Improving the Performance of E-Learning Systems Using Extracted Web Server Log File Suggestions

There are many opportunities to improve E-Learning web-based applications with regard to continue challenges e.g. their lack of interaction between teachers and students and structural evaluation of the presented learning activity. In this paper, E-Learning system web server log data and information about its control panel are provided; then the gathered data were preprocessed. After extracting association rules form these data and selecting results using more confidence coefficients, they were used as a virtual consultant for improving the E-Learning system. MySQL was used as the database for storing and extracting patterns. Extracting association rules with more confidence coefficients was done using Weka Software. The selected E-Learning web application was Moodie and the virtual university case study was Iran University of Science and Technology. The obtained results showed the needs which cannot be granted by a human consultant easily and cannot be inferred from current application log directly.

Keywords:

E-Learning systems , Web server log data , Data mining