Abstract
Intelligent tutoring systems achieve much of their success by adapting to individual students. One potential avenue for personalization is feedback generality. This paper presents two evaluation studies that measure the effects of modifying feedback generality in a web-based Intelligent Tutoring System (ITS) based on the analysis of student models. The object of the experiments was to measure the effectiveness of varying feedback generality, and to determine whether this could be performed en masse or if personalization is needed. In an initial trial with a web-based ITS it appeared that it is feasible to use a mass approach to select appropriate concepts for generalizing feedback. A second study gave conflicting results and showed a relationship between generality and ability, highlighting the need for feedback to be personalized to individual students’ needs.
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
Anderson, J.R., Corbett, A.T., Koedinger, K.R., Pelletier, R.: Cognitive Tutors: Lessons Learned. Journal of the Learning Sciences 4(2), 167–207 (1995)
Koedinger, K.R., Mathan, S.: Distinguishing Qualitatively Different Kinds of Learning Using Log Files and Learning Curves. In: Mostow, J., Tedesco, P. (eds.) Proc. Workshop on Data Mining of Student Logs at ITS 2004, Maceio, Brazil, pp. 39–46 (2004)
Martin, B.: Constraint-Based Modelling: Representing Student Knowledge. New Zealand Journal of Computing 7(2), 30–38 (1999)
Mitrovic, A.: An Intelligent SQL Tutor on the Web. Artificial Intelligence in Education 13(2-4), 173–197 (2003)
Mitrovic, A., Martin, B., Mayo, M.: Using evaluation to shape ITS design: Results and experiences with SQL-Tutor. User Modelling and User Adapted Interaction 12(2-3), 243–279 (2002)
Newell, A., Rosenbloom, P.S.: Mechanisms of skill acquisition and the law of practice. In: Anderson, J.R. (ed.) Cognitive skills and their acquisition, pp. 1–56. Lawrence Erlbaum Associates, Hillsdale (1981)
Ohlsson, S.: Constraint-Based Student Modeling. In: Greer, J., McCalla, G. (eds.) Student Modeling: The Key to Individualized Knowledge-Based Instruction, pp. 167–189. Springer, Heidelberg (1994)
Zapata-Rivera, J.D., Greer, J.E.: Interacting with Inspectable Bayesian Student Models. Artificial Intelligence in Education 14(2), 127–163 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Martin, B., Mitrovic, A. (2006). The Effect of Adapting Feedback Generality in ITS. In: Wade, V.P., Ashman, H., Smyth, B. (eds) Adaptive Hypermedia and Adaptive Web-Based Systems. AH 2006. Lecture Notes in Computer Science, vol 4018. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11768012_21
Download citation
DOI: https://doi.org/10.1007/11768012_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34696-8
Online ISBN: 978-3-540-34697-5
eBook Packages: Computer ScienceComputer Science (R0)