Student Performance in E-Learning Environments

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.

Teleworking—An Economic and Social Impact during COVID-19 Pandemic: A Data Mining Analysis

“… establishing the degree of filling for each attribute and eliminating the empty records or with very few data; By degree of filling is meant how many values an attribute had completed. the excel file with the preprocessed data was transformed into an arff type file that was used in the Weka data mining software for the analysis [ 40 , 41 ]. …”

“…the excel file with the preprocessed data was transformed into an arff type file that was used in the Weka data mining software for the analysis [ 40 , 41 ].…”