Does Program Compliance with CDIO Warrant Automatic Compliance with CEAB Accreditation Criteria for 2014?
Bibliographic record
Abstract
An accreditation board takes the responsibility of evaluating an institute’s engineering program, granting it accreditation upon the satisfaction that it meets a minimum standard in terms of academic and professional quality of the faculty, laboratories, equipment, computing facilities, and students’ work within the engineering curriculum. In Canada, the Canadian Engineering Accreditation Board (CEAB) ensures that engineering programs meet the necessary educational standards as acceptable for licensure, and that engineering education delivered by the institute continues to improve. In recent years, accreditation boards have prescribed “outcome-based” assessments of engineering design curriculums. These criteria focus on the ability of students to apply knowledge of mathematics, science, and engineering science, extending to designing and conducting experiments, analyzing data, as well as developing a system, component, or process to meet certain needs. A recent 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 adapted by several universities within their engineering departments. But should a program’s compliance with the CDIO standards warrant automatic compliance with CEAB (Canadian Engineering Accreditation Board) accreditation standards? Following the CDIO approach and using the outcome-based standards of accreditation boards may suggest so. Herein, we will provide an assessment of the Mechanical Engineering program in terms of the CDIO approach and look at its relationship with the CEAB 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 | 0.123 | 0.328 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".