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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.

美国国家工程院将推进个性化学习确定为其工程学14大挑战之一。为了在这一挑战中取得进展，一个在认知和学习科学、物理教育、计算机系统、生物传感和机器学习方面拥有专业知识的研究团队将共同努力，带来先进的数据采集和处理技术，以支持对认知和学习科学假设的快速测试和评估。该项目将使用一种融合的方法来推进主动学习的研究，并在物理入门课程中使用主动学习来研究压力在课堂上的作用。这项努力集中在两个目标上。首先是开发技术，建立一个灵活的框架，创建用于大学物理入门课程的压力测量仪器。第二项是先导研究，以发展共同的认知和学习科学为基础的理解压力的作用和学习机制的背景下，学生在多角度对话(MPC)的物理入门。虽然已知MPC在发展物理等学科的概念理解方面发挥着重要作用，但对MPC过程中刺激和压力的影响却知之甚少。由于几十个MPC同时出现在一个教室里，它们的记录和学习都是具有挑战性的。该项目开发的自动成绩单和机器学习工具将能够收集和研究更多的对话。认知科学实验室实验使用先进的生物传感器，最初将在受控环境中研究压力和学习。随后生物传感器和音频/视频数据的混合将允许在更大的课堂环境中首次研究压力和学习问题。该项目的一个关键的更广泛的成果将是建立一个跨学科的融合团队。活动将围绕建立共享词汇、跨学科会议、公共讲习班以及通过研究生课程和参与该项目对未来汇聚研究人员的培训展开。该奖项由OIA、EHR、ENG和SBE提供资金支持。技术开发工作分为两个方面，每个方面都有一个由认知科学家、学习型科学家和工程师组成的跨学科团队。Aspect I研究团队将开发先进的生物传感器，通过不显眼的可穿戴贴片和教室皮质醇传感器来测量压力。这些传感器将用于认知科学实验室，通过测量注意力、即时记忆和构建知识图式的能力来研究压力对学习的影响。在第二方面，将开发基于拓扑数据分析的机器学习方法，将音频/视频记录和生物传感器数据相结合，使学习和认知科学家能够在规模上研究主动学习环境。这些试点工作的重点将是研究入门物理课堂中的多视角对话(MPC)。配备压力传感器、自动成绩单和机器学习工具的灵活学习环境框架将首先在少数并发的MPC上进行测试，然后在课堂规模上进行测试。研究人员将验证这项技术，并开始调查MPC与学习结果之间的关系，培养学生之间MPC的条件，以及压力在塑造MPC潜在认知过程中的作用。基于线程的传感器将使用新材料，并且是非侵入性、可穿戴和无线的。拓扑数据分析(TDA)的使用将提供一个灵活的机器学习框架，该框架可以随着数据集的大小及其异构性而增长和适应。这些技术将由计算机系统研究人员整合并部署在教室规模的系统中。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。