Re‐imagining professional learning communities in education: Placing teacher leadership in <scp>STEM</scp> context
Bibliographic record
Abstract
Abstract This conceptual analysis paper discusses the characteristics of teacher leadership (TL) in Science, Technology, Engineering, and Mathematics (STEM) education, presenting benefits for its development within the professional learning communities (PLCs). We describe our STEM education approach and argue that TL in STEM is different and more complex than leadership in any particular discipline. We compare two pathways for STEM learning and professional development (PD): engineering design approach and modeling approach. Then, we answer two research questions pertaining to the characteristics of STEM teacher leaders' (TLRs) knowledge, dispositions, and skill set; the support TLRs need to empower STEM educators; and consequently, we discuss how PLCs can become vehicles for growing STEM TLRs and empowering teachers. When promoting integrated STEM, educators likely find themselves in an out‐of‐field teaching situation, where communication with their PLC's leaders and peers is crucial in developing epistemological multiliteracy and confidence. We elaborate on the four main characteristics of STEM PLCs: (1) collaborative nature; (2) focus on boosting teachers' pedagogical content knowledge and confidence; (3) evidence‐based decision making; and (4) advocacy for high‐quality STEM education, teacher education, and PD. Each feature serves different but complementary goals, suitable for developing and utilizing the seven dimensions of TL discussed in the literature.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".