Identifying and Extracting Meaningful Indicators of Children's Moment-to-Moment Programming Processes in Scratch

在 Scratch 中识别和提取儿童即时编程过程的有意义指标

• 批准号: 2119818
• 负责人: Gregory Chung
• 金额: $ 85万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2119818&HistoricalAwards=false
• 关键词: Identifying; Extracting; Meaningful; Indicators; Children

This project aims to serve the national interest in STEM and computational thinking by developing and validating indicators of students' programming processes. The proposed exploratory research will develop a data logging module for Scratch to collect students' moment-to-moment programming behavior. The project team will develop data processing rules or algorithms to derive indicators of programming processes (e.g., debugging). The project will advance the understanding of what programming processes students use, how these processes unfold over time, and how these processes relate to measures of programming and computational thinking. The data logging module and algorithms will be distributed to the Scratch community to allow the study of programming processes at scale. The research will also produce a systematic and replicable methodology to accelerate the development of algorithms and widespread dissemination of the tools, techniques, and methods used to study programming processes.The research will observe novice fifth grade and undergraduate students learning to program in Scratch. Students' process data will be used to derive indicators of programming processes. The indicators will be compared between age groups, within each age group over time, and to existing external measures of programming concepts and skills. This research will generate insights about what, how, and potentially why students perform the way they do. The capability to derive indicators of programming processes will complement existing methods of scoring static Scratch code. Algorithm development will focus on theoretically-driven, rule-based indicators of programming processes that are directly interpretable and on methods that systematize and accelerate the algorithm development process. The algorithm development methodology will apply to other block-based environments and applications involving process data such as games, simulations, and innovative item types in educational assessment.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.

该项目旨在通过开发和验证学生编程过程的指标，为STEM和计算思维的国家利益服务。这项探索性研究将为Scratch开发一个数据记录模块，以收集学生的即时编程行为。项目小组将制定数据处理规则或算法，以得出方案拟订过程的指标（例如，调试）。该项目将促进学生使用什么编程过程的理解，这些过程如何随着时间的推移展开，以及这些过程如何与编程和计算思维的措施相关。数据记录模块和算法将分发给Scratch社区，以允许大规模研究编程过程。这项研究还将产生一个系统的和可复制的方法，以加速算法的开发和广泛传播的工具，技术，和用于研究编程过程的方法。这项研究将观察新手五年级和本科生学习在Scratch编程。学生的过程数据将被用来得出编程过程的指标。这些指标将在不同年龄组之间进行比较，在每个年龄组内进行比较，并与现有的方案拟订概念和技能的外部衡量标准进行比较。这项研究将产生关于什么，如何以及为什么学生表现出他们这样做的见解。获得编程过程指标的能力将补充现有的静态Scratch代码评分方法。算法开发将侧重于理论驱动的，基于规则的编程过程的指标，这些指标是直接可解释的，以及系统化和加速算法开发过程的方法。该算法开发方法将适用于其他基于块的环境和涉及过程数据的应用程序，如游戏，模拟和教育评估中的创新项目类型。该奖项反映了NSF的法定使命，并被认为值得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来支持。