Making writing practices visible and sustainable in the engineering curriculum: a practice architectures theory analysis
Bibliographic record
Abstract
Engineering practice requires engineers who have strong spoken and written communication skills, but the development of these skills, notably writing practices, is often invisible in the engineering curriculum, and rarely embedded. Decades of reviews of engineering education have identified the gap between the engineering curriculum and engineering practice, such as engineering graduates’ level of writing skills being inadequate for the workplace. This paper draws on research from a qualitative study which investigates the perspectives of engineering educators about writing practices in the engineering curriculum, utilizing the theory of practice architectures as a theoretical and methodological lens. Using examples from the case studies, we explore some constraints of the development of writing practices in the engineering curriculum. We then focus on case studies where the development of writing practices is enabled within a subject, across a sequence of subjects and throughout an engineering degree program, and identify elements that contribute to these practices. Our findings suggest that the development of writing practices can be integrated into engineering studies, but certain pre-conditions are required.
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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 | 0.018 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".