Predicting Academic Performance Using an Efficient Model Based on Fusion of Classifiers Abstract: In the past few years, educational data mining (EDM) has attracted the attention of researchers to enhance the quality of education. Predicting student academic performance is crucial to improving the value of education. Some research studies have been conducted which mainly focused on prediction of students’ performance at higher education. However, research related to performance prediction at the secondary level is scarce, whereas the secondary level tends to be a benchmark to describe students’ learning progress at further educational levels. Students’ failure or poor grades at lower secondary negatively impact them at the higher secondary level. Therefore, early prediction of performance is vital to keep students on a progressive track. This research intended to determine the critical factors that affect the performance of students at the secondary level and to build an efficient classification model through the fusion of single and ensemble-based classifiers for the prediction of academic performance. Firstly, three single classifiers including a Multilayer Perceptron (MLP), J48, and PART were observed along with three wellestablished ensemble algorithms encompassing Bagging (BAG), MultiBoost (MB), and Voting (VT) independently. To further enhance the performance of the abovementioned classifiers, nine other models were developed by the fusion of single and ensemble-based classifiers. The evaluation results showed that MultiBoost with MLP outperformed the others by achieving 98.7% accuracy, 98.6% precision, recall, and F-score. The study implies that the proposed model could be useful in identifying the academic performance of secondary level students at an early stage to improve the learning outcomes. Keywords: educational data mining; supervised learning; secondary education; academic performance Introduction: Educational data mining (EDM) is a growing area of research that is being used to explore educational data for different academic purposes. The main application of EDM is the prediction of students’ academic performance [1,2]. In data mining, the analysis and interpretation of student academic performance are regarded as suitable analysis, evaluation, and assessment tools [3]. In the present era of a knowledge economy, the students are the key element for the socio-economic growth of any country, so keeping their performance on track is essential. Data mining (DM) methods are applied to learn hidden knowledge and patterns which assist administrators and academicians in decision making regarding the delivery of instructions. DM techniques have applications in numerous areas including retail business, the health sector, marketing, banking, bioinformatics, counterterrorism, and many others are also using it to enhance productivity and efficiency