LESSONS LEARNED FROM TEACHING SYSTEM THINKING TO ENGINEERING STUDENTS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 it