A Bayesian Network Meta-Analysis to Synthesize the Influence of Contexts of Scaffolding Use on Cognitive Outcomes in STEM Education

Autor/inn/en	Belland, Brian R.; Walker, Andrew E.; Kim, Nam Ju
Titel	A Bayesian Network Meta-Analysis to Synthesize the Influence of Contexts of Scaffolding Use on Cognitive Outcomes in STEM Education
Quelle	In: Review of Educational Research, 87 (2017) 6, S.1042-1081 (40 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0034-6543
DOI	10.3102/0034654317723009
Schlagwörter	Bayesian Statistics; Meta Analysis; STEM Education; Computer Assisted Instruction; Scaffolding (Teaching Technique); Monte Carlo Methods; Teaching Methods; Problem Based Learning; Intelligent Tutoring Systems; Coding; Effect Size; Elementary Secondary Education; Higher Education; Adult Education; Disabilities; English Language Learners; Markov Processes + Suchen Sie Ihr Suchwort? Meta-analysis; Metaanalyse; STEM; Computer based training; Computerunterstützter Unterricht; Monte-Carlo-Methode; Teaching method; Lehrmethode; Unterrichtsmethode; Problem-based learning; Problemorientiertes Lernen; Intelligentes Tutorsystem; Codierung; Programmierung; Hochschulbildung; Hochschulsystem; Hochschulwesen; Adult; Adults; Education; Adult basic education; Adult training; Erwachsenenbildung; Handicap; Behinderung; Markowscher Prozess
Abstract	Computer-based scaffolding provides temporary support that enables students to participate in and become more proficient at complex skills like problem solving, argumentation, and evaluation. While meta-analyses have addressed between-subject differences on cognitive outcomes resulting from scaffolding, none has addressed within-subject gains. This leaves much quantitative scaffolding literature not covered by existing meta-analyses. To address this gap, this study used Bayesian network meta-analysis to synthesize within-subjects (pre-post) differences resulting from scaffolding in 56 studies. We generated the posterior distribution using 20,000 Markov Chain Monte Carlo samples. Scaffolding has a consistently strong effect across student populations, STEM (science, technology, engineering, and mathematics) disciplines, and assessment levels, and a strong effect when used with most problem-centered instructional models (exception: inquiry-based learning and modeling visualization) and educational levels (exception: secondary education). Results also indicate some promising areas for future scaffolding research, including scaffolding among students with learning disabilities, for whom the effect size was particularly large (g-bar = 3.13). (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01