Narrative as a Pedagogical Approach to Teaching Leadership and Engineering
Bibliographic record
Abstract
Making the link between theory and practice remains one of the most challenging tasks in engineering education. Leadership, as one of the desired educational outcomes, presents the same challenge: how to move from theory to practice or how to leverage theory and practice to develop leadership skills and attitudes.Simply learning about leadership does not guarantee a student can act as a leader effectively in a variety of situations. “The Power of Story: Discovering Your Leadership Narrative” uses narrative to link theory and practice. Narrative provides opportunities for students to learn about relational and authentic leadership as they examine, reflect on personal experiences and learn about themselves as leaders. Narrative is used both as a source of information about leadership and leadership practices,and as a tool for reflecting on and making meaning from experience, [2], and finally, as a means of sharing those meanings with others.This paper examines the design and development of a course grounded in narrative as both process and product of learning. Pedagogical decision made in the design of the course will be discussed. These include decisions made to foster the trust and commitment to the class necessary to establish a safe space for personal exploration, the tension between the need to evaluate student subject knowledge and evaluating personal growth, the challenge moving students from learning as product to learning as process and product.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".