Curriculum Integration of the Canadian Engineering Grand Challenges in a First-year Undergraduate Design Course Using Multi-layered Peer Learning: A Methodology
Bibliographic record
Abstract
This paper introduces a methodology to investigate the impact of utilizing multilayered peer learning pedagogical strategies to integrate the Canadian Engineering Grand Challenges into a large first-year engineering design course. The Canadian Engineering Grand Challenges (CEGC) evolved from the seventeen UN sustainable development goals (UNSDG). The CEGC focus on achieving access to safe water; resilient infrastructure; sustainable energy, industry, and cities; and inclusive STEM education. The incorporation of the CEGC into higher education can be viewed as a tool to empower students to understand the significance of engineering in society with respect to the achievement of the UNSDG. Consequently, their inclusion in the first-year engineering education curriculum serves to engage students with urgent and complex societal problems and the socio-contextual impact of engineering decisions and designs. Peer learning has regularly been applied as an active learning strategy, often with smaller class sizes. A multilayered peer learning strategy was implemented to engage students with the CEGC in a large class of ~1100 students. This strategy is reviewed with respect to delivery logistics and observed efficacy.
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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".