Barriers, incentives, and benefits of the open educational resources (OER) movement: An exploration into instructor perspectives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Open educational resource (OER) barriers, incentives, and benefits are at the forefront of educator and institution interests as global use of OER evolves. Research into OER use, perceptions, costs, and outcomes is becoming more prevalent; however, it is still in its infancy. Understanding barriers to full adoption, administration, and acceptance of OER is paramount to fully supporting its growth and success in education worldwide. The purpose of this research was to replicate and extend Kursun, Cagiltay, and Can’s (2014) Turkish study to include international participants. Kursun, et al. surveyed OpenCourseWare (OCW) faculty on their perceptions of OER barriers, incentives, and benefits. Through replication, these findings provide a glimpse into the reality of the international educators’ perceptions of barriers, incentives, and benefits of OER use to assist in the creation of practical solutions and actions for both policy makers and educators alike. The results of this replication study indicate that barriers to OER include institutional policy, lack of incentives, and a need for more support and education in the creating, using, and sharing of instructional materials. A major benefit to OER identified by educators is the continued collegial atmosphere of sharing and lifelong learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it