Crossing Boundaries: Developing Transdisciplinary Skills in Engineering Education
Bibliographic record
Abstract
Transdisciplinary engineering curricula prepare future engineers with a holistic understanding of complex real-world problems, and the ability to tackle these problems with knowledge and skills in both engineering and non-engineering areas. What are transdisciplinary skills in the engineering education context? What learning activities can we design and implement to develop students’ transdisciplinary skills in the first-year engineering program? How can we assess transdisciplinary skills and evaluate the instructional effectiveness of these learning activities?The current study is an initial attempt to explore these questions. We introduce a conceptual framework ofusing systems thinking, empathy and metacognition asproxy indicators of transdisciplinary skills, and presentthe learning activities we have designed to developstudent competencies in these areas. In addition, wepropose an evaluation approach that includes a surveyinstrument and formative learning assessment, with which we investigate the relationships among empathy, systems thinking, and metacognitive skills in the context ofengineering education.
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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 | 0.005 | 0.012 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".