Collaborative Research [FW-HTF-RL]: Enhancing the Future of Teacher Practice via AI-enabled Formative Feedback for Job-Embedded Learning

协作研究 [FW-HTF-RL]:通过人工智能支持的工作嵌入学习形成性反馈增强教师实践的未来

基本信息

  • 批准号:
    2326169
  • 负责人:
  • 金额:
    $ 131.64万
  • 依托单位:
  • 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:
    2023-11-01 至 2027-10-31
  • 项目状态:
    未结题

项目摘要

This project envisions a future of work where advanced technologies provide automated, job-embedded, individualized feedback to drive professional learning of the future worker. To achieve this goal, it addresses a fundamental question: Are evaluative or non-evaluative feedback systems more effective in driving professional learning? This question will be tested on professionals where objective, fine-grained feedback is especially critical to improvement--the teaching professions. This research will be situated within English and language arts (ELA) instruction in middle- and high school classrooms, where underperformance and inequality in literacy outcomes are persistent problems facing the U.S. Current methods of supporting teacher learning through feedback are sparse, cumbersome, subjective, and evaluative. Thus, a major reconceptualization is needed to provide feedback mechanisms that- meaningfully affect teacher practice and are accessible to all. In partnership with TeachFX, an industry leader in technology-enabled instructional feedback, this project will work with teachers to design and test systems of automated feedback. Insights from the study will lead to feedback systems that empower teaching professionals, generate continued professional learning, and ultimately, increase student achievement. The scientific merits of the project are centered around the foundational question of whether instruction can be construed entirely along a continuum of ineffective to more effective practice. The hypothesis is that the richest opportunities for on-the-job feedback in the professions are agnostic technologically-driven feedback systems, which offer choice, withhold evaluation, make room for varied teacher practices, and promote a greater locus of control. The project has several goals towards testing this hypothesis, including: (1) to work with a diverse panel of teachers to design and refine automated feedback systems; (2) to enhance the robustness and fairness of computational models that underlie automated feedback; and finally, (3) to test fundamental design principles of professional feedback. The project will begin by leveraging TeachFX's corpus of instructional observations from approximately 5,000 educators to develop automated, robust, accurate, unbiased, generalizable, and interpretable feedback models. Next, working with teacher participants, the feedback interfaces will be co-designed and iteratively refined. Further, a variety of observational and survey-based measures will be used to assess teacher responsiveness to feedback. The project will culminate in a longitudinal, experimental study contrasting the effects of evaluative- with non-evaluative feedback on teacher learning, empowerment, and student achievement outcomes with a sample of 300 teachers. The study will create a blueprint for effective and efficient professional observation and feedback, and working systems to implement that feedback, driving the next generation of advancement in the sciences, technology, engineering, and mathematics.This project is supported by two programs at NSF: Primary support comes from the Future of Work at the Human-Technology Frontier program which supports multi-disciplinary research to sustain economic competitiveness, promote worker well-being, lifelong and pervasive learning, and quality of life, and illuminate the emerging social and economic context and drivers of innovations that are shaping the future of jobs and work. Additional support is from the Discovery Research preK-12 program (DRK-12) which seeks to significantly enhance the learning and teaching of science, technology, engineering, and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models, and tools.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.
该项目设想了未来的工作,先进的技术提供自动化,工作嵌入式,个性化的反馈,以推动未来工人的专业学习。为了实现这一目标,它解决了一个基本问题:评价或非评价反馈系统更有效地推动专业学习?这个问题将在专业人员中进行测试,在这些专业人员中,客观、精细的反馈对改进尤为重要--教师职业。这项研究将位于英语和语言艺术(ELA)教学中的初中和高中课堂,在识字成果的表现不佳和不平等是美国目前通过反馈支持教师学习的方法所面临的持久性问题是稀疏,繁琐,主观和评价。因此,一个重大的重新概念化是必要的,以提供反馈机制,有意义地影响教师的做法,并提供给所有人。该项目与TeachFX合作,TeachFX是技术支持的教学反馈的行业领导者,该项目将与教师合作设计和测试自动反馈系统。从研究的见解将导致反馈系统,使教学专业人员,产生持续的专业学习,并最终提高学生的成绩。该项目的科学价值集中在一个基本问题上,即教学是否可以完全沿着一个从无效到更有效的实践的连续体来解释。该假设是,最丰富的机会,在职反馈的专业是不可知论的技术驱动的反馈系统,提供选择,保留评价,为不同的教师的做法,并促进更大的控制点。该项目有几个目标来测试这一假设,包括:(1)与不同的教师小组合作,设计和完善自动反馈系统;(2)增强自动反馈基础计算模型的鲁棒性和公平性;最后,(3)测试专业反馈的基本设计原则。该项目将开始利用TeachFX的语料库的教学观察约5,000名教育工作者开发自动化,强大的,准确的,公正的,可推广的,可解释的反馈模型。接下来,与教师参与者合作,反馈界面将被共同设计和迭代改进。此外,各种观察和调查为基础的措施将被用来评估教师对反馈的反应。该项目将最终在一个纵向的,实验性的研究对比评价性的效果-与非评价性的反馈对教师的学习,授权,和学生的成绩成果与300名教师的样本。该研究将为有效和高效的专业观察和反馈以及实施该反馈的工作系统创建蓝图,推动下一代科学,技术,工程和数学的进步。该项目由NSF的两个项目支持:主要支持来自未来的工作在人类技术前沿计划,支持多学科研究,以维持经济竞争力,促进工人福祉、终身和普及学习以及生活质量,并阐明正在塑造就业和工作未来的新兴社会和经济背景以及创新驱动因素。额外的支持来自发现研究preK-12计划(DRK-12),该计划旨在通过研究和开发创新资源,模型,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进行评估,被认为值得支持的搜索.

