Collaborative Research: Further Characterizing Active Learning Environments in Physics

合作研究：进一步表征物理中的主动学习环境

• 批准号: 2111128
• 负责人: Eric Brewe
• 金额: $ 46.46万
• 依托单位: Drexel University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2111128&HistoricalAwards=false
• 关键词: Collaborative; Research; Further; Characterizing; Active

This project aims to serve the national interest by improving teaching and learning in college and university physics courses. This collaborative project involves investigators at Drexel University (Award DUE-2111128) and Wright State University (Award DUE-2111275). Education research has shown that using an "active learning" approach is central to improving students' learning in STEM classes. In physics, many different teaching methods fall under the umbrella of "active learning," but the specifics of how the methods differ from one another are not clear. One way in which all active learning classes are the same is that the students interact more with each other than they do in standard lectures. In this project, the investigators will attempt to identify the important features of active learning in physics by collecting data on what students and instructors are doing during class and how students are interacting with each other. Then they will attempt to relate these data to improved student learning. This research will help instructors make decisions about which teaching methods are best for their population of students.This project will build on exploratory research that the investigators conducted during a previous project that was funded by NSF Awards DUE-1711017 and DUE-1712341, "Collaborative Research: Characterizing Active Learning Environments in Physics." That research analyzed differences among active learning environments using network analysis and the Classroom Observation Protocol for Undergraduate STEM (COPUS). The focus was on a small number of sites that represented high-fidelity, high-quality implementations of six active learning pedagogies. The investigators discovered recurring mid-scale network structures across pedagogies, with fine-grained models beginning to identify smaller-scale features. In addition, by using Latent Profile Analysis (LPA) on COPUS observations, the investigators found that the COPUS presents distinct profiles for the different pedagogies that were examined. In the new project, the investigators will extend the baseline data from the pilot project, exploring whether the patterns are broadly repeated at other implementation sites of the same pedagogies and how they relate to student outcomes. In particular, the investigators will extend their previous work by collecting and analyzing data from 32 different sites and by coordinating the existing network and COPUS measures with student outcome data. Key aims of the research will be (1) to characterize implementations of active learning curricula in physics in terms of student-student interactions and instructor and student activity within each learning environment, (2) to survey common features of student-student network development across active learning environments, and (3) to integrate data on student networks and COPUS profiles in relation to student outcomes. The additional data and analyses should enable researchers to identify the specific features of active learning environments that contribute to specific student outcomes. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目旨在通过改善学院和大学物理课程的教学来服务国家利益。该合作项目涉及德雷克塞尔大学（奖项 DUE-2111128）和莱特州立大学（奖项 DUE-2111275）的研究人员。教育研究表明，使用“主动学习”方法对于提高学生在 STEM 课堂上的学习至关重要。在物理学中，许多不同的教学方法都属于“主动学习”的范畴，但这些方法之间的具体差异尚不清楚。所有主动学习课程都相同的一种方式是，学生之间的互动比标准讲座中的互动更多。在这个项目中，研究人员将尝试通过收集学生和教师在课堂上所做的事情以及学生之间如何互动的数据来确定物理主动学习的重要特征。然后他们将尝试将这些数据与改善学生的学习联系起来。这项研究将帮助教师决定哪种教学方法最适合他们的学生群体。该项目将建立在研究人员在先前由 NSF 奖 DUE-1711017 和 DUE-1712341 资助的项目“协作研究：描述物理中主动学习环境的特征。”该研究使用网络分析和本科生 STEM 课堂观察协议 (COPUS) 分析了主动学习环境之间的差异。重点是少数代表六种主动学习教学法的高保真、高质量实施的网站。研究人员发现整个教学法中反复出现的中型网络结构，细粒度模型开始识别较小规模的特征。此外，通过对 COPUS 观察结果使用潜在特征分析 (LPA)，研究人员发现 COPUS 为所检查的不同教学法提供了不同的特征。在新项目中，研究人员将扩展试点项目的基线数据，探索这些模式是否在相同教学法的其他实施地点广泛重复，以及它们与学生成绩的关系。特别是，研究人员将通过收集和分析来自 32 个不同地点的数据以及将现有网络和 COPUS 措施与学生成绩数据相协调来扩展他们之前的工作。该研究的主要目标是（1）在每个学习环境中的学生与学生互动以及教师和学生活动方面描述物理主动学习课程的实施，（2）调查跨学科学生与学生网络发展的共同特征积极的学习环境，(3) 整合学生网络和 COPUS 档案中与学生成绩相关的数据。额外的数据和分析应该使研究人员能够确定有助于特定学生成果的主动学习环境的具体特征。 NSF IUSE：EHR 计划支持研究和开发项目，以提高所有学生 STEM 教育的有效性。通过参与学生学习轨道，该计划支持有前途的实践和工具的创建、探索和实施。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。