Guidelines for the use of machine learning to predict student project group academic performance

Education plays a crucial role in the growth and development of a country. However, in South Africa, there is a limited capacity and an increasing demand of students seeking an education. In an attempt to address this demand, universities are pressured into accepting more students to increase their throughput. This pressure leads to educators having less time to give students individual attention. This study aims to address this problem by demonstrating how machine learning can be used to predict student group academic performance so that educators may allocate more resources and attention to students and groups at risk. The study focused on data obtained from the third-year capstone project for the diploma in Information Technology at the Nelson Mandela University. Learning analytics and educational data mining and their processes were discussed with an in-depth look at the machine learning techniques involved therein. Artificial neural networks, decision trees and naïve Bayes classifiers were proposed and motivated for prediction modelling. An experiment was performed resulting in proposed guidelines, which give insight and recommendations for the use of machine learning to predict student group academic performance