Planning Grant: Engineering Tools for Education Research (EnTER)
规划补助金:教育研究工程工具(EnTER)
基本信息
- 批准号:1937057
- 负责人:
- 金额:$ 10万
- 依托单位:
- 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:2019-09-01 至 2021-08-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The proposing team envisions an Engineering Research Center that will transform learning and instruction with a focus on active learning in STEM education. Current efforts to understand what takes place in such environments are limited to choosing between studies using compact, simple forms of data (such as grades or surveys), and studies employing video and written records of students' work. Compact data provide insight but are removed from the classroom setting, while detailed record reviews are small-scale because of the labor-intensive methods required to analyze these rich, qualitative data. The compromises associated with both strategies have severely limited progress in developing a scientific understanding of the classroom environment and using this information to impact real-world classrooms. Radically altering the way educational environments foster learning requires a multi-pronged approach bringing together researchers with expertise in cognitive and learning sciences, bio-sensing, data analytics and computer systems. The resulting engineered system will address fundamental research questions concerning how students learn best as individuals, in teams, and in classroom communities. This knowledge can subsequently be translated into practical tools to provide instructors with in-classroom insight into students' thinking and engagement in active learning environments at scale. This ERC planning grant will facilitate the development of a team and Center structure that the National Academies of Science and Engineering believe is necessary to Advance Personalized Learning in the next century. Knowledge developed through these tools will, in an inclusive and ethically sensitive manner, lead to advances in curriculum, pedagogy, and the design of engineered systems in support of learning and instruction.Active learning environments with multiple concurrent conversations and activities are a challenge to study; however, understanding their dynamics is central to the advancement of learning science. Our engineering team will invent new sensors, embedded in low-power wearable computing platforms, whose data will be processed by next-generation machine learning algorithms. The ERC planning grant will bring together researchers in convergent disciplines through workshops and data hackathons to understand the research problems and demonstrate potential technologies. These instrumented learning environments will allow researchers to formulate and evaluate the hypotheses rapidly and at a scale well beyond that which can be achieved today. The ERC planning team will directly engage a range of stakeholders with an interest in the education of the next generation workforce. These stakeholders include students, educators, researchers, administrators, industry and accreditation agencies that will be engaged to build an ecosystem around the proposed center. Given the nature of the effort, privacy and ethical treatment of human subjects will be central to all aspects of the project. The hackathons and workshop activities will impact the broader research community beyond Tufts University by showing the potential of these technologies for educational research.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.
