SBIR Phase I: Cloud-Based Voice Analysis and Machine Learning to Improve Teaching with Data

SBIR 第一阶段：基于云的语音分析和机器学习，以改善数据教学

• 批准号: 1818978
• 负责人: Amy Spinelli
• 金额: $ 22.5万
• 依托单位: Earshot LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2019-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1818978&HistoricalAwards=false
• 关键词: SBIR; Phase; Cloud; Based; Voice

This SBIR Phase I project will study how teachers use data to improve their instruction. In response to widespread understanding that feedback is a necessary component to improvement, school districts are increasingly investing in teacher coaches to provide feedback critical for teachers. However, this is not scalable, and typically only new or underperforming teachers have access to coaching. Earshot seeks to scale and democratize some aspects of coaching so every teacher can have access to the personalized data and feedback necessary for improvement. The goal is for all teachers to maximize their potential, better engaging students in learning.Earshot has developed a cloud-based system using voice analysis to provide data about instruction. The core innovation is a machine learning process to listen to dialogue between teachers and students and analyze the quality of the educational exchange. Funding for this project would enable study of two critical questions: How does providing data about teacher behavior affect what teachers do in their classrooms? And, what kinds of resources, content, and support are necessary to help teachers improve? This research has the potential to transform the way teachers use data about their own performance and receive the necessary help to improve.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.

SBIR第一阶段项目将研究教师如何使用数据来改善他们的教学。为了回应反馈是改进的必要组成部分这一广泛理解，学区越来越多地投资于教师教练，以提供对教师至关重要的反馈。然而，这是不可扩展的，通常只有新的或表现不佳的教师有机会获得辅导。Earshot旨在扩大和民主化辅导的某些方面，以便每个教师都可以获得改进所需的个性化数据和反馈。我们的目标是让所有教师最大限度地发挥他们的潜力，更好地让学生参与学习。Earshot开发了一个基于云的系统，使用语音分析来提供有关教学的数据。其核心创新是一个机器学习过程，用于倾听教师和学生之间的对话，并分析教育交流的质量。该项目的资金将使两个关键问题的研究：如何提供有关教师行为的数据影响教师在课堂上做什么？什么样的资源、内容和支持是帮助教师提高所必需的？这项研究有可能改变教师使用关于他们自己的表现数据的方式，并获得必要的帮助来提高。该奖项反映了NSF的法定使命，并被认为值得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来支持。