Collaborative Research: Community-Building and Infrastructure Design for Data-Intensive Research in Computer Science Education

合作研究:计算机科学教育数据密集型研究的社区建设和基础设施设计

基本信息

  • 批准号:
    1740798
  • 负责人:
  • 金额:
    $ 29.98万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2017
  • 资助国家:
    美国
  • 起止时间:
    2017-09-01 至 2021-12-31
  • 项目状态:
    已结题

项目摘要

The Building Community and Capacity in Data Intensive Research in Education program seeks to enable research communities to develop visions, teams, and capabilities dedicated to creating new, large-scale, next-generation data resources and relevant analytic techniques to advance fundamental research for areas of research covered by the Education and Human Resources Directorate. Successful proposals will outline activities that will have significant impacts across multiple fields by enabling new types of data-intensive research. Online educational systems, and the large-scale data streams that they generate, have the potential to transform education as well as our scientific understanding of learning. Computer Science Education (CSE) researchers are increasingly making use of large collections of data generated by the click streams coming from eTextbooks, interactive programming environments, and other smart content. However, CSE research faces barriers that slow progress: 1) Collection of computer science learning process and outcome data generated by one system is not compatible with that from other systems. 2) Computer science problem solving and learning (e.g., open-ended coding solutions to complex problems) is quite different from the type of data (e.g., discrete answers to questions or verbal responses) that current educational data mining focuses on. This project will build community and capacity among CSE researchers, data scientists, and learning scientists toward reducing these barriers and facilitating the full potential of data-intensive research on learning and improving computer science education. The project will bring together CSE tool building communities with learning science and technology researchers towards developing a software infrastructure that supports scaled and sustainable data-intensive research in CSE that contributes to basic science of human learning of complex problem solving. The project will support community-building and infrastructure capacity-building whose ultimate goal is to develop and disseminate infrastructure that facilitates three aspects of CSE research: (1) development and broader re-use of innovative learning content that is instrumented for rich data collection, (2) formats and tools for analysis of learner data, and (3) best practices to make large collections of learner data and associated analytics available to researchers in CSE, data science, or learning science. To achieve these goals, a large community of researchers will be engaged to define, develop, and use critical elements of this infrastructure toward addressing specific data-intensive research questions.The project will host workshops, meetings, and online forums leveraging existing communities and building new capacities toward significant research outcomes and lasting infrastructure support.This project will provide an infrastructure that can support various kinds of research in CSE domain as a one-stop-shop, and will be the first to focus on full-cycle educational research infrastructure in any domain. CSE tool developers and educators will become more productive at creating and integrating advanced technologies and novel analytics. Learning researchers will have better tools for analyzing the huge amounts of learner data that modern digital education software produces. Data scientists will have rich new datasets in which to explore new machine learning and statistical techniques. Collectively, these efforts will reduce barriers to educational innovation and support scientific discoveries about the nature of complex learning and how best to enhance it. The project will support scientific investigations through community meetings and mini-grants to others addressing questions such as: What is the optimal ratio of solution examples and problem-solving practice? How do computational thinking skills emerge? In what quanta are programming skills acquired? Can automated tutoring of programming be effective at scale in enhancing student learning?. Many of the innovations developed under this project will directly impact learning in any discipline. Educational software will more quickly be developed in the future, that more easily generates meaningful learner data, which in turn can be more easily analyzed.
在数据密集型研究教育计划的建设社区和能力,旨在使研究社区发展愿景,团队和能力,致力于创造新的,大规模的,下一代的数据资源和相关的分析技术,以推进基础研究的教育和人力资源局所涵盖的研究领域。成功的提案将概述通过实现新型数据密集型研究而对多个领域产生重大影响的活动。在线教育系统及其产生的大规模数据流有可能改变教育以及我们对学习的科学理解。计算机科学教育(CSE)研究人员越来越多地利用来自电子教科书,交互式编程环境和其他智能内容的点击流生成的大量数据。然而,CSE研究面临着阻碍其进展的障碍:1)由一个系统生成的计算机科学学习过程和结果数据的收集与来自其他系统的数据不兼容。2)计算机科学问题解决和学习(例如,复杂问题的开放式编码解决方案)与数据类型(例如,该项目将在CSE研究人员、数据科学家和学习科学家之间建立社区和能力,以减少这些障碍,促进数据密集型学习研究的充分潜力,并改善计算机科学教育。该项目将把CSE工具构建社区与学习科学和技术研究人员聚集在一起,共同开发一个软件基础设施,支持CSE中规模化和可持续的数据密集型研究,为人类学习复杂问题解决的基础科学做出贡献。该项目将支持社区建设和基础设施能力建设,其最终目标是发展和传播基础设施,促进社区和环境教育研究的三个方面:(1)开发和更广泛地重新使用创新的学习内容,以收集丰富的数据,(2)分析学习者数据的格式和工具,以及(3)最佳实践,使CSE,数据科学或学习科学的研究人员可以使用大量的学习者数据和相关分析。为了实现这些目标,一个庞大的研究人员社区将参与定义,开发和使用这个基础设施的关键要素,以解决特定的数据密集型研究问题。该项目将举办研讨会,会议,和在线论坛,利用现有的社区和建设新的能力,以取得重大的研究成果和持久的基础设施支持。该项目将提供一个基础设施,可以支持各种作为一个一站式服务中心,CSE领域的研究,并将是第一个专注于任何领域的全周期教育研究基础设施。CSE工具开发人员和教育工作者将在创建和集成先进技术和新颖分析方面变得更加富有成效。学习研究人员将有更好的工具来分析现代数字教育软件产生的大量学习者数据。数据科学家将拥有丰富的新数据集,以探索新的机器学习和统计技术。总的来说,这些努力将减少教育创新的障碍,并支持关于复杂学习的性质以及如何最好地加强它的科学发现,该项目将通过社区会议和小额赠款支持科学调查,以解决诸如解决问题的例子和解决问题的实践的最佳比例是多少?计算思维技能是如何产生的?编程技能的获得需要多少时间?编程的自动辅导在提高学生学习方面是否有效?在这个项目下开发的许多创新将直接影响任何学科的学习。教育软件在未来将得到更快的开发,更容易生成有意义的学习者数据,从而更容易进行分析。

