Abstract
Predicting student academic performance with a high accuracy facilitates admission decisions and enhances educational services at educational institutions. This raises the need to propose a model that predicts student performance, based on the results of standardized exams, including university entrance exams, high school graduation exams, and other influential factors. In this study, an approach to the problem based on the artificial neural network (ANN) with the two meta-heuristic algorithms inspired by cuckoo birds and their lifestyle, namely, Cuckoo Search (CS) and Cuckoo Optimization Algorithm (COA) is proposed. In particular, we used previous exam results and other factors, such as the location of the student’s high school and the student’s gender as input variables, and predicted the student academic performance. The standard CS and standard COA were separately utilized to train the feed-forward network for prediction. The algorithms optimized the weights between layers and biases of the neuron network. The simulation results were then discussed and analyzed to investigate the prediction ability of the neural network trained by these two algorithms. The findings demonstrated that both CS and COA have potential in training ANN and ANN-COA obtained slightly better results for predicting student academic performance in this case. It is expected that this work may be used to support student admission procedures and strengthen the service system in educational institutions.
Highlights
Predicting student academic performance has long been an important research topic [1,2].Among the issues of higher education institutions, questions concerning admissions remain important.The main objective of the admission system is to determine the candidates who would likely perform well after being accepted into the university
The results show that the artificial neural network (ANN)-Cuckoo Optimization Algorithm (COA) has a better overall performance in all criteria
The accurate prediction of student academic performance is of importance for making admission decisions as well as providing better educational services
Summary
Predicting student academic performance has long been an important research topic [1,2].Among the issues of higher education institutions, questions concerning admissions remain important.The main objective of the admission system is to determine the candidates who would likely perform well after being accepted into the university. Predicting student academic performance has long been an important research topic [1,2]. Among the issues of higher education institutions, questions concerning admissions remain important. The quality of admitted students has a great influence on the level of research and training within the institution. The failure to perform an accurate admission decision may result in an unsuitable student being admitted to the program. Admission officers want to know more about the academic potential of each student. Accurate predictions help admission officers to distinguish between suitable and unsuitable candidates for an academic program, and identify candidates who would likely do well in the university. The results obtained from the prediction of academic performance may be used for classifying students, which enables educational managers to offer them additional support, such as customized assistance and tutoring resources
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