Título: Analysis of student performance applying data mining techniques in a virtual learning environment

Autor(es): AGUAGALLO AIGAJE LEONARDO MOISES, GARCIA SANTILLAN IVAN DANILO, LANDETA LOPEZ PABLO ANDRES, POSSO YEPEZ MIGUEL ANGEL, SALAZAR FIERRO FAUSTO ALBERTO, GARCIA SANTILLAN JANNETH ALEXANDRA

Fecha de publicación: 07-jun-2023

Resumen: 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 understand-ing 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 associated with the academic perfor-mance of engineering university students, (ii) early prediction of academic success (student performance), and (iii) identification of use patterns in a vir-tual learning environment (VLE). The Knowledge Discovery in Databases (KDD) methodology was applied based on predictive and descriptive data min-ing techniques, using academic and socioeconomic data and interactions (re-sources and activities) with the VLE. The tools and programming languages used were Pentaho Data Integration for data integration and processing; Jupy-ter Notebook, Python, and Scikit-Learn for correlation analysis and prediction modeling; and R Studio for the clustering task. The results show that VLE re-sources such as files, links, and activities such as participation in forums are factors related to good academic performance. On the other hand, it was possi-ble to make predictions of academic success (pass or fail) 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.

Palabras clave: student performance, educational data mining, virtual learning environment, learning management systems, Knowledge Discovery in Databases, machine learning in education, learning analytics.

DOI: https://doi.org/10.3991/ijet.v18i11.37309

ISSN: 1863-0383

Tipo publicación: Artículo