The Effects and Implications of Using Open Educational Resources in Secondary Schools
Bibliographic record
Abstract
Open educational resources (OER) constitute a curriculum innovation that is considered revolutionary and has the potential to change the landscape of curriculum at all levels and content areas. OER have gained attention and widespread acceptance by educators and policy makers since 2002. The promise of OER is that they provide cost savings, promote collaboration, and are adaptable to the needs of teachers and students while providing a legitimate alternative to commercially produced print textbooks. Determining the relevance and viability of the movement to embrace OER requires an examination of theoretical foundations and empirical research to illuminate the effect of using OER as core curricula. While advocates promote the use of OER as a financially liberating model of curriculum and as a source of constructivist learning materials, more research is needed. The purpose of this study was to examine the relationship between OER and student learning. The study critically analyzed previous studies on OER and applied empirical analyses to the use of OER by a sample of middle schools. Twenty-eight middle schools from Washington State served as the subjects for the study. The study followed an ex post facto causal comparative model. Three research questions provided the focus for the study to investigate the effects of OER curriculum, duration of curriculum use, and other factors on student achievement in middle school mathematics. The results of the study found non-significant effects for OER use in relationship to school performance in mathematics, and significant effects on math scores for the variables of student poverty, curriculum duration, and cohort size.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".