BPC-A: Data Alliance on Persistence and Perception in Computing (DAPPIC)
BPC-A: Data Alliance on Persistence and Perception in Computing (DAPPIC)
批准号:
2216629
负责人:
Carla Brodley
金额:
$132.3万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2022
资助国家:
美国
项目状态:
已结题
起止时间:
2022-10-01 至 2024-09-30
中文摘要
东北大学的包容性计算中心(CIC),计算研究协会(CRA)的研究管道评估中心(CERP)和28所大学合作,提高定量和定性数据的可用性和使用,以支持各个学校和整个部门扩大本科计算机教育参与的努力。大学领导非常积极地增加计算机领域历史上少数群体的代表性。然而,虽然已经开发和评估了扩大计算参与(BPC)的最佳实践,但并不总是清楚哪些实践符合机构的特定挑战。为了准确诊断包容性障碍并评估干预措施的影响,领导者需要可靠地获取详细的入学、留校和毕业数据,以及关于不同身份学生经历的定性数据。该合作解决了改善定量和定性数据的可用性和使用的需要,以支持个别学校和整个行业。CIC逐学期、逐班收集学生入学、留校和毕业的数据,并按人口统计进行分类。目前,中投正在与57所学校开展这项工作。然后,这些数据在仪表板上显示出来,用户可以在不同的身份交叉点之间进行比较,比较各个术语的变化和趋势,并与57所合作学校的平均水平进行基准比较。CRA的CERP从参与数据伙伴调查(DBS)的150多个计算部门收集和传播数据。在本科阶段,星展银行收集有关学习者的学术和人口背景、职业道路以及与计算机招聘、保留和持久性相关的各种指标的信息。该联盟将整合这两个数据集,并为BPC中数据的使用设定新的标准。此外,该联盟将与国家学生信息中心(NCS)的中学后数据合作伙伴关系(PDP)合作,该合作伙伴关系提供深入的仪表板,使机构能够以汇总和分类的形式可视化学生的成果。我们看到了PDP在2022年的优先事项,即开发特定学科的仪表板,将我们与BPC数据的共同经验以及强大的“客户声音”观点结合起来。具体活动有:1)为CIC仪表板添加新功能(跨交叉身份的比较能力,针对不同的对等组进行基准测试,并连接到外部数据源,如NCES的IPEDS);2)整合CIC/DBS数据;3)评估整合并传播学习成果;4)探索与NSC的潜在整合;5)为实现一种提供长期可持续性和规模的模式而制定计划。我们将与28所学校一起评估该方法的有效性,所有这些学校都参与了CIC的数据收集和CERP的DBS。该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
Northeastern University’s Center for Inclusive Computing (the CIC), the Computing Research Association’s (CRA) Center for Evaluating the Research Pipeline (CERP) and 28 individual universities collaborate to improve the availability and use of quantitative and qualitative data to support the efforts of individual schools and the sector overall in broadening participation inundergraduate computing education. University leaders are highly motivated to increase the representation of populations historically minoritized in computing. However, while best practices for broadening participation in computing (BPC) have been developed and evaluated, it is not always clear which practices align with an institution’s particular challenges. To accurately diagnose barriers to inclusion and to assess the impact of interventions, leaders need reliable access to detailed enrollment, retention and graduation data as well as qualitative data on the experience of students of different identities. The collaboration addresses the need to improve the availability and use of quantitative and qualitative data to support individual schools and the sector as a whole. The CIC collects term by term, class by class data on student enrollment, retention, and graduation, disaggregated by demographics. At present the CIC does this work with 57 schools. This data is then visualized in dashboards that allow the user to compare between different intersections of identities, compare changes and trends across terms, and benchmark against an average of the 57 partner schools. CRA’s CERP collects and disseminates data from 150+ computing departments that participate in the Data Buddies Survey (DBS). At the undergraduate level, DBS gathers information about the academic and demographic background of learners, career pathways, as well as various indicators related to the recruitment, retention, and persistence in computing. The Alliance will integrate the two datasets and set a new standard for the usage of data in BPC. Additionally, the Alliance will work with the National Student Clearinghouse’s (NCS) Post-secondary Data Partnership (PDP), which offers in-depth dashboards that allow institutions to visualize student outcomes in aggregated and disaggregated forms. We see a point-in-time opportunity with the PDP’s priority in 2022 to develop discipline-specific dashboards to bring our collective experience working with BPC data as well as a robust “voice of the client” perspective. The specific activities are: 1) add new functionality to CIC dashboards (ability to compare across intersectional identities, to benchmark against different peer groups, and to connect to outside data sources such as NCES’ IPEDS); 2) integrate CIC/DBS data; 3) evaluate the integration and disseminate learnings; 4) explore potential integration with NSC; and 5) plan for achieving a model that provides long-term sustainability and scale. We will evaluate the efficacy of the approach with 28 schools, all of which participate in both the CIC’s data collection and CERP’s DBS.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.
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CUE-M: LEVEL UP: Charting a Pathway toward Inclusive Computing
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批准号:2246079
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项目类别:Standard Grant
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资助金额:$100.0万
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财政年份:2023
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负责人:Carla Brodley
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批准号:2208797
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BPC-AE: An Extension to Widening the Research Pipeline
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批准号:0739229
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负责人:Carla Brodley
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依托单位:
III-CXT-Medium: Interdisciplinary Machine Learning Research and Education
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批准号:0803409
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项目类别:Standard Grant
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资助金额:$87.58万
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财政年份:2008
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负责人:Carla Brodley
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依托单位:
Multidisciplinary Research Opportunities for Women
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批准号:0636325
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项目类别:Standard Grant
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资助金额:$0.0万
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财政年份:2007
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负责人:Carla Brodley
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依托单位:
SEI: Collaborative Research: Discovering Unexpected Planets and Other Astronomical Oddities
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批准号:0713259
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项目类别:Continuing Grant
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资助金额:$20.59万
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财政年份:2007
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负责人:Carla Brodley
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依托单位:
Collaborative Research: SGER: Mining for Planets
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批准号:0540902
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项目类别:Standard Grant
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资助金额:$6.18万
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财政年份:2005
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负责人:Carla Brodley
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依托单位:
SGER: Behavioral Authentication of Server Flows
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批准号:0446030
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项目类别:Standard Grant
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资助金额:$0.15万
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财政年份:2004
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负责人:Carla Brodley
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依托单位:
SGER: Behavioral Authentication of Server Flows
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批准号:0335574
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项目类别:Standard Grant
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资助金额:$5.07万
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依托单位:
Workshop: Student Scholarship Program for the International Conference on Machine Learning (ICML 2001)
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批准号:0004495
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项目类别:Standard Grant
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资助金额:$2.0万
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财政年份:2000
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负责人:Carla Brodley
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依托单位:
CAREER: A Foundation for Applied Machine Learning
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批准号:9733573
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项目类别:Continuing Grant
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资助金额:$23.67万
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负责人:Carla Brodley
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依托单位:
Content-Based Image Retrieval for Medical Databases
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批准号:9711535
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资助金额:$54.77万
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财政年份:1997
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负责人:Carla Brodley
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依托单位:
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