FW-HTF: The future of classroom work: Automated Teaching Assistants

人类技术前沿工作的未来(FW-HTF)是美国国家科学基金会宣布的未来投资的十大新想法之一。FW-HTF跨部门计划旨在通过支持融合研究来应对不断变化的工作和工作格局带来的挑战和机遇。这一奖项部分实现了这一目标。专业教师通过个人、小组和整个班级活动，有效地协调课堂中复杂的思想和产品流动。为了支持教师今后的工作，该项目将通过开发智能助教来促进课堂协调过程，该助教是以平板电脑仪表板的形式实施的。这将允许教师将与课堂活动相关的认知要求高的协调任务委托给智能助手，使他/她能够专注于支持学生学习的其他任务。建立在现有的中学数学教师共享文档系统的基础上，拟议的系统将有助于教师监控学生的工作，评估学生的工作，并做出结论(例如，指示学生的进步、错误和误解)，同时允许他们在单独或小组工作的学生之间传播。最终，该系统的行为将像一个自动化的教学助理，并允许教师更有效，通过允许他们专注于帮助最需要帮助的学习者来提高他们的工作满意度。基于团队的形成性评估和计算技术(FACT)系统，开发智能助教将提高教师对课堂上发生的事情的认识，并促进协调课堂活动的认知密集型任务。在第一个六个月的阶段，该项目将采用知识工程过程，以发现教师在管理课堂活动流程时用于决策的隐性知识。在第二阶段，在两年半的时间里，将进行一系列十项综合试验，通过收集数据和优化工作和想法的流程，仔细考虑教师的意见，反复制定系统的认知政策。这些知识将被编码到基于规则的人工智能中，智能助教可以在其中执行一些决策。在老师的授权下，它会在特定情况下直接向学生发送信息-一些是反馈，一些是提示，一些是访问其他学生的请求，还有一些只是为了重新引导注意力回到任务上来。自动化助教还将为教师确定任务的优先顺序；例如，访问教室中的哪些学生。作为这一过程的一部分，将在FACT中添加新的传感器，用于监控教师的讲话和位置。潜在的更广泛的影响包括促进教师进行更复杂和更有效的教学，以及在改善学生学习的同时提高教师的工作满意度。该奖项反映了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。

The Future of Work at the Human-Technology Frontier (FW-HTF) is one of 10 new Big Ideas for Future Investment announced by NSF. The FW-HTF cross-directorate program aims to respond to the challenges and opportunities of the changing landscape of jobs and work by supporting convergent research. This award fulfills part of that aim. Expert teachers effectively orchestrate the complex flow of ideas and products in the classroom through individual, small group, and whole class activities. In supporting the future work of teachers, this project will facilitate the classroom orchestration process through the development of an intelligent teaching assistant, implemented as a tablet-based dashboard. This will allow the teacher to delegate cognitively-demanding orchestration tasks related to classroom activities to the intelligent assistant, enabling him/her to focus on other tasks to support student learning. Building on an existing shared-document system for middle school mathematics teachers, the proposed system will facilitate teachers in monitoring student work, assessing it, and making conclusions (e.g., indicating student progress, errors, and misconceptions) while allowing them to circulate among students who are working individually or in small groups. Ultimately, the system will behave like an automated teaching assistant and allow teachers to be more effective and increase their job satisfaction by allowing them to concentrate on assisting learners who most need help. Building on the team's Formative Assessment with Computational Technologies (FACT) system, the development of an intelligent teaching assistant will enhance teachers' awareness of what is going on in the classroom and facilitate the cognitively-intensive tasks of orchestrating classroom activities. In the first six-month phase, the project will employ a knowledge engineering process to uncover the tacit knowledge teachers use for decision-making in managing the flow of classroom activities. In the second phase, over two and one-half years, a series of ten comprehensive trials will be conducted to iteratively develop the cognitive policies of the system through data collection and optimizing the flow of work and ideas, carefully considering teacher input. This knowledge will be encoded in a rule-based Artificial Intelligence where the intelligent teaching assistant can perform some of the decision-making. Authorized by the teacher, it will send messages directly to students in specific situations - some will be feedback, some will be hints, some will be requests to visit other students and some will just be to redirect attention to get back on task. The automated teaching assistant will also prioritize tasks for the teacher; e.g., which students to visit in the classroom. As part of this process, new sensors will be added to FACT that will monitor the teacher's speech and location. The potential broader impact includes facilitating teachers in conducting more pedagogically complex and effective lessons and increasing teacher job satisfaction while improving student learning.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.