Collaborative Research: Deep Insights Anytime, Anywhere (DIA2) - Central Resource for Characterizing the TUES Portfolio through Interactive Knowledge Mining and Visualizations

协作研究:随时随地深入洞察 (DIA2) - 通过交互式知识挖掘和可视化来表征 TUES 产品组合的中心资源

基本信息

  • 批准号:
    1444277
  • 负责人:
  • 金额:
    $ 37.23万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2014
  • 资助国家:
    美国
  • 起止时间:
    2014-04-18 至 2017-08-31
  • 项目状态:
    已结题

项目摘要

LEAD INSTITUTION: Purdue UniversityCOLLABORATORS: Arizona State University, Stanford University and Virginia Polytechnic Institute and State UniversityPROJECT DESCRIPTIONThis TUES Central Resource Project is designed to help those engaged in improving STEM education to synthesize knowledge produced through NSF investments through a web-based knowledge mining and interactive visualization platform. The Deep Insights Anytime, Anywhere (DIA2) project allows users (e.g., current and potential principle investigators, NSF/TUES program staff, and administrators at academic institutions) to interactively mine, synthesize, and visualize data at a scale that is not possible with currently available tools. DIA2 is based upon a more narrowly scoped Interactive Knowledge Networks for Engineering Education Research (iKNEER) prototype that targeted the engineering education research community, expanding the functionality by an order of magnitude in scale; integrating newer approaches in data mining and visualization into a fully deployed system. The project has three major goals: (1) Empower the TUES community to leverage TUES investments by understanding the knowledge hidden within its networks; (2) Develop and apply cutting-edge, large-scale knowledge mining and visualization techniques for characterizing the portfolio of TUES and predecessor programs; and (3) Leverage social media optimization and integration to catalyze diffusion of TUES innovations, build a community and sustain the project impact. DIA2 enables users to explore massive amounts of data and make sense of it using a highly intuitive process. The system development approach combines theories of user-centered design, large-scale data mining, community formation, social network analysis, and interactive visualization. The project's evaluation plan includes both formative and summative approaches for documenting, testing, measuring, and sharing community outcomes, internal team working, and system performance.BROADER SIGNIFICANCEDIA2 offers a framework for understanding and characterizing the TUES program along with its predecessor programs. It makes data available to a large community of TUES users and allows them to analyze the portfolio to garner an understanding of how ideas are adopted by others in the community. It allows current and future PIs, NSF program officers and administrators at academic institutions to identify best practices and explore synergistic projects in their environments. Since DIA2 is a knowledge portal, it provides a unique opportunity to showcase work undertaken at underserved and underprivileged institutions in new and novel ways. The project team is employing a methodology that attempts to understand the needs of these communities, in order to better address the DIA2 system design requirements. Ultimately, DIA2 is focused on providing knowledge that will allow the community to increase the impact of NSF STEM investments that improve student learning.
牵头机构:普渡大学 合作者:亚利桑那州立大学、斯坦福大学、弗吉尼亚理工学院和州立大学 项目描述 该 TUES 中央资源项目旨在帮助那些致力于改进 STEM 教育的人,通过基于网络的知识挖掘和互动,综合 NSF 投资产生的知识可视化平台。随时随地深度洞察 (DIA2) 项目允许用户(例如,当前和潜在的主要研究人员、NSF/TUES 项目工作人员和学术机构的管理人员)以交互方式挖掘、合成和可视化数据,其规模是当前可用的工具。 DIA2 基于范围更窄的工程教育研究互动知识网络 (iKNEER) 原型,该原型针对工程教育研究社区,将功能扩展了一个数量级;将数据挖掘和可视化方面的新方法集成到完全部署的系统中。该项目有三个主要目标:(1)通过了解隐藏在其网络中的知识,使 TUES 社区能够利用 TUES 投资; (2) 开发并应用尖端的大规模知识挖掘和可视化技术来表征 TUES 和前身项目的组合; (3) 利用社交媒体优化和整合来促进 TUES 创新的传播、建立社区并维持项目影响。 DIA2 使用户能够探索大量数据并使用高度直观的过程理解它。系统开发方法结合了以用户为中心的设计、大规模数据挖掘、社区形成、社交网络分析和交互式可视化的理论。该项目的评估计划包括用于记录、测试、衡量和共享社区成果、内部团队工作和系统性能的形成性和总结性方法。更广泛的意义DIA2 提供了一个框架,用于理解和描述 TUES 计划及其前身计划。它向 TUES 用户的大型社区提供数据,并允许他们分析投资组合,以了解社区中其他人如何采用想法。它允许当前和未来的 PI、NSF 项目官员和学术机构的管理人员确定最佳实践并探索其环境中的协同项目。由于 DIA2 是一个知识门户,它提供了一个独特的机会,以新颖的方式展示在服务不足和贫困机构开展的工作。项目团队正在采用一种方法来尝试了解这些社区的需求,以便更好地满足 DIA2 系统设计要求。最终,DIA2 致力于提供知识,使社区能够提高 NSF STEM 投资的影响力,从而改善学生的学习。

