Learning to teach science genres and language of science writing: Key change processes in a teacher’s critical SFL praxis
Bibliographic record
Abstract
This article examines the key change processes (KCPs) in the critical systemic functional linguistics praxis (CSFLP) of a science teacher, the first author, as she learned SFL theory as a metalanguage for thinking about language and for teaching science writing to culturally and linguistically diverse students. In this case study (Yin, 2012), which emerged from a large-scale action research project, we describe how the science teacher applied her developing knowledge of SFL to teach science writing over the course of five years. We analyze these changes through what we describe as three KCPs: (1) Changing conceptions of science writing; (2) Implementing the teaching learning cycle (TLC) to teach language and content; (3) Building teacher language awareness. Through the examination of these KCPs, we highlight the benefits of the TLC for learning to teach the genres of science writing over time (Rothery, 1996). Examples from the science teacher’s classroom show how her implementation of SFL-informed instruction helped her apply the theory to scaffold the teaching of writing. Implications include discussion of how praxis occurs when teachers learn SFL theory and apply it to developing genre-based pedagogy in science.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".