A Meta-Analysis of Online Learning Outcomes and Their Gender Differences

Autor/inn/en	Yu, Zhonggen; Yu, Liheng
Titel	A Meta-Analysis of Online Learning Outcomes and Their Gender Differences
Quelle	In: International Journal of Distance Education Technologies, 19 (2021) 3, S.33-50, Artikel 3 (18 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1539-3100
Schlagwörter	Gender Differences; Outcomes of Education; Interdisciplinary Approach; Online Courses; Effect Size; COVID-19; Pandemics; Databases; Research Reports; Teaching Methods; Comparative Analysis; School Closing; Meta Analysis; Distance Education + Suchen Sie Ihr Suchwort? Geschlechterkonflikt; Lernleistung; Schulerfolg; Fächerübergreifender Unterricht; Fächerverbindender Unterricht; Interdisziplinarität; Online course; Online-Kurs; Datenbank; Research report; Forschungsbericht; Teaching method; Lehrmethode; Unterrichtsmethode; School closings; Schule; Schließung; Schließung (von Schulen); Meta-analysis; Metaanalyse; Distance study; Distance learning; Fernunterricht
Abstract	In this COVID-19 pandemic, learners across the world are encouraged to stay at home receiving online education, which has nearly caused various learning outcomes. It is thus necessary to analyze online learning outcomes, as well as their gender differences to provide constructive references for learners, teachers, and technology developers. This study obtained reliable data from various databases using searching techniques. The study also selected the research articles based on inclusion and exclusion criteria. After analyzing the forest and funnel plots drawn via Review Manager 5.3, the study arrived at the conclusion that online learning outcomes were significantly higher than the traditional learning outcomes with a large effect size (d = 1.24), while the gender differences in learning outcomes were not significant with a small effect size (d = -0.03). In the future, experts could make every effort to develop advanced emotion detection applications via interdisciplinary cooperation to improve online learning outcomes. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01