Case-Based Reasoning and Profiling System for Learning Mathematics (CBR-PROMATH)

摘要

This paper discusses the architecture of a case-based reasoning profiling system for learning mathematics (CBR-PROMATH). The adaptive system has the ability to suggest suitable learning materials based on previous cases of learner profiles and individual learning styles. The developed learning materials use a learning tool which consists of so-called mastery, understanding, interpersonal and self-expressive styles. Two sets of experiments were carried out to test the system's functionality. The first consisted of 10 sets of learners' profile cases, stored previously in the database. The second presented the system with 10 real new cases. The system compared and calculated similarity values between the new and stored cases. The learning material that was most similar was presented as a solution for the new case. The experiment showed that the CBR algorithm was successfully applied in the development of the CBR-PROMATH.