提案团队设想了一个工程研究中心,该中心将改变学习和教学,重点是STEM教育中的主动学习。目前,了解在这种环境中发生的事情的努力仅限于在使用紧凑、简单的数据形式(如成绩或调查)的研究和使用学生作业的视频和书面记录的研究之间进行选择。紧凑的数据提供了洞察力,但从课堂环境中移除,而详细的记录审查是小规模的,因为分析这些丰富的定性数据需要耗费大量的劳动。这两种策略的妥协严重限制了发展对课堂环境的科学理解和利用这些信息影响现实世界课堂的进展。从根本上改变教育环境促进学习的方式需要多管齐下的方法,将具有认知和学习科学、生物传感、数据分析和计算机系统专业知识的研究人员聚集在一起。由此产生的工程系统将解决有关学生如何作为个人、团队和课堂社区学习最好的基本研究问题。这些知识随后可以转化为实用工具,为教师提供课堂洞察学生的思维和大规模主动学习环境的参与。这项ERC计划拨款将促进团队和中心结构的发展,美国国家科学与工程学院认为这是下个世纪推进个性化学习所必需的。通过这些工具开发的知识,将以包容和道德敏感的方式,导致课程、教学法和工程系统设计的进步,以支持学习和教学。具有多个同步对话和活动的主动学习环境是学习的挑战;然而,了解它们的动态是学习科学进步的核心。我们的工程团队将发明新型传感器,嵌入低功耗可穿戴计算平台,其数据将由下一代机器学习算法处理。ERC计划拨款将通过研讨会和数据黑客马拉松将融合学科的研究人员聚集在一起,以了解研究问题并展示潜在的技术。这些仪器化的学习环境将使研究人员能够快速地制定和评估假设,其规模远远超过今天所能实现的。ERC计划团队将直接与一系列对下一代劳动力教育感兴趣的利益相关者接触。这些利益相关者包括学生、教育工作者、研究人员、管理人员、行业和认证机构,他们将围绕拟议的中心建立一个生态系统。鉴于这项工作的性质,人类受试者的隐私和道德待遇将是该项目各个方面的核心。黑客马拉松和研讨会活动将通过展示这些技术在教育研究方面的潜力,影响塔夫茨大学以外的更广泛的研究界。该奖项反映了美国国家科学基金会的法定使命,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。
项目成果
期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Automatic coding of students' writing via Contrastive Representation Learning in the Wasserstein space
- DOI:
- 发表时间:2020-11
- 期刊:
- 影响因子:0
- 作者:Ruijie Jiang;J. Gouvea;David Hammer;S. Aeron
- 通讯作者:Ruijie Jiang;J. Gouvea;David Hammer;S. Aeron
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Mark Hempstead其他文献
SnackNoC: Processing in the Communication Layer
SnackNoC:通信层的处理
- DOI:
- 发表时间:
2020 - 期刊:
- 影响因子:0
- 作者:
Karthik Sangaiah;Michael Lui;Ragh Kuttappa;B. Taskin;Mark Hempstead - 通讯作者:
Mark Hempstead
Algorithms for CPU and DRAM DVFS under inefficiency constraints
低效率约束下的CPU和DRAM DVFS算法
- DOI:
- 发表时间:
2016 - 期刊:
- 影响因子:0
- 作者:
R. Begum;Mark Hempstead;Guru Prasad Srinivasa;Geoffrey Challen - 通讯作者:
Geoffrey Challen
Can You Trust Your Memory Trace? A Comparison of Memory Traces from Binary Instrumentation and Simulation
你能相信你的记忆痕迹吗?
- DOI:
- 发表时间:
2015 - 期刊:
- 影响因子:0
- 作者:
Siddharth Nilakantan;Scott Lerner;Mark Hempstead;B. Taskin - 通讯作者:
B. Taskin
Improving HLS with Shared Accelerators: A Retrospective
使用共享加速器改进 HLS:回顾
- DOI:
- 发表时间:
2021 - 期刊:
- 影响因子:0
- 作者:
Parnian Mokri;Mark Hempstead - 通讯作者:
Mark Hempstead
Characterizing the costs and benefits of hardware parallelism in accelerator cores
描述加速器内核中硬件并行性的成本和收益
- DOI:
10.1109/iccd.2013.6657021 - 发表时间:
2013 - 期刊:
- 影响因子:0
- 作者:
Steven J. Battle;Mark Hempstead - 通讯作者:
Mark Hempstead
Mark Hempstead的其他文献
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{{ truncateString('Mark Hempstead', 18)}}的其他基金
Travel: NSF Student Travel Grant for 2023 IEEE International Symposium on Workload Characterization (IISWC)
旅行:2023 年 IEEE 工作负载特征国际研讨会 (IISWC) 的 NSF 学生旅行补助金
- 批准号:
2330213 - 财政年份:2023
- 资助金额:
$ 10万 - 项目类别:
Standard Grant
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
- 批准号:
1931978 - 财政年份:2019
- 资助金额:
$ 10万 - 项目类别:
Standard Grant
CAREER: Combating Dark Silicon through Specialization: Communication-Aware Tiled Many-Accelerator Architectures
职业:通过专业化对抗暗硅:通信感知平铺多加速器架构
- 批准号:
1619816 - 财政年份:2015
- 资助金额:
$ 10万 - 项目类别:
Continuing Grant
CAREER: Combating Dark Silicon through Specialization: Communication-Aware Tiled Many-Accelerator Architectures
职业:通过专业化对抗暗硅:通信感知平铺多加速器架构
- 批准号:
1350624 - 财政年份:2014
- 资助金额:
$ 10万 - 项目类别:
Continuing Grant
SHF: Small: AfterBurner: Efficient Performance Scaling via Post-Retirement Processing
SHF:小型:AfterBurner:通过退役后处理实现高效性能扩展
- 批准号:
1017654 - 财政年份:2010
- 资助金额:
$ 10万 - 项目类别:
Standard Grant
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