Abstract
We present an automated detector that can predict a student’s future performance on a transfer post-test, a post-test involving related but different skills than the skills studied in the tutoring system, within an Intelligent Tutoring System for College Genetics. We show that this detector predicts transfer better than Bayesian Knowledge Tracing, a measure of student learning in intelligent tutors that has been shown to predict performance on paper post-tests of the same skills studied in the intelligent tutor. We also find that this detector only needs limited amounts of student data (the first 20% of a student’s data from a tutor lesson) in order to reach near-asymptotic predictive power.
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References
Aleven, V., Koedinger, K.R.: An effective metacognitive strategy: learning by doing and explaining with a computer-based Cognitive Tutor. Cognitive Science 26, 147–179 (2002)
Aleven, V., McLaren, B., Roll, I., Koedinger, K.: Toward meta-cognitive tutoring: A model of help seeking with a Cognitive Tutor. International Journal of Artificial Intelligence and Education 16, 101–128 (2006)
Baker, R.S.J.d., Corbett, A.T., Gowda, S.M., Wagner, A.Z., MacLaren, B.M., Kauffman, L.R., Mitchell, A.P., Giguere, S.: Contextual Slip and Prediction of Student Performance After Use of an Intelligent Tutor. In: Proceedings of the 18th Annual Conference on User Modeling, Adaptation, and Personalization, pp. 52–63 (2010)
Baker, R.S.J.d., Corbett, A.T., Roll, I., Koedinger, K.R.: Developing a Generalizable Detector of When Students Game the System. User Modeling and User-Adapted Interaction 18(3), 287–314 (2008)
Baker, R.S.J.d., Goldstein, A.B., Heffernan, N.T.: Detecting the moment of learning. In: Aleven, V., Kay, J., Mostow, J. (eds.) ITS 2010. LNCS, vol. 6094, pp. 25–34. Springer, Heidelberg (2010)
Bransford, J.D., Schwartz, D.: Rethinking transfer: A simple proposal with multiple implications. Review of Research in Education 24, 61–100 (1999)
Butcher, K.R.: How Diagram Interaction Supports Learning: Evidence from Think Alouds during Intelligent Tutoring. In: Goel, A.K., Jamnik, M., Narayanan, N.H. (eds.) Diagrams 2010. LNCS, vol. 6170, pp. 295–297. Springer, Heidelberg (2010)
Corbett, A.T., Anderson, J.R.: Knowledge Tracing: Modeling the Acquisition of Procedural Knowledge. User Modeling and User-Adapted Interaction 4, 253–278 (1995)
Corbett, A., Bhatnagar, A.: Student Modeling in the ACT Programming Tutor: Adjusting Procedural Learning Model with Declarative Knowledge. In: User Modeling: Proceedings of the 6th International Conference, pp. 243–254 (1997)
Corbett, A.T., Kauffman, L., MacLaren, B., Wagner, A., Jones, E.: A Cognitive Tutor for Genetics Problem Solving: Learning Gains and Student Modeling. Journal of Educational Computing Research 42(2), 219–239 (2010)
Efron, B., Gong, G.: A leisurely look at the bootstrap, the jackknife, and cross-validation. American Statistician 37, 36–48 (1983)
Gong, Y., Beck, J.E., Heffernan, N.T.: Comparing Knowledge Tracing and Performance Factor Analysis by Using Multiple Model Fitting Procedures. In: Aleven, V., Kay, J., Mostow, J. (eds.) ITS 2010. LNCS, vol. 6094, pp. 35–44. Springer, Heidelberg (2010)
Koedinger, K.R., Corbett, A.T., Perfetti, C.: (under review) The Knowledge-Learning-Instruction (KLI) Framework: Toward Bridging the Science-Practice Chasm to Enhance Robust Student Learning (manuscript under review)
Martin, J., VanLehn, K.: Student Assessment Using Bayesian Nets. International Journal of Human-Computer Studies 42, 575–591 (1995)
Pavlik, P.I., Anderson, J.R.: Using a Model to Compute the Optimal Schedule of Practice. Journal of Experimental Psychology: Applied 14(2), 101–117 (2008)
Pavlik, P.I., Cen, H., Koedinger, J.R.: Performance Factors Analysis – A New Alternative to Knowledge Tracing. In: Proceedings of the 14th International Conference on Artificial Intelligence in Education, pp. 531–540 (2009)
Salden, R.J.C.M., Koedinger, K.R., Renkl, A., Aleven, V., McLaren, B.M.: Accounting for Beneficial Effects of Worked Examples in Tutored Problem Solving. Educational Psychology Review 22, 379–392 (2010)
Shih, B., Koedinger, K.R., Scheines, R.: A response time model for bottom-out hints as worked examples. In: Proc. 1st Int’l Conf. on Educational Data Mining, pp. 117–126 (2008)
Shute, V.J.: SMART: Student modeling approach for responsive tutoring. User Modeling and User-Adapted Interaction 5(1), 1–44 (1995)
Singley, M.K., Anderson, J.R.: The Transfer of Cognitive Skill. Harvard University Press, Cambridge (1989)
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Baker, R.S.J.d., Gowda, S.M., Corbett, A.T. (2011). Towards Predicting Future Transfer of Learning. In: Biswas, G., Bull, S., Kay, J., Mitrovic, A. (eds) Artificial Intelligence in Education. AIED 2011. Lecture Notes in Computer Science(), vol 6738. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21869-9_6
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DOI: https://doi.org/10.1007/978-3-642-21869-9_6
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