Small Group and Individual Learning with Technology: A Meta-Analysis
Abstract
This study quantitatively synthesized the empirical research on the effects of social context (i.e., small group versus individual learning) when students learn using computer technology. In total, 486 independent findings were extracted from 122 studies involving 11,317 learners. The results indicate that, on average, small group learning had significantly more positive effects than individual learning on student individual achievement (mean ES = + 0.15), group task performance (mean ES = + 0.31), and several process and affective outcomes. However, findings on both individual achievement and group task performance were significantly heterogeneous. Through weighted least squares univariate and multiple regression analyses, we found that variability in each of the two cognitive outcomes could be accounted for by a few technology, task, grouping, and learner characteristics in the studies.
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Collaborative learning can be an effective strategy for promoting retention and problem solving.
“Several meta-analyses have thus been carried out showing that collaborative learning can be an effective strategy for promoting retention and problem solving (e.g., Lou et al. 2001;Roseth et al. 2008;Slavin 1980;Springer et al. 1999).”
From Making the Black Box of Collaborative Learning Transparent: Combining Process-Oriented and Cognitive Load Approaches (2010) · cites this paper as Lou et al. 2001
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Hypercard as a Psychological Research Tool — Experimental Studies
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Fantasy and Structure in Computer-mediated Courses
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The Effectiveness of Teams in Teaching COBAL Programming
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Cognitive load theory, educational research, and instructional design: some food for thought
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How can primary school students learn self-regulated learning strategies most effectively?
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A Meta-analysis of the Effects of Computer Technology on School Students’ Mathematics Learning
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Estimating the Difference Between Published and Unpublished Effect Sizes: A Meta-Review
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A meta-analysis of virtual reality training programs for social skill development
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Generative Learning: Which Strategies for What Age?
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Learning by creating and exchanging objects: the SCY experience
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Using Concept Maps to Facilitate Collaborative Simulation-Based Inquiry Learning
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Experiencing virtual patients in clinical learning: a phenomenological study
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A closer look on social presence as a causing factor in computer-mediated collaboration
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Beyond explicit feedback: new directions in adaptive collaborative learning support
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Beyond Knowledge: The Legacy of Competence
(2008) · doi:10.1007/978-1-4020-8827-8 · Cited by 43 · see citing works
- Authors
- Lou, Y., Abrami, P. C., & d’Apollonia, S.
- Affiliations
- Louisiana State University (US)
- Concordia University (CA)
- ORCID
- Abrami, Philip C.
- Venue
- Review of Educational Research
- Published
- 2001-09
- Volume
- 71
- Issue
- 3
- Pages
- 449-521
- ISSN
- 0034-6543 (ISSN-L)
- 1935-1046
- OpenAlex
- W2144282840
- Cited by
- 721
- References
- 170
- Citations by year
- 2026: 9 · 2025: 17 · 2024: 28 · 2023: 22 · 2022: 23 · 2021: 40 · 2020: 37 · 2019: 44 · 2018: 52 · 2017: 36 · 2016: 19 · 2015: 40 · 2014: 40 · 2013: 31 · 2012: 38
- FWCI
- 2.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Review of Educational Research (publishedVersion)
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Social Sciences › Education › Education and Technology Integration
- Social Sciences › Psychology › Developmental and Educational Psychology › Learning Styles and Cognitive Differences
- Keywords
- small group learning, individual learning, computer technology in education, social context of learning, academic performance, group task performance, affective learning outcomes, technology-enhanced learning, heterogeneous effects
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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