Convergence: RAISE: A Flexible Framework for Instrumented Learning Environments: Enhanced Learning Through Advanced Sensing, Processing, and Cognitive Technologies

融合：RAISE：仪器化学习环境的灵活框架：通过先进的传感、处理和认知技术增强学习

基本信息

批准号：
1931978
负责人：
Mark Hempstead
金额：
$ 100万
依托单位：
Tufts University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-15 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1931978&HistoricalAwards=false
关键词：
Convergence RAISE Flexible Framework Instrumented

项目摘要

The National Academy of Engineering identified advancing personalized learning as one of its 14 Grand Challenges for Engineering. To make progress on this challenge, a research team with expertise in cognitive and learning sciences, physics education, computer systems, bio-sensing, and machine learning will work together to bring advanced data acquisition and processing technologies to support rapid testing and evaluation of cognitive and learning science hypotheses. The project will use a convergent approach to advance the study of active learning and the role of stress in classes using active learning in introductory physics. The effort is centered on two objectives. The first is to develop technology to build a flexible framework for creating instruments to measure stress for use in introductory college physics. The second is to pilot studies to develop a joint cognitive and learning science-based understanding of the role of stress and the mechanisms for learning in the context of multi-perspective conversations (MPCs) among students in introductory physics. While MPCs are known to play an important role in developing conceptual understanding in disciplines such as physics, little is understood concerning the impact of stimulation and stress during MPCs. Because dozens of MPCs occur simultaneously in a classroom, they are challenging to record and study. The automated transcript and machine learning tools developed by this project will allow for the collection and study of significantly more conversations. Cognitive science lab experiments using advanced bio-sensors will study stress and learning initially in a controlled setting. Subsequent blending of biosensor and audio/video data will allow the issues of stress and learning to be studied for the first time in a larger classroom environment. One of the key broader outcomes of the project will be the construction of an interdisciplinary convergent team. Activities will focus around building shared vocabulary, cross-disciplinary meetings, public workshops and the training of future convergent researchers through graduate courses and participation in this project. The award is supported by funding from OIA, EHR, ENG, and SBE. The technology development efforts are divided into two aspects each with an interdisciplinary team of cognitive scientists, learning scientists and engineers. The Aspect I research team will develop advanced bio-sensors to measure stress through unobtrusive wearable patches and classroom cortisol sensors. These sensors will be used in the cognitive science lab to study the effects of stress in learning by measuring attention, immediate memory, and the ability to construct knowledge schemas. In Aspect II, machine learning methods based on topological data analysis will be developed that combine audio/video recording and the biosensors data to allow learning and cognitive scientists to study active learning environments at scale. The focus of these pilot efforts will be on the study of multi-perspective conversations (MPCs) in an introductory physics classroom. Flexible framework for instrumenting learning environments, complete with stress sensors and automated transcripts and machine learning tools, will be tested first with a small number of concurrent MPCs and then later at classroom scale. Researchers will verify the technology and begin investigating the relationship between MPC and learning outcomes, the conditions that foster MPCs between students, and the role of stress in shaping cognitive processes underlying MPCs. The thread-based sensors will use novel materials and are non-obtrusive and wearable and wireless. The use of topological data analysis (TDA) will provide a flexible machine learning framework that can grow and adapt both with the size of the data sets and their heterogeneity. These technologies will be integrated and deployed in a classroom-scale system by computer systems researchers.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.

美国国家工程学院将推进的个性化学习确定为其14项工程挑战之一。为了在这一挑战上取得进展，具有在认知和学习科学，物理教育，计算机系统，生物感应和机器学习方面具有专业知识的研究团队将共同努力，以带来先进的数据获取和处理技术，以支持对认知和学习科学假设的快速测试和评估。该项目将使用收敛的方法来推进主动学习的研究以及使用主动学习在入门物理学中的班级中的作用。这项工作以两个目标为中心。首先是开发技术来建立一个灵活的框架，以创建工具来衡量在大学物理学中使用的压力。第二个是试点研究，以在介绍性物理学中的学生中对压力的作用以及在多观点对话（MPC）的背景下建立对压力的作用的共同认知和学习的理解。尽管已知MPC在物理等学科中发展概念理解方面发挥了重要作用，但几乎没有理解有关MPC期间刺激和压力的影响。由于数十个MPC同时发生在教室中，因此他们在记录和学习方面具有挑战性。该项目开发的自动成绩单和机器学习工具将允许收集和研究更多的对话。使用高级生物传感器的认知科学实验室实验最初将在受控的环境中研究压力和学习。随后的生物传感器和音频/视频数据的融合将允许在更大的课堂环境中首次研究压力和学习问题。该项目的主要更广泛成果之一将是建造一个跨学科的融合团队。活动将集中在建立共享的词汇，跨学科会议，公共研讨会以及通过研究生课程和参与该项目的未来融合研究人员的培训。该奖项得到了OIA，EHR，ENG和SBE的资助。技术开发工作分为两个方面，每个方面与认知科学家，学习科学家和工程师的跨学科团队。我研究团队将通过不可感染的可穿戴贴片和教室皮质醇传感器来开发高级生物传感器，以测量压力。这些传感器将在认知科学实验室中使用，以通过衡量注意力，即时记忆和构建知识模式的能力来研究压力在学习中的影响。在II方面，将开发基于拓扑数据分析的机器学习方法，结合音频/视频记录和生物传感器数据，以允许学习和认知科学家大规模研究活动的学习环境。这些试点努力的重点将放在研究物理学课堂中的多人对话（MPC）的研究上。仪器学习环境的灵活框架，包括压力传感器和自动成绩单和机器学习工具，将首先使用少数并发的MPC进行测试，然后在课堂规模上进行。研究人员将验证该技术，并开始研究MPC与学习成果之间的关系，促进学生之间MPC的条件以及压力在塑造MPC基础认知过程中的作用。基于线的传感器将使用新颖的材料，并且是非引人注目的，可穿戴的，无线的。拓扑数据分析（TDA）的使用将提供一个灵活的机器学习框架，该框架可以增长和适应数据集的大小及其异质性。这些技术将由计算机系统研究人员集成和部署在课堂规模的系统中。该奖项反映了NSF的法定任务，并认为使用基金会的知识分子优点和更广泛的影响评估标准，认为值得通过评估来获得支持。