Institutional Research Using Data Mining: A Case Study in Online Programs

Constanta-Nicoleta Bodea (Academy of Economic Studies, Romania), Vasile Bodea (Academy of Economic Studies, Romania), and Radu Mogos (Academy of Economic Studies, Romania)
Copyright: © 2012 |Pages: 102
EISBN13: 9781466609884|DOI: 10.4018/978-1-60960-857-6.ch005
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The aim of this chapter is to explore the application of data mining for analyzing academic performance in connection with the participatory behavior of the students enrolled in an online two-year Master degree program 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. One hundred eighty-one enrolled students, and more than 150 distinct characteristics/ variables per student were used. 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. The association rules, clustering, and classification were applied in order to identify the factors explaining the students’ performance and the relationship between academic performance and behavior in the virtual learning environment. Data mining has revealed interesting patterns in data. These patterns indicate that academic performance is related to the intensity of the student activities in virtual environment. If the student understands how to work and she/he is motivated to communicate with others, then he might have a good academic performance. Based on clustering analysis, different student profiles were discovered, explaining the academic performance. The results are very encouraging and suggest several future developments.
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