Meta-Model Classification Based on the Naïve Bias Technique Auto-Regulated via Novel Metaheuristic Methods to Define Optimal Attributes of Student Performance

Abstract

Accurately assessing and predicting student performance is critical in today's educational environment. Schools are dependent on evaluating students' skills, forecasting their grades, and providing customized instruction to improve their academic performance. Early intervention is essential for pinpointing areas in need of development. By predicting students' futures in particular subjects, data mining, a potent technique for revealing hidden patterns within large datasets, helps lower failure rates. These methods are combined in the field of educational data mining, which focuses on the analysis of data from educators and students with the aim of raising academic achievement. In this study, the Naive Bayes classification (NBC) model is given the main responsibility for predicting student performance. However, two cutting-edge optimization strategies, Alibaba and the Forty Thieves (AFT) and Leader Harris Hawk's optimization (LHHO), have been used to improve the model's accuracy. The study's findings show that the NBC+AFT model performs more accurately than the other models. Accuracy, Precision, Recall, and F1-Score all display impressive performance metrics for a superior model, with values of 0.891, 0.9, 0.89, and 0.89, respectively. These metrics outperform those of competing models, highlighting how successful this strategy is. Because of the NBC+AFT model's strong performance, educational institutions are getting closer to a time when they will be able to predict students' success more precisely and help them along the way, making everyone's academic journey more promising and brighter.