Exploring the Impact of Observation Protocol Data in Changing Instructor Motivation and Practice

探索观察协议数据对改变教师动机和实践的影响

• 批准号: 2236327
• 负责人: Richard Edwards
• 金额: $ 99.5万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2028-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2236327&HistoricalAwards=false
• 关键词: Exploring; Impact; Observation; Protocol; Data

This project aims to serve the national interest by establishing how instructors use classroom observation data to change their teaching practices. Traditionally, teaching and learning centers (TLCs) have used unstructured or informal classroom observation protocols to assess university teaching. However, many evidence-based structured observation protocols have been developed to understand and measure components of teaching and classroom activities. This project intends to advance understanding of how university instructors use data from structured and unstructured protocols to inform their teaching. To this end, the project will provide 48 instructors with classroom observations focused on evidence-based teaching practices, including active learning; a comparable group of 12 instructors will receive the results of traditional unstructured observations. The instructors span two institutions, the University of California San Diego and University of California Riverside, and will represent various STEM disciplines. As a result, the project plans to investigate multiple contexts for the impact of examining classroom observation data on instructor motivation and change in teaching practices. The project hopes to generate new knowledge about how instructors interpret observation data, which could result in more efficient and effective faculty professional development and benefit student learning. The goals of this project are to 1) identify how college STEM instructors make sense of classroom observation data, 2) measure the extent to which interacting with their own classroom observation data affects instructor motivation to change their teaching practices, and 3) establish the extent to which instructors use observation data to change their teaching. Data from four structured observation protocols and one informal protocol will be collected and shared from each instructor’s course. The project will study the efficacy of this approach through data collected from surveys, interviews, and classroom observations to explore changes in teaching practices and conceptions of teaching. These data will allow the project team to identify best practices for sharing observation data with instructors. A mixed methods approach with quantitative and qualitative data will allow for comparisons across instructional contexts and capture nuanced changes in teaching and motivation. The NSF IUSE: EHR program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Institutional and Community Transformation track, the program supports efforts to transform and improve STEM education across institutions of higher education and disciplinary communities.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.

该项目旨在通过建立教师如何使用课堂观察数据来改变他们的教学实践，从而为国家利益服务。传统上，教学中心（TLCs）使用非结构化或非正式的课堂观察协议来评估大学教学。然而，已经开发了许多基于证据的结构化观察协议来理解和测量教学和课堂活动的组成部分。该项目旨在促进对大学教师如何使用结构化和非结构化协议中的数据来指导教学的理解。为此，该项目将为48名教师提供课堂观察，重点关注循证教学实践，包括主动学习；12名教员将接受传统的非结构化观察结果。讲师来自加州大学圣地亚哥分校和加州大学河滨分校两所院校，将代表不同的STEM学科。因此，该项目计划调查多种背景，以研究课堂观察数据对教师动机和教学实践变化的影响。该项目希望产生关于教师如何解释观测数据的新知识，这可能导致更高效和有效的教师专业发展，并有利于学生的学习。该项目的目标是：1)确定大学STEM教师如何理解课堂观察数据；2)衡量与自己的课堂观察数据互动对教师改变教学实践动机的影响程度；3)确定教师使用观察数据改变教学的程度。来自四个结构化观察协议和一个非正式协议的数据将从每个教师的课程中收集和共享。该项目将通过调查、访谈和课堂观察收集的数据来研究这种方法的有效性，以探索教学实践和教学观念的变化。这些数据将使项目团队能够确定与教师共享观测数据的最佳实践。定量和定性数据的混合方法将允许在教学环境中进行比较，并捕捉教学和动机的细微变化。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。通过其机构和社区转型轨道，该计划支持在高等教育机构和学科社区中转变和改善STEM教育的努力。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。