Developing transdisciplinarity in first‐year engineering
Bibliographic record
Abstract
Abstract Background For engineers who aim to address sustainability challenges, participating in transdisciplinary teams is key. Yet developing transdisciplinary knowledge, including systems thinking, metacognition, and empathic thinking, is not well supported in traditional engineering programs. Purpose The extent to which selected learning activities in the introduction to engineering courses support student development of systems thinking, metacognition, and empathic thinking is investigated. Design/Method Focus group discussions with instructional teams and student interviews are examined to elucidate how course activities improved student transdisciplinary knowledge. Threshold concepts frame the qualitative analysis of the collected data. Implications for teaching and learning are discussed. Findings Results suggest the investigated learning activities support student development of transdisciplinary knowledge as indicated by changes in systems thinking, metacognition, and empathic thinking. Where prior quantitative exploratory studies revealed little change in transdisciplinary knowledge indicators pre‐ and post‐course, deeper qualitative analysis uncovers students manifested improvements in transdisciplinary knowledge indicators as narrated by the students themselves and as observed by instructors and teaching assistants. Conclusions Integrating transdisciplinary knowledge development into engineering programs, starting with appropriate learning activities in first‐year engineering courses, may provide new pathways for transforming curricula aimed at educating the 21st‐century engineer.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".