How does technology-based embodied learning affect learning effectiveness? – Based on a systematic literature review and meta-analytic approach
Bibliographic record
Abstract
With the in-depth research on embodied learning in educational psychology, technology-based embodied learning (TBEL) has gained widespread popularity in the field of education. However, the impact of TBEL on learning efficiency remains controversial. The objective of this study is to determine the effect of TBEL on learning efficiency and identify the main factors influencing this efficiency. The research method employed is a systematic literature review and meta-analysis of 44 relevant English papers published over the past decade. The study found that TBEL has a statistically significant positive effect on learning outcomes (SMD = 0.41, p < .01). Four moderators—educational level, subject, type of embodiment, and experiment duration—have significant moderating effects on learning outcomes. Therefore, technology-based embodied learning can effectively improve students' learning effectiveness. In the future, efforts should be made to deepen and expand multidimensional embodied learning, providing guidance and inspiration for global educational practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.045 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.026 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".