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

这项研究项目将开发和研究一种创新的应用程序--对讲--以应对教育中的一项重大挑战：为教师提供关于课堂讨论策略的个性化反馈。该对讲应用程序建立在用于自然语言处理和语音识别的深度学习的进步之上，以自动分析课堂讨论，并可靠地生成有关在主动学习中发生的学生和教师之间的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，任何拥有课堂视频并希望改善课堂主动学习的教师都可以使用。对讲应用程序将由三个相互关联的组件组成：用于管理和处理课堂录音的基于云的大数据基础设施，可靠地检测谈话动作使用的深度学习模型，以及一个创新的界面，为教师提供个性化的反馈，让他们在个别教学环节中使用讨论策略，并纵向地在多个环节中使用。将进行两项用户研究，以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)。对讲应用程序将为大数据实现的一种新型翻译活动提供范例：在深度学习模型中将现有的、经过充分研究的理论框架具体化。基于美国国家科学基金会对谈话活动研究的投资，包括问责性谈话和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完全自动化的分析，并随着时间的推移纵向扩大到支持大量教师。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对课堂录音进行详细分析，以开发下一代学习环境(在本例中是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。具体地说，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洞察力，因为随着时间的推移，教师发展和扩大他们对谈话语步的使用。这项研究将开发大数据应用程序Talkback，根据教师自己录制的数学课向他们提供即时和可操作的反馈。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.