Open Textbooks as an innovation route for open science pedagogy
Bibliographic record
Abstract
The paper introduces the UK Open Textbook project and discusses its success factors with regards to promoting open practice and open pedagogy. Textbooks remain a core part of educational provision in science. Open Textbooks are openly licensed academic textbooks, wherein the digital version is available freely, and the print version at reduced cost. They are a form of Open Educational Resource (OER). In recent years a number of openly-licensed textbooks have demonstrated high impact in countries including the USA, Canada and South Africa. The UK Open Textbooks project piloted several established approaches to the use and promotion open textbooks (focusing on STEM subjects) in a UK context between 2017 and 2018. The project had two main aims: to promote the adoption of open textbooks in the UK; and to investigate the transferability of the successful models of adoption that have emerged in North America. Through a number of workshops at a range of higher education institutions and targeted promotion at specific education conferences, the project successfully raised the profile of open textbooks within the UK. Several case studies report existing examples of open textbook use in UK science were recorded. There was considerable interest and appetite for open textbooks amongst UK academics. This was partly related to cost savings for students, but more significant factors were the freedom to adapt and develop textbooks and OER. This is consistent with a range of research that has taken place in other countries and suggests the potential for impact on UK science education is high.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".