A neuro-fuzzy approach in the classification of students' academic performance

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Abstract

Classifying the student academic performance with high accuracy facilitates admission decisions and enhances educational services at educational institutions. The purpose of this paper is to present a neuro-fuzzy approach for classifying students into different groups. The neuro-fuzzy classifier used previous exam results and other related factors as input variables and labeled students based on their expected academic performance. The results showed that the proposed approach achieved a high accuracy. The results were also compared with those obtained from other well-known classification approaches, including support vector machine, Naive Bayes, neural network, and decision tree approaches. The comparative analysis indicated that the neuro-fuzzy approach performed better than the others. It is expected that this work may be used to support student admission procedures and to strengthen the services of educational institutions. © 2013 Quang Hung Do and Jeng-Fung Chen.

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Do, Q. H., & Chen, J. F. (2013). A neuro-fuzzy approach in the classification of students’ academic performance. Computational Intelligence and Neuroscience, 2013. https://doi.org/10.1155/2013/179097

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