Abstract
Tutorial dialogues are considered as one of the critical factors contributing to the effectiveness of human one-on-one tutoring. We discuss how we evaluated the effectiveness of a general model of adaptive tutorial dialogues in both an ill-defined and a well-defined task. The first study involved dialogues in database design, an ill-defined task. The control group participants received non-adaptive dialogues regardless of their knowledge level and explanation skills. The experimental group participants received adaptive dialogues that were customised based on their student models. The performance on pre- and post-tests indicate that the experimental group participants learned significantly more than their peers. The second study involved dialogues in data normalization, a well-defined task. The performance of the experimental group increased significantly between pre- and post-test, while the improvement of the control group was not significant. The studies show that the model is applicable to both ill- and well-defined tasks, and that they support learning effectively.
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
VanLehn, K., Graesser, A.C., Jackson, G.T., Jordan, P., Olney, A., Rose, C.P.: When are tutorial dialogues more effective than reading? Cognitive Science 31(1), 3–52 (2007)
Graesser, A.C., Lu, S., Jackson, G.T., Mitchell, H.H., Ventura, M., Olney, A., et al.: AutoTutor: A tutor with dialogue in natural language. Behavioral Research Methods, Instruments and Computers 36, 180–193 (2004)
Evens, M., Michael, J.: One-on-One Tutoring By Humans and Computers. Lawrence Erlbaum Associates, Mahwah (2006)
Aleven, V., Ogan, A., Popescu, O., Torrey, C., Koedinger, K.: Evaluating the Effectiveness of a Tutorial Dialogue System for Self-Explanation. In: Lester, J., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 443–454. Springer, Heidelberg (2004)
Weerasinghe, A., Mitrovic, A.: Facilitating Deep Learning through Self-Explanation in an Open-ended Domain. Knowledge-based and Intelligent Tutoring Systems 10(1), 3–19 (2006)
Weerasinghe, A., Mitrovic, A., Martin, B.: Towards Individualized Dialogue Support for Ill-Defined Domains IJAIED. Special Issue on Ill-Defined Domains 19(4), 357–379 (2009)
Mitrovic, A., Weerasinghe, A.: Revisiting the Ill-Definedness and Consequences for ITSs. In: Dimitrova, V., et al. (eds.) Proc. Artificial Intelligence in Education, Frontiers in Artificial Intelligence and Applications, vol. 200, pp. 375–382 (2009)
Mitrovic, A., Martin, B., Suraweera, P.: Intelligent Tutors for All: Constraint-based Modeling Methodology, Systems and Authoring. IEEE Intelligent Systems 22(4), 38–45 (2007)
Elmasri, R., Navathe, S.: Fundamentals of Database Systems, 5th edn. Addison-Wesley, Boston (2007)
Milik, N., Marshall, M., Mitrovic, A.: Teaching logical database design in ERM-Tutor. In: Ikeda, M., Ashley, K. (eds.) Proc. of ITS 2006, pp. 707–709 (2006)
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
Weerasinghe, A., Mitrovic, A., Thomson, D., Mogin, P., Martin, B. (2011). Evaluating a General Model of Adaptive Tutorial Dialogues. 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_51
Download citation
DOI: https://doi.org/10.1007/978-3-642-21869-9_51
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)