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.

教育数据密集型研究社区和能力建设计划旨在使研究社区能够发展愿景、团队和能力，致力于创建新的、大规模的下一代数据资源和相关分析技术，以推进以下领域的基础研究：研究由教育和人力资源局负责。成功的提案将概述通过启用新型数据密集型研究而对多个领域产生重大影响的活动。在线教育系统及其生成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研究面临着阻碍进展的障碍：1）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其他系统的不兼容。 2）计算机科学问题的解决和学习（例如，复杂问题的开放式编码解决方案）与当前教育数据挖掘所关注的数据类型（例如，对问题的离散答案或口头回答）有很大不同。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，以减少这些障碍并促进数据密集型研究在学习和改善计算机科学教育方面的全部潜力。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，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，为人类学习解决复杂问题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，其最终目标是开发和传播促进全面性教育研究三个方面的基础设施：（1）开发和更广泛地重用用于丰富数据收集的创新学习内容， (2) 用于分析学习者数据的格式和工具，以及 (3) 为 CSE、数据科学或学习科学的研究人员提供大量学习者数据和相关分析的最佳实践。为了实现这些目标，大型研究人员社区将参与定义、开发和使用该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，利用现有社区和构建取得重大研究成果和持久基础设施支持的新能力。该项目将提供一个基础设施，可以一站式支持 CSE 领域的各种研究，并将是第一个专注于 CSE 领域全周期教育研究基础设施的项目任何域。 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高效。学习研究人员将拥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，可以在其中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本质以及如何最好地增强它的科学发现。该项目将通过社区会议和向其他人提供小额赠款来支持科学研究，解决以下问题：解决方案示例和解决问题实践的最佳比例是多少？计算思维技能是如何出现的？编程技能是在哪些方面获得的？自动编程辅导能否有效地大规模提高学生的学习？该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，从而更容易分析这些数据。

项目成果

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

• DOI: 10.1145/3287324.3287487
• 发表时间: 2019-02
• 期刊: Proceedings of the 2019 ACM SIGCSE Technical Symposium on Computer Science Education (SIGCSE'19
• 作者: Ellis, Margaret;Shaffer, Clifford A.;Edwards, Stephen H.
• 通讯作者: Edwards, Stephen H.

An integrated practice system for learning programming in Python: design and evaluation

Python学习编程综合实践系统：设计与评估

• DOI: 10.1186/s41039-018-0085-9
• 发表时间: 2018-12
• 期刊: Research and Practice in Technology Enhanced Learning
• 影响因子: 3.2
• 作者: Brusilovsky, Peter;Malmi, Lauri;Hosseini, Roya;Guerra, Julio;Sirkiä, Teemu;Pollari
• 通讯作者: Pollari

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: 2018-09
• 期刊: 2018
• 作者: Chau, Hung;Barria;Brusilovsky, Peter
• 通讯作者: Brusilovsky, Peter

Explainable Recommendations in a Personalized Programming Practice System

个性化编程实践系统中的可解释建议

• DOI: 10.1007/978-3-030-78292-4_6
• 发表时间: 2021-06
• 期刊: Lecture notes in computer science
• 作者: Barria;Akhuseyinoglu, Kamil;Želem;Brusilovsky, Peter;Klasnja Milicevic, Aleksandra;Ivanovic, Mirjana
• 通讯作者: Ivanovic, Mirjana

PCEX: Interactive Program Construction Examples for Learning Programming

PCEX：学习编程的交互式程序构建示例

• DOI: 10.1145/3279720.3279726
• 发表时间: 2018-01
• 期刊: 18th Koli Calling International Conference on Computing Education Research
• 作者: Hosseini, Roya;Akhuseyinoglu, Kamil;Petersen, Andrew;Schunn, Christian D.;Brusilovsky, Peter
• 通讯作者: Brusilovsky, Peter