Effectiveness of Computer-Based Scaffolding in the Context of Problem-Based Learning: Bayesian Meta-Analysis

• In Event: Instructional and Technological Scaffolding in Enhancing Students' Problem-Based Learning Experiences

Abstract

This research determines the effectiveness of computer-based scaffolding (CS) in the context of problem-based learning in STEM education through Meta-analysis. Specifically, the application of Bayesian inferences overcame the limitations of traditional meta-analysis and generated accurate effect size estimates of four moderators related to the characteristics of CS. The results of Bayesian Meta-analysis indicated that metacognitive CS showed the highest effect size in problem-based learning in STEM education. The most interesting result in this paper is on the effects of scaffolding customization. When CS was faded and added according to students’ learning status, its effect size was highest. The result of Bayesian Meta-analysis contributes to an enhanced understanding of CS and guidelines about which strategies of scaffolding should be used.

Authors

• Nam Ju Kim, University of Miami

• Dayun Jeon, Utah State University