Collaborative Research: FW-HTF: Augmented Cognition for Teaching: Transforming Teacher Work with Intelligent Cognitive Assistants

合作研究:FW-HTF:增强教学认知:利用智能认知助手改变教师工作

基本信息

  • 批准号:
    1840120
  • 负责人:
  • 金额:
    $ 149.97万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2018
  • 资助国家:
    美国
  • 起止时间:
    2018-10-01 至 2024-09-30
  • 项目状态:
    已结题

项目摘要

The Future of Work at the Human-Technology Frontier (FW-HTF) is one of 10 new Big Ideas for Future Investment announced by NSF. The FW-HTF cross-directorate program aims to respond to the challenges and opportunities of the changing landscape of jobs and work by supporting convergent research. This award fulfills part of that aim. K-12 STEM teachers are critical to the US economy. With investments in teaching quality representing enormous economic value, the quality of education has been identified as a significant determinant of gross domestic product economic growth. However, the US teacher workforce is experiencing a crisis: teacher demand exceeds supply at every level, and attrition is extraordinarily high for new teachers. Further, while STEM teaching represents one of the areas of highest need, STEM teachers leave the profession at high rates. These developments call for innovative workforce augmentation technologies to improve K-12 STEM teachers' performance and quality of work-life. To address this critical national need, the project will investigate how intelligent cognitive assistants for teachers can transform teacher work to significantly increase teacher performance and teacher quality of work-life. The project centers on the design, development, and evaluation of the Intelligent Augmented Cognition for Teaching (I-ACT) framework for intelligent cognitive assistants for teachers. With a focus on assisting K-12 STEM teachers in technology-rich inquiry teaching that supports collaborative, problem-based STEM learning, I-ACT cognitive assistants provide teachers with (1) prospective pedagogical guidance (preparation support preceding classroom teaching), (2) concurrent pedagogical guidance (real-time support during classroom teaching), and (3) retrospective pedagogical guidance (reflection support within a community of practice following classroom teaching). The project will culminate with an experiment conducted with a fully implemented version of I-ACT in public middle schools in North Carolina and Indiana.The project realizes its objective through two primary thrusts. First, the research team will design and develop I-ACT cognitive assistants for K-12 STEM teachers and test them in public school classrooms. Utilizing AI-based multimodal learning analytics and a social constructivist theory of pedagogy, I-ACT cognitive assistants use machine-learned models of teacher orchestration to provide guidance throughout the full teaching workflow. I-ACT cognitive assistants operate in a tight feedback loop in which collected data will drive successive iterations of machine learning to train refined teacher support models for improved I-ACT cognitive assistant functionalities. Second, the research team will investigate how I-ACT cognitive assistants improve K-12 STEM teacher performance and teacher quality of work-life. The team will conduct focus groups, case studies, semi-structured interviews, and observations of teachers using I-ACT cognitive assistants in school implementations with middle school science teachers at the project's partner schools. The team will also conduct quasi-experimental studies to determine I-ACT impact on teacher performance and quality of work-life.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.
人类技术前沿的未来工作 (FW-HTF) 是 NSF 宣布的 10 个未来投资新大创意之一。 FW-HTF 跨部门计划旨在通过支持融合研究来应对不断变化的工作和工作格局的挑战和机遇。该奖项部分实现了这一目标。 K-12 STEM 教师对美国经济至关重要。由于对教学质量的投资代表着巨大的经济价值,教育质量已被确定为国内生产总值经济增长的重要决定因素。然而,美国教师队伍正在经历一场危机:各个级别的教师都供不应求,而且新教师的流失率极高。 此外,虽然 STEM 教学是最需要的领域之一,但 STEM 教师的离职率很高。这些发展需要创新的劳动力增强技术来提高 K-12 STEM 教师的表现和工作生活质量。为了满足这一关键的国家需求,该项目将研究教师的智能认知助手如何改变教师的工作,以显着提高教师的表现和教师的工作生活质量。该项目的重点是为教师智能认知助理设计、开发和评估智能教学增强认知(I-ACT)框架。 I-ACT 认知助理专注于协助 K-12 STEM 教师进行技术丰富的探究式教学,支持协作式、基于问题的 STEM 学习,为教师提供 (1) 前瞻性教学指导(课堂教学前的准备支持)、(2 )同时教学指导(课堂教学期间的实时支持),以及(3)回顾性教学指导(课堂教学后实践社区内的反思支持)。该项目最终将在北卡罗来纳州和印第安纳州的公立中学进行完全实施的 I-ACT 版本的实验。该项目通过两个主要目标实现其目标。首先,研究团队将为 K-12 STEM 教师设计和开发 I-ACT 认知助手,并在公立学校课堂上进行测试。 I-ACT 认知助理利用基于人工智能的多模态学习分析和社会建构主义教育学理论,利用机器学习的教师编排模型在整个教学工作流程中提供指导。 I-ACT 认知助理在紧密的反馈循环中运行,收集的数据将驱动机器学习的连续迭代,以训练完善的教师支持模型,从而改进 I-ACT 认知助理功能。其次,研究团队将研究 I-ACT 认知助理如何提高 K-12 STEM 教师的表现和教师的工作生活质量。该团队将与项目合作学校的中学科学教师一起进行焦点小组讨论、案例研究、半结构化访谈以及对在学校实施中使用 I-ACT 认知助理的教师进行观察。该团队还将进行准实验研究,以确定 I-ACT 对教师绩效和工作生活质量的影响。该奖项反映了 NSF 的法定使命,并通过使用基金会的智力优点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(11)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Human-centered Automation and Deliberately Limited Labels as Design Principles of Ambitious Learning Practices
以人为本的自动化和刻意限制的标签作为雄心勃勃的学习实践的设计原则
A Real-time Teacher Dashboard for a Game-based Collaborative Inquiry Learning Environment
基于游戏的协作探究学习环境的实时教师仪表板
Enhancing Stealth Assessment in Collaborative Game-based Learning with Multi-Task Learning.
通过多任务学习增强基于游戏的协作学习中的隐形评估。
Disruptive Talk Detection in Multi-Party Dialogue within Collaborative Learning Environments with a Regularized User-Aware Network
具有规范化用户感知网络的协作学习环境中多方对话中的破坏性谈话检测
Designing a Teacher Guidance Tool for Collaborative Inquiry Play
设计用于协作探究游戏的教师指导工具
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James Lester其他文献

