Workshop description: Does research in ODL make a difference? If so why does it have such a low profile?
Bibliographic record
Abstract
This workshop will be based on a series of short case studies from different parts of the world which illustrate where research has had a positive impact on decision-making in both policies and practices. Examples of where poor decisions may have been avoided if research had been undertaken will also be given. The following colleagues will contribute to this session: Som Naidu, University of Melbourne, Australia Sushmita Mitra, National Institute for Open Schooling, India Brian Sayer, University of London, UK Anne Gaskell, The UK Open University Jocelyn Calvert, Distance Education Consultant, Canada and, it is hoped, Evelyn Nonyongo from the University of South Africa Colleagues attending the session will be invited to give examples from their own experiences. A paper, which will be available at the conference, addresses the key issue of the role of research and evaluation in influencing policy and practice change and development in Open, Distance and elearning. (ODL) It takes as its hypothesis the fact that research does make a difference if well conceived and constructed and argues for this by providing recent examples of how research has made a difference across a range of institutions and countries. It reflects on the question of who should do the research, practitioners or specialized researchers. It is hoped that the paper will be of use to colleagues who may find themselves in the position of making a case for modest research expenditure to inform decision-making in their own institutions.
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How this classification was reachedexpand
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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".