the Canadian Education Statistics Council
Bibliographic record
Abstract
The driving question for this review is: What are the important dimensions of capacity building for ICT integration in education that have been identified, articulated, and experienced in different jurisdictions outside of Canada, and that have not [yet] been disseminated in the traditional research publication channels. We identified 12 Research and Development (R & D) initiatives involving 14 countries that either make explicit connection between national or state policies and research & development, have national or international scope, or both. We present the capacity building process that emerged out of studying the source, partners, activities, and results of R & D initiatives. The following dominant themes were identified: 1) The vision underlying educational reform, 2) partnerships, 3) leadership, 4) connectivity and access, 5) curriculum requirements, 6) teacher professional development, and 7) assessment of learning. We observe capacity building mostly around a few existing innovations in education: the networked computer, knowledge building, and collaborative project-based learning. Exciting results are growing out of the greenhouse R & D initiatives. Will efforts to scale them up lead to the loss of their rational and their innovative dimension? Too few studies consider both a leading-edge pedagogical practice of ICT-supported knowledge building in the classroom and an advanced perspective on school leadership and governance. We conclude generally that countries are acting proactively but are still far away from seeing network-supported innovative practices in teaching and learning being sustainable or adopted on a large scale. Such practices would be in coherence with the discourse on the knowledge society.
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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 | 0.009 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.016 | 0.037 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.181 | 0.087 |
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".