Using algorithms for evaluation in web based distance education

Abstract

Traditional assessment approaches are still being used in distance education environments. Positive changes have been experienced on dimensions of user, management and teacher in distance education systems at each passing day. In addition to these positive changes, new approaches to be used at the evaluation of distance education are emerging. Each of these approaches is an algorithm. In this study, the algorithms to be used at the evaluation of distance education platforms are analyzed and compared. Distance education algorithms as K-means, Apriori, C45, Support Vector Machines (SVM), KNN and Naive Bayes are created the universe and sample of this research. As a result, it is determined that which algorithms can be effective at analyzing of the student behavior, dimension of management and giving more impressive decision of the teachers.