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)
Seeing Beyond Expert Blind Spots: Online Learning Design for Scale and Quality
超越专家盲点:规模和质量的在线学习设计
Crossing the Borders: Re-Use of Smart Learning Objects in Advanced Content Access Systems
跨越国界:在高级内容访问系统中重复使用智能学习对象
  • DOI:
    10.3390/fi11070160
  • 发表时间:
    2019
  • 期刊:
  • 影响因子:
    3.4
  • 作者:
    Manzoor, Hamza;Akhuseyinoglu, Kamil;Wonderly, Jackson;Brusilovsky, Peter;Shaffer, Clifford A.
  • 通讯作者:
    Shaffer, Clifford A.
Comprehension Factor Analysis: Modeling student's reading behaviour: Accounting for reading practice in predicting students' learning in MOOCs
理解因素分析:对学生的阅读行为进行建模:考虑阅读实践来预测学生在 MOOC 中的学习情况
Approaches for Coordinating eTextbooks, Online Programming Practice, Automated Grading, and More into One Course
将电子教科书、在线编程练习、自动评分等整合到一门课程中的方法
{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Ken Koedinger其他文献

Improving Students’ Argumentation Skills Using Dynamic Machine-Learning–Based Modeling
使用基于动态机器学习的建模提高学生的论证技巧
  • DOI:
    10.1287/isre.2021.0615
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    4.9
  • 作者:
    Thiemo Wambsganss;Andreas Janson;Matthias Söllner;Ken Koedinger;J. Leimeister
  • 通讯作者:
    J. Leimeister

Ken Koedinger的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Ken Koedinger', 18)}}的其他基金

