EXPLORING PREDICTING PERFORMANCE OF ENGINEERING STUDENTS USING DEEP LEARNING

Abstract

A significant amount of research has gone into predicting student performance and many studies have been conducted to predict why students drop out. A variety of data including digital footprints, socio-economic data, financial data, and psychological aspects have been used to predict student performance at the test, course, or program level. Fairly good prediction results have been achieved using both traditional machine learning and more recently deep learning methods. While using diverse sets of data has achieved good results, this data is often difficult and expensive to collect, and may have privacy-related issues. This paper explores the extent to which only prior performance data readily available with registrars in most Universities can be used to predict student performance in future terms. Twenty term data from 789 students enrolled an engineering program at an American University were used to train long term short term (LSTM), Bi-directional LSTM and Gated Reference Units (GRU) models to predict student performance in future terms. The results are that all three types of models were able to reasonably predict the next term's performance (F1-score of about 0.70) regardless of the number of terms a student had spent the University. The models generally did not overfit. The prediction was reasonable until about trying to predict performance on seventh term in the future, but the performance dropped beyond this point primarily due to lack of sufficient data (F1-score of about 0.2).