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Student Performance in E-Learning Environments: An Empirical Analysis through Data Mining

Student Performance in E-Learning Environments: An Empirical Analysis through Data Mining
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Author(s): Constanta-Nicoleta Bodea (Academy of Economic Studies, Romania), Vasile Bodea (Academy of Economic Studies, Romania), Ion Gh. Rosca (Academy of Economic Studies, Romania), Radu Mogos (Academy of Economic Studies, Romania)and Maria-Iuliana Dascalu (Academy of Economic Studies, Romania)
Copyright: 2011
Pages: 46
Source title: Student Satisfaction and Learning Outcomes in E-Learning: An Introduction to Empirical Research
Source Author(s)/Editor(s): Sean B. Eom (Southeast Missouri State University, USA)and J. B. Arbaugh (University of Wisconsin Oshkosh, USA)
DOI: 10.4018/978-1-60960-615-2.ch008

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Abstract

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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