ExCEL: A Unique Approach to Providing Experiential Learning Opportunities
Bibliographic record
Abstract
McMaster University’s proposed Engineering Centre for Experiential Learning (ExCEL) is a novel example of providing a deep student learning experience outside of the traditional academic experience, and one in which experiential learning opportunities are intended to drive the development of additional learning opportunities. The ExCEL Initiative has students engaged in goal setting and fundraising, and actively involved in the design, construction and management of an engineering student centre building that will support future experiential learning opportunities for students. This integrates a diverse range of engineering pedagogical topics including open ended design, multi-disciplinary collaboration, sustainability, work-integrated learning, industry-academic collaboration, project-based learning and professional development. This learning opportunity will be analyzed on how it was leveraged and integrated to address this host of pedagogical topics through a real world project. Detailing this unique example will allow the rich learning aspects of it to be implemented by other educators in other engineering education institutions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".