Peer Learning and Leadership in Engineering Design and Professional Practice
Bibliographic record
Abstract
This work presents an assessment of the development of leadership skills in fourth-year engineering students, who are project leads of teams that comprise of third-year engineering students in a yearlong Engineering Design and Professional Practice course. It is proposed that the success of peer learning is directly related to the growth and change in the project leads, the progress of the followers, and the strength of the leader-follower engagement. As such, this work will compare classroom observations with Leader-MemberExchange (LMX) theory and the concepts of servant leadership and authentic leadership in the context of an engineering workplace. This work will also discuss both the value of the inputs and the measure of the outcomes in this peer mentorship scenario, i.e. starting with the importance of the individual in peer mentorship, through reflection, goal-setting, and self-awareness, to the importance and practice of designated project lead management meetings, to the significance of knowledge transfer in the learning process. The challenges of peer learning at the undergraduate level will also be discussed. In presenting this work, the authors would like to promote knowledge sharing of how peer mentorship and the development of leadership skills have been implemented and assessed effectively in Engineering at other universities. Keywords: peer learning and mentorship, project-based teamwork, authentic and servant leadership, leaderfollower exchange.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".