LESSONS LEARNED FROM TEACHING SYSTEM THINKING TO ENGINEERING STUDENTS
Bibliographic record
Abstract
The primary focus of Engineering Education programs has been to train engineers in various aspects of problem-solving techniques. However, there have been concerns about the types of problems engineering students are exposed to. Most engineering programs train students on solving routine problems, with extension to originative problems in design courses. However, highly complex or “wicked problems” are more rarely explored at the undergraduate level in spite of the fact that they are some of the most important problems faced in society. Systems Thinking has been suggested as a promising approach to addressing wicked problems. We have designed a course in Systems Thinking at the University of Toronto targeted toward students from all disciplines of engineering. The objective of this course is to encourage students to explore the inherent ambiguity of complex problems while introducing them to tools and approaches to visualize their problem space. This paper evaluates the learning experience of students in the first iteration of this course, through a series of analyses performed on their coursework, personal reflections, and interviews. It was hypothesized that teaching Systems Thinking to engineering students would increase their awareness of the problem space, push them to learn about other disciplines outside of engineering, and increase their ability to visualize the elements in the problem. Our results suggest ways in which Systems Thinking has helped engineering students in their problem solving abilities and looks at the specific skills in which engineering students have significantly improved.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".