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A Neuro-Fuzzy Approach in the Classification of Students’ Academic Performance
Joint Authors
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-11-04
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Do, Quang Hung& Chen, Jeng-Fung. 2013. A Neuro-Fuzzy Approach in the Classification of Students’ Academic Performance. Computational Intelligence and Neuroscience،Vol. 2013, no. 2013, pp.1-7.
https://search.emarefa.net/detail/BIM-452201
Modern Language Association (MLA)
Do, Quang Hung& Chen, Jeng-Fung. A Neuro-Fuzzy Approach in the Classification of Students’ Academic Performance. Computational Intelligence and Neuroscience No. 2013 (2013), pp.1-7.
https://search.emarefa.net/detail/BIM-452201
American Medical Association (AMA)
Do, Quang Hung& Chen, Jeng-Fung. A Neuro-Fuzzy Approach in the Classification of Students’ Academic Performance. Computational Intelligence and Neuroscience. 2013. Vol. 2013, no. 2013, pp.1-7.
https://search.emarefa.net/detail/BIM-452201
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-452201