Date of Award


Degree Type


Degree Name



Computer Science

First Advisor

Li, Liwu,


Computer Science.




The case-based reasoning (CBR) paradigm models how the reuse of stored experiences (cases) contributes to expertise. A case-based reasoning system solves a new problem by retrieving stored information on previous problem-solving episodes and adapting it to suggest solution(s) for the new problem. The results are then themselves added to the reasoner's memory (if necessary) in the form of new cases for future reuse. In this thesis, we build an intelligent tutorial system, with the case-based reasoning paradigm, which is intended to help university/college students solve integration problems in Calculus course. The system captures a fairly large amount of experience in solving certain types of integration problems from experienced human problem-solvers. The experience is mainly stored in the form of cases. The system simulates the way in which professors and textbook examples help students. For a given integral, the system searches its case base to find integrals similar to the given one, and makes the hints and answers for the retrieved integrals available to students. The system does not provide solutions to integrals, in order to encourage students to carry them out first-handed. The system also learns by retaining new cases into its case base. The system is built as an internet application, so that it can be accessed through the Internet by anyone anywhere anytime. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis1998 .X98. Source: Masters Abstracts International, Volume: 39-02, page: 0534. Adviser: Liwu Li. Thesis (M.Sc.)--University of Windsor (Canada), 1998.