Assessing Academic Performance Using Ensemble Machine Learning Models

Artificial Intelligence (AI) shall play a vital role in forecasting and predicting the academic performance of students. Societal factors such as family size, education and occupation of parents, and students' health, along with the details of their behavioral absenteeism are used as independent variables for the analysis. To perform this study, a standardized dataset is used with data instances of 1044 entries and a total of 33 unique variables constituting the feature matrix. Machine learning (ML) algorithms such as Support Vector Machine (SVM), Random Forest (RF), Multilayer Perceptron (MLP), LightGBM, and Ensemble Stacking (ES) are used to assess the specified dataset. Finally, an ES model is developed and used for assessment. Comparatively, the ES model outclassed other ML models with a test accuracy of 99.3%. Apart from accuracy, other parameters of metrics are used to evaluate the performance of the algorithms.