Does the group matter? Effects of trust, cultural diversity, and group formation on engagement in group work in higher education
Bibliographic record
Abstract
Group work is a common active learning strategy in higher education when the goal is to enhance deep learning and develop teamwork skills. Culturally diverse learning groups are particularly valuable in preparing university students to participate in a globalized world. Student engagement in group work is critical in realizing these benefits. Therefore, more insight into what factors promote engagement is necessary. This study investigates the extent to which trust in the group, cultural diversity in the group, and group formation contribute to behavioral and cognitive engagement in group work. A questionnaire was filled out by 1025 bachelor’s students from six universities in the Netherlands and Canada. Structural equation modeling analyses identified students’ trust in the group as the strongest positive predictor of both behavioral and cognitive engagement. Greater perceived cultural diversity was found to promote behavioral and cognitive engagement, but compared with trust, the impacts were relatively small. Whether students could choose their group members did not affect behavioral or cognitive engagement significantly. Contrary to what was expected, trust did not act as a mediator. That is, cultural diversity and group formation did not indirectly affect engagement through trust. These findings prompt some suggestions for how to enhance student engagement in group work.
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".