Random Forest Analysis of Factors Predicting Science Achievement Groups: Focusing on Science Activities and Learning in School

Autor/inn/en	Hong, Jeehye; Kim, Hyunjung; Hong, Hun-Gi
Titel	Random Forest Analysis of Factors Predicting Science Achievement Groups: Focusing on Science Activities and Learning in School
Quelle	In: Asia-Pacific Science Education, 8 (2022) 2, S.424-451 (28 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Hong, Jeehye) ORCID (Kim, Hyunjung) ORCID (Hong, Hun-Gi)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
Schlagwörter	Science Achievement; Prediction; Teaching Methods; Science Teachers; Learning Analytics; Foreign Countries; Achievement Tests; International Assessment; Secondary School Students; Student Attitudes; Parent Attitudes; Science Activities; Probability; Environmental Education; Context Effect; High Achievement; Low Achievement; Item Analysis; South Korea; Program for International Student Assessment + Suchen Sie Ihr Suchwort? Vorhersage; Teaching method; Lehrmethode; Unterrichtsmethode; Science; Teacher; Teachers; Science teacher; Wissenschaft; Lehrer; Lehrerin; Lehrende; Ausland; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Sekundarschüler; Schülerverhalten; Elternverhalten; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Umweltbildung; Umwelterziehung; Umweltpädagogik; Unterdurchschnittliche Leistung; Itemanalyse; Korea; Republik
Abstract	This study explored science-related variables that have an impact on the prediction of science achievement groups by applying the educational data mining (EDM) method of the random forest analysis to extract factors associated with students categorized in three different achievement groups (high, moderate, and low) in the Korean data from the 2015 Programme for International Student Assessment (PISA). The 57 variables of science activities and learning in school collected from PISA questionnaires for students and parents were analyzed. Variables related to students' past science activities, science teaching and learning methods, and environmental awareness were found to played important roles in predicting science achievement. When checking partial dependence plots for major variables, science activities and instructional strategies had a high probability of changing the prediction of an achievement group. This study focused on science-related contextual variables that can be improved through government policies and science teachers' efforts in the classroom. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01