The evolution of the Finite Element Analysis course: Steps towards human-centric engineering
Bibliographic record
Abstract
In this work, I, the chair of the Software Engineering program at the W Booth School of Engineering Practice and Technology at McMaster University, reflect on the evolution that has happened through an undergraduate engineering course, Finite Element Analysis, over the past 10 years at the W Booth School of Engineering Practice and Technology at McMaster University. I present a chronological sequence of transformations in this course based on internal and external influences. First, I outline the initial focus of the course on applied software skills training, which was advocated by industry partners and aligned with a college partnership, to ensure that students were employable in the automotive and aerospace industry almost immediately upon graduation. I then describe and reflect on two periods of significant change: (a) the enhancement of theoretical content to meet the accreditation and licensing requirements of Professional Engineers Ontario and to prepare students for graduate studies and (b) a recent push to graduate human-centric engineers capable of undertaking engineering work that considers technical, as well as social, human, and environmental issues. I also share my vision for future improvements to ensure that graduates have all the desired competencies. This reflection would serve as a good reference for anyone who wants to undertake such transformations in their course.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".