A Novel Concept Study on key factors affecting student performance using Machine Learning Algorithms.

This study examines the correlation between critical aspects, including study hours, sleep hours, attendance rate, dietary habits, and extracurricular contribution, and their impact on assessment performance through various machine learning algorithms. Five models were examined to forecast student performance: Linear Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and XGBoost. Data was generated for over 300 students, with each feature exerting a direct or indirect impact on the assessment performance. Each model has been developed on a training dataset and tested on a distinct test set to measure performance. The evaluation employed two principal metrics: Root Mean Squared Error (RMSE) and R-squared (R²). The XGBoost model exhibited optimal performance, evidenced by the lowest RMSE and maximum R², signifying its capacity to explain intricate relationships within the data. The Random Forest and Decision Tree models demonstrated favourable outcomes, with the Random Forest exhibiting greater resilience to overfitting compared to the Decision Tree. The SVM model, while successful, exhibited reduced efficiency owing to the non-linear correlations present in the data. Although interpretable, the Linear Regression model exhibited fewer errors than the alternative models. The study also examined learning curves, feature significance, and residual analysis to understand the model's strengths and limitations better. Scatterplots and histograms were employed to evaluate model performance and discern important features. The results indicate that XGBoost is a very efficient tool for performance forecasting, particularly in educational datasets characterised by numerous factors. This study evaluates machine learning models and illustrates how real-world variables influencing student performance can potentially be efficiently analysed using complex algorithms, offering useful insights for educational stakeholders seeking to enhance student outcomes.