Lessons Learned From Teaching System Thinking To Engineering Students
Bibliographic record
Abstract
There are frequent calls for engineers to build integrated approaches to complex social and environmental problems. However, engineering education provides little opportunity to explore these "wicked problems".
 Given the complexity of the challenges, engineers face upon graduation, introducing students to systems thinking approaches and wicked problems could greatly benefit their ability to deal with complex challenges in the real world. At the University of Toronto, we have been taking part in the initiative to design a course with the primary topic of Systems Thinking targeted towards upper-year students from all disciplines. The objective of this course is not for student teams to get to a solution, but more so to develop an understanding of the wicked problem they are working on while educating them to leverage system thinking tools for visualizing their problem space and system mapping techniques to look at open systems. 
 This presentation will share our observations of the interactions, lessons learned, and challenges we faced during our first iteration of this course. The objective of this paper is to start an ongoing thread about the progress of teaching systems thinking concepts to engineering students throughout the upcoming years, along with the learning outcomes established from this course. In the future, we want to extract the data gathered from this course and research systems thinking principles and their benefits in approaching wicked problems.
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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 | 0.005 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".