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

Brian R. Belland, Andrew E. Walker, Nam Ju Kim

Research output: Contribution to journalArticle

9 Scopus citations

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 (ḡ = 3.13).

Original languageEnglish (US)
Pages (from-to)1042-1081
Number of pages40
JournalReview of Educational Research
Volume87
Issue number6
DOIs
StatePublished - Dec 1 2017

Keywords

  • Bayesian network meta-analysis
  • STEM
  • cognitive tutor
  • intelligent tutoring systems
  • problem-centered instruction
  • scaffold

ASJC Scopus subject areas

  • Education

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