Using AI to Focus Teacher-Student Troubleshooting in Classroom Robotics

在教师和学生之间围绕内容保持有效的教学互动是具有挑战性的，特别是在计算机编程等开放式问题解决领域。在课堂规模上对学生项目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，教师的适应性、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。这个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，同时也将重点放在教师与学生之间的故障排除互动上，这些互动是围绕通过算法识别的学生正在进行的作业的片段来进行的。这种方法与当前的技术状态的不同之处在于，它既不取代教师，也不简单地通知教师，而是召集学生和教师关注学生自己的代码和输出中具有丰富教学意义的部分。中学机器人编程召集人工智能系统原型的设计、开发和完善将直接影响十多名教育工作者和他们的2000名学生，其中包括几所服务于代表不足的少数族裔人口的学校。这个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一个概念验证形成性评估建议工具(FAST)来解决人工智能驱动的召集人的技术和社会技术集成挑战。FAST将比较不同的基于概率和神经网络的自我监督学习方法在从学生的源代码和模拟的机器人运行遥测中确定学生预期解决方案的有效性，例如，通过比较学生生成的计划和最佳计划者的计划。然后，它使用回滚计划器来确定学生的当前实现不再具有通向该解决方案的可能路径的点，以便可以向教师表达这一点。FAST的ML模型最初是在35000个提交给同构机器人编程场景的学生代码的档案数据集上进行培训的。每个源文件都在仪表化环境中重新模拟，以重建位置、碰撞和其他信息。其他数据，包括学生的纵向代码编写行为，将通过在项目期间开发和部署到模拟器课程的活跃用户群的仪器升级来收集。来自课堂观察的数据将被用来模拟和监测在使用和不使用该工具的情况下从事不同教学行动的时间比例。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提炼。结构方程模型将被用来测试围绕该工具在课堂实践中的应用的行动理论：更快、更准确的故障排除增加了学生的学习和参与度以及教师的满意度，导致在良性循环中接受和继续使用该技术。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。

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.