Data-driven fuzzy rule generation and its application for student academic performance evaluation
Contribuinte(s) |
Department of Computer Science Advanced Reasoning Group |
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Data(s) |
15/01/2008
15/01/2008
01/12/2006
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Resumo |
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. 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. Peer reviewed |
Formato |
15 |
Identificador |
Shen , Q & Rasmani , K A 2006 , ' Data-driven fuzzy rule generation and its application for student academic performance evaluation ' Applied Intelligence , pp. 305-319 . DOI: 10.1007/s10489-006-0109-9 1573-7497 PURE: 74319 PURE UUID: 0b8a267e-efc3-4f3c-a1bc-e91178635539 dspace: 2160/438 |
Idioma(s) |
eng |
Relação |
Applied Intelligence |
Tipo |
/dk/atira/pure/researchoutput/researchoutputtypes/contributiontojournal/article |
Direitos |