Hybrid Machine Learning Models Based on CATBoost Classifier for Assessing Students' Academic Performance

This study addresses the imperative task of predicting and evaluating students' academic performance by amalgamating qualitative and quantitative factors, crucial in light of the persisting challenges undergraduates encounter in completing their degrees. Educational institutions wield significant influence in prognosticating student outcomes, necessitating the application of data mining (DM) techniques such as classification, clustering, and regression to discern and forecast student study behaviors. Through this research, the potential of deriving demonstrates valuable insights from educational data, empowering educational stakeholders with enhanced decision- making capabilities and facilitating improved student outcomes. Employing a hybrid approach, models developed within the realm of educational DM, leveraging the CATBoost Classifier (CATC) in conjunction with two cutting-edge optimization algorithms: Victoria Amazonica Optimization (VAO) and Artificial Rabbits Optimization (ARO). Initially, the models undergo partitioning into training and testing sets for performance evaluation utilizing statistical metrics. After classifying 649 students according to their final scores, VAO outperformed ARO in terms of maximizing CATC's classification ability, resulting in an approximate 6% enhancement in accuracy and precision. Moreover, the VAO model adeptly categorizes 606 out of 649 students accurately. This research furnishes invaluable predictive models for educators, researchers, and policymakers endeavoring to enrich students' educational journeys and foster academic success.