A Socio-Emotional Framework to Understand Engagement in Engineering Student Project-Teams
Bibliographic record
Abstract
Project-based design courses typically involve self-managed teams whose goal is to develop solutions to open-ended engineering problems. Due to their collaborative nature, team-based design courses are largely effective only if students participate and are engaged with both individual and team level learning and tasks. Despite efforts to improve team effectiveness, low engagement persists as a critical issue in engineering student project-teams. In this work, we present a socio-emotional conceptual framework to better understand how team member interactions affect the engagement of team members within engineering-student project-teams over time. It is proposed that this framework will be used to investigate previously overlooked characteristics of a team's social climate (including task interdependence, intra-team trust, and psychological safety) which may affect student engagement in the engineering project-team context. It is anticipated that this framework will provide novel insight into the social climate of the team environment and its effects on student engagement. Through better understanding these phenomena, we will be able to design curriculum to leverage this understanding and improve student engagement at a course-wide level.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".