Leading by Experiment: Phased Leadership Integration from Rotational Training to Systematic Curriculum Development in Engineering Education
Bibliographic record
Abstract
Engineering education emphasizes technical skills while often overlooking leadership development, a critical factor for professional success. This study implements a three-phase approach to systematically integrate leadership training into Materials Engineering (MTRL) curriculum. In Phase I, a leadership model was developed through a literature review, identifying four key elements: core personal values and character (ethics), awareness, project management, and communication, aligned with CEAB graduate attributes. In Phase II, a rotational leadership activity (RLA) was introduced in a third-year lab course using a flipped classroom model. Results suggested that the RLA was well-received, with students reporting increased leadership awareness, confidence, and appreciation for leadership in engineering settings. Additionally, students found the leadership model highly relevant, particularly in communication, project management, and awareness, reinforcing the effectiveness of integrating structured leadership training into engineering education. In Phase III, a curriculum survey revealed that fewer than 30% of courses explicitly incorporated leadership sub-components. Leadership elements were most prominent in team-based and project-driven courses, while individual technical courses had minimal leadership integration. Some courses implicitly fostered leadership skills but lacked structured training. These findings highlight gaps in leadership education and reinforce the need for a progressive and systematic approach to leadership development in engineering curricula.
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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 | 0.024 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".