Viewing science teacher learning and curriculum enactment through the lens of theory of practice architectures
Bibliographic record
Abstract
Abstract Science teachers struggle to implement and sustain new curricular ideas from professional development (PD) experiences. These PD opportunities are crucial for enacting real‐world changes to teaching practice and address pressing global challenges, such as the teaching and learning of socioscientific topics nested in school communities. Additionally, it is important to consider how school situative conditions are an important aspect in how science teachers learn, develop, and enact curricular practices in their classrooms. This paper is part of a special issue on Teacher Learning and Practice within Organizational Contexts. The purpose of this conceptual paper is to illustrate how researchers can frame research using the theory of practice architectures (TPA) as a lens to develop a dynamic socio‐material understanding of teacher learning within teachers' working environments and their local school communities. An ongoing multi‐year professional learning study with science teachers in an elementary school and secondary school was analyzed using TPA. Using a philosophical‐empirical approach, observations from PD sessions and collaborative meetings illustrated teachers' practices in the form of sayings, doings, and relatings and their changes over the duration of the observations with associated modifications in schools' practice architectures. Although specific school conditions, such as timetable restrictions and curriculum accountability, constrained teachers' practices they were still enabled to learn and develop their practices. Overall, TPA was found to be an insightful framework for theorizing changes in science teaching practices of teachers' saying, doings, and relatings at their school sites. Future research focused on PD within schools would benefit from using a TPA approach to theorizing science teacher learning and curriculum enactment practices.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.032 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".