Aprendizaje y liderazgo docente para el mejoramiento del sistema y la docencia en las salas de clase y establecimientos escolares
Bibliographic record
Abstract
This article presents a study of the Teacher Learning and Leadership Program (TLLP), a joint initiative between the government (Ontario Ministry of Education) and the teachers’ unions (Ontario Teachers’ Federation and affiliates) in Ontario, Canada. The goals of TLLP are to support experienced teachers’ professional learning, leadership and knowledge exchange. Our research utilized a mixed methods design to examine to what extent the goals of TLLP have been realized. The main features for teachers’ learning identified were the importance of an emphasis on experienced teachers’ leading their own learning and the learning of other teachers, and the empowering nature of teachers making their practice public and collaborating with benefits for new knowledge, improved understanding and changes in practice. For developing teachers’ leadership, the main benefits were: the development of leadership skills and experiences; opportunities to influence school and system improvements; and recognition as a teacher leader. Knowledge exchange involved developing professional learning, fostering collaboration, creating resources and using communication strategies. The main challenges were time, team dynamics, overcoming resistance and practicalities of project delivery. We conclude that the TLLP is professionally valuable for supporting teachers’ learning and leadership and educationally valuable by generating improvements in practices.
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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 | 0.004 | 0.008 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".