SCH: A Data-driven Classroom Intervention Framework for Children with Social Peer Engagement Deficits

SCH：针对有社交同伴参与缺陷的儿童的数据驱动的课堂干预框架

• 批准号: 2306499
• 负责人: Subir Biswas
• 金额: $ 60万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2306499&HistoricalAwards=false
• 关键词: SCH; Data; driven; Classroom; Intervention

Individuals with autism and other developmental disabilities show marked difficulty establishing and maintaining social peer engagements. These difficulties result in limited friendships, high rates of social rejection and isolation, and increased risk of anxiety, depression, and suicidal ideation. Individuals with autism and developmental disabilities also experience lower rates of employment, post-secondary participation, and independent living in comparison to their peers. Despite their social challenges, children with autism and developmental disabilities desire to engage socially but require support. Early social skills interventions such as proximity training can help such children improve social engagement and mental health. This project develops an assistive technology for aiding such corrective social skills interventions by teachers in classroom settings.This project develops human sensing and AI-enabled processing infrastructure for children’s peer-engagement behavior monitoring. Teachers use the sensed behavior and AI-inferred mood information to administer targeted behavioral interventions to children diagnosed with autism and other related behavioral disorders. A key technology enabler in this project is a Wearable Human Interaction Tracker (WHET) tag, which measures pair-wise human interaction between individuals. When two people, each wearing the nametag-sized tag, interact, their interaction/engagement dynamics are tracked and quantitatively captured by the WHET sensors for subsequent wireless upload to an access point connected to cloud storage. This assistive technology for teachers helps improve the quality of intervention, as well as the number of children that a teacher can simultaneously and effectively provide interventions to. The newly-developed technology and algorithms will also improve support for children with many other disorders including depression, anxiety, and ADHD.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.

自闭症患者和其他发育障碍的人表明，难以建立和维持社交同伴参与。这些困难导致朋友有限，社会排斥和孤立的率很高，并增加了焦虑，抑郁和自杀念头的风险。与同龄人相比，患有自闭症和发育障碍的人还经历了较低的就业率，专上参与和独立的生活。尽管面临社会挑战，但自闭症和发育障碍的儿童渴望在社交上参与，但需要支持。早期的社交技能干预措施（例如接近培训）可以帮助这些孩子改善社交参与和心理健康。该项目开发了一种辅助技术，用于帮助教师在课堂环境中进行正确的社交技能干预措施。该项目为儿童的同伴参与行为监控而发展了人类的敏感性和支持AI的处理基础架构。教师使用感知的行为和AI提取的情绪信息来对被诊断为自闭症和其他相关行为障碍的儿童进行有针对性的行为干预措施。该项目中的一个关键技术推动器是可穿戴的人类交互跟踪器（WHET）标签，该标签可以测量个人之间的成对互动。当两个人戴着名字大小的标签时，互动时，他们的交互/参与动力学会被WHET传感器跟踪并定量捕获，以便随后的无线上传到连接到云存储的访问点。这项针对教师的辅助技术有助于提高干预质量，以及教师可以简单有效地提供干预措施的儿童数量。新开发的技术和算法还将改善对许多其他疾病的儿童的支持，包括抑郁症，焦虑和多动症。该奖项反映了NSF的法定任务，并通过使用该基金会的知识分子优点和更广泛的影响来评估NSF的法定任务。