Discrimination of the Contextual Features of Top Performers in Scientific Literacy Using a Machine Learning Approach

Autor/inn/en	Chen, Jiangping; Zhang, Yang; Wei, Yueer; Hu, Jie
Titel	Discrimination of the Contextual Features of Top Performers in Scientific Literacy Using a Machine Learning Approach
Quelle	In: Research in Science Education, 51 (2021), S.129-158 (30 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Hu, Jie)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0157-244X
DOI	10.1007/s11165-019-9835-y
Schlagwörter	Scientific Literacy; Science Achievement; High Achievement; Foreign Countries; International Assessment; Achievement Tests; Secondary School Students; Questionnaires; Classification; Accuracy; Prediction; Teaching Methods; Parent Background; Educational Technology; Context Effect; Time on Task; Learner Engagement; Mass Media; Educational Equipment; Self Efficacy; Program for International Student Assessment + Suchen Sie Ihr Suchwort? Ausland; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Sekundarschüler; Fragebogen; Classification system; Klassifikation; Klassifikationssystem; Vorhersage; Teaching method; Lehrmethode; Unterrichtsmethode; Elternhaus; Unterrichtsmedien; Zeitaufwand; Massenmedien; Ausrüstung; Self-efficacy; Selbstwirksamkeit
Abstract	Science excellence is associated not only with a student's inherent aptitude but also a range of contextual factors. The objective of this paper was to identify the most important contextual characteristics of top performers in scientific literacy, by simultaneously considering factors at the PISA questionnaire-based student, family, and school levels. The data were based on the science scores of 380,771 PISA 2015 secondary students from 58 countries/economies, of whom 25,181 were top performers at proficiency level 5 or 6, as well as the responses of students and school principals to PISA questionnaires. Overall, 141 contextual variables (derived from the questionnaire responses) were ranked according to their relevance to top performers through a machine learning algorithm--specifically, support vector machine recursive feature elimination (SVM-RFE). An optimal set of 20 features (factors/variables) was then selected from the ranked list due to the high accuracy of these features in classifying and predicting top performers compared to non-top performers based on the support vector machine (SVM) classifier. The research findings indicate that the quality of teachers' instructional practices, parents' educational/occupational status, disciplinary climate, time spent on and involvement in learning, schools' mass media facilities/equipment, the quantity of teachers in the school, and students' self-efficacy played the most predictive roles in the target students' superior performance in science. The features identified in this study may provide important information for the future studies on students' performance in science literacy. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01