A Neuro-Fuzzy Approach in the Classification of Students' Academic Performance. (4th November 2013)

• Record Type: Journal Article
• Authors: Do, Quang Hung; Chen, Jeng-Fung
• Other Names: Zhang Daoqiang Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2013(2013)
• Publication Date: 2013-11-04
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2013/179097
• Languages: English
• ISSNs: 1687-5265

Abstract

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.