Analysis of Student Performance Applying Data Mining Techniques in a Virtual Learning Environment

Abstract

Students' academic performance is a key factor for educational institutions and society, which is an important indicator of the quality of the teaching-learning process and the appropriation of knowledge. Its analysis allows an understanding of the behavior of students and teachers, generating valuable knowledge for making timely academic decisions. In this study, the following phases were carried out: (i) identification of factors that influence the academic performance of engineering university students, (ii) early prediction of academic success, and (iii) identification of use patterns in a virtual learning environment (VLE). The Knowledge Discovery in Databases (KDD) methodology was applied based on predictive and descriptive data mining techniques, using academic and socioeconomic data and interactions (resources and activities) with the VLE. The tools and programming languages ​​used were Pentaho Data Integration for data integration and processing; Jupyter Notebook, Python, and Scikit-Learn for correlation analysis and prediction modeling; and R Studio for the clustering task. The results show that VLE resources such as files, links, and activities such as participation in forums are factors that lead to good academic performance. On the other hand, it was possible to make predictions of academic success (pass or not) with an accuracy greater than 95% and to identify the main patterns of use of the VLE. The group with excellent academic performance (grades 9 to 10) is recognized for using file-type resources and high participation in class and forum activities.