The evolution of experiential learning in the Faculty of Engineering: From rogue experiment to curricular innovation and beyond
Bibliographic record
Abstract
Experiential-based learning in engineering education is nearly synonymous with student-centred, hands-on learning using design projects. The pedagogical model adopted by pioneers of this kind of learning in engineering is one step removed from lecture-based classes. That is, the pencil and paper assignment is simply replaced with a hands-on experience with little or no change in the learning outcomes, methods of assessment, and learner-support strategies. This paper argues that this pedagogical model has outlived its usefulness. Experiential learning ought to be employed as a means to train students in durable mindsets, behaviours, and ways of thinking. Just offering students a hands-on experience is not sufficient. In short, there is a need for a new way to think about experiential education in engineering. This reflective essay maps out the journey of one engineering educator’s rogue experiment with experiential education to new ways of thinking about it.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".