Performance of linear regression in predicting academic outcome

Predicting academic outcomes is crucial for enhancing educational strategies, improving student performance, and facilitating early interventions. Machine learning (ML) has emerged as a powerful tool in educational data analysis, with linear regression being one of the most widely used algorithms due to its simplicity, interpretability, and effectiveness in modeling relationships between variables. This study investigates the performance of linear regression in predicting academic outcomes, focusing on its accuracy, strengths, and limitations compared to other predictive models. The research begins with a comprehensive review of existing literature on academic performance prediction, highlighting traditional statistical methods and advanced ML techniques. It then details the methodology, including data collection, preprocessing, and the implementation of a linear regression model. Key evaluation metrics such as mean squared error (MSE), and root mean squared error (RMSE) are employed to assess model performance. The findings reveal that linear regression provides a robust baseline for academic outcome prediction, particularly when interpretability and simplicity are prioritized. However, its performance is influenced by data quality, feature selection, and preprocessing techniques. The study also discusses the implications of these results for personalized learning, educational policy, and future research directions. Recommendations are provided for improving prediction accuracy, including the integration of feature engineering and hybrid modelling approaches. This work contributes to the growing body of research on educational data analytics by validating the utility of linear regression while acknowledging scenarios where more complex models may be advantageous. Future work could explore ensemble methods, deep learning techniques, and real-time predictive systems to further enhance academic outcome forecasting.