Collaborative Research: CCRI: New: An Infrastructure for Sustainable Innovation and Research in Computer Science Education
合作研究:CCRI:新:计算机科学教育可持续创新和研究的基础设施
  • 批准号:
    2213791
  • 财政年份:
    2022
  • 资助金额:
    $ 29.98万
  • 项目类别:
    Standard Grant
Learning Depends on Knowledge: Using Interaction Designs and Machine Learning to Contrast the Testing and Worked Example Effects
学习取决于知识:使用交互设计和机器学习来对比测试和工作示例的效果
  • 批准号:
    1824257
  • 财政年份:
    2018
  • 资助金额:
    $ 29.98万
  • 项目类别:
    Standard Grant
PFI: AIR-TT: Commercializing a new genre of Intelligent Science Stations for informal and formal learning
PFI:AIR-TT:将新型智能科学站商业化,用于非正式和正式学习
  • 批准号:
    1701107
  • 财政年份:
    2017
  • 资助金额:
    $ 29.98万
  • 项目类别:
    Standard Grant
Intelligent Science Exhibits: Transforming Hands-on Exhibits into Mixed-Reality Learning Experiences
智能科学展览:将动手展览转变为混合现实学习体验
  • 批准号:
    1612744
  • 财政年份:
    2016
  • 资助金额:
    $ 29.98万
  • 项目类别:
    Standard Grant
CIF21 DIBBs: Building a Scalable Infrastructure for Data-Driven Discovery and Innovation in Education
CIF21 DIBB:为数据驱动的教育发现和创新构建可扩展的基础设施
  • 批准号:
    1443068
  • 财政年份:
    2015
  • 资助金额:
    $ 29.98万
  • 项目类别:
    Standard Grant
Conference: A Proposal to the National Science Foundation for Support of the Seventh Annual Inter-Science of Learning Centers (iSLC) Student / Postdoctoral Scholar Conference
会议:向国家科学基金会提出的支持第七届年度跨科学学习中心 (iSLC) 学生/博士后学者会议的提案
  • 批准号:
    1430662
  • 财政年份:
    2014
  • 资助金额:
    $ 29.98万
  • 项目类别:
    Standard Grant
Toward a Decade of PSLC Research: Investigating Instructional, Social, and Learner Factors in Robust Learning through Data-Driven Analysis and Modeling
PSLC 研究十年:通过数据驱动的分析和建模研究稳健学习中的教学、社会和学习者因素
  • 批准号:
    0836012
  • 财政年份:
    2010
  • 资助金额:
    $ 29.98万
  • 项目类别:
    Cooperative Agreement
First Annual Inter-Science of Learning Center (iSLC) Student/Post-doc Summer Conference
第一届年度跨科学学习中心 (iSLC) 学生/博士后夏季会议
  • 批准号:
    0751038
  • 财政年份:
    2007
  • 资助金额:
    $ 29.98万
  • 项目类别:
    Standard Grant
SLC Center: Pittsburgh Science of Learning Center: Studying Robust Learning with Learning Experiments in Real Classrooms
SLC 中心:匹兹堡学习科学中心:通过真实课堂中的学习实验研究稳健学习
  • 批准号:
    0354420
  • 财政年份:
    2004
  • 资助金额:
    $ 29.98万
  • 项目类别:
    Cooperative Agreement
ROLE: Implementation of an American-German Research Network in the Field of Technology-Supported Education
作用:在技术支持的教育领域实施美德研究网络
  • 批准号:
    0310420
  • 财政年份:
    2003
  • 资助金额:
    $ 29.98万
  • 项目类别:
    Standard Grant

相似国自然基金

Research on Quantum Field Theory without a Lagrangian Description
  • 批准号:
    24ZR1403900
  • 批准年份:
    2024
  • 资助金额:
    0.0 万元
  • 项目类别:
    省市级项目
Cell Research
  • 批准号:
    31224802
  • 批准年份:
    2012
  • 资助金额:
    24.0 万元
  • 项目类别:
    专项基金项目
Cell Research
  • 批准号:
    31024804
  • 批准年份:
    2010
  • 资助金额:
    24.0 万元
  • 项目类别:
    专项基金项目
Cell Research (细胞研究)
  • 批准号:
    30824808
  • 批准年份:
    2008
  • 资助金额:
    24.0 万元
  • 项目类别:
    专项基金项目
Research on the Rapid Growth Mechanism of KDP Crystal
  • 批准号:
    10774081
  • 批准年份:
    2007
  • 资助金额:
    45.0 万元
  • 项目类别:
    面上项目

