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Prediction of student academic performance by using an adaptive neuro-fuzzy inference system

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dc.contributor.authorSevindik, Tuncay
dc.date.accessioned2026-06-27T13:13:05Z
dc.date.issued2011
dc.description.abstractAn assessment model based on adaptive neuro-fuzzy inference system (ANFIS) was designed for prediction of the students' academic performance. ANFIS is a composition of fuzzy system and neural networks. For enhancing the systems' speed, adaptability and flexibility, neural networks adapted to fuzzy inference system. The data used in this paper was collected from the students of computer and instructional technology education. Data sets include exam 1, exam 2, project, final and attitude points of the students at graphics and animation course for education in one semester. The performance of the assessment model is presented by graphics and percentages of success.en
dc.identifier.endpage646
dc.identifier.issn1308-7711
dc.identifier.issue4
dc.identifier.startpage635
dc.identifier.urihttps://hdl.handle.net/20.500.14981/50638
dc.identifier.volume3
dc.identifier.wos000287470500022
dc.language.isoeng
dc.publisherSILA SCIENCE
dc.relation.ispartofENERGY EDUCATION SCIENCE AND TECHNOLOGY PART B-SOCIAL AND EDUCATIONAL STUDIES
dc.subjectEvaluation methodologies
dc.subjectHuman-computer interface
dc.subjectIntelligent tutoring systems
dc.subjectNETWORK
dc.subjectTEACHERS
dc.subjectMODEL
dc.subjectEducation & Educational Research
dc.titlePrediction of student academic performance by using an adaptive neuro-fuzzy inference system
dc.typeArticle
dspace.entity.typePublication
local.import.sourceWOS

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