Machine Learning Research 1989-90: Final Report
机器学习研究 1989-90:最终报告
  • DOI:
  • 发表时间:
    1990-09-14
  • 期刊:
  • 影响因子:
    0
  • 作者:
    A. Souther;K. Murray;James Lester;L. Acker;Erik Eilerts;David Severinsen
  • 通讯作者:
    David Severinsen
A multi-level growth modeling approach to measuring learner attention with metacognitive pedagogical agents
使用元认知教学代理衡量学习者注意力的多层次增长建模方法
  • DOI:
    10.1007/s11409-023-09336-z
  • 发表时间:
    2023-03-03
  • 期刊:
  • 影响因子:
    3.3
  • 作者:
    Megan D. Wiedbusch;James Lester;R. Azevedo
  • 通讯作者:
    R. Azevedo
Affective Dynamics and Cognition During Game-Based Learning
基于游戏的学习过程中的情感动态和认知
  • DOI:
    10.1109/taffc.2022.3210755
  • 发表时间:
    2022-10-01
  • 期刊:
  • 影响因子:
    11.2
  • 作者:
    Elizabeth B. Cloude;Daryn A. Dever;Debbie L. Hahs;Andrew Emerson;R. Azevedo;James Lester
  • 通讯作者:
    James Lester
Enhancing Stealth Assessment in Game-Based Learning Environments with Generative Zero-Shot Learning
通过生成零样本学习增强基于游戏的学习环境中的隐形评估
  • DOI:
  • 发表时间:
    2024-09-14
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Nathan L. Henderson;Halim Acosta;Wookhee Min;Bradford W. Mott;Trudi Lord;F. Reichsman;Chad Dorsey;Eric N. Wiebe;James Lester
  • 通讯作者:
    James Lester
Disruptive Talk Detection in Multi-Party Dialogue within Collaborative Learning Environments with a Regularized User-Aware Network
具有规范化用户感知网络的协作学习环境中多方对话中的破坏性谈话检测
  • DOI:
    10.18653/v1/2022.sigdial-1.47
  • 发表时间:
    2024-09-14
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Kyungjin Park;Hyunwoo Sohn;Wookhee Min;Bradford W. Mott;Krista D. Glazewski;C. Hmelo‐Silver;James Lester
  • 通讯作者:
    James Lester