项目成果

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Sean Kelly其他文献

Specular highlights as a guide to perceptual content
镜面高光作为感知内容的指南
  • DOI:
  • 发表时间:
    2008
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Alva Noë;Sean Kelly;Bruce W. Brower;A. Clark
  • 通讯作者:
    A. Clark
Modeling Classroom Discourse: Do Models of Predicting Dialogic Instruction Properties Generalize across Populations?
课堂话语建模:预测对话教学属性的模型是否可以在人群中推广?
  • DOI:
  • 发表时间:
    2015
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Borhan Samei;A. Olney;Sean Kelly;M. Nystrand;S. D’Mello;Nathaniel Blanchard;A. Graesser
  • 通讯作者:
    A. Graesser
AC 2010-622: PREDICTION OF SOPHOMORE RETENTION
AC 2010-622:大二学生保留率预测
  • DOI:
  • 发表时间:
    2010
  • 期刊:
  • 影响因子:
    0
  • 作者:
    C. Pieronek;Kerry Meyers;Sean Kelly;L. H. Mcwilliams
  • 通讯作者:
    L. H. Mcwilliams
Autoerotic nonlethal filmed hangings: a case series and comments on the estimation of the time to irreversibility in hanging.
自体性非致命性绞刑:一系列案例和对绞刑不可逆转时间估计的评论。
Hydrographic maintenance of deep anoxia in a tidally influenced saline lagoon
受潮汐影响的咸水湖深度缺氧的水文维持
  • DOI:
    10.1071/mf17199
  • 发表时间:
    2017
  • 期刊:
  • 影响因子:
    1.8
  • 作者:
    Sean Kelly;E. Eyto;M. Dillane;R. Poole;G. Brett;M. White
  • 通讯作者:
    M. White

Sean Kelly的其他文献

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{{ truncateString('Sean Kelly', 18)}}的其他基金

SBIR Phase II: Artificially Intelligent Solution to Maximize Value Creation and Upcycling Potential of Aluminum Scrap
SBIR 第二阶段:人工智能解决方案,最大限度地提高废铝的价值创造和升级回收潜力
  • 批准号:
    2026106
  • 财政年份:
    2020
  • 资助金额:
    $ 131.64万
  • 项目类别:
    Cooperative Agreement
SBIR Phase I: Artificially Intelligent Solution to Maximize Value Creation and Upcycling Potential of Aluminum Scrap
SBIR 第一阶段:人工智能解决方案,最大限度地提高废铝的价值创造和升级回收潜力
  • 批准号:
    1843858
  • 财政年份:
    2019
  • 资助金额:
    $ 131.64万
  • 项目类别:
    Standard Grant
CREST-Postdoctoral Research Fellowship: Cross-Ecosystem Interactions and the Transport of Aquatic Contaminants to Terrestrial Food Webs within Mangrove Forests
CREST-博士后研究奖学金:跨生态系统相互作用和水生污染物向红树林内陆地食物网的传输
  • 批准号:
    1914750
  • 财政年份:
    2019
  • 资助金额:
    $ 131.64万
  • 项目类别:
    Standard Grant
EXP: Collaborative Research: Cyber-enabled Teacher Discourse Analytics to Empower Teacher Learning
EXP:协作研究:基于网络的教师话语分析,增强教师学习能力
  • 批准号:
    1735785
  • 财政年份:
    2017
  • 资助金额:
    $ 131.64万
  • 项目类别:
    Standard Grant
The material basis of function in the brain: Connections to philosophy of biology and phenomenology
大脑功能的物质基础:与生物学哲学和现象学的联系
  • 批准号:
    1026632
  • 财政年份:
    2010
  • 资助金额:
    $ 131.64万
  • 项目类别:
    Standard Grant

