SBIR Phase I: The Teacher Practice Feedback Tool- real-time information for teachers who want to measure and improve their talk time, wait time, and question-asking skills.

SBIR第一阶段项目旨在通过为教师提供实时、个性化的反馈来改善教学。大多数教师很少收到关于他们教学的定期、可操作的反馈，以帮助他们提高水平。提问和讨论是优秀教师的财富，特别是在教师帮助学生培养批判性思维、协作、沟通和创造力技能的情况下，这些技能是科学、技术、工程和数学职业中最需要的技能。该项目旨在设计、开发、测试和完善一款移动应用程序，为所有年级和学科领域的教师提供强大的实时教学数据。通过自动分析问题和讨论，教师将获得有关课堂交流的有用数据，帮助他们不断改进教学。该项目旨在帮助教师将更多的探究式教学方法融入到他们的教学实践中。探究是一种以研究为基础的方法，以提高学生的能力。使用这一应用程序的预期结果是让教师少说多问，在召唤学生之前留出足够的等待时间，并让教师在教学中融入更多更高层次的问题。这项技术可以在订阅的基础上直接出售给美国各地的学校和大学的教师，使教师能够控制自己的数据并决定如何使用这些数据，同时确保该工具用于改进，而不是用于评估目的。为该项目开发的技术建立在自然语言处理和语音分析的最新进展基础上。该项目使用机器学习和算法开发了一款用户友好的应用程序，该应用程序围绕四个关键数据点分析教师语音数据：交谈时间、等待时间、问题级别(基于专有分类的较高和较低级别的问题)和问题频率。该项目的目标是设计、测试和改进该应用程序的第一个版本，该应用程序将使用外部麦克风在iPhone上使用。代表不同年级水平、学科领域、经验水平和声音特征的教师将试验这项技术，并提供反馈，为发展提供信息。该项目将采用研究方法，如事前和事后调查、观察、访谈和有声思考会议，以告知可用性和可行性。

This SBIR Phase I project seeks to improve teaching by providing teachers with real-time, personalized feedback. Most teachers receive little regular, actionable feedback about their teaching to help them improve. Questions and discussions are the currency of great teachers, especially as teachers help students develop critical thinking, collaboration, communications and creativity skills, those skills in highest demand for careers in science, technology, engineering and math. This project is to design, develop, test and refine a mobile app that provides powerful, real-time instructional data for teachers at all grade levels and subject areas.Through the automatic analysis of questions and discussion, teachers will have access to useful data about communication in their classrooms to help them continuously improve instruction. This project is designed to help teachers incorporate more inquiry-based instruction methods into their teaching practice. Inquiry is a research-based approach to increasing student competencies. The intended outcomes for using this application are for teachers to talk less and ask more questions, to allow sufficient wait time before calling on students, and for teachers to incorporate more and higher-level questioning into their teaching. The technology can be sold to directly teachers at schools and universities throughout the United States on a subscription basis, enabling teachers to control their own data and decide how it is used, as well as ensuring that the tool is used for improvement, rather than for evaluative purposes. The technology being developed for this project builds upon recent advancements in natural language processing and voice analysis. This project uses machine learning and algorithms to develop a user-friendly app that analyzes teacher voice data around four key data points: talk time, wait time, question level (higher-order and lower-order questions based on a proprietary taxonomy), and question frequency. The objective of this project is to design, test and refine the first version of the app, to be used on an iPhone using an external microphone. Teachers representing various grade levels, subject areas, levels of experience, and voice characteristics will pilot this technology and provide feedback to inform development. This project will employ research methodologies such as pre and post surveys, observations, interviews, and think-aloud sessions, to inform usability and feasibility.