The Case for Peer Coaching in Undergraduate Leadership Education
Bibliographic record
Abstract
The integration of leadership coaching in higher education has become increasingly prevalent, evolving from a specialized tool into a mainstream practice aimed at cultivating essential leadership skills in undergraduates. This paper explores the implementation of peer coaching interventions in undergraduate curricula, focusing on two models: The Coaching Trio Exercise and a Peer Coaching Groups (PCGs) class. These models promote self-awareness, emotional intelligence, and adaptability—skills crucial for leadership in today’s dynamic workforce. The paper argues that peer coaching offers a scalable, compassionate approach to leadership development, fostering student growth by helping them achieve personal goals while learning to guide others. Peer coaching, distinguished from compliance-based academic and sports coaching, emphasizes co-creation and mutual learning, equipping students with teamwork, communication, and emotional regulation skills sought by modern employers. Through case studies of coaching exercises integrated into leadership courses, this paper highlights the potential of coaching to enhance leadership education and career readiness in undergraduate students, bridging critical gaps in leadership competency development.
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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 | 0.032 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.021 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".