Self Evaluation for Compliance with the 12 CDIO Standards
Bibliographic record
Abstract
Throughout the 1980’s and 1990’s, collaboration began between universities, industry, and government to improve the quality and state of engineering education. Their paramount goal was to provide better ways to help students become successful engineers, possessing the necessary technical skills and expertise, exhibiting creativity, and having awareness of social, lawful, ethical, and environmental impacts as related to their profession. Traditionally, engineering programs emphasized the theoretical aspects required, while placing little emphasis on practical applications. An approach that has been introduced to provide a better learning experience for engineering students and to educate them as well-rounded engineers to be able to develop complex, value-added engineering products and processes is the CDIO (Conceive-Design-Implement-Operate) approach. This approach has been adopted by several universities within their engineering departments. At UOIT, the Mechanical Engineering curriculum has been developed around and continually evolves to line up with the goals of CDIO in terms of course and curriculum offerings for core and complementary engineering design courses, science, math, communications, engineering ethics, and humanities courses. Herein, we present an evaluation of the Mechanical Engineering program at UOIT against the twelve CDIO standards.
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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 | 0.066 | 0.168 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".