Using AI to Focus Teacher-Student Troubleshooting in Classroom Robotics

利用人工智能集中解决课堂机器人中的师生故障

• 批准号: 2118883
• 负责人: Ross Higashi
• 金额: $ 84.78万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118883&HistoricalAwards=false
• 关键词: Using; AI; Focus; Teacher; Student

Maintaining effective instructional interactions between teachers and students around content is challenging, especially in open-ended problem-solving domains such as computer programming. Troubleshooting student programs at the classroom scale becomes difficult, even more so in remote or hybrid instructional contexts. Yet an instructor’s adaptability, insight, rapport with students, and leadership role in the classroom remain indispensable. This project will explore the use of Machine Learning (ML) algorithms to offload the time-consuming tasks of finding and deciphering student errors while also focusing teacher-student troubleshooting interactions around algorithmically identified episodic "clips" of student work-in-progress. This approach differs from the current state of the art in that it neither replaces nor simply informs the teacher, but instead convenes students and instructors around instructionally rich portions of the students’ own code and output. Design, development, and refinement of a prototype Convening AI system for middle school robotics programming will directly impact more than a dozen educators and 2000 of their students, including several schools serving underrepresented minority populations. It will also produce generalizable know-how about the design of Convening AI systems for other educational domains and ultimately inform future directions for the design of human-AI systems.This project will address the technical and sociotechnical integration challenges of an AI-driven convener through design-based research by developing a proof-of-concept Formative Assessment Suggestion Tool (FAST) in the context of middle school robotics programming. FAST will compare the efficacy of different probabilistic and neural network-based self-supervised learning approaches in identifying a student’s intended solution from their source code and simulated robot run telemetry, e.g., by comparing the plan generated by a student with that by an optimal planner. It then uses a rollback planner to identify the point at which the student’s current implementation no longer has a likely path to that solution, such that this point can be expressed to the teacher. FAST’s ML models are initially trained on an archival data set of 35,000 student code submissions to isomorphic robot programming scenarios. Each source file is re-simulated in an instrumented environment to reconstruct position, collision, and other information. Additional data including longitudinal student code-writing behavior will be collected using instrumentation upgrades developed and deployed to the simulator curriculum’s active user base during the project. Data from classroom observation will be used to model and monitor proportions of time spent engaged in different instructional actions with and without the tool. User experience around convening will be refined through participatory co-design with teachers and students. Structural equation modeling will be used to test a theory of action around uptake of the tool into classroom practice: faster, more accurate troubleshooting increases student learning and engagement as well as teacher satisfaction, leading to acceptance and continued use of the technology in a virtuous cycle.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.

保持教师和学生之间围绕内容的有效教学互动是具有挑战性的，特别是在开放式问题解决领域，如计算机编程。在课堂规模上对学生程序进行故障排除变得困难，在远程或混合教学环境中更是如此。然而，教师的适应能力、洞察力、与学生的融洽关系以及在课堂上的领导作用仍然是不可或缺的。该项目将探索使用机器学习（ML）算法来卸载查找和破译学生错误的耗时任务，同时还将师生故障排除互动集中在学生工作进展中的算法识别的情节“剪辑”周围。这种方法与当前的技术状态不同，因为它既不取代也不简单地通知教师，而是召集学生和教师讨论学生自己的代码和输出的丰富部分。设计、开发和完善用于中学机器人编程的原型召集人工智能系统将直接影响十多名教育工作者和2000名学生，其中包括几所为少数民族人口服务的学校。该项目还将为其他教育领域的召集人工智能系统的设计提供可推广的专业知识，并最终为人类-人工智能系统的设计提供未来方向。该项目将通过基于设计的研究，通过在中学机器人编程的背景下开发概念验证形成性评估建议工具（FAST），解决人工智能驱动的召集人的技术和社会技术整合挑战。FAST将比较不同的概率和基于神经网络的自监督学习方法在从源代码和模拟机器人运行遥测中识别学生预期解决方案方面的功效，例如，通过将学生生成的计划与最佳计划者生成的计划进行比较。然后，它使用回滚规划器来确定学生当前实现不再具有通向该解决方案的可能路径的点，以便可以将该点表达给教师。FAST的ML模型最初是在35，000个学生代码提交的档案数据集上训练的，这些代码提交给同构机器人编程场景。每个源文件都在一个装有仪器的环境中重新模拟，以重建位置、碰撞和其他信息。其他数据，包括纵向学生的代码编写行为，将收集使用仪器升级开发和部署到模拟器课程的活跃用户群在项目期间。从课堂观察的数据将被用来建模和监测的时间比例在不同的教学行动和没有工具。围绕召集的用户体验将通过与教师和学生的参与式共同设计来完善。结构方程模型将用于测试围绕将该工具纳入课堂实践的行动理论：更快、更准确的故障排除提高了学生的学习和参与度以及教师的满意度，该奖项反映了NSF的法定使命，并通过使用基金会的智力价值进行评估，更广泛的影响审查标准。