Session Timeout Thresholds Impact on Quality and Quantity of Extracted Sequence Rules

The effort of using web usage mining methods in the area of educational data mining is to reveal the knowledge hidden in the log files of the web and database servers of contemporary virtual learning environments. By applying data mining methods to these data, interesting patterns concerning the students’ behavior can be identified. These methods help us to find the most effective structure of the e-learning courses, optimize the learning content, recommend the most suitable learning path based on student’s behavior or provide more personalized learning environment. We prepared six datasets of different quality obtained from logs of the virtual learning environment Moodle and pre-processed in different ways. We used three datasets with identified users’ sessions based on 15, 30 and 60 minute session timeout threshold and three another datasets with the same thresholds including reconstructed paths among course activities. We tried to assess the impact of different session timeout thresholds with or without paths completion on the quantity and quality of the sequence rules that contribute to the representation of the students’ behavioral patterns in virtual learning environment. The results show that the session timeout threshold has significant impact on quality and quantity of extracted sequence rules. On the contrary, it is shown that the completion of paths has neither significant impact on quantity nor quality of extracted rules.