Collaborative Research: New Pathways into Data Science: Extending the Scratch Programming Language to Enable Youth to Analyze and Visualize Their Own Learning

协作研究：数据科学的新途径：扩展 Scratch 编程语言，使青少年能够分析和可视化自己的学习

• 批准号: 1417663
• 负责人: Benjamin Mako Hill
• 金额: $ 12.44万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1417663&HistoricalAwards=false
• 关键词: Collaborative; Research; New; Pathways; into

This REAL project arises from the 2013 solicitation on Data-intensive Research to Improve Teaching and Learning. The intention of that effort is to bring together researchers from across disciplines to foster novel, transformative, multidisciplinary approaches to using the data in large education-related data sets to create actionable knowledge for improving STEM teaching and learning environments in the medium term and to revolutionize learning in the longer term. This project addresses the issue of how to represent and communicate data to young people so that they can track their learning and weaknesses and take advantage of what they learn through that tracking. The project team aims to address this challenge by giving young people (middle schoolers) the tools and support to create, manipulate, analyze, and share representations of their own understanding, capabilities, and participation within the Scratch environment. Scratch is a programming language and online community in which youngsters (mostly middle schoolers) engage in programming together, sometimes to make scientific models and sometimes to express themselves artistically using sophisticated computer algorithms. Scratch community participants are often interested in keeping track of what they are learning, so this population is a good one for exploring ways of helping young people make sense of data that records their participation and learning. The team will extend the Scratch programming language with facilities for manipulating, analyzing, and representing such data, and Scratch participants will be challenged to make sense of their learning and participation data and helped to use the new facilities to do write programs to carry out such interpretation. Scratch participants will become visualizers of their participation patterns and learning trajectories; research will address how such data explorations influence their learning trajectories. Scratch and its community are the place for the proposed investigations, but what is learned will apply far more broadly to construction of tools for allowing learners to understand their participation and learning across a broad range of environments.This project addresses the sixth challenge in the program solicitation: how can information extracted from large datasets be represented and communicated to maximize its usefulness in real-time educational stings, and what delivery mechanisms are right for that? The PIs go right to the learners; rather than looking for delivery mechanisms for communicating the data representations, they give young people tools and support to create manipulate, analyze, and share those representations, bringing together approaches to quantitative evidence-based learning analytics with the constructionist tradition of learning through design experiences. In addition to helping us learn about how to help youngsters analyze data about their perforance and self-assess, the PIs expect that their endeavor will help us better learn how to help young people become data analyzers, an important part of computational thinking. Learners will, in the process of engaging with data representing their development and participation, interact with visualizations, model and troubleshoot data sets, and search for patterns in large data sets. In addition, the tools being developed as part of this project will be applicable for analysis of other types of data sets. The results that will transfer beyond Scratch and the Scratch community, are (1) the kinds of tools that make such analysis possible for youngsters, (2) the kinds of challenges that will get youngsters interested in doing such analyses, (3) the kinds of data youngsters can handle, and (4) the kinds of scaffolding and coaching youngsters need to make sense of that data.

这个真实的项目源于2013年关于数据密集型研究以改善教学的征集。这项工作的目的是将来自不同学科的研究人员聚集在一起，以促进新的，变革性的，多学科的方法，使用大型教育相关数据集中的数据，创造可操作的知识，以改善STEM教学和学习环境在中期和革命性的学习在长期内。该项目处理的问题是如何向年轻人展示和传达数据，使他们能够跟踪自己的学习情况和弱点，并利用他们通过跟踪学到的东西。项目团队旨在通过为年轻人（中学生）提供工具和支持来解决这一挑战，以创建，操作，分析和分享他们自己在Scratch环境中的理解，能力和参与的表现。Scratch是一种编程语言和在线社区，年轻人（主要是中学生）一起参与编程，有时制作科学模型，有时使用复杂的计算机算法艺术地表达自己。Scratch社区的参与者通常对跟踪他们正在学习的内容感兴趣，所以这个群体是探索帮助年轻人理解记录他们参与和学习的数据的方法的好人群。该团队将扩展Scratch编程语言，使其具有操作，分析和表示此类数据的功能，Scratch参与者将面临挑战，以理解他们的学习和参与数据，并帮助他们使用新的工具来编写程序来执行此类解释。Scratch参与者将成为他们参与模式和学习轨迹的可视化者;研究将解决这些数据探索如何影响他们的学习轨迹。Scratch及其社区是拟议调查的地方，但所学到的知识将更广泛地应用于工具的构建，以使学习者能够了解他们在广泛环境中的参与和学习。该项目解决了项目征集中的第六个挑战：如何表示和传达从大型数据集提取的信息，以最大限度地发挥其在实时教育中的作用，什么样的传递机制才是正确的呢PI直接面向学习者;而不是寻找用于传达数据表示的交付机制，他们为年轻人提供工具和支持，以创建、操作、分析和分享这些表示，将基于定量证据的学习分析方法与通过设计经验学习的建构主义传统结合在一起。除了帮助我们学习如何帮助年轻人分析有关他们的表现和自我评估的数据外，PI还希望他们的奋进将帮助我们更好地学习如何帮助年轻人成为数据分析师，这是计算思维的重要组成部分。学习者将在参与代表其发展和参与的数据的过程中，与可视化交互，建模和故障排除数据集，并在大型数据集中搜索模式。此外，作为该项目一部分正在开发的工具将适用于分析其他类型的数据集。结果将转移到Scratch和Scratch社区之外，是（1）使年轻人能够进行这种分析的工具类型，（2）让年轻人对进行这种分析感兴趣的挑战类型，（3）年轻人可以处理的数据类型，以及（4）年轻人需要理解这些数据的脚手架和教练类型。