STEM TRansfer Students Opportunity for Nurtured Growth (STRONG)

STEM 转学生提供培育成长的机会(强)

基本信息

项目摘要

With funding from the National Science Foundation's Scholarships in Science, Technology, Engineering and Mathematics (S-STEM) program, the STEM TRansfer Students Opportunity for Nurtured Growth (STRONG) is providing support to low-income students with demonstrated financial need and academic promise to succeed in STEM disciplines at the University of Central Florida (UCF). This S-STEM Track 2 project is funding up to 36 scholarships over 5 years for transfer students who are pursuing bachelor's degrees in STEM. Compared to first time in college (FTIC) students who begin their academic careers at a four-year institution, transfer students generally find the path to obtaining a bachelor's degree more challenging, and they are significantly less likely to graduate than their FTIC counterparts. Finding ways to improve the educational experiences of these students is an urgent national priority. The aim of UCF's STRONG project is to substantially increase retention, graduation rates, and career success of STEM transfer students. This goal is achieved in part by giving each student a role in various STEM activities within a community of high-achieving scholars, who are majoring in similar fields. Providing financial support, mentorship, and opportunities for academic engagement outside the classroom promises to reduce the obstacles students face, keep them interested in their field of study, and motivate them to excel academically. A thorough study of how the program affects its participants will maximize the impact of the intervention and informs future efforts to improve the educational experiences of STEM transfer students.The UCF STRONG project consists of three components, which are all essential for reaching the program goal of increasing retention, graduation rates, and career success of STEM transfers. The first component is to design, implement, and improve a high-accuracy prediction model for identifying high-risk STEM transfers. Using known information about transfers, along with statistical analysis and machine learning, it will establish a means of predicting which students are least likely to complete a STEM degree, thereby helping UCF to know which students will benefit most from STRONG. The second component is to increase retention of transfers in STEM fields. STRONG scholars will be selected from financially needy incoming transfers who have been predicted to be unlikely to graduate, with the purpose of retaining them through graduation due to their participation in STRONG. This outcome is an expected result of increased student success through stimulation of academic integration and a cultivation of proficiency in their chosen fields. More specifically, each student is given a role within a community of like-minded students from closely related fields; they also meet regularly with mentors and participate in STEM activities that promote their success. The third component is to learn how the interventions influence STEM transfer students' ability to become active participants in their own learning. The project team will study the effects of the intervention on metacognition, motivation, and behavior, which are the three pillars of Self-Regulated Learning and are essential for student success.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.
在国家科学基金会科学、技术、工程和数学奖学金(S-STEM)项目的资助下,STEM转学学生培育成长机会(STRONG)正在为有经济需求和学术承诺能够在中佛罗里达大学(UCF)STEM学科取得成功的低收入学生提供支持。S-STEM Track 2项目将在5年内为正在攻读STEM学士学位的转校生提供多达36个奖学金。与在四年制院校开始学业生涯的第一次上大学(FTIC)学生相比,转学学生通常发现获得学士学位的道路更具挑战性,他们毕业的可能性明显低于FTIC同龄人。寻找改善这些学生的教育体验的方法是国家的当务之急。UCF STRONG项目的目标是大幅提高STEM转校生的保留率、毕业率和职业成就率。这一目标的实现部分是通过让每个学生在类似领域的高成就学者社区内的各种STEM活动中发挥作用。为学生提供经济支持、辅导和课外参与的机会,可以减少学生面临的障碍,保持他们对学习领域的兴趣,并激励他们在学业上出类拔萃。深入研究该计划如何影响其参与者,将最大限度地发挥干预措施的影响,并为未来改善STEM转学学生的教育体验提供信息。UCF Strong项目由三个组成部分组成,这三个部分对于实现提高STEM转学的保留率、毕业率和职业成功都是必不可少的。第一个组成部分是设计、实施和改进用于识别高风险STEM转移的高精度预测模型。使用有关转学的已知信息,以及统计分析和机器学习,它将建立一种方法来预测哪些学生最不可能完成STEM学位,从而帮助UCF了解哪些学生将从Strong中受益最大。第二个组成部分是增加STEM领域的转让保留率。优秀的学者将从经济上有需求的转学人员中挑选出来,这些人预计不太可能毕业,目的是因为他们参与了STRONG项目,所以可以留住他们直到毕业。这一结果是通过促进学术融合和培养他们所选择的领域的熟练程度来提高学生成功的预期结果。更具体地说,每个学生都被分配到一个由来自密切相关领域的志同道合的学生组成的社区中的一个角色;他们还定期与导师会面,并参加STEM活动,以促进他们的成功。第三个部分是了解干预措施如何影响STEM转学学生成为自己学习的积极参与者的能力。该项目团队将研究干预对元认知、动机和行为的影响,这三大支柱是自主学习的三大支柱,对学生的成功至关重要。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势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(0)
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Mubarak Shah其他文献

