Collaborative Research: Replication of a Community-Engaged Educational Ecosystem Model in Rust Belt Cities

合作研究:在铁锈地带城市复制社区参与的教育生态系统模式

基本信息

项目摘要

This project aims to serve the national interest by improving educational environments that contribute to strengthening and diversifying the regional STEM workforce. The challenges of building a STEM workforce in shrinking Rust Belt cities are addressed by replicating and examining an effective STEM learning environment that applies and innovates high impact practices. Rust Belt cities refer to areas of the northern and Midwest United States that were once known for steel production and heavy industry. These cities often have high poverty rates and lower educational attainment in STEM, highlighting the need to bridge the divide of communities that can engage in the knowledge economy. This project aims to build STEM attraction and retention by immersing high school and college students in the STEM knowledge, skill, and capacity needs of deindustrialized cities using project-based learning, community engagement, and community development techniques. The Community-Engaged Educational Ecosystem Model (C-EEEM) was first developed and implemented in South Bend, Indiana in the Center for Civic Innovation at the University of Notre Dame. It produced a sustainable network of educational institutions and community stakeholders in delivering a high impact STEM learning environment. This project will replicate and study the C-EEEM in two other cities, Youngstown, Ohio and Erie, Pennsylvania. The ultimate goal of the project is to establish an interconnected network of STEM education initiatives to benefit the regional workforce.This project seeks to answer questions about the implementation of the C-EEEM in two sites with different community characteristics and types of anchor institutions. The project will also investigate differences in learning and dispositional outcomes in students in these new contexts. Consequently, units of analysis are nested: 1) the learning environment created by the C-EEEM and 2) the individual students. Both levels of examination use structured mixed-methods data collection. For students, the research will explore STEM motivation and persistence in STEM career pathways using theoretical framing derived from Self Determination Theory (SDT), as well as explore and expand on pilot findings regarding place attachment. For the C-EEEM learning environment, researchers will examine the core elements and critical factors specified from the pilot C-EEEM against the development of the new sites, allowing for comparison and assessment of relevance of the C-EEEM specifications in different conditions as well as identification of adaptations for generalizability. Concurrently, researchers will look for differences in student outcomes correlating with model differences. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Institutional and Community Transformation track, the program supports efforts to transform and improve STEM education across institutions of higher education and disciplinary communities.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.
该项目旨在通过改善教育环境,促进区域STEM劳动力的加强和多样化,为国家利益服务。通过复制和检查应用和创新高影响力实践的有效STEM学习环境,解决了在萎缩的铁锈地带城市建立STEM劳动力的挑战。锈带城市指的是美国北方和中西部的地区,这些地区曾经以钢铁生产和重工业而闻名。这些城市的贫困率往往很高,在科学、技术、工程和数学方面的教育程度较低,这突出表明有必要弥合能够参与知识经济的社区之间的鸿沟。该项目旨在通过使用基于项目的学习,社区参与和社区发展技术,使高中和大学生沉浸在去工业化城市的STEM知识,技能和能力需求中,从而建立STEM吸引力和保留率。社区教育生态系统模型(C-EEEM)首先在印第安纳州的南本德由圣母大学的公民创新中心开发和实施。它建立了一个可持续的教育机构和社区利益相关者网络,提供了一个高影响力的STEM学习环境。该项目将在另外两个城市,俄亥俄州的扬斯敦和宾夕法尼亚州的伊利复制和研究C-EEEM。该项目的最终目标是建立一个STEM教育举措的互联网络,以造福区域劳动力。该项目旨在回答有关在两个具有不同社区特征和锚机构类型的地点实施C-EEEM的问题。该项目还将调查学生在这些新环境中学习和性格结果的差异。因此,分析单元是嵌套的:1)由C-EEEM创建的学习环境和2)个别学生。这两个级别的检查使用结构化的混合方法的数据收集。对于学生来说,该研究将探索STEM动机和坚持STEM职业道路使用来自自我决定理论(SDT)的理论框架,以及探索和扩大关于地方依恋的试点结果。对于C-EEEM学习环境,研究人员将检查从试点C-EEEM指定的核心要素和关键因素对新网站的发展,允许在不同条件下的C-EEEM规范的相关性进行比较和评估,以及确定适应的普遍性。同时,研究人员将寻找与模型差异相关的学生成绩差异。NSF IUSE:EHR计划支持研究和开发项目,以提高所有学生STEM教育的有效性。通过机构和社区转型的轨道,该计划支持的努力,以改变和改善高等教育机构和学科社区的STEM教育.这个奖项反映了NSF的法定使命,并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估支持.

