TEACHING TEAMWORK TO ENGINEERING TECHNOLOGY STUDENTS: THE IMPORTANCE OF SELF-REFLECTION AND ACKNOWLEDGING DIVERSITY IN TEAMS
Bibliographic record
Abstract
Abstract – In their quest to find work-ready graduates, employers are increasingly prioritizing graduates with so-called transferable skills. These transferable skills include critical thinking and problem-solving skills, communication skills, and the ability to work in diverse teams. With the plethora of engineering education literature on the topic of developing undergraduates’ teamwork abilities, there are numerous suggestions and little consensus on the best way to develop these skills in engineering classrooms. This paper adds to this literature and provides an overview of group work workshops for first-year undergraduates. The hope for these workshops was to better equip students for future group work activities by providing them easy-to-remember teamwork tools that were first learned and practiced in low-stakes workshop environments. Following their participation in these workshops, students participated in focus groups and feedback demonstrated an appreciation for these workshops as well as the opportunity to self-reflect on their role as a team member. Further, there appeared to be a shift in the awareness and tolerance of the diversity found among group members, which demonstrates a potential area for further investigation. The authors conclude with a call for more research in order to better understand the role of teamwork as a means for developing tolerance toward diversity among first-year undergraduate students.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".