Open Educational Resources Help Promote Student Success
Bibliographic record
Abstract
BACKGROUND: A significant economic barrier for prelicensure nursing students is the skyrocketing cost of textbooks and learning activities. Adopting high-quality open educational resources (OER) is an innovative teaching strategy that promotes student success by making education more accessible and more affordable. METHOD: The Open Resources for Nursing (Open RN) project has published 15 OER textbooks reviewed by more than 350 peer reviewers from across the United States and Canada. Open RN textbooks have received widespread international usage with adoption by more than 1,300 institutions. RESULTS: The NCLEX-RN pass rate can be used to objectively measure student success in programs adopting OER. The annual 2023-2024 NCLEX-RN pass rate of graduates from the associate degree nursing program that led the creation of Open RN resources was 98%. CONCLUSION: High-quality OER created by nursing faculty that is aligned with competency-based curriculum and the NCLEX Test Plan can help promote success for all nursing students.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.006 |
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".