Professional Learning in a Digital Age / L’apprentissage professionnel à l’ère numérique
Bibliographic record
Abstract
While professional development (PD) has always been central to the teaching profession, increasingly traditional models of PD are out of step with contemporary ways of learning. Commiserate with the literature, we see the field moving along a continuum which reflects changes in what, how and when teachers learn. Following a brief sketch of the online teacher professional development (oTPD) field, we identify important considerations of emerging models of technology-mediated professional learning (TMPL). We posit the catalyst for the transformation of education, as envisioned by countless educational leaders, may lie in reimaging professional development as professional learning in a networked age. Alors que le perfectionnement professionnel (« PP ») a toujours été au cœur de la profession d’enseignant, les modèles traditionnels de PP sont de plus en plus décalés par rapport aux méthodes contemporaines d’apprentissage. Nous voyons ce domaine progresser dans un continuum qui reflète les changements dans ce que les enseignants apprennent, dans la façon et le moment où ils l’apprennent, et cette progression correspond à la littérature. Après un survol du domaine du perfectionnement professionnel en ligne pour les enseignants, nous cernons des considérations importantes sur les modèles émergents de la formation professionnelle assistée par ordinateur. Nous postulons que le catalyseur de la transformation de l’éducation, comme conçue par d’innombrables chefs de file de la pédagogie, pourrait être de ré-imaginer le perfectionnement professionnel comme une formation professionnelle à l’ère des réseaux.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".