Fostering Holistic Thinking: Design of an Interdisciplinary Systems-Thinking Course
Bibliographic record
Abstract
In an era dominated by intricate challenges, navigating complex systems and wicked problems is paramount for the engineering discipline's evolution. This necessity beckons a departure from conventional educational paradigms towards a holistic and transdisciplinary pedagogical approach. This paper delineates the development of a systems-thinking course tailored to instill such competencies. Grounded in a robust framework of learning outcomes, the course is strategically crafted to facilitate a comprehensive examination of complex systems and the nuanced nature of wicked problems. The curriculum, rich in experiential learning opportunities, weaves together practical exercises, theoretical discussions, and collaborative project work, enabling students to cultivate a deep, holistic understanding of the challenges at the intersection of political, environmental, social, technological, economic, and legal dimensions. By employing a variety of system-mapping techniques, along with primary and secondary research methods, and honing effective communication skills, students are equipped to confront real-world issues with enhanced analytical and critical thinking capabilities.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".