USING RUBRICS IN A CAPSTONE ENGINEERING DESIGN COURSE
Bibliographic record
Abstract
Rubrics have been developed to assess the proposal,interim report, final report and proposal as related to theengineering capstone project, to better assess theperformance of the students and provide feedback to thestudents. The presentation will outline how the rubricswere developed and highlight some the challenges thatarose in implementing them. In addition, the rubrics werestructured to address the majority of graduate attributesoutlined by the Canadian Engineering AccreditationBoard (CEAB). As such the rubrics assess studentperformance in the following graduate attributes: design,problem analysis, investigation, communication skills,impact of engineering on society and environment, andeconomics and project management. Through thelearning management system used at the SOE, the rubricdata can also be collected and reviewed as part of thegraduate attribute process that has become an importantcomponent of the CEAB accreditation process.Challenges in using the learning management system willalso be discussed.
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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 | 0.014 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.016 |
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".