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

EXP：协作研究：基于网络的教师话语分析，增强教师学习能力

• 批准号: 2002875
• 负责人: Patrick Donnelly
• 金额: $ 2.46万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-21 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2002875&HistoricalAwards=false
• 关键词: EXP; Collaborative; Research; Cyber; enabled

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.

该项目将利用多种来源的中学课堂数据为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）以及学生和教师之间课堂讨论的匿名现有录音。音频数据将用于分析学生与教师的讨论，以确保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，将在设计周期中向教师提供教学改进的反馈，以实现持续改进。技术创新在于对学生与教师讨论的分析、对学生与教师讨论的自然语言理解，以及对有效和无效学生与教师讨论进行分类的机器学习。该项目将推动网络驱动的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。该示例性实施将自动分析来自现实世界英语和语言艺术课程的音频，以获取有效话语的指标，从而实现数据驱动的反思性实践的新范式。该项目强调与学生成绩增长相关的话语的六个理论维度：目标清晰度、学科概念和教师主导话语的策略使用，以及交易话语的挑战、联系和详细反馈。该创新旨在帮助教师发展这些方面的专业知识，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，对教师话语如何预测学生成绩产生初步见解。接下来，他们将设计并迭代完善硬件/软件接口，以便教师高效、灵活、可扩展的音频数据收集。这些数据将用于通过将音频的语言学、话语、声学和语境分析与监督和半监督深度循环神经网络相结合，对有效话语的维度进行计算建模。基于模型的估计将被纳入交互式分析/可视化平台中，以促进数据驱动的反思实践。通过设计研究进行完善后，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推广的见解，以促进向未来网络支持的教师分析技术的可转移性。

项目成果

Toward the automated analysis of teacher talk in secondary ELA classrooms

• DOI: 10.1016/j.tate.2021.103584
• 发表时间: 2021-12-04
• 期刊: TEACHING AND TEACHER EDUCATION
• 影响因子: 3.9
• 作者: Dale, Meghan E.;Godley, Amanda J.;Kelly, Sean P.
• 通讯作者: Kelly, Sean P.