E- LEARNING PLATFORM ACCESS AND USAGE STATISTICS THROUGH DATA MINING: AN EXPERIMENTAL STUDY IN MOODLE

learning platforms use databases to store detailed log data on the access, usage and interaction of individual users with the platform and the educational material. Actions and quantities typically registered include user login times, user access of the various parts of the material, duration of sessions, number and times of keystrokes (clicks, scrolls, page loads), file downloads or file shows, etc. Detailed logging produces large volumes of data; data mining methods are therefore necessary for the selective extraction of useful data from the logs. All this is transparent to the user (student); yet, it produces a wealth of data for extraction and analysis by the administrator and/ or the instructor or researcher. An experimental study is described in this paper, where access and usage data from a moodle e-learning platform are extracted and exploited to answer a series of research questions having to do with students' practices, strategies and academic performance. A methodology is proposed and its experimental application is outlined, on a specific e-learning course. Data analysis following data extraction focuses (i) on the type of relations among the various quantities extracted from the moodle database (causality, linearity, correlation, etc.), (ii) the type of relations among the access and usage data and the learning outcomes of the electronic course, and (iii) the feasibility of prediction of the learning outcomes (students' performance, in terms of grades) on the basis of access, usage and interaction data. Results reveal interesting relations among the various quantities, varying from strongly linear to highly non-linear. Clustering methods reveal the interconnection between students' performance and platform interaction data. Finally, a clearly positive prospect arises as to the feasibility of predicting student performance from platform interaction data.