Effectiveness of conceptual change strategies in science education: A meta-analysis

Pacaci C., Ustun U., ÖZDEMİR Ö. F.

Journal of Research in Science Teaching, 2023 (SSCI) identifier

  • Publication Type: Article / Article
  • Publication Date: 2023
  • Doi Number: 10.1002/tea.21887
  • Journal Name: Journal of Research in Science Teaching
  • Journal Indexes: Social Sciences Citation Index (SSCI), Scopus, ASSIA, IBZ Online, Periodicals Index Online, Applied Science & Technology Source, Compendex, EBSCO Education Source, Education Abstracts, Educational research abstracts (ERA), ERIC (Education Resources Information Center), Psycinfo
  • Keywords: cognitive bridging, cognitive conflict, conceptual change, meta-analysis, ontological category shift
  • Middle East Technical University Affiliated: Yes


There is extensive literature focusing on students' misconceptions in various subject domains. Several conceptual change approaches have been trying to understand how conceptual change occurs to help learners handle these misconceptions. This meta-analysis aims to integrate studies investigating the effectiveness of three types of conceptual change strategy: cognitive conflict, cognitive bridging, and ontological category shift in science learning. We conducted a random-effects meta-analysis to calculate an overall effect size in Hedges' g with a sample of 218 primary studies, including 18,051 students. Our analyses resulted in a large overall effect size (g = 1.10, 95% CI [1.01, 1.19], k = 218, p < 0.001). We also performed a robust Bayesian meta-analysis to calculate an adjusted effect size, which specified a large effect (adjusted g = 0.93, 95% CI [0.68, 1.07], k = 218). Results are also consistent across the conceptual change strategies of cognitive conflict (g = 1.10, 95% CI [0.99, 1.21], k = 150, p < 0.001), cognitive bridging (g = 1.06, 95% CI [0.84, 1.28], k = 30, p < 0.001), and ontological category shift (g = 0.88, 95% CI [0.50, 1.26], k = 9, p < 0.001). However, a wide-ranging prediction interval [0.19, 2.38] points out a high level of heterogeneity in the distribution of effect sizes. Thus, we investigated the moderating effects of several variables using simple and multiple meta-regression. The final meta-regression model we created explained 35% of overall heterogeneity. This meta-analysis provides robust evidence that conceptual change strategies significantly enhance students' learning in science.