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

这个REAL项目源于2013年“数据密集型研究以改善教学”的招标。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，培养新颖的、变革性的、多学科的方法，利用大型教育相关数据集中的数据，创造可操作的知识，以在中期改善STEM教学环境，并在长期内彻底改变学习方式。该项目解决了如何向年轻人展示和传达数据的问题，以便他们能够跟踪自己的学习和弱点，并利用他们通过跟踪学到的东西。项目团队的目标是通过给年轻人（中学生）提供工具和支持来创建、操作、分析和共享他们自己的理解、能力和在Scratch环境中的参与的表示来解决这个挑战。Scratch是一种编程语言和在线社区，年轻人（主要是中学生）在这里一起编程，有时是为了制作科学模型，有时是为了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，因此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一个很好的群体。该团队将扩展Scratch编程语言，为其提供操作、分析和表示这些数据的工具，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编写程序来执行这些解释。Scratch参与者将成为他们的参与模式和学习轨迹的可视化者；研究将讨论这些数据探索如何影响他们的学习轨迹。Scratch和它的社区是建议调查的地方，但是学到的东西将更广泛地应用于工具的构建，使学习者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：如何从大型数据集中提取信息，并将其表达和交流，以最大限度地提高其在实时教育项目中的实用性，以及什么样的交付机制适合于此？pi直接到学习者那里；他们不是寻找传递数据表示的机制，而是为年轻人提供工具和支持，以创建、操作、分析和共享这些表示，将定量循证学习分析方法与通过设计经验学习的建构主义传统结合起来。除了帮助我们学习如何帮助年轻人分析他们的表现和自我评估的数据外，pi们希望他们的努力能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。学习者将在与代表他们发展和参与的数据打交道的过程中，与可视化、数据集建模和故障排除进行交互，并在大型数据集中搜索模式。此外，作为该项目的一部分正在开发的工具将适用于分析其他类型的数据集。将转移到Scratch和Scratch社区之外的结果是(1)使年轻人能够进行此类分析的各种工具，(2)将使年轻人对进行此类分析感兴趣的各种挑战，(3)年轻人可以处理的数据类型，以及(4)年轻人需要的脚手架和指导类型理解这些数据。

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.