Beyond Dyadic Coordination: Multimodal Behavioral Irregularity in Triads Predicts Facets of Collaborative Problem Solving
Beyond Dyadic Coordination: Multimodal Behavioral Irregularity in Triads Predicts Facets of Collaborative Problem Solving
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DOI：
10.1111/cogs.12787
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发表时间：
2019-10-01
影响因子：
2.5
通讯作者：
D'Mello, Sidney K.
中科院分区：
文献类型：
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作者：
Amon, Mary Jean;Vrzakova, Hana;D'Mello, Sidney K.
我们假设，当个体和环境成分形成协同作用时，有效的协作是促进的，在这种协同作用中，它们一起工作并相互调节，以产生稳定的行为模式或规律性，以及适应性地重组以形成新的行为或不规则性。我们在一项研究中测试了这一假设，32个黑社会合作解决了一个具有挑战性的视觉计算机编程任务20分钟后，介绍热身阶段。使用多维递归量化分析来检查细粒度（即，每10秒）跨团队成员的语速、身体运动和与共享用户界面的团队交互的集体规律性模式。我们发现，与洗牌基线相比，团队表现出明显的规律性模式，但随着时间的推移，规律性没有系统的趋势。我们还发现，规律性的时间段与整体行为的减少有关。值得注意的是，不规则行为的产生预测了协作活动的专家编码指标，例如团队构建共享知识以及有效协商和协调解决方案执行的能力，以及整体行为产生和行为自相似性。我们的研究结果支持的理论，群体可以相互作用，形成人际协同作用，并表明，系统级动态的信息是一种可行的方式来理解和预测有效的协作过程。
We hypothesize that effective collaboration is facilitated when individuals and environmental components form a synergy where they work together and regulate one another to produce stable patterns of behavior, or regularity, as well as adaptively reorganize to form new behaviors, or irregularity. We tested this hypothesis in a study with 32 triads who collaboratively solved a challenging visual computer programming task for 20 min following an introductory warm-up phase. Multidimensional recurrence quantification analysis was used to examine fine-grained (i.e., every 10 s) collective patterns of regularity across team members' speech rate, body movement, and team interaction with the shared user interface. We found that teams exhibited significant patterns of regularity as compared to shuffled baselines, but there were no systematic trends in regularity across time. We also found that periods of regularity were associated with a reduction in overall behavior. Notably, the production of irregular behavior predicted expert-coded metrics of collaborative activity, such as teams' ability to construct shared knowledge and effectively negotiate and coordinate execution of solutions, net of overall behavioral production and behavioral self-similarity. Our findings support the theory that groups can interact to form interpersonal synergies and indicate that information about system-level dynamics is a viable way to understand and predict effective collaborative processes.