Adoption of Sharing and Reuse of Open Resources by Educators in Higher Education Institutions in the Netherlands: A Qualitative Research of Practices, Motives, and Conditions
Bibliographic record
Abstract
To find out what is needed to speed up the adoption of open sharing and reuse of learning materials and open online courses in publicly funded Dutch institutions of Higher Education, a qualitative research study was conducted in fall 2016. This study examined issues of willingness of educators and management, barriers and enablers of adoption, and the role of institutional and national policy in the adoption of open sharing and reuse of learning materials and online courses. Fifty-five stakeholders (educators, board members, and support staff) in 10 Dutch Higher Education Institutions were interviewed. The main findings of this study are: motivation for sharing and reuse of learning materials for educators and managers is directly related to the ambition to achieve better education for students; sharing and reuse of learning materials is common practice, very diverse and not open accessible for the whole world, and important barriers include lack of awareness of opportunities for open sharing and reuse and lack of time. Based on the findings from the interviews, the last section of this paper presents conclusions and recommendations regarding how Dutch institutions for Higher Education can formulate effective policies to raise awareness, organize adequate support and provide time to experiment.
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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 | 0.020 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".