a neuro-fuzzy approach in the classification of students’ academic performance
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2013
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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.
| Reference Key |
do2013computationala
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|---|---|
| Authors | ;Quang Hung Do;Jeng-Fung Chen |
| Journal | Organic Chemistry Frontiers |
| Year | 2013 |
| DOI |
10.1155/2013/179097
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| URL | |
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