A Meta-Analysis of the Effects of Feedback in Computer-Based Instruction
Abstract
A quantitative research synthesis (meta-analysis) was conducted on the literature concerning the effects of feedback on learning from computer-based instruction (CBI). Despite the widespread acceptance of feedback in computerized instruction, empirical support for particular types of feedback information has been inconsistent and contradictory. Effect size calculations from twenty-two studies involving the administration of immediate achievement posttests resulted in a weighted mean effect size of .80. Also, a mean weighted effect size of .35 was obtained from nine studies involving delayed posttest administration. Feedback effects on learning and retention were found to vary with CBI typology, format of unit content and access to supplemental materials. Results indicate that the diagnostic and prescriptive management strategies of computer-based adaptive instructional systems provide the most effective feedback. The implementation of effective feedback in computerized instruction involves the computer's ability to verify the correctness of the learner's answer and the underlying causes of error.
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Previous meta-analyses have produced inconsistent results of feedback on cognitive variables.
“Previous meta-analyses have produced inconsistent results of feedback on cognitive variables (Kulik and Kulik, 1988; Bangert-Drowns et al., 1991; Azevedo and Bernard, 1995; Kluger and DeNisi, 1996; Lyster and Saito, 2010) and significant heterogeneity remains within the sub-group defined by this moderator in our analysis.”
From The Power of Feedback Revisited: A Meta-Analysis of Educational Feedback Research (2020) · cites this paper as Azevedo and Bernard, 1995
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- Authors
- Azevedo, R., & Bernard, R. M.
- Affiliations
- McGill University (CA)
- Concordia University (CA)
- Venue
- Journal of Educational Computing Research
- Publisher
- SAGE Publications
- Published
- 1995-09
- Volume
- 13
- Issue
- 2
- Pages
- 111-127
- ISSN
- 0735-6331 (ISSN-L)
- 1541-4140
- OpenAlex
- W2107946292
- Cited by
- 308
- References
- 45
- Citations by year
- 2026: 8 · 2025: 7 · 2024: 12 · 2023: 13 · 2022: 12 · 2021: 20 · 2020: 22 · 2019: 14 · 2018: 10 · 2017: 17 · 2016: 20 · 2015: 17 · 2014: 16 · 2013: 11 · 2012: 11
- FWCI
- 0.87
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Educational Computing Research (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Education › Student Assessment and Feedback
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Keywords
- feedback, computer-assisted instruction, learning outcomes, retention, adaptive instructional systems, diagnostic feedback, error analysis, effect size
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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