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

• 批准号: 1740775
• 负责人: Peter Brusilovsky
• 金额: $ 27.12万
• 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: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2022-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1740775&HistoricalAwards=false
• 关键词: Collaborative; Research; Community; Building; Infrastructure

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。未来教育软件的开发速度将更快，更容易生成有意义的学习者数据，而这些数据又可以更容易地进行分析。

项目成果

Architecting Analytics Across Multiple E-Learning Systems to Enhance Learning Design

• DOI: 10.1109/tlt.2021.3072159
• 发表时间: 2021-04
• 期刊: IEEE Transactions on Learning Technologies
• 影响因子: 3.7
• 作者: Katerina Mangaroska;B. Vesin;V. Kostakos;Peter Brusilovsky;M. Giannakos

Course-Adaptive Content Recommender for Course Authoring

用于课程创作的课程自适应内容推荐器

• DOI: 10.1007/978-3-319-93846-2_9
• 发表时间: 2018
• 作者: Chau, Hung;Barria-Pineda, Jordan;Brusilovsky, Peter
• 通讯作者: Brusilovsky, Peter

Making it Smart: Converting Static Code into an Interactive Trace Table

让它变得聪明：将静态代码转换为交互式跟踪表

• 发表时间: 2020
• 期刊: Proceedings of Sixth SPLICE Workshop "Building an Infrastructure for Computer Science Education Research and Practice at Scale" at ACM Learning at Scale 2020
• 作者: Risha, Zak;Brusilovsky, Peter
• 通讯作者: Brusilovsky, Peter

A Study of Worked Examples for SQL Programming

SQL 编程实例研究

• 发表时间: 2022
• 期刊: Proceedings of the 27th ACM Conference on Innovation and Technology in Computer Science Education
• 作者: Akhuseyinoglu, Kamil;Hardt, Ryan;Barria-Pineda, Jordan;Brusilovsky, Peter;Pollari-Malmi, Kerttu;Sirkiä, Teemu;Malmi, Lauri
• 通讯作者: Malmi, Lauri

Knowledge-Based Design Analytics for Authoring Courses with Smart Learning Content

基于知识的设计分析，用于编写具有智能学习内容的课程

• DOI: 10.1007/s40593-021-00253-3
• 发表时间: 2021
• 期刊: International Journal of Artificial Intelligence in Education
• 影响因子: 4.9
• 作者: Albó, Laia;Barria-Pineda, Jordan;Brusilovsky, Peter;Hernández-Leo, Davinia
• 通讯作者: Hernández-Leo, Davinia