Advancing Engineering Education: Linking Systems Thinking Skills to the Tools through a Revised Framework
Bibliographic record
Abstract
Abstract Scholars have advanced the argument advocating for the integration of Systems Thinking (ST) into engineering education, positing its efficacy in equipping engineering students with the capacity to discern and comprehend intricate challenges and opportunities embedded within complex problem domains. This pedagogical approach fosters a holistic mindset that enables students to address multifaceted predicaments while remaining attuned to the dynamic shifts occurring in the market and the broader external landscape (Sterman, 2000; Halecker & Hartmann, 2013; Halecker & Hartmann, 2013). Nevertheless, a noticeable research gap persists in the elucidation of the specific skillsets and cognitive paradigms that each ST tool augments. Within this paper, we embark upon a systematic literature review encompassing an array of studies that have explored the prominent tools employed in instructing Systems Thinking. Our objectives are threefold: firstly, to delineate the diverse skill sets these tools serve to enhance; secondly, to expound upon the distinct modes of cognition with which they are intricately associated; and finally, to proffer a comprehensive rubric designed for the evaluation of a particularly salient ST tool, ascertained to be exceptionally pertinent in the context of opportunity identification during entrepreneurial endeavors.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.002 | 0.030 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".