Utilizing Artificial Neural Network Models for Predicting High School Students' Final Grades and the Impact of Social Factors

الملخص

In response to ongoing technical changes, it has become imperative to evolve educational methodologies to provide information when needed for making appropriate decisions. This necessitates a shift in the skills possessed by learners to keep pace with such advancements and carry out associated tasks. The primary challenge lies in the scarcity of using Artificial Neural Networks (ANN), one of the most important artificial intelligence tools due to their immense capability for prediction, classification, and simulation of human intelligence. They aid institutions in achieving their objectives and addressing the challenges they face. Hence, this research aims to utilize neural networks in predicting the outcomes of high school students and examine how neural networks integrate in forecasting final academic performance, focusing on social factors such as gender, family size, and parental occupations, among others. The research seeks to enhance the accuracy of academic performance predictions by incorporating these social aspects into the neural model, enabling a deeper understanding of the relationships between these factors and academic success.

      The findings of this research offer valuable insights for educational institutions, which can be used to enhance academic support and develop educational plans. The attention to social factors is attributed to their impact and significance in shaping an active context that reflects the challenges students may encounter during their academic journey.

      The research recommends using neural networks and their applications, employing artificial intelligence in machine learning and deep learning applications, such as other predictions, classification, and clustering. These aids in enhancing the educational proces