Artificial neural network analysis of the academic performance of students in virtual learning environments

Abstract

Educational institutions continually strive to improve the services they offer, their aim is to have the best possible teaching staff, increase the quality of teaching and the academic performance of their students. A knowledge of the factors that affect student learning could help universities and study centres adjust their curricula and teaching methods to the needs of their students. One of the first measures employed by teaching institutions was to create Virtual Learning Environments (VLEs). This type of environment makes it possible to attract a larger number of students because it enables them to study from wherever they are in the world, meaning that the student’s location is no longer a constraint. Moreover, VLEs facilitate access to teaching resources, they make it easier to monitor the activity of the teaching staff and of the interactions between students and teachers. Thus, online environments make it possible to assess the factors that cause the students’ academic performance to increase or decrease.To understand the factors that influence the university learning process, this paper applies a series of automatic learning techniques to a public dataset, including tree-based models and different types of Artificial Neural Networks (ANNs). Having applied these techniques to the dataset, the number of times students have accessed the resources made available on VLE platforms has been identified as a key factor affecting student performance. This factor has been analysed by conducting a real case study which has involved 120 students doing a masters degree over a VLE platform. Concretely, the case study participants were masters degree students in areas related to computer engineering at the University of Salamanca.