Predicting Academic Performance of Students Through Supervised Learning Approaches

Abstract

In the contemporary educational environment, where the success of millions of students depends on precise forecasting, it is essential to conduct in-depth research into the many factors that influence academic achievement. Beyond simply analyzing students’ grades, our research aims to provide a holistic picture of student achievement by examining a wide range of student demographics, academic backgrounds, and behavioral factors. We use advanced machine learning techniques, such as regression and classification, to decipher the complex patterns embedded in the data. This enables us to gain nuanced insights into the factors that predict student performance. We hope that by using these approaches, we will not only forecast academic outcomes but also identify the underlying factors that influence overall student success. In addition, our research seeks to determine the primary factors that have the greatest
impact on students’ academic performance. Educators receive vital insights that enable them to personalize interventions that target both academic and non-academic aspects that affect student progress. After an in-depth investigation, we concluded that the Artificial Neural Network (ANN) and Decision Tree (DT) models were the most accurate predictors. These models achieved accuracy rates of 81% and 76%, respectively. The results of this study demonstrate that the use of sophisticated machine learning algorithms is an effective method for predicting student performance and guiding interventions specifically designed to support student achievement.