Data-driven fuzzy rule generation and its application for student academic performance evaluation

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dc.contributor.author Shen, Qiang
dc.contributor.author Rasmani, Khairul A.
dc.date.accessioned 2008-01-15T21:18:14Z
dc.date.available 2008-01-15T21:18:14Z
dc.date.issued 2006-12
dc.identifier.citation Shen , Q & Rasmani , K A 2006 , ' Data-driven fuzzy rule generation and its application for student academic performance evaluation ' Applied Intelligence , pp. 305-319 . , 10.1007/s10489-006-0109-9 en
dc.identifier.issn 1573-7497
dc.identifier.other PURE: 74319
dc.identifier.other dspace: 2160/438
dc.identifier.uri http://hdl.handle.net/2160/438
dc.description K. Rasmani and Q. Shen. Data-driven fuzzy rule generation and its application for student academic performance evaluation. Applied Intelligence, 25(3):305-319, 2006. en
dc.description.abstract Several approaches using fuzzy techniques have been proposed to provide a practical method for evaluating student academic performance. However, these approaches are largely based on expert opinions and are difficult to explore and utilize valuable information embedded in collected data. This paper proposes a new method for evaluating student academic performance based on data-driven fuzzy rule induction. A suitable fuzzy inference mechanism and associated Rule Induction Algorithm is given. The new method has been applied to perform Criterion-Referenced Evaluation (CRE) and comparisons are made with typical existing methods, revealing significant advantages of the present work. The new method has also been applied to perform Norm-Referenced Evaluation (NRE), demonstrating its potential as an extended method of evaluation that can produce new and informative scores based on information gathered from data. en
dc.format.extent 15 en
dc.language.iso eng
dc.relation.ispartof Applied Intelligence en
dc.title Data-driven fuzzy rule generation and its application for student academic performance evaluation en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.identifier.doi http://dx.doi.org/10.1007/s10489-006-0109-9
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Advanced Reasoning Group en
dc.description.status Peer reviewed en


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