Barriers, incentives, and benefits of the open educational resources (OER) movement: An exploration into instructor perspectives
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.
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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 | 0.022 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".