Current Developments and Best Practice in Open and Distance Learning
Bibliographic record
Abstract
Through the many documents regularly emitted by those dedicated to this activity, it is comparatively easy to describe factual developments in the field of open and distance education in different places in the world. However, it is much more difficult to produce judgements of value about their quality. Quality is a subjective rather than an absolute concept and may be examined from different analytical perspectives: consumers' satisfaction level, intrinsic value of scientific and technical content of learning materials, soundness of learning strategies, efficiency of organisation and procedures, adequate use of advanced technologies, reliability of student support mechanisms, etc. These parameters should be put into the context of specific objectives, nature of target populations and availability of different kinds of resources. In a specific geographic, social, economic and cultural situation a given set of solutions might be judged as adequate and deserving the qualification of "good practice", while in a different context it could be considered of rather poor quality. The selection of examples in this article is the sole responsibility of the authors: neither should the chosen cases be considered as clearly better than any other one, nor missing cases be interpreted as lack of appreciation or a negative judgement. Finally, the authors are aware of the risks of interpreting trends and trying to extrapolate them into the near future: readers should use their own judgement in accepting (or forcefully rejecting) these projections.
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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 | 0.029 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".