IMPLEMENTING EXPERIENTIAL EDUCATION ON ENGINEERING AND SOCIETY
Bibliographic record
Abstract
Recent educational research in engineeringhas examined the challenges Canadian universities arefacing when implementing graduate attributes, especiallythose attributes that involve significant social components(such as ethics and equity, impact of technology onsociety, and communication skills). In response to thesechallenges, this paper asks: how might experientialeducation be used as an approach to teach non-technicalgraduate attributes? Having asked this question at ourown institution, we are in the process of implementingexperienced-based approaches to engineering education.We describe our efforts in curricular and non-curricularspaces which include adding project-based components toour existing courses on technology and society andcommunication, designing a new experiential course oncreativity and innovation, serving as clients for capstonecourses, facilitating reflection for our co-op program,developing a workshop on community engagement, andorganizing design competitions in our innovation centre.We analyze the challenges and the benefits of theseapproaches. Our argument is that experience alone maynot lead to planned learning outcomes, so finding creativeways to promote reflection on experience becomescritical. In our programs, this has meant: playing the roleof both client and facilitator in projects; partnering withfaculty members in other disciplines; and having studentsdirectly interact with users from very differentbackgrounds. Through these approaches, we are findingways to help students visualize the lived context oftechnology use in communities, and ways to help themunderstand the non-technical components of design andco-op work that are essential if we want to create just andsustainable outcomes though technology. The implicationof this preliminary reflexive account is that experientialeducation holds much promise for improving instructionrelated to non-technical graduate attributes.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".