What Explains Macau Students' Achievement? An Integrative Perspective Using a Machine Learning Approach (¿Cuál es la explicación del rendimiento de los estudiantes macaenses? Una perspectiva integradora mediante la adopción del enfoque del aprendizaje automático)

Autor/inn/en	Wang, Yi; King, Ronnel; Haw, Joseph; Leung, Shing on
Titel	What Explains Macau Students' Achievement? An Integrative Perspective Using a Machine Learning Approach (¿Cuál es la explicación del rendimiento de los estudiantes macaenses? Una perspectiva integradora mediante la adopción del enfoque del aprendizaje automático)
Quelle	In: Journal for the Study of Education and Development, 46 (2023) 1, S.71-108 (38 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (King, Ronnel) ORCID (Leung, Shing on)
Sprache	englisch; spanisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0210-3702
DOI	10.1080/02103702.2022.2149120
Schlagwörter	Foreign Countries; High School Students; Reading Achievement; Predictor Variables; Influences; Secondary School Students; Student Characteristics; Artificial Intelligence; Macau; Program for International Student Assessment + Suchen Sie Ihr Suchwort? Ausland; High school; High schools; Student; Students; Oberschule; Schüler; Schülerin; Studentin; Leseleistung; Prädiktor; Influence; Einfluss; Einflussfaktor; Sekundarschüler; Künstliche Intelligenz
Abstract	Although Macau students have consistently been recognized as top performers in international assessments, little research has been conducted to explore the various factors that are associated with their achievement. This paper aimed to identify factors that could best predict Macau students' reading achievement using PISA 2018 data provided by 2,979 15-year-old students. An integrative theoretical model that considered the critical roles of demographic, personal and social-contextual factors was used to understand the relative importance of 41 different factors in predicting reading achievement. A machine learning approach, specifically Random Forest Algorithm, was used to analyse the data. Results indicated that variables classified under personal factors (e.g., metacognitive strategies, reading enjoyment and perceived difficulty) were the most important predictors of Macau students' achievement. A supplementary analysis using Hierarchical Linear Modelling confirmed the findings from the machine learning approach. Implications of the findings were discussed. [Spanish translation by Mercè Rius.] (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01