| With the growing popularity of the World Wide Web (WWW), Web-based distance education is increasingly popular in order to overcome various problems that central classroom teaching faces, such as distance, time scheduling, size limits, cost, and individual learning barriers. The WebCT system is an example of a web-based instruction tool that enables instructors to create and customize their courses for distance post-secondary education. A WebCT course allows students to do assignments, quizzes, and a final examination on the World Wide Web. If a student fails the final examination, then the student needs to study the course material again, Therefore, the performance of online students and the lack of contact and feedback between online students and the instructor, inherent to the course delivery mode, become growing concerns.; Inductive Learning is a research area in Artificial Intelligence. It has been used to model the knowledge of human experts by using a carefully chosen sample of expert decisions to infer decision rules. Rough Set based Inductive Learning uses Rough Set theory to compute decision rules.; The primary goal of the thesis is to investigate how to provide contact between students and teacher in distance education. In particular, we focus on WebCT education. We discuss how to use Rough Set theory in WebCT to permit decision rules to be induced that are important to both students and instructors. We propose the Rough Set Based Distance Learning Algorithm and describe its implementation using Java to make it more portable in a distance delivery environment. |
