Framework for Evaluating the Creation and Adaptation of Open Textbooks
Bibliographic record
Abstract
The COVID-19 pandemic resulted in dramatic growth in the use of open education resources, such as open textbooks, as classes moved online around the world. This highlighted the importance of evaluating the creation and adaptation of open textbooks used in online courses. In reviewing the research literature, I found existing rubrics or frameworks were outdated and lacked criteria that are important in today’s digital age. Therefore, I synthesized the evaluation and quality criteria from 31 articles into a single framework, which online instructors can consider using as they create or adapt an open textbook. The criteria include content, accessibility, technology, open licenses, instructional design, and a new category I termed verification.
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 | 0.221 | 0.315 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.049 | 0.020 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".