Campus and Online U.S. College Students’ Attitudes Toward an Open Educational Resource Course Fee: A Pilot Study
Bibliographic record
Abstract
Convincing faculty to accept, create, adapt, and adopt open educational resources (OERs) instead of textbooks for their courses has proven challenging because incentives are lacking. One approach to provide incentive to faculty members is an OER course fee, which could be employed in courses that use OERs approved by the institution for courses that do not utilize textbooks or other resources students must purchase. This fee would provide sustained incentive for using OERs while also decreasing student expense compared with what most currently pay for textbooks. We set out to determine if campus and online students who had used a free OER textbook replacement would support the idea, and implementation at their institution, of an OER course fee. Among online students (n = 17), those who supported an OER course fee at their institution (n = 6) the mean appropriate course fee amount was $9.58/credit hour. Subsequent campus (n = 46) and online students (n = 57) were asked whether they supported a $10/credit hour OER course fee, greater than 67% of somewhat agreed, agreed, or strongly agreed. While these pilot results are encouraging, it is important to note that they are from one course, using one OER, by one instructor at one institution. More research is needed to determine if there is similar support for OER course fees in a broader base of students. If so, OER course fees may be a legitimate approach to increase the acceptance, creation, adaptation, and adoption of OER.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".