Assessing the Gaps between Alberta K-12 Competency Development and University of Alberta Engineering Introductory-level Graduate Attribute Indicators
Bibliographic record
Abstract
Canadian engineering education is in high demand for students. The University of Alberta (U of A) internal surveys have shown that the first year of engineering program is often the most challenging due to the greater expectations of knowledge and skills, different learning environments, and higher requirements of independent study skills compared to high school. Hence, high school graduates should be equipped with the competencies and knowledge to be more prepared for their first-year engineering studies, which is critical to their success. Therefore, this paper aims first to explore the engineering-specific high school competencies to enter the Faculty of Engineering, U of A. Further, it seeks to identify the gaps between these competencies and the introductory-level graduate attributes (GAs) in the U of A engineering programs. Using a document analysis method, we compared the ENGG introductory-level GAs with the learning outcomes of high school required courses. The findings indicate that high school courses prepare students for first-year engineering courses in terms of knowledge-based and other competencies. However, despite the alignment, gaps also exist in terms of life-long learning, professionalism, ethics and equity, economics and project management that need to be addressed by the program’s first year. Having these skills developed would better prepare students for the first year of engineering and should be a focus of first-year engineering programs.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".