Embracing the Promise of Open Educational Resources: Faculty Insights and Implications in Higher Education
Bibliographic record
Abstract
This study examines faculty perceptions, awareness, and utilization of Open Educational Resources (OER) in higher education and identifies barriers hindering their adoption. The research encompasses multiple perspectives, including demographic information, teaching practices involving technology, familiarity and opinions regarding OER, types of OER utilized by faculty, ease of searching for OER, a comparison between open and traditional resources, intentions to use OER in the future, and factors deterring the adoption of OER. The study employed a quantitative approach using an online survey questionnaire to gather data. A random sample of faculty members from the University of Jeddah in Saudi Arabia was recruited (n = 139). Using descriptive and MANOVA tests, the findings highlight a preference for blended teaching styles, significant awareness of OER, ease in searching for OER, and a strong intention to use OER in the future. The results emphasize the importance of addressing concerns related to institutional support, intellectual property policies, recognition of contributions, and creating a supportive environment to enhance faculty engagement with OER. The study implications suggest comprehensive and targeted approaches to support faculty members in adopting and utilizing OER effectively, including promoting gender equity, enhancing ease of adoption, considering workload impact, recognizing differences based on teaching experience, tailoring support based on teaching style, and actively promoting the benefits and opportunities 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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".