Crowne Plaza Montreal Centre Hotel
Bibliographic record
Abstract
The opinions expressed in this paper are those of the authors and do not represent the views of the Canadian Education Statistics Council The importance of information and communication technology (ICT) in education has been proven. For several years now, at increasing warp speed, university teaching has been coping with changing relationships to knowledge and has been plunged into the digital world of the Internet and e-learning (Karsenti and Larose, 2001). In the context of educating future teachers, can pedagogical integration of ICT be achieved despite the new problems facing faculties of education? The answer now appears self-evident as teacher education officials and instructors can no longer ignore ICT, at the risk of censure by future teachers, the education sector and the general public. The key factor is a better understanding of how this pedagogical integration of ICTwhich adds value to the teaching-learning continuumcan be achieved. Do instructor practices have an impact on the practices of future teachers? Have the obstacles and factors linked to change and efficient pedagogical integration of ICT evolved in recent years? Are faculties of education meeting the professional development needs of future and practising teachers? These are
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.768 | 0.342 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".