Using Open Educational Resources at Viterbo University: Faculty and Student Feedback
Bibliographic record
Abstract
This study evaluated a coordinated and collaborative pilot implementation of open educational resources (OER) across multiple disciplines including nursing, accounting, environmental science, religious studies, and finance. Participating faculty were qualitatively surveyed regarding their experience creating and implementing OER in a course. Students were surveyed on their perceptions of OER quality, cost savings, and ease of use. Faculty had an overall positive experience with OER, believing there was a significant benefit to students in cost savings while maintaining learning quality. Faculty felt the OER implementation process took a significant investment of time and recommended that faculty should be compensated for creating and implementing OER materials in future courses. Students overall showed positive responses to using OER in their course; the majority of students agreed with the OER cost savings, quality of OER resources, ease of using OER, and they trusted the use of OER materials. Older students (over 30 years) were more likely to state they would print out OER materials rather than read them online (as compared with students 30 and under). Senior-level students agreed significantly more than did freshman-level students that OER presented a cost savings. Faculty recommendations from this study included focusing on courses with very high textbook costs and courses that would impact the greatest number of students. Additionally, faculty recommended a follow-up revision process to keep OER materials current after implementation.
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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 | 0.018 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".