The aim of this chapter is to explore the application of data mining for analyzing performance and satisfaction of the students enrolled in an online two-year master degree programme in project management. This programme is delivered by the Academy of Economic Studies, the biggest Romanian university in economics and business administration in parallel, as an online programme and as a traditional one. The main data sources for the mining process are the survey made for gathering students’ opinions, the operational database with the students’ records and data regarding students activities recorded by the e-learning platform are. More than 180 students have responded, and more than 150 distinct characteristics/ variable per student were identified. Due the large number of variables data mining is a recommended approach to analysis this data. Clustering, classification, and association rules were employed in order to identify the factor explaining students’ performance and satisfaction, and the relationship between them. The results are very encouraging and suggest several future developments.

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The aim of this chapter is to explore the behavior of the students enrolled in an online two-year master degree program in project management. One hundred twenty-nine enrolled students and more than 195 distinct characteristics/variables per student were analyzed. Due to the large number of variables, an exploratory data analysis through data mining was chosen, and a model-based discovery approach was designed and executed in Weka environment. Association rules, clustering, and classification were applied in order to identify behavior patterns and to discover the factors explaining the students’ behavior in virtual communities. Three actual behavior patterns were discovered for the first and second academic year. The students associated with the first behavior pattern tend especially to visit the administrative area of the e-learning platform, not being interested in communicating with colleagues and teachers. The students associated with the second pattern have a high interest for the administrative issues, but the teaching topics are not neglected either. The students tend to interact with their colleagues to a large degree, making proposals for new topics. Students presenting the last behavioral pattern are clearly focused on the academic activities and have a low interest for the administrative issues. Differences between the behavior in the first and second year are not relevant. The attribute with the biggest influence on the actual behavior in the first year is the volume of communication with the teacher, while for the second year it is the volume of materials for reading. The results of the data analysis are very encouraging and suggest several future developments.

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.