Performance Analysis of Machine Learning Algorithms in Prediction of Student Academic Performance

Abstract

The advancement in technology has contributed largely to the application of data mining in education in recent times. However, selecting appropriate algorithm(s) to “mine” knowledge about educational data presents a difficult challenge to researchers and analyst. This paper contributes to the use of classification algorithms in academic performance prediction. The predictive ability of four popular algorithms; C4.5 Decision tree (CDT), Multilayer Perceptron (MLP), Naïve Bayes (NB) and Random Forest (RF) algorithms were compared. The models were built using student dataset from selected private senior high schools in Ghana. The comparative analysis of the algorithms was made based on their Accuracy, Recall, Specificity, F-Measure and Running time. On all the training and test ratios; 80:20, 70:30 and 10-fold cross validation, the results indicated that all the algorithms performed well in the classification. However, the Naïve Bayes algorithm performed significantly better than the MLP and CDT on some ratios. The running time of the NB, CDT and RF were the quickest while MLP took the longest time.