Developing Leadership Skills in Engineering Students – Foundational Approach through Enhancement of Self-Awareness and Interpersonal Communication
Bibliographic record
Abstract
Engineering leadership education is emerging as a vital addition to the development of theprofession. However, practitioners of engineering leadership education are still defining outcomes, objectives and curricula. The assumptions, desired outcomes, and our pedagogical approach to engineering leadership education discussed in this paper starts with a strategic assumption to minimize the emphasis on development of “vision” that is a clear focus of leadership training in business and other disciplines. While vision is clearly a critical leadership characteristic, engineering schools already excel at developing students who envision solutions to complex problems. Therefore, less effort is needed for the engineer to transition “problem solving” into “leadership vision.” Instead, the focus is placed on interpersonal communication (vs. organizational communication) and understanding of motivation and behaviors of self and with respect to interactions with others. This paper will present the methodology and reflective assessments in teaching engineering students “leadership communication,” and “self-awareness.”Leadership communication consists of techniques to develop intentional listening skills and questioning/interviewing approaches to define problems and understand motivations with emphasis on application of lessons learned from behavior inventory assessment. Further, the use of self and group reflection will be discussed in the context of both learning leadership concepts and increasing self-awareness.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".