Abstract
Effective pedagogical strategies are important for e-learning environments. While it is assumed that an effective learning environment should craft and adapt its actions to the user’s needs, it is often not clear how to do so. In this paper, we used a Natural Language Tutoring System named Cordillera and applied Reinforcement Learning (RL) to induce pedagogical strategies directly from pre-existing human user interaction corpora. 50 features were explored to model the learning context. Of these features, domain-oriented and system performance features were the most influential while user performance and background features were rarely selected. The induced pedagogical strategies were then evaluated on real users and results were compared with pre-existing human user interaction corpora. Overall, our results show that RL is a feasible approach to induce effective, adaptive pedagogical strategies by using a relatively small training corpus. Moreover, we believe that our approach can be used to develop other adaptive and personalized learning environments.
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VanLehn, K., Jordan, P.W., Rosé, C.P., Bhembe, D., et al.: The architecture of why2-atlas: A coach for qualitative physics essay writing. In: Cerri, S.A., Gouardéres, G., Paraguaçu, F. (eds.) ITS 2002. LNCS, vol. 2363, pp. 158–167. Springer, Heidelberg (2002)
Chi, M.T.H., Siler, S.A., Jeong, H., Yamauchi, T., Hausmann, R.G.: Learning from human tutoring. Cognitive Science 25, 471–533 (2001)
Singh, S.P., Litman, D.J., Kearns, M.J., Walker, M.A.: Optimizing dialogue management with reinforcement learning: Experiments with the njfun system. J. Artif. Intell. Res. (JAIR) 16, 105–133 (2002)
Raux, A., Langner, B., Bohus, D., Black, A.W., Eskenazi, M.: Let’s go public! taking a spoken dialog system to the real world. In: Proceedings of Interspeech, Eurospeech (2005)
Collins, A., Brown, J.S., Newman, S.E.: Cognitive apprenticeship: Teaching the craft of reading, writing and mathematics. In: Resnick, L.B. (ed.) Knowing, learning and instruction: Essays in honor of Robert Glaser, pp. 453–494. Lawrence Erlbaum Associates, Hillsdale (1989)
Chi, M.T.H., de Leeuw, N., Chiu, M.H., LaVancher, C.: Eliciting self-explanations improves understanding. Cognitive Science 18(3), 439–477 (1994)
Conati, C., VanLehn, K.: Toward computer-based support of meta-cognitive skills: a computational framework to coach self-explanation. International Journal of Artificial Intelligence in Education 11, 398–415 (2000)
Katz, S., O’Donnell, G., Kay, H.: An approach to analyzing the role and structure of reflective dialogue. International Journal of Artificial Intelligence and Education 11, 320–343 (2000)
Singh, S.P., Kearns, M.J., Litman, D.J., Walker, M.A.: Reinforcement learning for spoken dialogue systems. In: Solla, S.A., Leen, T.K., Müller, K.R. (eds.) NIPS, pp. 956–962. The MIT Press, Cambridge (1999)
Sutton, R.S., Barto, A.G.: Reinforcement Learning. MIT Press Bradford Books, Cambridge (1998)
Tetreault, J.R., Litman, D.J.: A reinforcement learning approach to evaluating state representations in spoken dialogue systems. Speech Communication 50(8-9), 683–696 (2008)
VanLehn, K., Jordan, P.W., Litman, D.: Developing pedagogically effective tutorial dialogue tactics: Experiments and a testbed. In: Proceedings of SLaTE Workshop on Speech and Language Technology in Education ISCA Tutorial and Research Workshop (2007)
Anderson, J.R.: The architecture of cognition. Harvard University Press, Cambridge (1983)
Newell, A. (ed.): Unified Theories of Cognition. Harvard University Press, Cambridge (1994); Reprint edition
Moore, J.D., Porayska-Pomsta, K., Varges, S., Zinn, C.: Generating tutorial feedback with affect. In: Barr, V., Markov, Z. (eds.) FLAIRS Conference. AAAI Press, Menlo Park (2004)
Beck, J., Woolf, B.P., Beal, C.R.: Advisor: A machine learning architecture for intelligent tutor construction. In: AAAI/IAAI, pp. 552–557. AAAI Press / The MIT Press (2000)
Forbes-Riley, K., Litman, D.J., Purandare, A., Rotaru, M., Tetreault, J.R.: Comparing linguistic features for modeling learning in computer tutoring. In: Luckin, R., Koedinger, K.R., Greer, J.E. (eds.) AIED. Frontiers in Artificial Intelligence and Applications, vol. 158, pp. 270–277. IOS Press, Amsterdam (2007)
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Chi, M., VanLehn, K., Litman, D., Jordan, P. (2010). Inducing Effective Pedagogical Strategies Using Learning Context Features. In: De Bra, P., Kobsa, A., Chin, D. (eds) User Modeling, Adaptation, and Personalization. UMAP 2010. Lecture Notes in Computer Science, vol 6075. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13470-8_15
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DOI: https://doi.org/10.1007/978-3-642-13470-8_15
Publisher Name: Springer, Berlin, Heidelberg
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