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: 2213789
• 负责人: Peter Brusilovsky
• 金额: $ 65.88万
• 依托单位: University of Pittsburgh
• 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
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2213789&HistoricalAwards=false
• 关键词: Collaborative; Research; CCRI; New; Infrastructure

Researchers from the University of Pittsburgh, Carnegie Mellon University, North Carolina State University, and Virginia Tech collaborate to support the Computer Science Education Hub, social and technical infrastructure to accelerate research on teaching and learning of computing disciplines. The Hub will host community events and provide a web repository for computing education datasets, tools, and common analysis methods. The Hub facilitates creation and adoption of new tools to support computing educators and students, and new standards to support data collection and data-enabled research. Hub community events help researchers and educators from many learning contexts to develop and improve computing education resources. The Hub provides a pioneering example of infrastructure to support data-enabled research on education in Computing and beyond. It advances Computing Education Research through: (1) building a community and website that helps instructors adopt evidence-based practices and innovative learning technologies, (2) creating data standards and large, high-quality datasets, (3) developing advanced algorithms using artificial intelligence, statistics, and analytics methods that leverage data to optimize student learning effectiveness, efficiency, and engagement, (4) developing rigorous evaluation methods to demonstrate large, lasting, and replicable impacts on student achievement. Cross-disciplinary scientific advances are disseminated through the Hub infrastructure community and through scientific publications.The Hub will have a direct and immediate impact on hundreds of researchers and educators, and tens of thousands of students, during the project phase and beyond. It will reduce barriers to educational innovation and support discoveries in the many scientific communities that contribute to computing education research, where many researchers and educators are currently isolated into separate silos. Through community development and outreach efforts, this team directly engages with instructors and students from underserved communities in the design and use of the infrastructure. Discoveries and innovations enabled by the Hub will help tens of thousands of students in the strategically important field of computer science.To distribute project-related information, the website at http://cssplice.org/ is maintained, where all project information including standards, best practices, and resources will be shared. It informs the community about events and opportunities to contribute. The site provides links to project publications, code, data, and learning content hosted in archival repositories such as GitHub and DataShop. The website will be maintained at least through 2027. The resources contributed to archival repositories will be maintained indefinitely.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.

来自匹兹堡大学、卡内基梅隆大学、北卡罗来纳州立大学和弗吉尼亚理工大学的研究人员合作支持计算机科学教育中心、社会和技术基础设施，以加速计算学科教学和学习的研究。该中心将举办社区活动，并提供一个用于计算教育数据集、工具和通用分析方法的网络存储库。该中心促进创建和采用新工具来支持计算教育工作者和学生，以及新标准来支持数据收集和数据支持的研究。 Hub 社区活动可帮助来自多种学习环境的研究人员和教育工作者开发和改进计算教育资源。该中心提供了基础设施的开创性示例，以支持计算及其他领域的数据驱动的教育研究。它通过以下方式推进计算教育研究：(1) 建立一个社区和网站，帮助教师采用基于证据的实践和创新学习技术，(2) 创建数据标准和大型高质量数据集，(3) 使用人工开发先进算法利用数据来优化学生学习效果、效率和参与度的情报、统计和分析方法，(4) 开发严格的评估方法，以证明对学生成绩的巨大、持久和可复制的影响。跨学科的科学进步通过中心基础设施社区和科学出版物传播。该中心将在项目阶段及之后对数百名研究人员和教育工作者以及数万名学生产生直接和即时的影响。它将减少教育创新的障碍，并支持许多为计算机教育研究做出贡献的科学界的发现，目前许多研究人员和教育工作者被隔离在不同的孤岛中。通过社区发展和外展工作，该团队直接与来自服务欠缺社区的教师和学生合作设计和使用基础设施。该中心的发现和创新将帮助计算机科学这一具有重要战略意义的领域的数以万计的学生。 为了分发项目相关信息，我们维护了 http://cssplice.org/ 网站，其中包含包括标准在内的所有项目信息、最佳实践和资源将被共享。它向社区通报有关活动和做出贡献的机会。该网站提供了托管在 GitHub 和 DataShop 等档案存储库中的项目出版物、代码、数据和学习内容的链接。该网站将至少维护到 2027 年。为档案存储库提供的资源将无限期维护。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。

项目成果

Analysis of an Explainable Student Performance Prediction Model in an Introductory Programming Course

编程入门课程中可解释的学生表现预测模型的分析

• 发表时间: 2024-09-13
• 期刊: Environmental Research Letters
• 影响因子: 6.7
• 作者: Muntasir Hoq;Peter Brusilovsky;Bita Akram
• 通讯作者: Bita Akram