Using analogues to predict student's performance and generate advice / Chong Lee Kian

The report provides an overview and technical report of what i did in my thesis. The main objective of this project is to develop a system to predict student's performance and generate advice for all the computer science students in University Malaya. Nowadays, student advising become more important because of the increasingly amount of the computer science student problem in the Faculty Computer Science & Information Technology (FCSIT). Students receive counseling help in planning their academic schedules and in clarifying career goals throughout their few years in University Malaya. Academic advisor and students work together to review academic success to verify comoletion of study requirements, and to discuss student's interest. Therefore, it is essential for the academic advisor to have a system to assist them in the administration work. The system will enhance the quality of work and reduce the workload of the academic advisor, also as a lecturer. The effectiveness of the advising task may be enhanced with the use of procedures or tools that forecast the student's future class performance. This project presents a prototype intelligent system that uses case-based reasoning in order to forecast a student's performance. The system draws conclusion on the basis of similarities between a student's current class performance and the performance of other students that attended the same class. Presented here are the problem domain, my approach for an operational system, and the results achieved by the developed prototype system. Findings of the prototype implementation indicate that the potential utility of this predictive approach is high. Any educator may develop a similar system that is customized to the structure of his/her own classes and be capable of assisting in advising students on their class progress way before it is too late for the student