A Data Mining Approach for the Prediction of Low Performing Students

Abstract

By maximising the greatest results and decreasing the number of students who fail, educational institutions aim to improve the quality of education. Improving and developing academic achievement requires the ability to accurately estimate student performance in advance. Using data mining techniques, educational institutions can get valuable insights from the study of educational data. The purpose of this research is to present educational data mining approaches and incorporate them into a web-based platform for forecasting low-performing pupils. Prediction models were compared in a comparative study. High-performance models were then designed to get better results. Hybrid RF, a hybrid random forest algorithm, is the most effective at classifying data. In order to enhance the learning outcomes and intervention methods, a new feature selection approach called MICHI is presented, which is the mixture of similarity measure and chi-square techniques based on ranking feature scores. Using the suggested methodologies for educational data mining, a system for predicting student academic performance will be constructed for academic entrepreneurs to obtain an early forecast of student achievement for prompt treatment. Experiments and surveys demonstrate the utility and efficacy of the academic prediction system that was built. Use of the system aids educators in intervening and enhancing student outcomes.