Bibliographic record
Abstract
Integrating pedagogies from the humanities into engineering curricula can help students communicate, collaborate and situate their technical learning in broader social and cultural contexts, supporting their success as future professionals working in complex sociotechnical environments. There is a large body of literature that argues for such an interdisciplinary, liberal arts education, providing reasons to believe that transdisciplinary competencies improve a student’s problem-solving readiness, promote an ethical mindset and encourage the inclusion of multiple perspectives and epistemologies. However, these arguments for integration are often theoretical or aspirational, supported by minimal in-depth qualitative research into how specific outcomes are achieved by these experiences. My dissertation project addresses this shortcoming by studying the experience of engineering students working with pedagogical tools from Theatre & Performance Studies, exploiting my position as an educator and researcher at the intersection of these fields. My project analyzes three sites: a theatre course for engineering students, a science communication course for Engineering students that uses the tools of performance analysis, and a student-led theatre revue. I adopted a grounded theory approach to collect and analyze site observations, semi-structured interviews, course materials and student work at these intersections. This analysis was guided by three questions: 1. How might interdisciplinary pedagogical approaches to engineering education such as those from Theatre and Performance Studies disrupt or challenge engineering epistemologies and student mindsets?2. What can such disruptive pedagogical approaches reveal about the relationship between classroom environment—both physical and in terms of community—and student learning? 3. How do students understand the impact of those approaches, especially as it pertains to their futures in engineering? Adopting a narrative inquiry approach to my data, my analyses explore affordances and enabling pedagogies from across the three sites. By grounding the analysis in this qualitative data, my study moves beyond the conventionally cited “benefits” of interdisciplinary learning in the field, such as greater communication or collaboration skills, to identify some new important thematic concerns: building community, promoting contingency and relativism, ethical practice, and opportunities for professional and personal reflection.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.025 |
| Scholarly communication | 0.024 | 0.014 |
| Open science | 0.001 | 0.021 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.048 | 0.004 |
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".