Exploiting advanced machine learning techniques for predictive analysis of novice learners' programming performance

Programming education is evolving quickly, thus new methods are needed to help beginners learn and grow. This study predicts novice learners' performance utilising sophisticated machine learning methods including K-nearest neighbours, decision tree, random forest, and XGBoost. We assessed these models on accuracy, precision, recall, and F1-score utilising 2,111 samples, 11 beginning features, and derived attributes including correctness, error, performance, and final choice. Ensemble models like random forest and XGBoost capture complex data patterns better since they generalise robustly. Simple KNN and decision tree ensembles provide a foundation but have weak feature interactions and class distributions. Performance and prediction are improved via hyperparameter adjustment and feature engineering in this research. This research personalises/adapts novice learners' learning aids using predictive models. Educational data mining is growing, and machine learning may revolutionise programming education. This dataset may be expanded, environmental variables researched, or improved using deep learning.