Using Machine Learning for Automatic Correction of Numerical Analysis Assignments Towards Sustainable Education Development

Education is a significant tool for change. It contributes to social stability, develops livelihoods, improves health, and motivates long-term economic growth. One of the critical aspects of the educational process is student assessment, which enables teachers to address current issues in education and measure teaching effectiveness and student performance. Correcting assessments takes teachers time and effort. This research proposed a solution to this issue by automatically correcting the true percent relative error equation with less human interference as well as decrease grading bias by using a machine learning approach. In order to use machine learning algorithms, a true percent relative error dataset were created. A total of550 solutions by different numerical analysis students at Umm Al-Qura University were collected and labeled in this work. Then this research tried and tested six machine learning classification algorithms. The results showed that the gradient boosting classifier algorithm achieved the highest percentage in all metrics. It achied 86% in accuracy, 87% in precision, 86% in recall, and 86% in F1. While the K-neighbors obtained the lowest percentage in all metrics.