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

该研究项目将开发和研究一个创新的应用程序- TalkBack -以解决教育中的一个重大挑战：为教师提供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深度学习的基础上，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。这项研究将显着扩展现有的机器学习，从简单的描述和自动分类，对话到更复杂的描述和自动分类，使用深度学习的对话。TalkBack将是一个云应用程序，适用于任何拥有课堂视频并希望改善课堂主动学习的教师。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，深度学习模型，可靠地检测谈话的使用，以及一个创新的界面，为教师提供个性化，对他们在个别教学片段中以及在多个片段中使用讨论策略的反馈。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有关的信息。这些用户研究将包括第二年的试点研究（n = 20名教师）和第三年的实地研究（n = 100名教师）。TalkBack应用程序将为大数据支持的新型翻译活动提供范例：在深度学习模型中具体化现有的、经过充分研究的理论框架。基于NSF对谈话动作研究的投资，包括问责制谈话和IQA框架，这项工作展示了如何使用这些框架对教学实践进行分析，并随着时间的推移进行扩展，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，支持使用语音和语言处理对课堂录音进行详细分析，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），并揭示大规模教学实践的新见解。具体来说，这项研究将提供前所未有的洞察力的方式，课堂讨论和学生参与的变化，教师发展和扩大他们的使用谈话移动随着时间的推移。这项研究将开发大数据应用程序TalkBack，根据教师的数学课自我记录，为教师提供即时和可操作的反馈。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

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.