“Accreditation VS COVID”: The Canadian Engineering Accreditation Board’s transition to virtual accreditation visits in 2020/2021
Bibliographic record
Abstract
“Accreditation VS COVID.” This was the subject line of an email that a representative from a CEAB-accredited program sent to the CEAB Secretariat in Spring 2020. The COVID-19 global pandemic created multiple shifts in how engineering programs operated, which necessitated a reimagining of how CEAB accreditation (operating in the same novel environment) could survive and thrive. This work provides an overview of the CEAB’s efforts between March 2020 and June 2021 as theory, plans and implementation activities came together to transition the CEAB accreditation system to a completely virtual modality for the 2021/2022 visit cycle; it speaks briefly to identified best practices and lessons learned. Administrators and faculty of baccalaureate engineering programs will find this work to be of interest for the insight it provides into the CEAB’s processes. Moreover, accreditors of other disciplines and members of the general higher education community may find value in the work as part of a larger discussion of best practices for virtual quality assurance evaluations. This work presents the results of an environmental scan and literature review that was undertaken in 2020 (and updated in 2021) and includes reflections on the transition process from members of the CEAB Task Force on Virtual Visits and the CEAB Secretariat. The work this reflection piece will present has enabled the CEAB to offer ongoing accreditation reviews for Canadian baccalaureate engineering programs regardless of disruptions caused by the COVID-19 pandemic.
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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 | 0.030 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.027 | 0.014 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".