Are open educational resources (OER) and practices (OEP) effective in improving learning achievement? A meta-analysis and research synthesis
Bibliographic record
Abstract
Abstract While several studies have investigated the various effects of open educational resources (OER) and open educational practices (OEP), few have focused on its connection to learning achievement. The related scientific literature is divided about the effects of OER and OEP with regards to their contribution to learning achievement. To address this tension, a meta-analysis and research synthesis of 25 studies ( N = 119,840 participants) was conducted to quantitatively investigate the effects of OER and OEP on students’ learning achievement. The analysis included course subject, level of education, intervention duration, sample size, geographical distribution, and research design as moderating variables of the obtained effects. The findings revealed that OER and OEP have a significant yet negligible ( g = 0.07, p < 0.001) effect. Additionally, the analysis found that the obtained effect can be moderated by several variables, including course subject, level of education and geographical distribution. The study findings can help various stakeholders (e.g., educators, instructional designers or policy makers) in understanding what might hinder OER and OEP effect on learning achievement, hence accommodating better learning outcomes and more effective interventions.
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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 | 0.063 | 0.114 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.052 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".