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Education Data Mining on PISA 2015 Best Ranked Countries: What Makes the Students go Well

Education Data Mining on PISA 2015 Best Ranked Countries: What Makes the Students go Well Abstract The demand for in-depth studies on educational data presupposes the application of technologies that allow data analysis of vast quantities, and subsequently, drawing relevant information and knowledge. The research objective herein is to employ data mining techniques on PISA databases to identify potential patterns that may explain the top-performing countries’ success. Accounting for the methodology, data acquisition, bank creation, and countries’ data extraction, we ran preprocessing and data cleaning and mining stages, respectively; in the last phase, we used the J48 method for classification purposes. From the decision trees, the study identified the relevant attributes which relate to student educational level aspiration; failure; motivation and anxiety; socioeconomic factors; scientific approaches; the use of information and communication technologies; interactions with friends; physical activity practice; paid work; home assignments; learning time for each discipline; cooperation and teamwork; the student’s study program; the teacher’s fairness; and the school year in which the student is enrolled. In this regard, results were considered satisfactory for allowing the analyses of these aforementioned relevant attributes associated with PISA best-ranked countries. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png "Technology, Knowledge and Learning" Springer Journals

Education Data Mining on PISA 2015 Best Ranked Countries: What Makes the Students go Well

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Publisher
Springer Journals
Copyright
2021 The Author(s), under exclusive licence to Springer Nature B.V.
ISSN
2211-1662
eISSN
2211-1670
DOI
10.1007/s10758-021-09572-9
Publisher site
See Article on Publisher Site

Abstract

Abstract The demand for in-depth studies on educational data presupposes the application of technologies that allow data analysis of vast quantities, and subsequently, drawing relevant information and knowledge. The research objective herein is to employ data mining techniques on PISA databases to identify potential patterns that may explain the top-performing countries’ success. Accounting for the methodology, data acquisition, bank creation, and countries’ data extraction, we ran preprocessing and data cleaning and mining stages, respectively; in the last phase, we used the J48 method for classification purposes. From the decision trees, the study identified the relevant attributes which relate to student educational level aspiration; failure; motivation and anxiety; socioeconomic factors; scientific approaches; the use of information and communication technologies; interactions with friends; physical activity practice; paid work; home assignments; learning time for each discipline; cooperation and teamwork; the student’s study program; the teacher’s fairness; and the school year in which the student is enrolled. In this regard, results were considered satisfactory for allowing the analyses of these aforementioned relevant attributes associated with PISA best-ranked countries.

Journal

"Technology, Knowledge and Learning"Springer Journals

Published: Sep 27, 2021

Keywords: learning and instruction; educational technology; science education; creativity and arts education

References