Mining Student Participatory Behavior in Virtual Learning Communities

Abstract

The aim of this chapter is to explore the application of data mining for analyzing participatory behavior of the students enrolled in an online two-year Master degree programme in Project Management. The main data sources were the operational database with the students’ records and the log files and statistics provided by the e-learning platform. 129 enrolled students and more than 195 distinct characteristics/ variables per student were used. Due to the large number of variables, an exploratory data analysis through data mining is decided, and a model-based discovery approach was designed and executed in Weka environment. The association rules, clustering, and classification were applied in order to describe the participatory behavior of the students, as well as to identify the factors explaining the students’ behavior, and the relationship between academic performance and behavior in the virtual learning environment. The results are very encouraging and suggest several future developments.