Fostering a technology-rich professional learning community: design-based research cycle
Bibliographic record
Abstract
Teachers' knowledge and skills is one of the most important factors affecting student learning. This thesis describes the early process of a teacher professional development project during which a technology-rich professional learning community was developed and validated to support ongoing teacher development. The aim of this project was to improve teachers' disciplinary understanding and digital literacy in the mathematics classroom, with a focus on student learning in the transition from elementary to secondary schools. In particular, the author highlights the use of design-based research in the design and the validation of this learning community. The results indicate that this learning community fostered a shared vision of continued development among teachers. Teachers shared their experience of practice and supported each other's learning to improve student learning. In addition, they explored the effective use of digital tools for student learning in the classroom. This study reports baseline data that will be used to examine how the conversations, teaching practice, and inquiry activities evolve in the next iterations of the professional learning community.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.013 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".