Becoming an engineering communicator : a study of novices' trajectories in learning genres of their profession
Bibliographic record
Abstract
The study presented in this dissertation focusses on the analysis of novices' trajectories in learning genres of their profession, engineering. The goals of the study are: (a) to refine the current understanding of what constitutes professional genre knowledge and of how novices learn and use genres of professional communication, and (b) to test the effectiveness of the suggested pedagogy for an Engineering Communication course. This qualitative longitudinal exploration includes ten case studies that span eight years and trace the participants' trajectories through the university and workplaces. I use a combination of three theoretical perspectives---Rhetorical Genre Studies, Activity Theory, and situated learning---as a lens for the analysis of novices' learning trajectories on their way to becoming professional communicators. The study demonstrates that in addition to the knowledge of genre conventions and understanding of an audience's expectations, genre knowledge is a result of a summative effect of such ingredients accumulated from different sources at different time periods as (a) cultural capital, (b) domain content expertise, (c) the novice's understanding of the improvisational qualities of genre, (d) agency, as reflected in the novice's ability to both seize and create kairotic moments in the chronological flux of time and enact genres in the ways that are recognizable by the community of practice, (e) formal education, (f) workplace experiences, and (g) private intention. The study indicates that the ingredients of genre knowledge accumulated in one context may be used in another, that is, that rhetorical strategy may be portable, thus allowing novices to adapt genres learned elsewhere to a new rhetorical situation. The study also shows that communication practices can be successfully taught outside of local contexts, for example, in the academic classroom. In addition I draw pedagogical implications of the inquiry for the communication classroom; for example, that communication instructors need to extend their pedagogies beyond teaching genre conventions and audience awareness and provide classroom contexts that would allow students to develop the understanding of genre as allowing for flexibility and educated intervention. The study also shows that the timing of the offering of domain-specific communication courses is crucial for the students to be able to develop the sense of connections among communication courses, subject matter courses, and professional practice.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".