Simulating Challenging Social Situations with Intelligent Agents to Support Novice Computer Science Teachers in Self- and Social- Regulation Strategies

有效的教育者必须经常参与需要情绪自我调节（管理自己的策略）和社会调节（管理自己和他人之间互动的策略）的困难对话，以取得成功的结果。在处理愤怒的家长、困惑的学生或沮丧的同事时，教育者必须在保持冷静的同时处理紧张的情况，关注学生的需求，同时实施有效的教学实践。当这些对话涉及到身份、自尊或归属感的问题时，教育者的言行可能会对年轻人的生活产生重大影响——或好或坏。在这个项目中，团队建立在一个现有的移动网络应用程序的数字模拟，教师时刻。新手教师将沉浸在以文字、动画和视频形式呈现的课堂生活小片段中。参与者对涉及学生、家长或其他学校工作人员的场景提供即兴的文字和音频回应。该项目将在系统内开发智能代理，分析参与者的音频数据，以获取情感和社会语言学线索，以帮助评估新手教师是否在练习有效的自我调节和社会调节策略，从而降低互动中的紧张程度。这些“监管传感器”将与智能、个性化的干预措施相结合，通过脚手架式的反思和反馈来支持新手教师，同时为教师教育工作者提供指导数据。如果成功，这些模式可以适用于其他领域——比如社会工作、牧师关怀、执法和医学——在这些领域，从业者必须有效地管理困难的对话。这个研究项目是一个结合学习科学家、计算语言学家和教师教育工作者的专业知识的研究与实践伙伴关系。该团队将招募10名研究员，培训和支持计算机科学教师的教师教育工作者。关于学习和教育研究的具体活动，团队将：(1)在教师朋友圈中共同设计和实地测试反映高杠杆、高公平计算机科学实践的新场景；(2)确定职前计算机科学教师在数字模拟中应对困难教学对话时的基线情感体验和会话策略；(3)利用技术支持职前计算机科学教师进行情感自我调节，以期对学生产生积极影响；(4)支持职前教师在模拟的困难教学会话中使用有效的会话策略；(5)培养职前计算机科学教师的元交际意识。在技术研发方面，项目将：(1)开发基于自然语言处理的传感器，能够识别有效的情绪和社会调节；(2)构建支持新教师情绪调节、社会调节和教学实践的教学脚手架；(3)根据调节传感器收集的数据，对适当的支架进行个性化部署——直接面向新手教师或通过教师教育者；(4)评估具有调节传感器和教学支架的数字临床模拟系统如何适应不同的教师教育环境；(5)为具有其他领域应用潜力的新手教师智能反馈系统提供技术支持。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。

Effective educators must often engage in difficult conversations that require emotional self-regulation (strategies of managing oneself) and social-regulation (strategies of managing the interaction between oneself and others) to have successful outcomes. In dealing with angry parents, confused students, or frustrated colleagues, educators must address stressful situations while remaining calm, attending to student needs, and simultaneously deploying effective teaching practice. When these conversations engage issues of identity, self-esteem, or sense of belonging, the consequences of educators' words and actions can make a significant impact--for good or for ill--on the lives of young people. In this project, the team builds upon an existing mobile web application for digital simulations, Teacher Moments. Novice teachers will be immersed in vignettes of classroom life presented in text, animation, and video. Participants provide improvised text and audio responses to scenarios involving students, parents or other school personnel. The project will develop intelligent agents within the system to analyze participant audio data for emotional and sociolinguistic cues to help assess whether novice teachers are practicing effective self-regulation and social-regulation strategies that may lower the level of tension within the interaction. These "regulation sensors" will be combined with intelligent, personalized interventions to support novice teachers through scaffolded reflection and feedback, while supporting teacher educators with data for coaching. If successful, the models can be adapted to other fields--such as social work, pastoral care, law enforcement, and medicine--where practitioners must effectively manage difficult conversations. This research project is implemented as a research-practice partnership combining the expertise of learning scientists, computational linguists, and teacher educators. The team will recruit a cohort of ten Fellows, teacher educators who train and support computer science teachers. Regarding specific activities for the learning and educational research, the team will: (1) co-design and field test new scenarios in Teacher Moments that reflect high-leverage, high-equity computer science practices; (2) determine the baseline affective experience of and conversational strategies employed by preservice computer science teachers in responding to difficult teaching conversations in a digital simulation; (3) use technology to support preservice computer science teachers in affective self-regulation with the aim of a positive impact on students; (4) support preservice teachers' use of effective conversational strategies during difficult teaching conversations in the simulation; and (5) foster metacommunicative awareness in preservice computer science teachers. With respect to technology research and development, the project will: (1) develop sensors based on natural language processing that can identify more and less effective emotional and social regulation; (2) develop instructional scaffolds that can support novice teachers in emotional regulation, social regulation, and teaching practice; (3) personalize the deployment of appropriate scaffolds--directly to novice teachers and through teacher educators--based on data collected by regulation sensors; (4) evaluate how digital clinical simulation systems with regulation sensors and instructional scaffolds need to be adapted to diverse teacher education contexts; and (5) support technical foundations for an intelligent teacher feedback system for novice educators with potential for applications in other fields.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.