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A Framework for Learning From Erroneous Examples and Meta-Analysis of Empirical Research

Bibliographic Data

ID21380992
AuthorsEcenaz Alemdag (0000-0003-2645-4732, Dresden University of Technology, corresponding author), Anja Eichelmann (FSD - Central Agency for Periodic Technical Inspection), Susanne Narci (0000-0002-4280-6534, Dresden University of Technology)
Year2025
Publication date2025-11-20
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueReview of Educational Research (JOURNAL)
Journal identifiersISSN: 0034-6543 • E-ISSN: 1935-1046
PublisherAmerican Educational Research Association (AERA) (PUBLISHER)
DOI10.3102/00346543251390901
OpenAlexW4416411508
LanguageEN
References cited113

While there is ample theoretical and empirical evidence detailing which conditions benefit learning from one’s own errors, the evidence on learning from others’ errors has not yet been synthesized. In this meta-analysis, we examine the overall impact of erroneous examples on learning and the effects of potential moderating variables based on a novel framework. Following the robust variance estimation method, we synthesized findings from 42 papers (177 effect sizes) comparing erroneous examples with correct examples or problem-solving in experimental studies. The results revealed a statistically significant but weak effect of erroneous examples on learning (g = .136). Further analysis indicated a statistically significant moderating effect of the design of error-explanation activities. Specifically, providing self-explanation prompts or instructional explanations enhanced learning from erroneous examples more than not providing any error explanations. Our findings draw attention to the design of error explanation activities as well as several areas for future research

Design of experiments · Empirical evidence · Empirical research · Error Analysis · Estimation · Learning effect · Moderation · Variance (accounting) · Educational Strategies and Epistemologies · Innovative Teaching and Learning Methods · Visual and Cognitive Learning Processes

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