Abstract
Interactive narrative-centered learning environments offer significant potential for scaffolding guided discovery learning in rich virtual storyworlds while creating engaging and pedagogically effective experiences. Within these environments students actively participate in problem-solving activities. A significant challenge posed by narrative-centered learning environments is devising accurate models of narrative-centered tutorial decision making to craft customized story-based learning experiences for students. A promising approach is developing empirically driven models of narrative-centered tutorial decision-making. In this work, a dynamic Bayesian network has been designed to make narrative-centered tutorial decisions. The network parameters were learned from a corpus collected in a Wizard-of-Oz study in which narrative and tutorial planning activities were performed by humans. The performance of the resulting model was evaluated with respect to predictive accuracy and yields encouraging results.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Johnson, L., Wu, S.: Assessing aptitude for learning with a serious game for foreign language and culture. In: Woolf, B.P., Aïmeur, E., Nkambou, R., Lajoie, S. (eds.) ITS 2008. LNCS, vol. 5091, pp. 520–529. Springer, Heidelberg (2008)
Rowe, J., Shores, L., Mott, B., Lester, J.: Integrating learning and engagement in narrative-centered learning environments. In: Aleven, V., Kay, J., Mostow, J. (eds.) ITS 2010. LNCS, vol. 6095, pp. 166–177. Springer, Heidelberg (2010)
Bruner, J.: The Act of Discovery. Harv. Educ. Rev. 31, 21–32 (1961)
de Jong, T., Joolingen, W.: Scientific Discovery Learning with Computer Simulations of Conceptual Domains. Rev. Educ. Res. 68(2), 179–201 (1998)
Mayer, R.: Should There Be a Three-Strike Rule Against Pure Discovery Learning? American Psychologist 59(1), 4–19 (2004)
Kirschner, P., Sweller, J., Clark, R.: Why Minimal Guidance During Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-based, Experiential, and Inquiry-based Teaching. Educational Psychologist 41, 75–86 (2006)
Shulman, L., Keisler, E.: Learning by Discovery: A Critical Appraisal. Rand McNally, Chicago (1966)
Machado, I., Brna, P., Paiva, A.: Learning by Playing: Supporting and Guiding Story-Creation Activities. In: 10th International Conference on Artificial Intelligence in Education, Amsterdam, Netherlands, pp. 334–342 (2001)
Marsella, S., Johnson, W.L., LaBore, C.: Interactive Pedagogical Drama for Health Interventions. In: 11th International Conference on Artificial Intelligence in Education, Sydney, Australia (2003)
Aylett, R., Louchart, S., Dias, J., Paiva, A., Vala, M.: FearNot! - an experiment in emergent narrative. In: Panayiotopoulos, T., Gratch, J., Aylett, R.S., Ballin, D., Olivier, P., Rist, T. (eds.) IVA 2005. LNCS (LNAI), vol. 3661, pp. 305–316. Springer, Heidelberg (2005)
Gratch, J., Wang, N., Gerten, J., Fast, E., Duffy, R.: Creating rapport with virtual agents. In: Pelachaud, C., Martin, J.-C., André, E., Chollet, G., Karpouzis, K., Pelé, D. (eds.) IVA 2007. LNCS (LNAI), vol. 4722, pp. 125–138. Springer, Heidelberg (2007)
Thomas, J., Young, R.M.: Using Task-Based Modeling to Generate Scaffolding in Narrative-Guided Exploratory Learning Environments. In: 14th International Conference on Artificial Intelligence in Education, Brighton, U.K, pp. 107–114 (2009)
Dean, T., Kanazawa, K.: A Model for Reasoning about Persistence and Causation. Computational Intelligence 147(3), 142–150 (1989)
Murray, R., VanLehn, K.: DT Tutor: A Decision-Theoretic, Dynamic Approach for Optimal Selection of Tutorial Actions. In: 5th International Conference on Intelligent Tutoring System, Montreal, Canada, pp. 153–162 (2000)
Lee, S., Mott, B., Lester, J.: Optimizing story-based learning: An investigation of student narrative profiles. In: Aleven, V., Kay, J., Mostow, J. (eds.) ITS 2010. LNCS, vol. 6095, pp. 155–165. Springer, Heidelberg (2010)
Mott, B., Lester, J.: Narrative-centered tutorial planning for inquiry-based learning environments. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 675–684. Springer, Heidelberg (2006)
Druzdzel, M.: SMILE: Structural Modeling, Inference, and Learning Engine and Genie: A Development Environment for Graphical Decision-Theoretic Models. In: 16th National Conference on Artificial Intelligence, Orlando, Florida, pp. 342–343 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lee, S.Y., Mott, B.W., Lester, J.C. (2011). Modeling Narrative-Centered Tutorial Decision Making in Guided Discovery 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_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-21869-9_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21868-2
Online ISBN: 978-3-642-21869-9
eBook Packages: Computer ScienceComputer Science (R0)