E-Learning for Teacher Development: Global Perspectives of Policy and Planning Issues
Bibliographic record
Abstract
The panel session will focus on key topics and issues related to the use of e-Learning for teacher development. The international group of panelists are chapter authors of a new book commissioned by UNESCO entitled e-Learning for Teacher Development: A Policy and Planning Guide. Four basic categories of e-learning for teacher development will be presented including: accessing online resources; online courses and degree programs; blended learning environments; and communities of practice. Each category is framed within two axes of e-learning: content and communication. Panelists will share their diverse views of the global context of e-learning for teacher development, the global challenge of preparing 15-35 million teachers needed in the next 15 years to meet UNESCO's Education for All goals and the major organizational, financial, policy and planning issues related to effective use of elearning for teacher development in both developed and developing 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 | 0.017 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.030 | 0.029 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.020 | 0.023 |
| Insufficient payload (model declined to judge) | 0.015 | 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".