Science(), vol 7926. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39112-5_16
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DOI: https://doi.org/10.1007/978-3-642-39112-5_16
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