项目成果

期刊论文数量(4)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Replicating a Community-Engaged Educational Ecosystem: First-Year Findings
复制社区参与的教育生态系统:第一年的发现
  • DOI:
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Wood, D.;Marie, H.;Brockman, J. B.;Lapsley, D.;Meyers, K.;LaPorte, E. C.;Bello, K. O.
  • 通讯作者:
    Bello, K. O.
How a Civic Internship Impacts Student Professional Discernment
公民实习如何影响学生的专业辨别力
A Program to Engage Undergraduate and High School Students in Community-Based Research
让本科生和高中生参与社区研究的计划
  • DOI:
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Bello, K. O.;Aqlan, F.;Wood, D.;Brockman, J. B.;Marie, H.;Meyers, K.;Lapsley, D.
  • 通讯作者:
    Lapsley, D.
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Faisal Aqlan其他文献

Applying Product Manufacturing Techniques to Teach Data Analytics in Industrial Engineering: A Project Based Learning Experience
应用产品制造技术教授工业工程中的数据分析:基于项目的学习体验
Optimal Cholera Vaccine Allocation Policies in Developing Countries: A Case Study
发展中国家霍乱疫苗的最佳分配政策:案例研究
  • DOI:
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    0
  • 作者:
    A. G. Qasem;Abdulrahman Shamsan;Faisal Aqlan
  • 通讯作者:
    Faisal Aqlan
A risk-based optimization framework for integrated supply chains using genetic algorithm and artificial neural networks
使用遗传算法和人工神经网络的基于风险的集成供应链优化框架
Job shop planning and scheduling of reconfigurable manufacturing systems
  • DOI:
    10.1007/s12063-025-00551-2
  • 发表时间:
    2025-04-30
  • 期刊:
  • 影响因子:
    5.300
  • 作者:
    Jad Imsetif;Nasim Nezamoddini;Faisal Aqlan
  • 通讯作者:
    Faisal Aqlan
Sensor-Based Virtual Reality for Clinical Decision Support in the Assessment of Mental Disorders
基于传感器的虚拟现实用于精神障碍评估中的临床决策支持
  • DOI:
    10.1109/cog47356.2020.9231896
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Bryant Niederriter;Alice Rong;Faisal Aqlan;Hui Yang
  • 通讯作者:
    Hui Yang

Faisal Aqlan的其他文献

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{{ truncateString('Faisal Aqlan', 18)}}的其他基金

REU Site in Advanced Manufacturing and Supply Chain
REU 先进制造和供应链基地
  • 批准号:
    2244119
  • 财政年份:
    2023
  • 资助金额:
    $ 74.58万
  • 项目类别:
    Standard Grant
Collaborative Research: An Extended Reality Factory Innovation for Adaptive Problem-solving and Personalized Learning in Manufacturing Engineering
协作研究:制造工程中自适应问题解决和个性化学习的扩展现实工厂创新
  • 批准号:
    2302833
  • 财政年份:
    2023
  • 资助金额:
    $ 74.58万
  • 项目类别:
    Standard Grant
Integrating Undergraduate Learning in Engineering and Business to Improve Manufacturing Education
将工程和商业本科学习相结合以改善制造教育
  • 批准号:
    2211066
  • 财政年份:
    2022
  • 资助金额:
    $ 74.58万
  • 项目类别:
    Standard Grant
Collaborative Research: Replication of a Community-Engaged Educational Ecosystem Model in Rust Belt Cities
合作研究:在铁锈地带城市复制社区参与的教育生态系统模式
  • 批准号:
    2111377
  • 财政年份:
    2021
  • 资助金额:
    $ 74.58万
  • 项目类别:
    Continuing Grant
Research Initiation: Advanced Modeling of Metacognitive Problem Solving and Group Effectiveness in Collaborative Engineering Teams
研究启动:协作工程团队中元认知问题解决和团队有效性的高级建模
  • 批准号:
    2208680
  • 财政年份:
    2021
  • 资助金额:
    $ 74.58万
  • 项目类别:
    Standard Grant
RET Site in Manufacturing Simulation and Automation
制造仿真和自动化中的 RET 站点
  • 批准号:
    2055384
  • 财政年份:
    2021
  • 资助金额:
    $ 74.58万
  • 项目类别:
    Standard Grant
RET Site in Manufacturing Simulation and Automation
制造仿真和自动化中的 RET 站点
  • 批准号:
    2204719
  • 财政年份:
    2021
  • 资助金额:
    $ 74.58万
  • 项目类别:
    Standard Grant
GOALI: Stochastic Optimization Framework for Energy-Smart Re/Manufacturing Systems
GOALI:能源智能再造/制造系统的随机优化框架
  • 批准号:
    2038325
  • 财政年份:
    2021
  • 资助金额:
    $ 74.58万
  • 项目类别:
    Standard Grant
RET Site in Manufacturing Simulation and Automation
制造仿真和自动化中的 RET 站点
  • 批准号:
    2204601
  • 财政年份:
    2021
  • 资助金额:
    $ 74.58万
  • 项目类别:
    Standard Grant
Integrating Undergraduate Learning in Engineering and Business to Improve Manufacturing Education
将工程和商业本科学习相结合以改善制造教育
  • 批准号:
    2021303
  • 财政年份:
    2020
  • 资助金额:
    $ 74.58万
  • 项目类别:
    Standard Grant

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