CodaMal: Contrastive Domain Adaptation for Malaria Detection in Low-Cost Microscopes
CodaMal:低成本显微镜中疟疾检测的对比域适应
  • DOI:
    10.48550/arxiv.2402.10478
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    I. Dave;Tristan de Blegiers;Chen Chen;Mubarak Shah
  • 通讯作者:
    Mubarak Shah
Lung-CADex: Fully automatic Zero-Shot Detection and Classification of Lung Nodules in Thoracic CT Images
Lung-CADex:胸部 CT 图像中肺结节的全自动零样本检测和分类
  • DOI:
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Furqan Shaukat;Syed Muhammad Anwar;Abhijeet Parida;Van Lam;M. Linguraru;Mubarak Shah
  • 通讯作者:
    Mubarak Shah
Out-of-Distribution Detection Using Union of 1 -Dimensional Subspaces: Supplementary Materials
使用一维子空间并集进行分布外检测:补充材料
  • DOI:
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Alireza Zaeemzadeh;N. Bisagno;Zeno Sambugaro;Nicola Conci;Nazanin Rahnavard;Mubarak Shah
  • 通讯作者:
    Mubarak Shah
Robust Image Geolocalization
强大的图像地理定位
  • DOI:
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Akhil Arularasu;P. Kulkarni;Gaurav Kumar;Mubarak Shah
  • 通讯作者:
    Mubarak Shah
Machine Vision and Applications Understanding Human Behavior from Motion Imagery
机器视觉和应用从运动图像理解人类行为
  • DOI:
  • 发表时间:
    2003
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Mubarak Shah
  • 通讯作者:
    Mubarak Shah

Mubarak Shah的其他文献

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{{ truncateString('Mubarak Shah', 18)}}的其他基金

REU Site: Research Experience for Undergraduates in Computer Vision
REU 网站:计算机视觉本科生的研究经验
  • 批准号:
    2349386
  • 财政年份:
    2024
  • 资助金额:
    $ 100万
  • 项目类别:
    Standard Grant
Scholarships, Academic, and Social Supports to Provide Low-Income Transfers Students Opportunities for Nurtured Growth in AI
奖学金、学术和社会支持为低收入转学生提供促进人工智能发展的机会
  • 批准号:
    2321986
  • 财政年份:
    2024
  • 资助金额:
    $ 100万
  • 项目类别:
    Continuing Grant
REU Site: Research Experience for Undergraduates in Computer Vision
REU 网站:计算机视觉本科生的研究经验
  • 批准号:
    2050731
  • 财政年份:
    2021
  • 资助金额:
    $ 100万
  • 项目类别:
    Standard Grant
CRI: II-New: Cognitive Mechanisms and Computational Modeling of Gaze Control During Scene Free Viewing, Visual Search, and Daily Tasks
CRI:II-新:场景自由观看、视觉搜索和日常任务期间注视控制的认知机制和计算模型
  • 批准号:
    1823276
  • 财政年份:
    2018
  • 资助金额:
    $ 100万
  • 项目类别:
    Standard Grant
REU Site: Research Experience for Undergraduates in Computer Vision
REU 网站:计算机视觉本科生的研究经验
  • 批准号:
    1757858
  • 财政年份:
    2018
  • 资助金额:
    $ 100万
  • 项目类别:
    Standard Grant
BIGDATA: IA: Distributed Semi-Supervised Training of Deep Models and Its Applications in Video Understanding
BIGDATA:IA:深度模型的分布式半监督训练及其在视频理解中的应用
  • 批准号:
    1741431
  • 财政年份:
    2017
  • 资助金额:
    $ 100万
  • 项目类别:
    Standard Grant
RET Site: Research Experiences for Teachers in Computer Vision and Bio-Medical Imaging
RET 网站:计算机视觉和生物医学成像教师的研究经验
  • 批准号:
    1542439
  • 财政年份:
    2016
  • 资助金额:
    $ 100万
  • 项目类别:
    Standard Grant
REU Site: NSF Research Experience for Undergraduates in Computer Vision
REU 网站:NSF 计算机视觉本科生研究经验
  • 批准号:
    1461121
  • 财政年份:
    2015
  • 资助金额:
    $ 100万
  • 项目类别:
    Standard Grant
REU Site: Research Experience for Undergraduates in Computer Vision
REU 网站:计算机视觉本科生的研究经验
  • 批准号:
    1156990
  • 财政年份:
    2012
  • 资助金额:
    $ 100万
  • 项目类别:
    Standard Grant
Students Actualizing Talent at Education?s Subsequent Stages (STATESS)
学生在教育后续阶段实现才能(STATESS)
  • 批准号:
    0966249
  • 财政年份:
    2010
  • 资助金额:
    $ 100万
  • 项目类别:
    Standard Grant

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具有时序迁移能力的Spiking-Transfer learning (脉冲-迁移学习)方法研究
  • 批准号:
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  • 批准号:
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