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

Authors:
Quang Hung Do;Jeng-Fung Chen
Affiliations:
Department of Industrial Engineering and Systems Management, Feng Chia University, Taichung, Taiwan;Department of Industrial Engineering and Systems Management, Feng Chia University, Taichung, Taiwan
Venue:
Computational Intelligence and Neuroscience
Year:
2013

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Abstract

Classifying the student academic performancewith 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 examresults 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.