A SNAPSHOT OF THE CANADIAN ENGINEERING EDUCATION SYSTEM: REFLECTIONS FROM AN EMERGING SCHOLAR TRYING TO SUPPORT NATIONAL CURRICULUM CHANGE
Bibliographic record
Abstract
The Canadian Engineering Education Challenge (CEEC) is an initiative with the goal of ‘developing a national collaboration to target engineering curriculum to graduate students who will be eminently prepared to take on the challenges of the future’. The National Coordinator performed a scan of the education system across Canadian engineering programs to determine existing initiatives, and common challenges. The objectives of this work are: 1. Identify needs and challenges of the Canadian Engineering Education system, and 2. Identify challenges and opportunities in providing support to curricular change initiatives. This work is conceptually framed and driven through the Coordinator’s perspectives, assumptions and goals, using an action research framework. A qualitative inductive thematic analysis is used to identify common themes and trends from across visits and conversations with over 60 individuals from 14 institutions. A secondary goal of this work is to share the process, lessons learned, and personal reflections, with those who have contributed to generating the data, and others entering into the field of Engineering Education. Several trends emerged from the data, which elucidate needs and challenges. These include: distinct roles in the system, curricular initiatives relating to non-technical skills and design spine, challenges associated with Engineering Education Research and the Scholarship of Teaching and Learning, the impact and inertia of culture, modeling and instructor training related to non-technical skills, funding promotes change, and the importance of terminology and shared understanding. Considerations became apparent which may inform how to best support individuals and the system. These include: the need to align with the existing priorities of others, the desire for engineering educators to learn from each other, the power of culture, and incentives to promote engagement. This work will serve to identify potential opportunities for the CEEC to leverage collaboration between institutions with common alignment, as well as important considerations to be incorporated for the CEEC to maximize impact. Finally, this work hopes to provide valuable insight to others who wish to engage more deeply in Engineering Education.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.083 | 0.020 |
| Scholarly communication | 0.019 | 0.005 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".