New Literacies for Engineering Students: Critical Reflective-Writing Practice
Bibliographic record
Abstract
Engineering education has long resulted in professional engineers with the required technical skills to meet the profession’s needs. Yet in today’s rapidly changing, globalised world, engineers will need more than technical competencies to meet the requirements of their professional work. Incorporating different literacies in engineering education might help with this shift. We introduce the idea of including critical reflective writing practice on the idea of being an engineer into engineering curricula. Our study explored how fourteen engineering graduate students were mentored on how to reflect critically on their professional identities through narrative writing. The students wrote the narratives while attending a pilot co-curricular Institute that focused on developing leadership, communication, and professional skill-building. We analysed the narrative writing produced by participants using the constant comparison method of analysis. Key findings show that (a) narrative methodologies are valuable for tapping into the reflective non-technical, process aspects of the profession; and (b) critical reflective writing practice was challenging for participants and required comprehensive scaffolding. If scaffolded and embedded in engineering curricula, critical reflective writing practice could contribute significantly to a 21st century engineering identity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.052 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.012 | 0.030 |
| Scholarly communication | 0.024 | 0.016 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".