Enhancing Team Performance in Hybrid-Flexible Course Learning: The Role of Team Communication and Atmosphere
Bibliographic record
Abstract
Background: Hybrid-Flexible (Hyflex) courses have become a prominent teaching method post-COVID-19, offering students the flexibility to attend classes online, offline, or both. While this model promotes instructional continuity and student-centered learning, it presents challenges in managing effective team collaboration. Aims: to investigate the underexplored relationship between effective communication, team atmosphere, and team performance in Hyflex learning environments, the study seeks to provide novel insights into optimizing team dynamics and enhancing learning outcomes in flexible educational settings. Sample(s): The study involved 310 college students enrolled in the authors' Hyflex course.. Methods: An online survey was conducted using validated scales, Confirmatory Factor Analysis (CFA) and Multiple linear regression analyses were used to test the hypotheses. Results: Effective communication was found to significantly enhance team performance (β = 0.389, p < .001). Team atmosphere moderated this relationship (β = -0.118, p < .05), indicating that while a positive atmosphere generally supports performance, an overly positive atmosphere may reduce the critical engagement necessary for optimal outcomes. Conclusions: Both effective communication and a balanced team atmosphere are critical for maximizing team performance in Hyflex courses. Educators should foster communication strategies that encourage open dialogue and critical thinking while maintaining a supportive team environment. Overemphasis on maintaining positivity may hinder constructive critique and diminish performance.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".