Engineering Education: Does Our Training Reflect Student Employment Trajectories?
Bibliographic record
Abstract
Departmental/disciplinary differences aside, newly graduated engineers can be considered to have one of four general and non-exclusive initial employment trajectories: operations, technological innovation, research, and teaching. Survey data from engineering students at the University of Saskatchewan will describe the proportions of students focused on these employment trajectories by year of study, and by discipline. An important implication of this classification is that the desired graduate attributes of these four employment trajectories require divergent knowledge and skills, aside from technical competence. Operations engineers need training in hazard assessment, economics, optimization, schematics, controls, constrained design, and quality control. Technology Innovators require training in creativity, abstract thinking, taking initiative, open-ended design, technical graphics, prototyping, and market research. Research engineers need training in experimental design, statistics, the scientific method, programming, instrumentation, and data analytics. Teaching engineers require training in pedagogy, communications, curriculum design, and social-media tools. All Canadian engineering schools train for Operations. Most have an option/certificate/specialization for Technological Innovation. Some have a minor emphasis on training for the Research stream. Very few systematically prepare for the Teaching role. Are we losing some good engineers by lack of curricular support for these latter three aspirations? Equally important, are sufficient numbers of engineers being prepared in each trajectory? These questions will also be addressed in this study, as data reflecting on the personality characteristics of student respondents was collected and analyzed while looking at their employment trajectories. The potential implications of this type of analysis on attrition and retention, innovation in Canada, and more effective teaching of STEM, will be
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".