Restricted Versus Unrestricted Learning: Synthesis of Recent Meta-Analyses
Bibliographic record
Abstract
Meta-analysis is a method of quantitatively summarizing the results of experimental research. This article summarizes four meta-analyses published since 2003 that compare the effect of DE and traditional education (TE) on student learning. Despite limitations, synthesis of these metaanalyses establish, at the very least, equivalent learning outcomes for DE and TE. Research efforts should now be directed toward the more important question: Why is DE not clearly superior to TE? TE is restricted by time-place-pace: DE is marginally influenced by such restrictions. Intuitively, learning opportunities that are convenient and individualized, as opposed to those that are fixed and inflexible, should result in higher student achievement. DE demands learner responsibility and affords learner autonomy, both of which facilitate academic achievement. Instructional technology, virtually synonymous with DE, is a force that can transform instruction. Why is DE not clearly superior to TE?
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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 | 0.064 | 0.172 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.038 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".