A machine learning prediction of academic performance of secondary school students using radial basis function neural network

Background: Predictive models for academic performance forecasting have been a useful tool in the improvement of the administrative, counseling and instructional personnel of academic institutions. Aim: The aim of this work is to develop a Radial Basis Function Neural Network for prediction of students’ performance using their past academic records as well as their cognitive and psychomotor abilities. Methods: We obtained data from a secondary school repository containing academic, cognitive and psychomotor scores of the students. The preprocessed dataset was used to train the RBFNN model. The impact of Principal Component Analysis on the model performance was also measured. Results: The results gave a sensitivity (pass prediction) of 93.49%, specificity (failure prediction) of 75%, overall accuracy of 86.59% and an AUC score (aggregate measure of performance across the possible classification thresholds) of 94%. Conclusion: We established in this study that psychomotor and cognitive abilities also predict students’ performance. This study helps students, parents and teachers to get a projection of academic success even before sitting for the examination. © 2022