CAREER: Engaging Rural Students with Next Generation Physiological Interfaces

下一代生理计算系统使用传感器来测量大脑活动或心脏活动等数据。这些新兴系统处于人机交互应用的最前沿，涉及健康技术、自适应智能环境、自我调节、安全和认证。未来的STEM专业人员需要设计依赖于生理计算的应用程序和工具。此外，生理传感器是激发学生学习计算机科学，数据处理，技术和设计的机会。该项目将为农村学生和教师提供生理计算方面的实践学习经验。该项目将调查学生如何学习生理数据处理和计算概念。产品将包括高中科学和技术课程的学习模块。拟议研究的结果将为未来K-12在人类生理学和技术交叉点的工作提供信息。该项目由CS for All资助：研究和RPPs计划。该项目还得到了学生和教师创新技术体验（ITEST）计划的资助，该计划支持建立对实践，计划元素，背景和过程的理解的项目，有助于增加学生对科学，技术，工程，信息和通信技术（ICT）学生将获得生理数据处理和计算技能，同时参与该项目中开发的技术。更具体地说，生理传感器让学生学习波的特性和其他信号处理主题。生理计算还需要在数据处理，计算思维和科学现象的跨学科学习。该项目将通过多项研究探索高中教室中的生理计算，这些研究探索了各种方法来吸引学生参与生理计算，例如（1）预先记录和实时数据（2）整合生理数据处理和计算概念，（3）文本和视觉编程接口，以及（4）数字和物理反馈组件。该项目包括以下研究问题。首先，如何动手的经验与预先记录和实时的生理数据影响学生的能力，以获得知识的生理数据处理的概念？这些经历如何影响学生对STEM的兴趣？第二，在多大程度上整合计算和数据处理概念建立计算思维和生理计算技能？与视觉界面相比，生理数据处理的文本界面将如何影响计算思维，生理数据处理概念的知识以及对STEM的兴趣？第三，与数字方法相比，基于物理生理的学习环境将如何影响计算思维，生理数据处理概念的知识以及对STEM的兴趣？该项目将从音频和视频分析、屏幕记录、学生作业和其他知识和兴趣评估中收集证据。该奖项反映了NSF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。

Next generation physiological computing systems use sensors to measure data like brain activity or heart activity. These emerging systems are at the forefront of human-computer interaction applications involving health technologies, adaptive smart environments, self-regulation, security and authentication. Future STEM professionals are needed to design applications and tools that rely on physiological computing. In addition, physiological sensors are motivating opportunities for students to learn about computer science, data processing, technology, and design. This project will provide rural students and teachers with hands-on learning experiences in physiological computing. The project will investigate how students learn about physiological data processing and computational concepts. Products will include learning modules for high school science and technology courses. Findings from the proposed research will inform future K-12 work at the intersection of human physiology and technology. This project is funded by the CS for All: Research and RPPs program. This project is also funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts, and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers.Students are expected to gain physiological data processing and computational skills while engaging with the technology developed in the project. More specifically, physiological sensors engage students in learning about wave properties and other signal processing topics. Physiological computing also requires interdisciplinary learning in data processing, computational thinking, and scientific phenomenon. This project will explore physiological computing in high school classrooms through multiple studies that explore various ways to engage students with physiological computing such as (1) pre-recorded and real-time data (2) integrating physiological data processing and computational concepts, (3) textual and visual programming interfaces, and (4) digital and physical feedback components. The project includes the following research questions. First, how does hands-on experience with pre-recorded and real-time physiological data influence students’ ability to gain knowledge of physiological data processing concepts? How do these experiences influence students’ interest in STEM? Second, to what extent does integrating computational and data processing concepts build computational thinking and physiological computing skills? When compared to a visual interface, how will a textual interface for physiological data processing influence computational thinking, knowledge of physiological data processing concepts, and interest in STEM? Third, when compared to digital approaches, how will physical physiological-based learning environments influence computational thinking, knowledge of physiological data processing concepts, and interest in STEM? The project will gather evidence from audio and video analysis, screen recordings, student work and other assessments of knowledge and interest.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.