Pedagogy of Engagement: Exploring Three Methods in an Engineering Ethics and Professionalism Course
Bibliographic record
Abstract
This paper explores the role of three pedagogical interventions in engineering students' learning about ethical and professional conduct, with a particular focus on affective engagement.Many transformative efforts involving equity, diversity, inclusion, and decolonization are centered on ethics as a justifying principle, which further stresses the need to cultivate an ethical orientation in engineering practice, beyond specific knowledge.A new course on professionalism and ethics was introduced as a platform to explore scalable pedagogical approaches to enhance engagement and achieve affective learning outcomes in engineering ethics.The learning activities were designed to stimulate critical thinking about social aspects of engineering and to reframe the traditionally technical obligations of the engineer within sociopolitical and equity-oriented structures.Through a qualitative analysis of student experiences, assignments, and reflections as part of the course, this paper evaluates the impact of three pedagogical methods on student engagement with ethical questions surrounding their decision-making as both individuals and as future engineers.The three methods being studied are Virtue Points, a tool that encourages self-reflection by contrasting personal and professional virtues, an adapted 'Spectrum Game' based on concepts presented by Jubilee Media, and a modified Pisces Game used to explore Tragedy of the Commons.Early findings show positive engagement with both the Pisces Game and Spectrum Game, with many students describing these two as particularly impactful and enjoyable.Virtue Points yielded results that surprised many students, and there are indications that clarifying and amending the scoring system for the game may promote better understanding of how it can support self-reflection on virtues.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".