Designing open access, educational resources / Développer des ressources éducatives en libre accès
Bibliographic record
Abstract
<p>The recent bourgeoning of open educational resources has meant greater access to materials with open licenses in the public domain than ever before. Open educational resources are learning tools, such as textbooks, that are freely available and typically accessed online. Despite the expansion of open educational resources, many educators are still unacquainted with the nature and process of producing such resources. The purpose of this discussion paper is to share our experience in developing an open educational e-textbook for students in post-secondary programs for nursing and other health professions while highlighting practical tips for educators. The exemplar referenced in this paper focuses on vital signs’ measurement, a familiar concept for nurse educators, and a topic ubiquitous in healthcare. This paper is suited for any user curious about designing open educational resources with consideration of key elements to produce quality and educational resources that support excellence in nursing pedagogy. We begin by providing a background to our specific project followed by a discussion of the planning phase, the design phase, and other considerations. The e-textbook falls under a Creative Commons license and can be accessed for free by educators and learners.</p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.015 | 0.021 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".