Revolutionizing Education: Cutting-Edge Predictive Models for Student Success

Abstract

Student performance prediction systems are crucial for improving educational outcomes in various institutions, including universities, schools, and training centers. These systems gather data from diverse sources such as examination centers, registration departments, virtual courses, and e-learning platforms. Analyzing educational data is challenging due to its vast and varied nature, and to address this, machine learning techniques are employed. Dimensionality reduction, enabled by machine learning algorithms, simplifies complex datasets, making them more manageable for analysis. In this study, the Support Vector Classification (SVC) model is used for student performance prediction. SVC is a powerful machine-learning approach for classification tasks. To further enhance the model's efficiency and accuracy, two optimization algorithms, the Sea Horse Optimization (SHO) and the Adaptive Opposition Slime Mould Algorithm (AOSMA), are integrated. Machine learning (ML) reduces complexity through techniques like feature selection and dimensionality reduction, improving the effectiveness of student performance prediction systems and enabling data-informed decisions for educators and institutions. The combination of SVC with these innovative optimization strategies highlights the study's commitment to leveraging the latest advancements in ML and bio−inspired algorithms for more precise and robust student performance predictions, ultimately enhancing educational outcomes. Based on the obtained outcomes, it reveals that the SVSH model registered the best performance in predicting and categorizing the student performance with Accuracy=92.4%, Precision=93%, Recall=92%, and F1_Score=92%. Implementing SHO and AOSMA optimizers to the SVC model resulted in improvement of Accuracy evaluator outputs by 2.12% and 0.89%, respectively.