Adapting Existing Assessment Tools For Use in Assessing Engineering Graduate Attributes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it