Statistical and clustering based rules extraction approaches for fuzzy model to estimate academic performance in distance education

Abstract

The demand for distance education has been increasing at a rapid pace all around the world. This, in turn, places a special importance on the need for the development of more distance education systems. However, there is an alarming rise in the number of distance education students that drop out of the system without asking for any help. The present study focuses on forming three fuzzy-based models through K-Means, C-Means and subtractive clustering. The models are designed to predict students' year-end academic performance based on the 8-week data kept in the learning management system (LMS). Next, the models are evaluated in terms of their accuracy in order to determine the most suitable one. Then, the data was analyzed through various statistical methods and the results were compared. The model provides invaluable information regarding the students' year-end success or failure by analyzing the data on Basic Computer Skills, a course included in the curriculum for sophomores at a local university. Thanks to such information, those who are likely to drop out can be determined and accordingly, the institution can start to take measures to encourage students not to drop out early in the semester, which, in turn, can increase the extent to which distance education can be successful. The present study will hopefully decrease the number of students that drop out of distance education systems.