Deep Dense Neural Network for Early Prediction of Failure-Prone Students

Abstract

In recent years, the constant technological advances in computing power as well as in the processing and analysis of large amounts of data have given a powerful impetus to the development of a new research area. Deep Learning is a burgeoning subfield of machine learning which is currently getting a lot of attention among scientists offering considerable advantages over traditional machine learning techniques. Deep neural networks have already been successfully applied for solving a wide variety of tasks, such as natural language processing, text translation, image classification, object detection and speech recognition. A great deal of notable studies has recently emerged concerning the use of Deep Learning methods in the area of Educational Data Mining and Learning Analytics. Their potential use in the educational field opens up new horizons for educators so as to enhance their understanding and analysis of data coming from varied educational settings, thus improving learning and teaching quality as well as the educational outcomes. In this context, the main purpose of the present study is to evaluate the efficiency of deep dense neural networks with a view to early predicting failure-prone students in distance higher education. A plethora of student features coming from different educational sources were employed in our study regarding students’ characteristics, academic achievements and activity in the course learning management system. The experimental results reveal that Deep Learning methods may contribute to building more accurate predictive models, whilst identifying students in trouble soon enough to provide in-time and effective interventions.