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Algorithms 2014, 7(4), 538-553; doi:10.3390/a7040538

Predicting Student Academic Performance: A Comparison of Two Meta-Heuristic Algorithms Inspired by Cuckoo Birds for Training Neural Networks

1
Department of Industrial Engineering and Systems Management, Feng Chia University, Taichung 40724, Taiwan
2
Department of Electrical and Electronic Engineering, University of Transport Technology, Hanoi 100000, Vietnam
*
Author to whom correspondence should be addressed.
Received: 23 July 2014 / Revised: 26 September 2014 / Accepted: 8 October 2014 / Published: 16 October 2014
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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. View Full-Text
Keywords: cuckoo search; cuckoo optimization algorithm; meta-heuristic algorithms; neural networks; educational institutions cuckoo search; cuckoo optimization algorithm; meta-heuristic algorithms; neural networks; educational institutions
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Chen, J.-F.; Hsieh, H.-N.; Do, Q.H. Predicting Student Academic Performance: A Comparison of Two Meta-Heuristic Algorithms Inspired by Cuckoo Birds for Training Neural Networks. Algorithms 2014, 7, 538-553.

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