OER Quality and Adaptation in K-12: Comparing Teacher Evaluations of Copyright-Restricted, Open, and Open/Adapted Textbooks
Bibliographic record
Abstract
Conducted in conjunction with an institute on open textbook adaptation, this study compares textbook evaluations from practicing K-12 classroom teachers (n = 30) on three different types of textbooks utilized in their contexts: copyright-restricted, open, and open/adapted. Copyright-restricted textbooks consisted of those textbooks already in use by the teachers in their classrooms prior to the institute, open textbooks included alternatives from CK-12 and OpenStax, and open/adapted consisted of open textbooks that the teachers devoted time to adapting to their individual needs. Results indicate that open/adapted textbooks were evaluated as having the highest quality, and that open textbooks were of higher quality than copyright-restricted textbooks. Though some factors of quality might be influenced by cost differences (e.g., timeliness and the ability to adopt updated textbooks), results reveal that open and open/adapted textbooks may do a better job of meeting the needs of K-12 teachers in a variety of ways that may not be captured through traditional approaches to quality assurance. This study marks an early step in exploring the quality of K-12 open educational resources (OER) and the use of practicing teachers as authentic evaluators of textbooks for their local contexts.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".