title: A neuro-fuzzy approach in the classification of students' academic performance.
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.
authors.affiliation: Department of Industrial Engineering and Systems Management, Feng Chia University, No. 100, Wenhwa Road, Taichung 40724, Taiwan.
authors.forename: Quang Hung
authors.initials: QH
authors.lastname: Do
authors.forename: Jeng-Fung
authors.initials: JF
authors.lastname: Chen
mesh_terms: D057225:Data Mining / Q000379:methods*; D017143:Fuzzy Logic; D006801:Humans; D016571:Neural Networks, Computer; D013334:Students; D060388:Support Vector Machine