Engineering Student Experiences of Group Work
Bibliographic record
Abstract
Soft skills are a crucial component for success in today’s workplace as employers increasingly value work that is collaborative and encompasses diverse perspectives. Despite this, most engineering programs fail to explicitly teach students transferable skills, including the best practices of group work. This research sought to explore how undergraduate experiences of group work change over time. This research also investigated what reflecting on cooperative education (co-op) experiences tells us about teaching group work in academic settings. Despite frequently noting the influence of group work in developing their communication skills and brainstorming ideas over time, students become somewhat more frustrated over time with their experiences of group work, mainly due to conflicting personalities and ideas among team members and/or a “slacker” student. However, our findings also show that students become more confident working in teams over time, as upper-year students were more likely to assume a leadership role and self-reported higher past performance as a group member. This study offers insights into the changing group work experiences of undergraduate engineering students as they progress through coursework and engage in experiential learning and work-integrated learning opportunities, such as co-op placements. The findings of this study can inform educators on how to best incorporate methods for teaching transferable soft skills.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".