BUILDING THE ENGINEERING MINDSET: DEVELOPING LEADERSHIP AND MANAGEMENT COMPETENCIES IN THE ENGINEERING CURRICULUM
Bibliographic record
Abstract
In this paper we explore building the engineering mindset from the perspective of developing exceptional leadership and management competencies to guide and support the traditional technical competencies that are the primary focus of undergraduate engineering programs. A knowledge base for engineering, science, and design is developed throughout most engineering programs. Math and science are carefully scaffolded from first year engineering to ensure technical competence by graduation. We ask the questions: “How are leadership and management related to engineering work and design?” and “Can we develop a framework to guide the development of leadership and management skills in the engineering curriculum?” We argue leadership and management are integral to the engineering mindset and necessary to address the complex engineering problems society faces. There is discord between the responsibility of the engineer and the decision-making authority for engineering projects. This dissonance often results in engineers being technically accountable for their designs yet lacking the authority to make decisions with respect to the construction, commissioning, and operation of their designs. To address this gap, we suggest leadership and management training be carefully scaffolded in the same manner that technical competence has been stewarded in engineering programs and propose a framework to do so.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".