Abstract
The paper presents the application of data mining for analyzing performance of students enrolled in an online two-year master degree programme in project management. 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. 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 data. Clustering, classification and association rules were employed in order to identify the factor explaining students’ performance. The results are very encouraging and suggest several future developments.
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Bodea, CN., Bodea, V., Mogos, R. (2010). Student Performance in Online Project Management Courses: A Data Mining Approach. In: Lytras, M.D., Ordonez De Pablos, P., Ziderman, A., Roulstone, A., Maurer, H., Imber, J.B. (eds) Knowledge Management, Information Systems, E-Learning, and Sustainability Research. WSKS 2010. Communications in Computer and Information Science, vol 111. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16318-0_60
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DOI: https://doi.org/10.1007/978-3-642-16318-0_60
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