DESIGNING RUBRICS FOR COMMUNICATION COURSES IN ENGINEERING: A Work in Progress
Bibliographic record
Abstract
This paper will focus on the rubrics that we have developed for the technical communication course and the senior (capstone) design projects. As part of the C.E.A.B.’s and our own Faculty of Engineering’s mandate to more clearly define the goals of each course, the learning attributes associated with course content, and how these are assessed, we first developed rubrics that would help us track and assess students’ communicative competence. However, we soon learned that our presentation of the information impacts how well students assimilate it. Consequently, in our rubrics for the senior (capstone) design courses, we began to phrase the assignment requirements as action items, as something that must be done; for example, a document’s “layout and document design” must use “clear markers to create a visually appealing document,” and the illustrations must “communicate design elements and results.” In this way, students are encouraged to reflect on their individual performance, and one outcome for them is the opportunity to engage in a meaningful dialogue with the professor. One outcome for the professor is having the means to indicate a student’s position on a spectrum of performance. Finally, although linking attributes to learning objectives and determining “competency levels” can be very challenging, we hope to show how the rubrics we have designed may indeed make the task less daunting and more manageable for all stakeholders in the education of our engineering students.
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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 | 0.058 | 0.163 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".