Open Textbooks: Quality and Relevance for Postsecondary Study in The Bahamas
Bibliographic record
Abstract
Open educational resources (OER), are openly licensed text, media, and other digital and analog assets that are useful for teaching, learning, and research. Recent research has shown that in courses where open textbooks are assigned, students perform as well as or better than students in similar courses with commercially licensed textbooks. Despite cost savings and demonstrated effectiveness, perceptions about quality, relevance, ease of access, and other concerns persist. The objectives of this study were twofold: (a) to develop a practical and reusable measure for evaluating open textbook quality in terms of pedagogy, openness, accessibility, and relevance; and (b) to use the measure to rate the quality and relevance of open textbooks for use in higher education in The Bahamas. The study confirmed the viability of the quality measure as a practical tool to assess open resources and found that the open textbooks studied were accessible and well matched to course content, but of varying quality. More study is needed to explore ways to increase faculty adoption, use, adaption and production of OER.
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 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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".