Forecasting Learner Attrition for Student Success at a South African University

In this paper we attempt to deduce student attrition at a South African higher-education institution with the aim of identifying students who are likely to be in need of academic support so that a focus could be provided on improving their academic performance. The significance of this paper is on using computer science and information technology to address learner attrition (an African reality) and thereby impact the low university throughput and retention rates positively. We trained several machine learning classification models to deduce the student into four risk classes using only Grade 12 marks and background characteristics of the learner. We provide the following contributions: (a) the first known published trained classifier able to calculate the distribution over a students' risk profile for a South African university focused on the conceptual framework; (b) a ranking of employed features according to their entropy to correctly classify the class variable; (c) a comparison of trained classifiers able to calculate the probability of a students' risk profile for a South African higher-education research-intensive university; and (d) an interactive program which is able to calculate the posterior probability over the student's risk profile so that support can be provided to them. The random forest classification model achieves the best performance with a 82% accuracy over these four risk profiles. We argue for introducing predictive tools to enhance student success and student support initiatives in Higher-Education Institutions. This work will benefit academic developers and staff who provide support to students who are at academic risk of completing their undergraduate Science programmes.