CO-DESIGNING CURRICULUM: FIRST-HAND EXPERIENCES OF UNDERGRADUATES CREATING EXPERIENTIAL LEARNING ACTIVITIES FOR THEIR PEERS
Bibliographic record
Abstract
Abstract – The Bachelor of Technology (B.Tech.) program at McMaster University, W. Booth School of Engineering Practice and Technology differentiates itself through its experiential and industry-driven approach to teaching and learning. The B.Tech program initiated a pilot faculty-student co-design project for the Project Management course delivered to third-year engineering technology students. In the past, the faculty has struggled to find a major project assignment that gives students workplace readiness skills in project management in a real-world context. The faculty and a fourth-year undergraduate student worked together to co-design a term long project, which treated their personal educational deliverables (e.g. course work, assessment deadlines, financial accountabilities), as a project to manage. The paper will bring together key perspectives from this pilot co-design experience, namely, the undergraduate course developer, faculty liaison, as well as feedback from the students in the course. The authors found that while students appreciated the accompanying project documentation, the co-design team must continue to demonstrate the usefulness of working with MS Project as software enabling workplace readiness.
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| 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".