Abstract
Andes, an intelligent tutoring system for Newtonian physics, provides an environment for students to solve quantitative physics problems. Andes provides immediate correct/incorrect feedback to each student entry during problem solving. When a student enters an equation, Andes must (1) determine quickly whether that equation is correct, and (2) provide helpful feedback indicating what is wrong with the student’s entry. To address the former, we match student equations against a pre-generated list of correct equations. To address the latter, we use the pre-generated equations to infer what equation the student may have been trying to enter, and generate hints based on the discrepancies. This paper describes the representation of equations and the procedures Andes uses to perform these tasks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cristina Conati, Abigail S. Gertner, Kurt VanLehn, and Marek J. Druzdzel. Online student modeling for coached problem solving using Bayesian networks. In Proceedings of UM-97, Sixth International Conference on User Modeling, pages 231–242, Sardinia, Italy, June 1997. Springer. 258
Abigail S. Gertner, Cristina Conati, and Kurt VanLehn. Procedural help in Andes: Generating hints using a Bayesian network student model. In Proceedings of the 15th National Conference on Artificial Intelligence, Madison, WI, 1998. to appear. 259
Denise Gürer. A Bi-Level Physics Student Diagnostic Utilizing Cognitive Models for an Intelligent Tutoring System. PhD thesis, Lehigh University, 1993. 257
Joel Martin and Kurt VanLehn. Student assessment using Bayesian nets. International Journal of Human-Computer Studies, 42:575–591, 1995. 259
Douglas C. Merril, Brian J. Reiser, Michael Ranney, and J. Gregory Trafton. Effective tutoring techniques: A comparison of human tutors and intelligent tutoring systems. The Journal of the Learning Sciences, 3(2):277–305, 1992. 261
A. G. Priest and R. O. Lindsay. New light on novice-expert differences in physics problem solving. British Journal of Psychology, 83:389–405, 1992. 256
Derek H. Sleeman. Inferring student models for intelligent computer-aided instruction. In R.S. Michalski, J.G. Carbonnel, and T.M. Mitchell, editors, Machine Learning: An Artificial Intelligence Approach, pages 483–510. Tioga Publishing Company, Palo Alto, CA, 1983. 257
K. VanLehn. Conceptual and meta learning during coached problem solving. In C. Frasson, G. Gauthier, and A. Lesgold, editors, Proceedings of the 3rd International Conference on Intelligent Tutoring Systems ITS’ 96, pages 29–47. Springer, 1996. 258, 262
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gertner, A.S. (1998). Providing Feedback to Equation Entries in an Intelligent Tutoring System for Physics. In: Goettl, B.P., Halff, H.M., Redfield, C.L., Shute, V.J. (eds) Intelligent Tutoring Systems. ITS 1998. Lecture Notes in Computer Science, vol 1452. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-68716-5_31
Download citation
DOI: https://doi.org/10.1007/3-540-68716-5_31
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64770-6
Online ISBN: 978-3-540-68716-0
eBook Packages: Springer Book Archive