EAGER: Early Stage Research on Automatically Identifying Instructional Moves in Mathematics

EAGER：自动识别数学教学动作的早期研究

• 批准号: 1600325
• 负责人: Tamara Sumner
• 金额: $ 29.99万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-15 至 2018-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1600325&HistoricalAwards=false
• 关键词: EAGER; Early; Stage; Research; Automatically

This is an Early-concept Grant for Exploratory Research research project to develop automated tools to aid in the development of mathematics teaching expertise in preservice teachers. Current research on preservice mathematics teacher instruction relies on observing preservice teachers interacting with students and recording their mathematical interactions to provide guidance and advice as how the preservice teachers can improve their teaching. This research requires highly trained observers and highly trained analysts to record and interpret the student teacher verbal interactions in order to give teachers feed back in how to improve their instruction. The aim of this project is to automate the observation, recording, and interpretation of student-teacher interactions. This would result in more effective research on instructional strategies for the preservice teachers and ultimately lead to changes in teacher professional development when feedback only available in research environments becomes feasible for all preservice teacher professional development. The project will use as an initial basis the observation toolkit Accountablity Talk for providing teachers with both formative and summative feedback on their instruction. Automatic speech recognition and natural language technologies will be used to record and interpret student teacher verbal interactions. The results of this research have the potential to democratize preservice mathematics teacher professional development and, over time, provide insight into teacher learning that can result in restructuring teacher learning environment to make them more effective in developing high quality mathematics teachers.

这是一个探索性研究项目的早期概念补助金，旨在开发自动化工具，以帮助数学教师发展数学教学专业知识。目前对中学数学教师教学的研究主要是通过观察中学数学教师与学生的互动，并记录他们的数学互动，为中学数学教师如何提高教学水平提供指导和建议。这项研究需要训练有素的观察员和训练有素的分析师来记录和解释学生教师的口头互动，以便给教师反馈如何改善他们的教学。这个项目的目的是自动化的观察，记录和解释学生与教师的互动。这将导致更有效的研究教学策略的副教师，并最终导致教师专业发展的变化时，只有在研究环境中提供的反馈成为可行的所有副教师的专业发展。该项目将使用观察工具包问责制谈话作为初步基础，为教师提供关于其教学的形成性和总结性反馈。自动语音识别和自然语言技术将用于记录和解释学生与教师的口头互动。本研究的结果有可能民主化的数学教师专业发展，随着时间的推移，提供洞察教师学习，可以导致重建教师学习环境，使他们更有效地发展高质量的数学教师。