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2 references
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- Vol 3 RE: Chapter 5
computer-supported collaborative learninglearner agencycollaborative interactionlearning analyticsadaptive supportresearch paradigmssocial media learningeducational changeJournal article Evidence summaryAt a glance
Wise and Schwarz consult the computer-supported collaborative learning community to provoke debate about the field’s identity and future. Eight paired dialogues present opposing positions rather than recommendations.
Key ideas
They question whether CSCL needs one unifying framework; whether learner agency should take priority over scripted collaboration; and how rigorously researchers should decide that collaboration or community exists. Further provocations ask whether analytic and interpretive traditions should be reconciled, whether computational approaches deserve greater emphasis, whether learning analytics and adaptive support should become priorities, and whether the field must engage social media and learning at scale. The final exchange challenges the ambition to produce educational-system change. Each “provocateur” is answered by a “conciliator” defending achievements and complexities in existing traditions.
Why it matters
The article arises from interviews and interactive presentations, not an empirical test, and intentionally offers no resolution. Its value is diagnostic: make assumptions about collaboration explicit, examine agency and power, justify units and methods of analysis, connect small-group theory with new scales, and decide how scholarly identity relates to educational impact.
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- Vol 2 RE: Chapter 14
digital technologydeep learninghigher-order thinkingknowledge transferblended learninginstructional guidancecollaborative learningimplementation heterogeneityJournal article Evidence summaryAt a glance
Wu meta-analyses 60 experimental or quasi-experimental studies examining digital technology and educational “deep learning,” meaning understanding, higher-order thinking, and transfer rather than machine learning. The studies span 2007–2023, multiple levels and subjects, and 13,187 participants.
Key ideas
The pooled standardised mean difference favours technology-supported conditions (SMD = 0.68, 95% CI 0.45–0.92), but heterogeneity is extremely high (I² = 99%), so the average does not predict every implementation. Moderator analyses report stronger outcomes when digital work is blended with offline activity, accompanied by guidance, used systematically rather than fragmentarily, and connected with collaborative learning. Tool function, duration, educational level, and several design variables were not significant moderators. Because categories and measures of deep learning vary, subgroup findings are observational across studies rather than causal comparisons among implementation features. The practical message is not that technology itself produces depth.
Why it matters
Educators should align tools with substantive goals, provide guidance, integrate use coherently with teaching and interaction, and evaluate understanding and transfer. Decisions should consider study quality, context, and the wide variability hidden by the pooled effect.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
Citation checks
References are checked against publisher records, scholarly indexes, DOI or ISBN metadata, repositories and author records where available. An entry marked [Citation not verified] preserves the wording found in the relevant volume without attributing findings to an unconfirmed work.
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