Implementing a Support Vector Classifier for Student Risk Assessment in Colegio De Getafe: A Machine Learning Approach

Abstract

This study addresses the persistent need for technological advancement in higher education to provide quality higher education and early classification for students who are struggling academically. The study's focus is Colegio de Getafe (CDG), a higher educational institution in Bohol, Philippines. The current process of manually collecting and analyzing data employed by CDG is time-consuming and often results in inaccurate assessments. The study emphasizes the importance of implementing machine learning algorithms for risk assessment systems in identifying at-risk students. The research aims to develop a machine learning-based student risk assessment system that automates data processes and classifies at-risk students. The system enables data collection, enhances data management, and facilitates early identification of students facing academic challenges by implementing the support vector classifier that will identify at-risk students. The study evaluated the machine learning algorithm's performance using the confusion matrix, accuracy, precision, recall, and F1 score. It also assessed the system's quality using the International Organization for Standardization (ISO 9126) software quality standards. Results indicate that the system is highly effective in identifying at-risk students, achieving 100% accuracy, precision, recall, and F1 score in student risk classification. The system complies with international software quality standards, scoring an overall rating of 4.38 out of 5. This research provides a strong solution for early identification and support for at-risk students, aligning with CDG's commitment to providing globally competitive students.