The Nature of the Teacher Knowledge Constructed in a Multimodal Professional Learning Community
Bibliographic record
Abstract
This research explores the nature of teacher knowledge constructed over four years in a multi-modal environment. Fourteen K-12 teachers, from Ontario, Canada and Michigan, USA, met online for two hours during a monthly research release day, participated in online chats, a forum site called Virtual Professional Learning Community, as well as participated in four three-day summer institutes. The teachers used these times to engage in knowledge building, creating research for their specific schools/classes in a recurring cycle of learning. The data consist of multiple forms, including the online meetings, the forum, and the summer institute discussions. The research investigated what types of knowledge teachers developed and how those knowledges informed their professional learning. Grounded Theory informed the data gathering and an hermeneutical approach was used for the analyses. Three types of professional knowledge emerged, associated with the classical Aristotelian intellectual virtues: episteme, techne and phronesis. Teachersâ discourse revealed the complexity of their professional knowledge and the wide range of scope of their action â a finding that goes beyond many neoliberal conceptions of the profession. Findings provide ways to develop models of professional development with teachers leading to their sustained journey for meaningful professional learning.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".