Using Machine Learning Techniques to Predict Learner Drop-out Rate in Higher Educational Institutions

Abstract

Recently, students dropping out of school at the tertiary level without prior notice or permission has intrigued deep concern among academic authorities, instructors, and counsellors. It has therefore become necessary to understand factors that lead to high attrition rates among learners and identify at-risk students for urgent academic counselling. In providing a proactive response to learner attrition, the study deployed a machine learning algorithm with high model accuracy to predict students’ drop-out rates and identify dominant attributes that affect learner attrition and retention. An attrition model was built and validated among support vector machine, decision tree, multilayer perceptron, and random forest algorithms. The machine learning algorithms were tested for accuracy, precision, recall, F-measure, and ROC using the 10-fold and the 5-fold comparative cross-validation techniques. In addition to the cross-validation technique, the chi-square feature selection mechanism was implemented to understand the algorithms’ training time and accuracy. The random forest emerged as the best-performing algorithm, with an accuracy of 70.98% and 69.74% for the 10-fold and the 5-fold cross-validation implementations, respectively.