COMPARING AND ALIGNING OUTCOMES OF TWO ENGINEERING AND TECHNOLOGY DISCIPLINES IN ONTARIO
Bibliographic record
Abstract
Abstract –The separate development of engineering and technology programs in Ontario has made transfer between these program types a complicated process. The process often requires assessment on a case-by-case basis and considers different aspects of knowledge, skills, and performance. This study was conducted to determine the level equivalency between two engineering and technology disciplines with the purpose of informing the development of transfer policy and comprehensive bridging programs in the province. Outcomes, content, and function of engineering and technology programs in Ontario were analyzed using a common framework in two disciplines: mechanical and electrical. Material from 7 engineering and 10 technology programs, including syllabi, learning outcomes, and reports was collected and analyzed, along with publically available information about programs. Slightly less than 40% of the courses in representative first year Mechanical and first and second year Electrical/Electronics Technology programs had equivalency to courses in engineering degree programs. The level of cognitive process expected for problemsolving outcomes is higher in the engineering programs than technology programs, and vice versa for outcomes related to hands-on skills. Overall, the analysis indicated sufficient alignment between engineering and technology programs to suggest transfer students may have acquired the necessary skills and knowledge of introductory level courses that are similar in content. Through hybrid bridging subjects and tests on prior knowledge, engineering programs can ensure incoming transfer students meet all CEAB accreditation criteria.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".