Enhancing distance education using advanced distance learning algorithm

With the growing popularity of the World Wide Web, Web-based Distance education is becoming more and more popular because of its flexibility in time and distance. As Web-based education becomes prominent on university campuses, increasing numbers of instructors and students are becoming involved in distance education. However, with the prevalence and explosive growth of distance education, a lack of interaction or sufficient contact and feedback between instructors and online students has become a growing concern. As a result, the Rough Set based Distance Learning Algorithm has been proposed and implemented. Using this former algorithm, students in distance education can be informed of the important sections in terms of passing the final exam in an online course.;In my research, I study possible improvements to providing extensive self-assessment and evaluation for online students. The definition of classification table is extended to deal with multiple target decisions by providing distributed probability rules. The Advanced Distance Learning Algorithm is presented and implemented. This algorithm can be used to evaluate learners' performance in courses delivered via the World Wide Web and it can provide timely feedback and advice to learners in distance education. Online experiments of students' performance evaluation using rules derived from real-life course data sufficiently demonstrated the effectiveness of the approach.