Collaborative Research: Interpersonal Coordination and Coregulation during Collaborative Problem Solving

在我们这个联系日益紧密和全球化的世界里，协作解决问题（CPS）是一项基本技能。然而，关于如何定义、衡量和发展这一技能的知识缺乏，特别是在STEM学习的背景下。来自圣母大学、佛罗里达州立大学和亚利桑那州立大学的一组研究人员将试图发现数字STEM学习环境中人际互动是如何产生并影响CPS过程和结果的。这项研究的重点是高中生和大学生在一个名为“物理游乐场”的STEM教育游戏中进行虚拟合作。假设CPS的有效性可以通过对正在进行的协作提供实时的自动化反馈来提高。研究人员将收集一系列数据，从个体生理测量到学习结果测量，再到群体交流模式。他们的目标是改进未来CPS学习环境的设计，使他们更愉快、更吸引人、更有效。该项目由EHR核心研究（ECR）项目资助，该项目支持推进STEM学习基础研究文献的工作。这项研究将涉及二人组和三合组在一个名为物理游乐场的学习环境中进行虚拟合作，这是一款STEM教育游戏。数据将在实验室和课堂上收集，使用来自美国三个地点的高中生和大学生的不同样本。该团队将整合来自一系列来源的数据，包括小组问题解决和协作学习的建模，以及来自眼动追踪，面部特征追踪，心理生理学和语言/副语言语音分析的低级数据建模。此外，该团队将研究任务约束和小组组成对CPS过程和结果的调节作用。进一步的目标是使用非线性时间序列分析和多模态深度递归神经网络对动态CPS过程进行建模。计算模型将集成到学习环境中，以测试通过对展开的协作过程提供自动反馈可以改善CPS结果的假设。因此，通过混合基础实验研究、多传感器-多模态分析、计算建模和动态计算机化干预，研究人员将尝试取得基础理论、方法和技术进步。

Collaborative problem solving (CPS) is an essential skill in our increasingly connected and globalized world. Yet, there is a paucity of knowledge on how to define, measure, and develop this skill, especially in the context of STEM learning. A team of investigators from Notre Dame University, Florida State University, and Arizona State University will seek to discover how interpersonal interactions arise and influence CPS processes and outcomes in digital STEM learning environments. The research will focus on groups of high school and college students collaborating virtually within a STEM educational game called Physics Playground. The hypothesis is that CPS effectiveness can be improved by providing real-time automated feedback on the ongoing collaboration. The investigators will collect an array of data, ranging from individual physiological measures to learning outcome measures to group communication patterns. Their goal is to improve the design of future CPS learning environments, making them more enjoyable, engaging, and effective. The project is funded by the EHR Core Research (ECR) program, which supports work that advances the fundamental research literature on STEM learning.The research will involve dyads and triads collaborating virtually within a learning environment called Physics Playground, a STEM educational game. Data will be collected in both the lab and classroom, using a diverse sample of high school and college students from three sites across the U.S. The team will integrate data from a range of sources including modeling of small group problem solving and collaborative learning and from the modeling of low-level data from eye tracking, facial feature tracking, psychophysiology, and linguistic/paralinguistic speech analysis. Additionally, the team will study the moderating effects of task constraints and group composition on CPS processes and outcomes. A further goal is to model dynamic CPS processes using nonlinear time series analyses and multimodal deep recurrent neural networks. The computational models will be integrated into the learning environment to test the hypothesis that CPS outcomes can be improved by providing automated feedback on unfolding collaborative processes. Thus, by blending basic experimental research, multisensor-multimodal analysis, computational modeling, and dynamic computerized intervention, the researchers will attempt to make foundational theoretical, methodological, and technological advances.