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 indexes and metrics that would facilitate their qualitative and quantitative measurement. The purpose of this paper is to describe the use of data mining techniques, such as clustering, classification, 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 classification 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 relationship 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. Improvement in course quality gives students the opportunity of asynchronous study of courses with actualized 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, ecommerce, e-banking, blogs, etc.