Curricular and Co-curricular Leadership Learning for Engineering Students
Bibliographic record
Abstract
In recent years engineering educators have been encouraged to blend technical and professional learning in their curricular and co-curricular programing (EC, 2009; NAE, 2004). Our paper describes a multifaceted leadership learning program developed to achieve this goal by infusing reflective, experiential learning into an otherwise technically oriented discipline. The program was designed by a collaborative team of educators and researchers with backgrounds in engineering, education, psychology, and industry and offers a range of learning experiences using diverse pedagogical strategies. The content covers four realms of leadership corresponding to four levels of analysis: self, team, organization, and society. Learning experiences include elective academic courses, co-curricular workshop programs, guest lectures in core courses, seminars, department based leadership groups, and panel discussions. In this paper, we describe the program goals, curricular and co-curricular initiatives and early research findings in order to scaffold an emerging discussion about engineering leadership education in Canada. Informal feedback from students who have participated in our program provide us with preliminary evidence that students are learning, that they value the learning opportunities afforded by our program and that our initiative is enabling significant personal growth.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".