Using content validity measures to evaluate the Biosystems Engineering Program at the University of Manitoba
Bibliographic record
Abstract
An exploratory case study was designed to determine the relative importance of the Canadian Engineering Accreditation Board (CEAB) graduate attributes as perceived by University of Manitoba engineering stakeholders. Findings were used to examine the content validity of the Biosystems Engineering program. The overarching objective was to explore how graduate attribute emphasis in engineering programs reflect graduate attribute importance reported by key stakeholders. Problem Analysis, Investigation, Design, Communication Skills, Impact of Engineering on Society & the Environment, and Use of Engineering Tools had similar expected (mean relative importance) and observed (content and assessment program coverage) data percentages. The gap was wider for other graduate attributes, with the most surprising being Knowledge Base. Overall, the pattern of results suggests that various professional attributes (e.g., Professionalism, Ethics & Equity, and Lifelong Learning) should be more prominent in content and assessments within an engineering program. Recommendations to improve methods to assess content validity in engineering programs are 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 | 0.044 | 0.126 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".