Designing Rubrics to Assess Engineering Design, Professional Practice, and Communication Over Three Years of Study
Bibliographic record
Abstract
When using rubric-based assessment of students’ understanding of design process in project based courses, it is important to provide specific feedback for major design process elements while avoiding overly prescriptive descriptors [8]. This paper details the development process of a sequence of rubrics used for assessment in successive second, third and fourth year project-based courses. A major consideration in the rubric development process was to ensure the alignment of assessment with course learning outcomes that can be easily mapped to the CEAB graduate attribute accreditation requirements. In the second year course, the rubrics are used to provide students with directed feedback as they learn the basics of engineering design process. The third and fourth year rubrics progress from the second year analytic rubrics by employing elements of holistic assessment. The purpose of evolving these rubrics year over year is to find a balance between the students’ learning and development in design process whilst accommodating variation in projects. This ultimatelyprovides students with greater flexibility and encourages responsibility as they progress through their program.
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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 | 0.029 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".