Multi-models of Educational Data Mining for Predicting Student Performance in Mathematics: A Case Study on High Schools in Cambodia

Abstract

Education is crucial for the development of any country. Analysis of education datasets requires effective algorithms to extract hidden information and gain the fruitful results to improve academic performance. Multiple models were used to maximize the contribution to the education environment. In this study, we used the spot-checking algorithm to compare these methods and find the most effective method. We propose three main classes of education research tools: a statistical analysis method, machine learning algorithms, and a deep learning framework. The data were obtained from many high schools in Cambodia. We introduced feature selection techniques to figure out the informative features that affect the future performance of students in mathematics. The proposed ensemble methods of tree-based classifiers provide satisfiying results, and in that, random forest algorithm generates the highest accuracy and the lowest predictive mean squared error, thus showing potential in this prediction and classification problem. The results from this work can be used as recipe and recommendation for mining various material settings in improving high school student performance in Cambodia.