BIGDATA: IA: Automating Analysis and Feedback to Improve Mathematics Teachers' Classroom Discourse

大数据：IA：自动化分析和反馈以改善数学教师的课堂讨论

• 批准号: 1837986
• 负责人: Tamara Sumner
• 金额: $ 199.85万
• 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: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1837986&HistoricalAwards=false
• 关键词: BIGDATA; IA; Automating; Analysis; Feedback

This research project will develop and study an innovative application - TalkBack - for addressing a significant challenge in education: providing teachers with personalized feedback on classroom discussion strategies. The TalkBack application builds on advances in deep learning for natural language processing and speech recognition to automatically analyze classroom discussions and reliably generate information about specific classroom dialog between student and teacher that occur in active learning. This research will significantly extend existing machine learning from simple descriptions, and automated classification, of dialog to more complex descriptions, and automated classification, of dialog using deep learning. TalkBack will be a cloud application available to any teacher who has classroom video and who wants to improve active learning in the classroom.The TalkBack application will consist of three interrelated components: a cloud-based big data infrastructure for managing and processing classroom recordings, deep learning models that reliably detect the use of talk moves, and an innovative interface that provides teachers with personalized, feedback on their use of discussion strategies during individual teaching episodes and longitudinally over multiple episodes. Two user studies will be conducted to gather information from math teachers related to the design and impact of the application. These user studies will include a pilot study in year 2 (n = 20 teachers) and a field study in year 3 (n = 100 teachers). The TalkBack application will provide an exemplar for a new type of translational activity enabled by big data: the reification of existing, well-researched theoretical frameworks in deep learning models. Building on NSF's investment in research on talk moves, including the Accountable Talk and the IQA frameworks, this work demonstrates how analyses of teaching practices using these frameworks can be fully automated and scaled up to support large numbers of teachers longitudinally over time. Furthermore, this effort will demonstrate how a cloud-based infrastructure supporting the detailed analysis of classroom recordings using speech and language processing can be used to develop next generation learning environments (in this case, personalized feedback on teaching practices) and to uncover new insights into teaching practices at scale. Specifically, this research will provide unprecedented insight into the ways that classroom discussions and student participation changes as teachers develop and expand their use of talk moves over time. This study will develop the big data application, TalkBack, providing immediate and actionable feedback to teachers based on self-recordings of their mathematics lessons.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个研究项目将开发和研究一个创新的应用程序——反馈——以解决教育中的一个重大挑战：为教师提供关于课堂讨论策略的个性化反馈。TalkBack应用程序建立在自然语言处理和语音识别的深度学习进步的基础上，可以自动分析课堂讨论，并可靠地生成有关主动学习中学生和教师之间特定课堂对话的信息。这项研究将极大地扩展现有的机器学习，从简单的对话描述和自动分类，到使用深度学习的更复杂的对话描述和自动分类。TalkBack将是一个云应用程序，任何有课堂视频的老师都可以使用，谁想要提高课堂上的主动学习。TalkBack应用程序将由三个相互关联的组件组成：一个基于云的大数据基础设施，用于管理和处理课堂录音；一个深度学习模型，用于可靠地检测谈话动作的使用；以及一个创新的界面，为教师提供个性化的反馈，反馈他们在单个教学章节和多个章节中使用的讨论策略。将进行两项用户研究，从数学教师那里收集与应用程序的设计和影响相关的信息。这些用户研究将包括第2年的试点研究（20名教师）和第3年的实地研究（100名教师）。TalkBack应用程序将为大数据支持的一种新型翻译活动提供范例：将现有的、研究充分的理论框架具体化为深度学习模型。基于NSF在谈话行动研究上的投资，包括问责谈话和IQA框架，这项工作展示了如何使用这些框架对教学实践的分析可以完全自动化，并随着时间的推移扩大规模，以支持大量教师。此外，这项工作将展示如何使用基于云的基础设施来支持使用语音和语言处理的课堂录音的详细分析，以开发下一代学习环境（在这种情况下，对教学实践的个性化反馈），并发现对大规模教学实践的新见解。具体来说，这项研究将为课堂讨论和学生参与的方式提供前所未有的见解，因为教师随着时间的推移发展和扩大了他们对谈话动作的使用。本研究将开发大数据应用TalkBack，根据教师的数学课程录音，为教师提供即时和可操作的反馈。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

The TalkMoves dataset: K-12 mathematics lesson transcripts annotated for teacher and student discursive moves.

TalkMoves 数据集：K-12 数学课程成绩单，注释了教师和学生的话语动作。

• 发表时间: 2022
• 期刊: Proceedings of the 13th Conference on Language Resources and Evaluation
• 作者: Suresh, A.;Jacobs, J.;Clevenger, C.;Perkoff, M.;Martin, J.;Sumner, T.
• 通讯作者: Sumner, T.

Automating analysis and feedback to improve mathematics teachers’ classroom discourse

自动分析和反馈以改善数学教师的课堂讨论

• 发表时间: 2019
• 期刊: Ninth Symposium on Educational Advances in Artificial Intelligence
• 作者: Suresh, A.;Sumner, T.;Jacobs, J.;Foland, B.;Ward, W.
• 通讯作者: Ward, W.

Automated Feedback on Discourse Moves: Teachers' Perceived Utility of a Big Data Tool

话语移动的自动反馈：教师对大数据工具的感知效用

• DOI: 10.3102/1887987
• 发表时间: 2022
• 期刊: Proceedings of the 2022 AERA Annual Meeting
• 作者: Karla Scornavacco

"Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humans

• DOI: 10.1145/3313831.3376873
• 发表时间: 2020-01
• 期刊: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
• 作者: Vivian Lai;Han Liu;Chenhao Tan

Promoting rich discussions in mathematics classrooms: Using personalized, automated feedback to support reflection and instructional change

促进数学课堂上的丰富讨论：使用个性化、自动化的反馈来支持反思和教学变革

• DOI: 10.1016/j.tate.2022.103631
• 发表时间: 2022
• 期刊: Teaching and Teacher Education
• 影响因子: 3.9
• 作者: Jacobs, Jennifer;Scornavacco, Karla;Harty, Charis;Suresh, Abhijit;Lai, Vivian;Sumner, Tamara
• 通讯作者: Sumner, Tamara