Advancing Knowledge Creation in Education Through Tripartite Partnerships
Bibliographic record
Abstract
The purpose of this paper is to highlight the work of one tripartite partnership with stakeholders to improve and strengthen novice teachers’ pedagogical designs using design based professional learning guided by the principles of knowledge building/knowledge creation. The tripartite partnership involved 450 novice teachers from an urban school division, a practitioner-research university team, and the provincial government. Drawing upon one case, this paper analyzes the ways in which the design-based professional learning mirrored the knowledge building/knowledge creation processes highlighting the ways in which teachers worked in collaborative, collective, and connected ways to progressively improve pedagogical designs for collective knowledge building. Computer supported, networked digital technologies provided a community to develop an audit trail to keep track of progressive improvements and refinements to their pedagogical designs and to support, enable, and enhance knowledge building discourse. Design-based professional learning informed by the 12 principles of knowledge building/knowledge creation provided novice teachers with a process to work collectively as a community, progressively improving and refining their pedagogical designs, identifying the role of their pedagogical designs in their students’ work, and engaging with other teachers in their respective schools.
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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 | 0.059 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.013 | 0.022 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.005 | 0.052 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".