An International Comparison Study Exploring the Influential Variables Affecting Students' Reading Literacy and Life Satisfaction

Autor/inn/en	Chung Hyewon; Kim, Jung-In; Jung, Eunjin; Park, Soyoung
Titel	An International Comparison Study Exploring the Influential Variables Affecting Students' Reading Literacy and Life Satisfaction
Quelle	In: International Journal of Educational Psychology, 11 (2022) 3, S.261-292 (33 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Chung Hyewon) ORCID (Kim, Jung-In) ORCID (Jung, Eunjin) ORCID (Park, Soyoung)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2014-3591
Schlagwörter	Comparative Education; Predictor Variables; Literacy; Life Satisfaction; Foreign Countries; Achievement Tests; Secondary School Students; International Assessment; Reading Achievement; Artificial Intelligence; Statistical Analysis; Student Characteristics; Teacher Characteristics; Institutional Characteristics; United States; South Korea; Program for International Student Assessment + Suchen Sie Ihr Suchwort? Vergleichende Erziehungswissenschaft; Prädiktor; Alphabetisierung; Schreib- und Lesefähigkeit; Lebensvollendung; Ausland; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Sekundarschüler; Leseleistung; Künstliche Intelligenz; Statistische Analyse; USA; Korea; Republik
Abstract	The Program for International Student Assessment (PISA) aims to provide comparative data on 15-year-olds' academic performance and well-being. The purpose of the current study is to explore and compare the variables that predict the reading literacy and life satisfaction of U.S. and South Korean students. The random forest algorithm, which is a machine learning approach, was applied to PISA 2018 data (4,677 U.S. students and 6,650 South Korean students) to explore and select the key variables among 305 variables that predict reading literacy and life satisfaction. In each random forest analysis, one for the U.S. and another for South Korea, 23 variables were derived as key variables in students' reading literacy. In addition, 23 variables in the U.S. and 26 variables in South Korea were derived as important variables for students' life satisfaction. The multilevel analysis revealed that various student-, teacher- or school-related key variables derived from the random forest were statistically related to either U.S. and/or South Korean students' reading literacy and/or life satisfaction. The current study proposes to use a machine learning approach to examine international large-scale data for an international comparison. The implications of the current study and suggestions for future research are discussed. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01