Three presenters from LUNDSTEP presented their work on communication dynamics in the context of medical student training (van Klaveren) and to newly formed teams in an escape room board game (Schelin, Nordbeck) at University of Montpellier this summer. Find the abstracts below. (Link to workshop.)
RQA of Dynamic Team Learning Behaviors and Their Relation to Performance
Michelle Schelin∗ , Lisa-Maria Van Klaveren , and Patric C. Nordbeck∗
In complex work environments teams must continuously learn and adapt to perform effectively. While theoretical advancements conceptualize teams as complex, adaptive systems shaped by non-linear interactions and emergent processes, empirical team learning research continues to rely on static methodologies that do not capture these dynamics (Mulder, 2022). To address this gap we apply Recurrence Quantification Analysis (RQA) and chromatic (cRQA) to explore the temporal dynamics of team learning behaviors: knowledge sharing, co-construction, constructive conflict and procedural coordination (Decuyper et al., 2010). Using transcribed conversational data from teams completing escape room simulations in a controlled laboratory experiment, this study explores (1) intra-team patterns of learning behaviors, (2) inter-team differences, (3) the relationship between behavioral patterns and team performance, and (4) the role of constructive conflict as necessary for higher performance. Key RQA metrics (including recurrence rate, determinism, entropy, and laminarity) will quantify behavioral stability, adaptability, and complexity over time. We expect high-performing teams to exhibit mixed structured-variable recurrence patterns, while lower performing teams display either excessive rigidity or erratic behavioral shifts. Exploring the ability of RQA to quantify team learning behavior patterns for performance prediction, the findings have practical implications for designing organizational training and guidelines to develop adaptive and resilient teams.
Keywords: team learning, dynamic systems, recurrence quantification analysis, team performance
Opening the black box of team-based learning: Exploring dynamics of team conversations in online application sessions
Lisa-Maria Van Klaveren∗, Patric C. Nordbeck , Tobias Boerboom , Rien De Vos, and Linda Roossien
Team-Based Learning (TBL) promotes in-depth discussions and deep learning. To better understand how conversational dynamics emerge from task and team constraints during online TBL-application sessions and their relation to learning outcomes, we conducted an observational study involving twelve TBL-teams. Each team participated in two sessions, completing five different tasks. We collected preparation and outcome scores, and audio- recorded the conversations. Transcripts were coded for team learning behaviors, including sharing, co-construction, and constructive conflict. We applied chromatic recurrence plots and recurrence quantification analysis at team and task levels. Wilcoxon Signed-Rank tests assessed differences in conversational dynamics, while General Linear Mixed Models examined relationships between preparation, dynamics, and performance. Findings indicate that entropy varied across teams, suggesting that teams exhibited distinct dynamics. Additionally, differences in recurrence rate and entropy across tasks highlight how they shape team learning. Relationships between preparation, conversational dynamics and performances provided mixed results that may hint at the complexity of team learning. These insights contribute to how to intentionally vary task design to enhance team learning in TBL. Understanding how conversational dynamics emerge from task and team constraints can inform evidence-based approaches to stimulate in-depth discussions and deep learning.
Keywords: Conversational dynamics, team learning, constraints, recurrence quantification analysis