项目成果

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Aditya Johri其他文献

Representational literacy and participatory learning in large engineering classes using pen-based computing
使用笔式计算在大型工程课程中进行表征素养和参与式学习
Teaching Multidimensional Ethical Decision-Making Through a Role-Play Case Study
通过角色扮演案例研究教授多维道德决策
A Systematic Review of AI Literacy Conceptualization, Constructs, and Implementation and Assessment Efforts (2019-2023)
人工智能素养概念化、构建、实施和评估工作的系统回顾(2019-2023)
  • DOI:
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    3.6
  • 作者:
    Omaima Almatrafi;Aditya Johri;Hyuna Lee
  • 通讯作者:
    Hyuna Lee
Learning Analytics in Higher Education
高等教育中的学习分析
  • DOI:
  • 发表时间:
    2017
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Jaime Lester;Carrie Klein;H. Rangwala;Aditya Johri
  • 通讯作者:
    Aditya Johri
Generative Artificial Intelligence in Higher Education: Evidence from an Analysis of Institutional Policies and Guidelines
高等教育中的生成人工智能:来自机构政策和指南分析的证据
  • DOI:
    10.48550/arxiv.2402.01659
  • 发表时间:
    2024-01-12
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Nora McDonald;Aditya Johri;Areej Ali;Aayushi Hingle
  • 通讯作者:
    Aayushi Hingle

Aditya Johri的其他文献

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{{ truncateString('Aditya Johri', 18)}}的其他基金

Education DCL: EAGER: An Embedded Case Study Approach for Broadening Students' Mindset for Ethical and Responsible Cybersecurity
教育 DCL:EAGER:一种嵌入式案例研究方法,用于拓宽学生道德和负责任的网络安全思维
  • 批准号:
    2335636
  • 财政年份:
    2024
  • 资助金额:
    $ 37.23万
  • 项目类别:
    Standard Grant
EAGER: Impact of Generative Artificial Intelligence (GAI) on Engineering Education Practices
EAGER:生成人工智能 (GAI) 对工程教育实践的影响
  • 批准号:
    2319137
  • 财政年份:
    2023
  • 资助金额:
    $ 37.23万
  • 项目类别:
    Standard Grant
Workshop: ProVis-EER: Developing Professional Vision into Empirical Practices within Engineering Education Research (EER) though Digital Apprenticeship
研讨会:ProVis-EER:通过数字学徒制将专业愿景发展为工程教育研究 (EER) 中的实证实践
  • 批准号:
    2112775
  • 财政年份:
    2021
  • 资助金额:
    $ 37.23万
  • 项目类别:
    Standard Grant
Collaborative EAGER: Novel Ethnographic Investigations of Engineering Workplaces to Advance Theory and Research Methods for Preparing the Future Workforce
协作 EAGER:对工程工作场所进行新颖的民族志调查,以推进为未来劳动力做好准备的理论和研究方法
  • 批准号:
    1939105
  • 财政年份:
    2020
  • 资助金额:
    $ 37.23万
  • 项目类别:
    Standard Grant
Workshop: Building an Inclusive Foundation of Engineering Education Research Scholarship for Future Growth
研讨会:为未来发展建立工程教育研究奖学金的包容性基础
  • 批准号:
    1941186
  • 财政年份:
    2020
  • 资助金额:
    $ 37.23万
  • 项目类别:
    Standard Grant
Situated Algorithmic Thinking: Preparing the Future Computing Workforce for Ethical Decision-Making through Interactive Case Studies
情境算法思维:通过交互式案例研究为未来的计算劳动力进行道德决策做好准备
  • 批准号:
    1937950
  • 财政年份:
    2020
  • 资助金额:
    $ 37.23万
  • 项目类别:
    Standard Grant
Deeper Learning of Data Science (DLDS): Studying Real-world Experiences of Engineering Professionals to Prepare the Future Workforce
数据科学深度学习 (DLDS):研究工程专业人员的真实经验,为未来的劳动力做好准备
  • 批准号:
    1712129
  • 财政年份:
    2017
  • 资助金额:
    $ 37.23万
  • 项目类别:
    Standard Grant
EAGER: Social Media Participation as Indicator of Actors, Awareness, Attitudes, and Activities Related to STEM Education
EAGER:社交媒体参与度作为与 STEM 教育相关的参与者、意识、态度和活动的指标
  • 批准号:
    1707837
  • 财政年份:
    2017
  • 资助金额:
    $ 37.23万
  • 项目类别:
    Standard Grant
RAPID: Collaborative Research: Technology Adoption during Environmental Jolts: Mobile Phone Use and Digital Services Appropriation during India's Demonetization Crisis
RAPID:合作研究:环境动荡期间的技术采用:印度废钞危机期间的手机使用和数字服务挪用
  • 批准号:
    1733634
  • 财政年份:
    2017
  • 资助金额:
    $ 37.23万
  • 项目类别:
    Standard Grant
Collaborative Research (EAGER): Data Ecosystem for Catalyzing Transformative Research in Engineering Education
协作研究(EAGER):促进工程教育变革性研究的数据生态系统
  • 批准号:
    1306373
  • 财政年份:
    2014
  • 资助金额:
    $ 37.23万
  • 项目类别:
    Standard Grant

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