Bibliographic record
Abstract
Following the burst of the dot-com bubble in 2000, scepticism about e-learning replaced over-enthusiasm. Rhetoric aside, where do we stand? Why and how do different kinds of tertiary education institutions engage in e-learning? What do institutions perceive to be the pedagogic impact of e-learning in its different forms? How do institutions understand the costs of e-learning? How might e-learning impact staffing and staff development? This book addresses these and many other questions. The study is based on a qualitative survey of practices and strategies carried out by the OECD Centre for Educational Research and Innovation (CERI) at 19 tertiary education institutions from 11 OECD member countries – Australia, Canada, France, Germany, Japan, Mexico, New Zealand, Spain, Switzerland, the United Kingdom and the United States – and 2 non-member countries – Brazil and Thailand. This qualitative survey is complemented by the findings of a quantitative survey of e-learning in tertiary education carried out in 2004 by the Observatory on Borderless Higher Education (OBHE) in some Commonwealth countries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.010 |
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".