Prediction of Achievement in Primary School Students Using Support Vector Machine Algorithm
Keywords:
Prediction, Achievement, Platform, Algorithm, Support Vector MachineAbstract
This academic article focuses on the application of the Support Vector Machine (SVM) algorithm to predict the academic performance of Grade 6 students in the 2024 academic year. Artificial intelligence technology, specifically ChatGPT, was employed to simulate a dataset of 1,000 student records, based on real data collected from three schools in Loei Province, Thailand. The dataset included variables such as gender, grade point average (GPA), cumulative GPA, study hours, and academic status, while personal identifiers were excluded to ensure privacy. The data were divided into two sets: 70% for model training and 30% for testing to evaluate the model’s performance. The model development process consisted of five main stages: (1) data collection, (2) data preparation, (3) model construction, (4) performance evaluation using 10-fold cross-validation, and (5) implementation using Altair AI Studio Educational 2025.0.0. The results revealed that the SVM model demonstrated high levels of performance in terms of accuracy, precision, and recall, confirming its potential as a reliable tool for academic performance prediction. Additionally, the model was integrated into the One Compiler application, an online coding platform that supports HTML-based user interface design. This enabled users to input student data and conveniently receive predictive outcomes. The tool serves as a practical resource for school administrators, teachers, and stakeholders to enhance decision-making and improve the effectiveness of teaching and learning management.
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