Assessing Student Perspectives on Engineering Leadership Education in Canada
Bibliographic record
Abstract
Engineering leadership (EL) is gaining global attention due to its importance in engineering practices. This study aims to explore undergraduate students' experiences with EL programs in Canada by analyzing 13 peer-reviewed full papers published in CEEA-ACÉG Proceedings over the last 15 years. Findings revealed that EL programs successfully developed students' leadership competencies, such as self-awareness, interpersonal skills, collaboration, and organizational skills. Moreover, students were satisfied with their EL experience and appreciated activities that have a tangible impact on their community. However, communication skills were a major barrier to effective leadership in engineering teams. Nonetheless, only a small number of research studies have examined minoritized students' experiences in EL programs and the implementation of EL in capstone design courses. Future research could leverage the findings from this study to explore the experiences of underrepresented engineering students in leadership-related programs, as well as the potential for implementing EL in capstone design courses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".