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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

基本信息

批准号：
1735793
负责人：
Sidney D'Mello
金额：
$ 26.25万
依托单位：
University of Colorado at Boulder
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1735793&HistoricalAwards=false
关键词：
EXP Collaborative Research Cyber enabled

项目摘要

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.

本计画将利用多个来源的中学课堂资料，提供教师回馈与评量资讯，以提升教师的教学能力。这些数据包括匿名的学生表现数据（成绩和标准化考试结果）以及学生和教师之间课堂讨论的匿名现有音频记录。音频数据将用于分析师生讨论，以提高师生讨论在学生学习中的有效性。在制订有效措施后，将在设计周期内向教师提供有关教学改进的反馈，以便不断改进。该项目的技术创新在于对师生讨论的分析、对师生讨论的自然语言理解以及对有效和无效师生讨论进行分类的机器学习。该项目将推动网络化的教师分析作为一种新的技术类型，提供对教师表现的自动反馈，旨在提高教学效率和学生成绩。示例性实现将自主分析来自真实世界的英语和语言艺术类的音频，以获得有效话语的指标，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：目标明确，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，连接和详细的反馈。这项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，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一个互动分析/可视化平台，以促进数据驱动的反思实践。通过设计研究进行改进后，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推广的见解，以促进未来网络支持的教师分析技术的可移植性。