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