Adapting Existing Assessment Tools For Use in Assessing Engineering Graduate Attributes
Bibliographic record
Abstract
Recently, changes to the Canadian Engineering Accreditation requirements, following the example set by ABET, have called for the measurement of 12 graduate attributes in the engineering curriculum. Some attributes, such as “Knowledge Base,” lend themselves to forms of quantitative measurement; others, such as “Investigation” and “Communication” are inherently difficult to measure quantitatively and comprehensively. To assess these attributes authentically within our current curriculum, methods for adapting existing tools – that both satisfy the objectives of the actual course and the needs of graduate attributes assessment – must be found. This paper describes the process and challenges involved in adapting existing tools for assessment to measure such graduate attributes, specifically in a large senior research thesis course in a multidisciplinary engineering program. These challenges include balancing both the needs of multiple parties involved in the assessment, maintaining rubric usability, reliability and validity, as well as appropriately matching rubric elements to attributes. Despite these tensions, the results provided by this process provide insight about rubric design, assessment strategies and the students’ strengths and weaknesses within the graduate attributes, providing valuable information to feed back into the graduate attribute and continual curriculum improvement processes.
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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 | 0.073 | 0.246 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".