EXP: Collaborative Research: Cyber-enabled Teacher Discourse Analytics to Empower Teacher Learning

本项目将使用多种来源的中学课堂数据，为教师提供反馈和评估信息，提高教师的教学能力。这些数据包括匿名的学生表现数据（成绩和标准化测试结果）和匿名的现有学生和老师之间课堂讨论的录音。音频数据将用于分析学生与教师的讨论，以了解学生与教师的讨论在学生学习中的有效性。随着有效性措施的制定，教学改进的反馈将在一个设计周期内提供给教师，以持续改进。技术创新体现在对师生讨论的分析，对师生讨论的自然语言理解，以及对有效和无效的师生讨论进行分类的机器学习。该项目将推动网络教师分析作为一种新型技术，为教师的表现提供自动反馈，以提高教学效率和学生成绩。示范性实施将自动分析来自现实世界的英语和语言艺术课程的音频，以寻找有效话语的指标，从而实现数据驱动的反思实践的新范式。该项目强调了与学生成绩增长相关的话语的六个理论维度：教师主导话语的目标清晰度、学科概念和策略使用，以及事务性话语的挑战、联系和详细反馈。这项创新旨在帮助教师培养这些方面的专业知识，并将在宾夕法尼亚州西部的九年级课堂上进行开发和测试。该团队将首先通过重新分析大量（128小时）的现有课堂音频，对教师话语如何预测学生成绩产生初步见解。接下来，他们将设计并迭代改进硬件/软件接口，以实现教师高效，灵活，可扩展的音频数据收集。这些数据将用于计算有效话语的维度，通过将音频的语言、话语、声学和上下文分析与监督和半监督深度递归神经网络相结合。基于模型的评估将被纳入交互式分析/可视化平台，以促进数据驱动的反思实践。经过设计研究的改进，创新对教学改进和学生读写能力结果的影响将在随机对照试验中进行评估。最后，将在项目的每个阶段确定可概括的见解，以促进未来网络支持的教师分析技术的可转移性。

This project will use multiple sources of middle school classroom data to give feedback and assessment information to teachers so that their teaching ability is enhanced. The data includes anonymized student performance data (grades and standardized test results) and anonymized existing audio recordings of classroom discussions between students and teachers. The audio data will be used to analyze the student-teacher discussions for effectiveness of the student-teacher discussions in student learning. As the effectiveness measures are developed, feedback for instructional improvement will be provided to the teachers in a design cycle for continuous improvement. The technological innovations are in the analysis of the student-teacher discussions, in natural language understanding of student-teacher discussions, and in machine learning to classify effective from non-effective student-teacher discussions.This project will advance cyber-enabled, teacher analytics as a new genre of technology that provides automated feedback on teacher performance with the goal of improving teaching effectiveness and student achievement. The exemplary implementation will autonomously analyze audio from real-world English and language arts classes for indicators of effective discourse to enable a new paradigm of datadriven reflective practice. The project emphasizes six theoretical dimensions of discourse linked to student achievement growth: goal clarity, disciplinary concepts, and strategy use for teacher-led discourse, and challenge, connection, and elaborated feedback for transactional discourse. The innovation aims to help teachers develop expertise on these dimensions and will be developed and tested in 9th grade classrooms in Western Pennsylvania. The team will first generate initial insights on how teacher discourse predicts student achievement via a re-analysis of large volumes (128 hours) of existingclassroom audio. Next, they will design and iteratively refine hardware/software interfaces for efficient,flexible, scalable audio data collection by teachers. The data will be used to computationally model dimensions of effective discourse by combining linguistic, discursive, acoustic, and contextual analysis of audio with supervised and semi-supervised deep recurrent neural networks. The model-based estimates will be incorporated into an interactive analytic/visualization platform to promote data-driven reflective practice. After refinement via design studies, the impact of the innovation on instructional improvement and student literacy outcomes will be evaluated in a randomized control trial. Finally, generalizable insights will be identified at every stage of the project to promote transferability to future cyber-enabled, teacher-analytics technologies.