CatBoost — An Ensemble Machine Learning Model for Prediction and Classification of Student Academic Performance

Abstract

In every educational institution, predicting pupils’ performance is a vital responsibility. Due to this, a variety of data mining techniques, such as clustering, classification, and regression, are applied to anticipate the learner’s study behavior. By Machine Learning’s arrival, it has become vital to forecast students’ academic achievement, and this study attracts significant attention within the scientific community. In addition, the findings from this work have tremendous socio-economic consequences. One area of major research in the world of education today is educational data mining, which is the study of techniques to reveal hidden patterns in educational data. Data mining strategies succeed or fail to depend on the type and quality of the data that is being mined. Here, we provide a novel method that enhances the accuracy of prior student performance prediction by identifying and providing an explanation as to why it is rising. Using our robust machine learning ensemble models, we propose and evaluate a prediction model. The findings demonstrate that our CatBoost — an ensemble machine learning model — is superior to standard machine learning models with an accuracy of 92.27%. This new model was able to show itself to be dependable by the use of smote and hyperparameter optimization, which proved to be valuable methods and approaches. Additional features are significant as well. More critically, a unique method is utilized to increase model transparency. The SHAP values are a valuable part of the student performance prediction system, which we think should be integrated. For those educators tasked with using prediction models in education, we have found that there is a preference for models that offer both insightful insights and easy to understand predictions, as by utilizing our experiment the educator will be able to identify those students who are at early risk and inspire and encourage these students in a positive way.