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

这个SBIR第一阶段项目将研究教师如何使用数据来改进他们的教学。由于普遍认识到反馈是改进的必要组成部分，学区正在越来越多地投资于教师培训，以提供对教师至关重要的反馈。然而，这是不可扩展的，通常只有新教师或表现不佳的教师才能获得指导。Eearshot寻求扩大辅导的某些方面并使其民主化，这样每一位教师都可以获得改进所需的个性化数据和反馈。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。核心创新是一个机器学习过程，用于倾听师生之间的对话，分析教育交流的质量。为这个项目提供资金将使我们能够研究两个关键问题：提供有关教师行为的数据如何影响教师在课堂上的行为？那么，需要什么样的资源、内容和支持来帮助教师提高呢？这项研究有可能改变教师使用有关自己表现的数据的方式，并获得必要的帮助来改进。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.