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This project aims to develop a predictive analytics model using machine learning to forecast student grades, helping educational institutions identify struggling students early for targeted support. By leveraging machine learning, the model can analyze large datasets to detect complex patterns, enhancing prediction accuracy in education. The project employs neural networks due to their ability to capture non-linear relationships in data. Two models were created: one trained with data from low-rated schools and tested on both low- and high-rated schools, achieving 85.7% and 83.3% accuracy, respectively. The second model, trained with high-rated school data, yielded 88.9% accuracy for high-rated schools but only 35.7% for low-rated ones. Results indicate that separate models for different school levels are more effective due to discrepancies in grade reporting accuracy among Iraqi schools.
Flayyih et al. (Mon,) studied this question.