Pedagogy of Science in Engineering
Bibliographic record
Abstract
Definitions of the engineering profession include “…application of scientific principles to design or develop machines, processes, works, etc.” Significant science-based content is therefore a feature of accredited engineering curricula — a feature tending to dominate the early years thereby establishing a particular learning environment. This paper 1) identifies how a dysfunctional relationship between learning of science and the practice of engineering can arise; and 2) presents ways to improve learning of both science and design through integrated science learning. Over the course of almost three decades of trying to improve engineering design learning at the University of Calgary — employing many of the approaches described at length in the engineering education literature — it became apparent that realization of our teaching goals (e.g. quality, innovation, agility, and establishing a basis for life-long learning) might require a fundamental change in the culture of learning. Through a process of re-design plus continuous improvement, the authors have sought to develop a learning environment that establishes a learning culture that can foster the desired attributes. A pivotal aspect of this learning environment lies in the integration of science and design learning at the most fundamental level. Observation of thousands of students working on hundreds of design projects has revealed that desired outcomes can be achieved (Fig. 1).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".