Application of classification models to predict students’ academic performance using classifiers ensemble and synthetic minority over sampling techniques

Abstract

The demand for data-driven decision making has resulted in the application of data mining in the educational sector and other disciplines. The needs for improving the performance of data mining models have been identified as an interesting area of research globally. Higher educational institutions keep a large amount of students’ data, but these data are rarely used effectively in decision and or policy-making processes. This research is an attempt to enhance the performance of data mining models to predict students’ academic performance using stacking classifiers ensemble and synthetic minority over-sampling techniques. The three (3) classifiers models J48, IBK and SMO were trained and tested on 206 students’ data set using previous academic performance records of Federal University Dutse, Nigeria. WEKA 3.9.1 data mining tool was used in predicting the final year student’s classes of degree at an undergraduate level, while Unified Tertiary Matriculation Examination, Senior Secondary Certificate Examinations and first-year Cumulative Grade Point Average of students served as inputs to the model. The result obtained showed that on training dataset after class balancing, stacking classifiers ensemble model out- performing the other three (3) classifiers models in both performance accuracy (96.7949%) and RSME (0.1098), suggesting that stacking classifiers ensemble is the best model in context of this research.