相似海外基金

Collaborative Research: BoCP-Implementation: Alpine plants as a model system for biodiversity dynamics in a warming world: Integrating genetic, functional, and community approaches
合作研究:BoCP-实施:高山植物作为变暖世界中生物多样性动态的模型系统:整合遗传、功能和社区方法
  • 批准号:
    2326020
  • 财政年份:
    2024
  • 资助金额:
    $ 29.98万
  • 项目类别:
    Continuing Grant
Collaborative Research: BoCP-Implementation: Alpine plants as a model system for biodiversity dynamics in a warming world: Integrating genetic, functional, and community approaches
合作研究:BoCP-实施:高山植物作为变暖世界中生物多样性动态的模型系统:整合遗传、功能和社区方法
  • 批准号:
    2326021
  • 财政年份:
    2024
  • 资助金额:
    $ 29.98万
  • 项目类别:
    Standard Grant
Collaborative Research: GEO OSE Track 2: Developing CI-enabled collaborative workflows to integrate data for the SZ4D (Subduction Zones in Four Dimensions) community
协作研究:GEO OSE 轨道 2:开发支持 CI 的协作工作流程以集成 SZ4D(四维俯冲带)社区的数据
  • 批准号:
    2324714
  • 财政年份:
    2024
  • 资助金额:
    $ 29.98万
  • 项目类别:
    Standard Grant
RAPID: Reimagining a collaborative future: engaging community with the Andrews Forest Research Program
RAPID:重新构想协作未来:让社区参与安德鲁斯森林研究计划
  • 批准号:
    2409274
  • 财政年份:
    2024
  • 资助金额:
    $ 29.98万
  • 项目类别:
    Standard Grant
Collaborative Research: CPS: NSF-JST: Enabling Human-Centered Digital Twins for Community Resilience
合作研究:CPS:NSF-JST:实现以人为本的数字孪生,提高社区复原力
  • 批准号:
    2420846
  • 财政年份:
    2024
  • 资助金额:
    $ 29.98万
  • 项目类别:
    Standard Grant
Collaborative Research: Mechanisms of community coalescence in synthetic microbiomes
合作研究:合成微生物组中群落合并的机制
  • 批准号:
    2328529
  • 财政年份:
    2024
  • 资助金额:
    $ 29.98万
  • 项目类别:
    Standard Grant
Collaborative Research: Mechanisms of community coalescence in synthetic microbiomes
合作研究:合成微生物组中群落合并的机制
  • 批准号:
    2328528
  • 财政年份:
    2024
  • 资助金额:
    $ 29.98万
  • 项目类别:
    Standard Grant
Collaborative Research: GEO OSE Track 2: Project Pythia and Pangeo: Building an inclusive geoscience community through accessible, reusable, and reproducible workflows
合作研究:GEO OSE 第 2 轨道:Pythia 和 Pangeo 项目:通过可访问、可重用和可重复的工作流程构建包容性的地球科学社区
  • 批准号:
    2324304
  • 财政年份:
    2024
  • 资助金额:
    $ 29.98万
  • 项目类别:
    Standard Grant
Collaborative Research: REU Site: MICRO-CCS: Microbial Interactions Create Research Opportunities for Community College Students
合作研究:REU 网站:MICRO-CCS:微生物相互作用为社区学院学生创造研究机会
  • 批准号:
    2349221
  • 财政年份:
    2024
  • 资助金额:
    $ 29.98万
  • 项目类别:
    Standard Grant
Collaborative Research: GEO OSE Track 2: Developing CI-enabled collaborative workflows to integrate data for the SZ4D (Subduction Zones in Four Dimensions) community
协作研究:GEO OSE 轨道 2:开发支持 CI 的协作工作流程以集成 SZ4D(四维俯冲带)社区的数据
  • 批准号:
    2324709
  • 财政年份:
    2024
  • 资助金额:
    $ 29.98万
  • 项目类别:
    Standard Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了