Predicting academically at-risk engineering students: A soft computing application

Abstract

This paper presents a study on predicting academically at-risk engineering students at the early stage of their education. For this purpose, some soft computing tools namely support vectors machines and artificial neural networks have been employed. The study population included all students enrolled in Pamukkale University, Faculty of Engineering at 2008-2009 and 2009-2010 academic years as freshmen. The data are retrieved from various institutions and questionnaires conducted on the students. Each input data point is of 38-dimension, which includes demographic and academic information about the students, while the output based on the first-year GPA of the students falls into either at-risk or not. The results of the study have shown that either support vector machine or artificial neural network methods can be used to predict first-year performance of a student in a priori manner. Thus, a proper course load and graduation schedule can be transcribed for the student to manage their graduation in a way that potential dropout risks are reduced. Moreover, an input sensitivity analysis has been conducted to determine the importance of each input used in the study