Re‐imagining professional learning communities in education: Placing teacher leadership in <scp>STEM</scp> context
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it