Student Performance Estimation Through Innovative Classification Techniques in Education

Abstract

In the current era of intense educational competition, institutions must effectively classify individuals based on their abilities, proactively forecast student performance, and work towards enhancing their forthcoming examination outcomes. Providing early guidance to students is crucial in helping them focus their efforts on specific areas to boost their academic achievements. This analytical approach supports educational institutions in mitigating failure rates by utilizing students' previous performance in relevant courses to predict their outcomes in a specific program. Data mining encompasses a variety of techniques used to reveal hidden patterns within vast datasets. In the context of educational data mining, these methods are applied within the educational sphere, with a specific emphasis on analyzing data from both students and educators. These patterns can offer significant value for predictive and analytical objectives. In this study, Gaussian Process Classification (GPC) was employed for the prediction of student performance. To improve the model's accuracy, two cutting-edge optimizers, namely the Golden Eagle Optimizer (GEO) and the Pelican Optimization Algorithm (POA), were incorporated. When assessing the model's performance, four widely used metrics were utilized: Accuracy, Precision, Recall, and F1-score. The results of this study underscore the effectiveness of both the POA and GEO optimizers in enhancing GPC performance. Specifically, GPC+GEO demonstrated remarkable effectiveness in the Poor grade, while GPC+POA excelled in the Acceptable and Excellent category. This highlights the positive impact of these optimization techniques on the model's predictive capabilities.