INTEGRATING COMMUNICATION INTO SENIOR ENGINEERING DESIGN COURSES AT THE UNIVERSITY OF MANITOBA
Bibliographic record
Abstract
In this paper, we will describe how we integrated communication into two capstone design courses in the Faculty of Engineering at the University of Manitoba. We will first look briefly at how the stand-alone technical communication course (offered early in the curriculum) serves as a cornerstone because it introduces students to the various genres of engineering communication and emphasizes the importance of communication within the practice of engineering. Integrating communication into courses like the Mechanical and Manufacturing Engineering design course (MECH 4860) and the Electrical and Computer Engineering design course (ENG 4600) means that technical and communications specialists work together toward helping senior engineering design students achieve their goal: designing a solution to an industry-based problem and then presenting their design in written, graphical and oral form. To do so, communications specialists become partners in the delivery of the course and in the assessment process. At the same time, the technical specialists can focus on assessing the design itself. Together, we can then evaluate a design according to what engineers must do on the job: solve problems and communicate solutions. The rubrics used to assess written communications are also intended as ways to help students see how each design element (like “project specifications”) is important to the “deliverable,” the report to the client. Finally, we will conclude with some observations about this past year and indicate what we would like to do next year.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".