E-Learning Platform Usage Analysis

Abstract

E-learning is technology-based learning, such as computer-based learning, web-based learning, virtual classroom, and digital collaboration. The usage of web applications can be measured with the use of indexes and metrics. However, in e-Learning platforms there are no appropriate in-dexes and metrics that would facilitate their qualitative and quantitative measurement. The pur-pose of this paper is to describe the use of data mining techniques, such as clustering, classifica-tion, and association, in order to analyze the log file of an eLearning platform and deduce useful conclusions. Two metrics for course usage measurement and one algorithm for course classifica-tion are used. A case study based on a previous approach was applied to e-Learning data from a Greek University. The results confirmed the validity of the approach and showed a strong rela-tionship between the course usage and the corresponding students' grades in the exams. From a pedagogical point of view this method contributes to improvements in course content and course usability and the adaptation of courses in accordance with student capabilities. Improve-ment in course quality gives students the opportunity of asynchronous study of courses with actu-alized and optimal educational material and, therefore, higher performance in exams. It should be mentioned that even though the scope of the method is on e-Learning platforms and educational content, it can be easily adopted to other web applications such as e-government, e-commerce, e-banking, blogs, etc