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Education Quality Measured by the Classification of School Performance Using Quality Labels

Applied Mechanics and Materials, 2014-10, Vol.670-671 (Applied Mechanics, Materials and Manufacturing IV), p.1675-1683 [Peer Reviewed Journal]

2014 Trans Tech Publications Ltd ;Copyright Trans Tech Publications Ltd. Oct 2014 ;ISSN: 1660-9336 ;ISSN: 1662-7482 ;ISBN: 3038352888 ;ISBN: 9783038352884 ;EISSN: 1662-7482 ;DOI: 10.4028/www.scientific.net/AMM.670-671.1675

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  • Title:
    Education Quality Measured by the Classification of School Performance Using Quality Labels
  • Author: Góes, Anderson Roges Teixeira ; Arns Steiner, Maria Teresinha ; Steiner Neto, Pedro José
  • Subjects: Artificial neural networks ; Classification ; Data mining ; Education ; Labels ; Methodology ; Pattern recognition
  • Is Part Of: Applied Mechanics and Materials, 2014-10, Vol.670-671 (Applied Mechanics, Materials and Manufacturing IV), p.1675-1683
  • Description: Quality in education has been the subject of many debates, be it among managers in schools, in the media or in literature. However, the literature appears not to include methods or techniques for exploring databases to obtain classifications for this quality; nor is there a consensus as to the definition of “education quality”. To address these issues, this article proposes a methodology similar to the KDD (Knowledge Discovery in Databases) to classify Education Quality in schools comparatively, based on grades scored in school performance tests. For the purposes of this study, the test used was the Prova Brasil examination, which is part of the Basic Education Development Index (IDEB) used in Brazil. The methodology was applied to public municipal schools in the town of Araucária in the metropolitan district of Curitiba in Paraná State. Seventeen schools that offer elementary and junior high school education were included. All the grades of every student were considered from the early and later years at the schools. During the Data Mining stage, the main stage of the KDD process, three comparative methods were used for Pattern Recognition: Artificial Neural Networks, Support Vector Machines and Genetic Algorithms. These methods supplied satisfactory results in the classification of schools represented by way of a “Quality Label”, with Artificial Neural Networks having the best performance for the problem in question. With this quality label, educational managers can decide on which measures to adopt at all the schools to help them achieve their goals.
  • Publisher: Zurich: Trans Tech Publications Ltd
  • Language: English
  • Identifier: ISSN: 1660-9336
    ISSN: 1662-7482
    ISBN: 3038352888
    ISBN: 9783038352884
    EISSN: 1662-7482
    DOI: 10.4028/www.scientific.net/AMM.670-671.1675
  • Source: Ebook Central Academic Complete
    AUTh Library subscriptions: ProQuest Central
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