Collaborative Research: Characterizing Active Learning Environments in Physics

该项目将改进工具，以区分主动学习环境，并对物理学和STEM教育都具有重要意义。主动学习上级纯粹被动授课的证据是决定性的，因此STEM教育研究现在必须建立一个更好的词汇来比较主动环境。这项工作将研究几个“旗舰”的大学物理课程，描述这些教室使用一个共同的观察框架和社会网络分析，以检查学生的合作。该项目将比较这些高影响力的例子之间的课堂特征和学生网络，并且还将促进教师收集关于他们自己的教室的类似信息的工具。使用本科生STEM课堂观察协议（COPUS），开始建立标准化词汇来比较这些环境;并开始将学习环境的具体特征与学生在学期中发展的社交网络的特征联系起来。学生网络将形成工作的重点措施，因为学生之间的互动，在几乎所有的主动学习课程的突出。关于这一课堂方面的数据目前远远落后于个人认知措施，如概念增益的细节。本项目将解决这一不足，同时记录支持或限制这些合作网络的课堂功能。收集的数据将包括网络调查和对六个经过研究验证的课程的详细课堂观察。分析将包括度分布、网络结构和集中化的比较和时间演变，以及将网络指标与COPUS分析确定的配置文件相结合的线性建模。

This project will improve tools to distinguish between active learning environments and is significant to both physics specifically and STEM education generally. Evidence that active learning is superior to purely passive lecturing is decisive, so STEM education research must now build a better vocabulary for comparing active environments. This work will study several "flagship" curricula in university physics, describing those classrooms using a common observation framework and social network analysis to examine student collaboration. The project will compare classroom features and student networks between these high-impact examples, and will also promote tools for faculty to gather similar information about their own classrooms.The goals of this project are: to characterize the social network development within several research-validated active learning curricula; to use the Classroom Observation Protocol for Undergraduate STEM (COPUS) to begin to build a standardized vocabulary to compare these environments; and to begin to link specific features of learning environments with the characteristics of student social networks that develop over the semester. Student networks will form the focal measure of the work because of the prominence of student-student interactions in virtually all active learning curricula. Data on this classroom aspect currently lag far behind the detail available for individual-cognitive measures such as conceptual gains. This project will address that deficiency while simultaneously documenting the classroom features that support or constrain these collaborative networks. Data collected will include network surveys and detailed classroom observations from six research-validated curricula. Analyses will include comparison and time evolution of degree distributions, network structure and centralization, and linear modeling combining network metrics with profiles identified from COPUS analysis.