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

该项目将利用多种来源的中学课堂数据，向教师提供反馈和评估信息，从而提高他们的教学能力。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)和匿名的现有师生课堂讨论录音。音频数据将被用来分析师生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设计周期中提供给教师，以进行持续改进。这些技术创新包括对师生讨论的分析，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。这个项目将推动基于网络的教师分析作为一种新的技术流派，为教师表现提供自动反馈，以提高教学效率和学生成绩。该示例性实现将自主地分析来自真实世界英语和语言艺术课的音频，以寻找有效话语的指示符，以实现数据驱动的反思实践的新范例。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：目标明确、纪律概念和教师主导话语的策略使用，以及交易性话语的挑战、联系和精心设计的反馈。这项创新旨在帮助教师在这些方面发展专业知识，并将在宾夕法尼亚州西部的九年级教室进行开发和测试。该团队将首先通过对大量(128小时)现有课堂音频的重新分析，产生关于教师话语如何预测学生成绩的初步见解。接下来，他们将设计并迭代优化硬件/软件接口，以实现教师高效、灵活、可扩展的音频数据收集。这些数据将被用来通过将音频的语言、话语、声学和语境分析与监督和半监督的深度递归神经网络相结合来计算有效话语的维度。基于模型的估计将纳入一个交互式分析/可视化平台，以促进数据驱动的反思实践。在通过设计研究进行细化后，将在随机对照试验中评估创新对教学改进和学生识字结果的影响。最后，将在项目的每个阶段确定可概括的见解，以促进向未来支持网络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 existing classroom 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 ofaudio 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.