Towards development of OER derived custom-built open textbooks: A baseline survey of university teachers at the University of the South Pacific
Bibliographic record
Abstract
Textbook prices have soared over the years, with several studies revealing many university students are finding it difficult to afford textbooks. Fortunately, two innovations – open educational resources (OER) and open textbooks – hold the potential to increase textbook affordability. Experts, though, have stated the obvious: that students can save money through open textbooks only if teachers are willing to develop and use them. Considering both the high price of textbooks and the benefits offered by OER and open textbooks, the aim of this study was to assess the University of the South Pacific (USP) teachers’ willingness towards development of custom-built OER derived open textbooks for their courses with a focus on providing a foundation for strategies to promote open textbook development at USP. This paper reports the findings of an online survey of 39 USP teachers. The results show that 17 teachers were willing to develop OER derived custom-built open textbooks for their courses. Besides this, there are findings relating to six important areas: teachers’ motivation to develop open textbooks; the frequency of more than one prescribed textbook per course; teachers’ awareness of the costs of the prescribed textbooks; the average cost of prescribed textbooks in a course; teachers’ awareness and utilization of OER and open textbooks; and teachers’ perceived barriers to using OER and types of challenges they encounter while using OER. These findings have been discussed in relation to research studies on OER and open textbooks.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".