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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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- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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