A comprehensive HLM analysis examining how group composition shapes the effects of socio-emotional climate and positive interdependence on performance.
Ucan, S., & Kadioglu-Akbulut, C. (2026). Examining group dynamics and composition characteristics with HLM in online collaborative instructional planning among pre-service teachers. Journal of Computing in Higher Education. https://doi.org/10.1007/s12528-026-09490-8
To investigate the relationships among socio-emotional climate (SEC), positive interdependence (PI), and group outcome, and examine how gender composition and group history moderate these relationships in online settings.
While collaborative planning is crucial, the "black box" of group dynamics remains opaque. Understanding these moderators helps educators move beyond "one-size-fits-all" grouping strategies.
Hierarchical Linear Modeling (HLM) was used to analyze nested data from pre-service teachers in an 8-week Online CIP project. Predictors included SEC, PI, gender, and collaboration history.
SEC and PI are reciprocal. Crucially, "All-Male" and "No History" groups are highly sensitive to these dynamics (volatile/steep slope). "Mixed", "Female", and "History" groups show a "ceiling effect" with high stability.
Adjust parameters to see real-time Interaction Graphs derived from the HLM analysis. Observe how group type changes the slope of success.
Gender Composition
Group History
Groups with no history are most vulnerable. Faculty must embed specific "storming" activities early on, as these groups rely heavily on climate for success.
All-male groups show the highest volatility but also the highest potential gain. Use assigned roles emphasizing socio-emotional maintenance for these cohorts.
For groups with history, a "ceiling effect" exists. Shift focus from climate-building to complex coordination and critical evaluation.