James Lester的其他文献

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{{ truncateString('James Lester', 18)}}的其他基金

AI Institute for Engaged Learning
人工智能参与学习研究所
  • 批准号:
    2112635
  • 财政年份:
    2021
  • 资助金额:
    $ 149.97万
  • 项目类别:
    Cooperative Agreement
ExplainIt: Improving Student Learning with Explanation-based Classroom Response Systems
ExplainIt:通过基于解释的课堂响应系统改善学生的学习
  • 批准号:
    2111473
  • 财政年份:
    2021
  • 资助金额:
    $ 149.97万
  • 项目类别:
    Standard Grant
EAGER: Collaborative Research: Building Capacity for K-12 Artificial Intelligence Education Research
EAGER:协作研究:K-12 人工智能教育研究能力建设
  • 批准号:
    1938778
  • 财政年份:
    2019
  • 资助金额:
    $ 149.97万
  • 项目类别:
    Standard Grant
Collaborative Research: PrimaryAI: Integrating Artificial Intelligence into Upper Elementary Science with Immersive Problem-Based Learning
合作研究:PrimaryAI:通过基于问题的沉浸式学习将人工智能融入高年级基础科学
  • 批准号:
    1934153
  • 财政年份:
    2019
  • 资助金额:
    $ 149.97万
  • 项目类别:
    Standard Grant
Supporting Student Planning with Open Learner Models in Middle Grades Science
通过中年级科学的开放学习者模型支持学生规划
  • 批准号:
    1761178
  • 财政年份:
    2018
  • 资助金额:
    $ 149.97万
  • 项目类别:
    Standard Grant
Multimodal Visitor Analytics: Investigating Naturalistic Engagement with Interactive Tabletop Science Exhibits
多模式访客分析:研究交互式桌面科学展览的自然参与
  • 批准号:
    1713545
  • 财政年份:
    2018
  • 资助金额:
    $ 149.97万
  • 项目类别:
    Continuing Grant
Improving Science Problem Solving with Adaptive Game-Based Reflection Tools
使用基于游戏的自适应反思工具提高科学问题的解决能力
  • 批准号:
    1661202
  • 财政年份:
    2017
  • 资助金额:
    $ 149.97万
  • 项目类别:
    Continuing Grant
Collaborative Research: PRIME: Engaging STEM Undergraduate Students in Computer Science with Intelligent Tutoring Systems
合作研究:PRIME:利用智能辅导系统让 STEM 本科生学习计算机科学
  • 批准号:
    1626235
  • 财政年份:
    2016
  • 资助金额:
    $ 149.97万
  • 项目类别:
    Standard Grant
ENGAGE: A Game-based Curricular Strategy for Infusing Computational Thinking into Middle School Science
ENGAGE:将计算思维融入中学科学的基于游戏的课程策略
  • 批准号:
    1640141
  • 财政年份:
    2016
  • 资助金额:
    $ 149.97万
  • 项目类别:
    Standard Grant
Collaborative Research: Big Data from Small Groups: Learning Analytics and Adaptive Support in Game-based Collaborative Learning
协作研究:来自小组的大数据:基于游戏的协作学习中的学习分析和自适应支持
  • 批准号:
    1561655
  • 财政年份:
    2016
  • 资助金额:
    $ 149.97万
  • 项目类别:
    Continuing 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:
    2326169
  • 财政年份:
    2023
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    $ 149.97万
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    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
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    $ 149.97万
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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:
    2326185
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  • 批准号:
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Collaborative Research: FW-HTF-RL: Collaborative Remote Physical Examination: Transforming Medical and Nursing Practice
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