UNDERGRADUATE WRITING ASSIGNMENTS IN ENGINEERING: TRACKING ATTRIBUTE 7 IN TWO ACCREDITATION CYCLES
Bibliographic record
Abstract
This paper reports on two iterations of our study of course syllabi in the Faculty of Engineering, University of Manitoba. The first iteration was part of a national study investigating the writing demands placed on students in a variety of disciplines, including those in the Social Sciences and the Humanities as well as Engineering. This first iteration followed an accreditation visit and the Faculty’s introduction of the C.E.A.B. graduate attributes and outcome-based assessment. Although one would expect Engineering to have far fewer written assignments than these other disciplines, such was not always the case. For example, the national study captured results for Political Science that closely matched those for Mechanical Engineering; Political Science students typically wrote, on average, 2.3 written assignments in year 2 of their program, 2.4 written assignments in year 3, and 4.2 written assignments in year 4, while Mechanical Engineering students wrote 4, 3 and 4.2 written assignments in those same years. Such a finding suggested that more writing was happening in the Faculty of Engineering than we might realize – and quiteapart from that done in the mandatory communication class. So, our second iteration of the study followed another accreditation cycle, but this time we focused solely on the Faculty of Engineering at the University of Manitoba. In this second iteration, our goal was to refresh the data so that we could clarify how Attribute 7, “communication skills,” is being met in the Faculty of Engineering at the University of Manitoba.
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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 | 0.017 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".