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相似海外基金

Collaborative Research [FW-HTF-RL]: Enhancing the Future of Teacher Practice via AI-enabled Formative Feedback for Job-Embedded Learning
协作研究 [FW-HTF-RL]:通过人工智能支持的工作嵌入学习形成性反馈增强教师实践的未来
  • 批准号:
    2326170
  • 财政年份:
    2023
  • 资助金额:
    $ 131.64万
  • 项目类别:
    Standard Grant
Collaborative Research: FW-HTF-RM: Human-in-the-Lead Construction Robotics: Future-Proofing Framing Craft Workers in Industrialized Construction
合作研究:FW-HTF-RM:人类主导的建筑机器人:工业化建筑中面向未来的框架工艺工人
  • 批准号:
    2326160
  • 财政年份:
    2023
  • 资助金额:
    $ 131.64万
  • 项目类别:
    Standard Grant
Collaborative Research: FW-HTF-RL: Trapeze: Responsible AI-assisted Talent Acquisition for HR Specialists
合作研究:FW-HTF-RL:Trapeze:负责任的人工智能辅助人力资源专家人才获取
  • 批准号:
    2326193
  • 财政年份:
    2023
  • 资助金额:
    $ 131.64万
  • 项目类别:
    Standard Grant
Collaborative Research: FW-HTF-RM: Artificial Intelligence Technology for Future Music Performers
合作研究:FW-HTF-RM:未来音乐表演者的人工智能技术
  • 批准号:
    2326198
  • 财政年份:
    2023
  • 资助金额:
    $ 131.64万
  • 项目类别:
    Standard Grant
FW-HTF-RL/Collaborative Research: Future of Digital Facility Management (Future of DFM)
FW-HTF-RL/协作研究:数字设施管理的未来(DFM 的未来)
  • 批准号:
    2326407
  • 财政年份:
    2023
  • 资助金额:
    $ 131.64万
  • 项目类别:
    Standard Grant
FW-HTF-RL/Collaborative Research: Future of Digital Facility Management (Future of DFM)
FW-HTF-RL/协作研究:数字设施管理的未来(DFM 的未来)
  • 批准号:
    2326408
  • 财政年份:
    2023
  • 资助金额:
    $ 131.64万
  • 项目类别:
    Standard Grant
Collaborative Research: FW-HTF-R: Future of Construction Workplace Health Monitoring
合作研究:FW-HTF-R:建筑工作场所健康监测的未来
  • 批准号:
    2401745
  • 财政年份:
    2023
  • 资助金额:
    $ 131.64万
  • 项目类别:
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Collaborative Research: FW-HTF-RL: Understanding the Ethics, Development, Design, and Integration of Interactive Artificial Intelligence Teammates in Future Mental Health Work
合作研究:FW-HTF-RL:了解未来心理健康工作中交互式人工智能队友的伦理、开发、设计和整合
  • 批准号:
    2326146
  • 财政年份:
    2023
  • 资助金额:
    $ 131.64万
  • 项目类别:
    Standard Grant
FW-HTF-RL/Collaborative Research: The Future of Aviation Inspection: Artificial Intelligence and Mixed Reality as Agents of Transformation
FW-HTF-RL/合作研究:航空检查的未来:人工智能和混合现实作为转型的推动者
  • 批准号:
    2326186
  • 财政年份:
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  • 资助金额:
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Collaborative Research: FW-HTF-RM: Human-in-the-Lead Construction Robotics: Future-Proofing Framing Craft Workers in Industrialized Construction
合作研究:FW-HTF-RM:人类主导的建筑机器人:工业化建筑中面向未来的框架工艺工人
  • 批准号:
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  • 财政年份:
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