In Search for the Open Educator: Proposal of a Definition and a Framework to Increase Openness Adoption Among University Educators
Bibliographic record
Abstract
The paper explores the change process that university teachers need to go through in order to become fluent with Open Education approaches. Based on a literature review and a set of interviews with a number of leading experts in the field of Open Educational Resources and Open Education, the paper puts forward an original definition of Open Educator which takes into account all the components of teachers’ work: learning design, teaching resources, pedagogical approaches and assessment methods- of teachers’ activities. Subsequently, to help the development of teachers’ openness capacity, the definition is further developed into a holistic framework for teachers, which takes into account all the dimensions of openness included in the definition and which provides teachers with self-development paths along each dimension. By working on the definition and on the framework with the interviewed experts, the paper concludes that a strong relation exists between the use of open approaches and the networking and collaboration attitude of university teachers, and that in order to overcome the technical and cultural barriers that hinder the use of open approaches in Higher Education, it is important to work on the transition phases – in terms of awareness and of capacity building - that teachers have to go through in their journey towards openness.
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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 | 0.041 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.010 | 0.039 |
| Scholarly communication | 0.018 | 0.030 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".