Collaborative Research: Characterizing Active Learning Environments in Physics

合作研究：描述物理中主动学习环境的特征

基本信息

批准号：
1712341
负责人：
Adrienne Traxler
金额：
$ 7.35万
依托单位：
Wright State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-01 至 2022-01-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1712341&HistoricalAwards=false
关键词：
Collaborative Research Characterizing Active Learning

项目摘要

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.

该项目将改进区分主动学习环境的工具，对于具体的物理和一般的 STEM 教育都具有重要意义。主动学习优于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更好的词汇来比较主动环境。这项工作将研究大学物理中的几门“旗舰”课程，使用通用观察框架和社交网络分析来描述这些课堂，以检查学生的协作。该项目将比较这些高影响力示例之间的课堂特征和学生网络，还将推广教师收集有关自己课堂的类似信息的工具。该项目的目标是：描述几个经过研究验证的社交网络发展的特征主动学习课程；使用本科生 STEM 课堂观察协议 (COPUS) 开始构建标准化词汇表来比较这些环境；并开始将学习环境的具体特征与整个学期发展的学生社交网络的特征联系起来。由于学生与学生之间的互动在几乎所有主动学习课程中都很突出，因此学生网络将成为衡量工作的焦点。目前，有关课堂方面的数据远远落后于概念增益等个人认知测量的详细信息。该项目将解决这一缺陷，同时记录支持或限制这些协作网络的课堂功能。收集的数据将包括网络调查和来自六项经过研究验证的课程的详细课堂观察。分析将包括度分布、网络结构和集中度的比较和时间演变，以及将网络指标与 COPUS 分析确定的概况相结合的线性建模。