With the introduction of computer programming in schools around the world, a myriad of guides are being published to support educators who are teaching this subject, often for the first time. Most of these books offer a learning path based on the experience of the experts who author them. In this paper we propose and investigate an alternative way of determining the most suitable learning paths by analyzing projects developed by learners hosted in public repositories. Therefore, we downloaded 250 projects of different types from the Scratch online platform, and identified the differences and clustered them based on a quantitative measure, the computational thinking score provided by Dr. Scratch. We then triangulated the results by qualitatively studying in detail the source code of the prototypical projects to explain the progression required to move from one cluster to the next one. The result is a data-driven itinerary that can support teachers and policy makers in the creation of a curriculum for learning to program. Aiming to generalize this approach, we discuss a potential recommender tool, populated with data from public repositories, to allow educators and learners creating their own learning paths, contributing thus to a personalized learning connected with students’ interests.

中文翻译：

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迈向数据驱动的学习路径，以从头开始发展计算思维

随着计算机编程在世界各地的学校中的引入，大量的指南被出版，以支持教授这门学科的教育工作者，通常是第一次。这些书籍中的大多数都提供了基于作者专家经验的学习路径。在本文中，我们通过分析托管在公共存储库中的学习者开发的项目，提出并研究了一种确定最合适学习路径的替代方法。因此，我们从 Scratch 在线平台下载了 250 个不同类型的项目，并根据 Scratch 博士提供的计算思维评分这一定量衡量标准，识别差异并对其进行聚类。然后，我们通过详细地定性研究原型项目的源代码来对结果进行三角测量，以解释从一个集群移动到下一个集群所需的进展。结果是一个数据驱动的行程，可以支持教师和政策制定者创建学习编程的课程。为了推广这种方法，我们讨论了一种潜在的推荐工具，其中包含来自公共存储库的数据，允许教育者和学习者创建自己的学习路径，从而有助于与学生兴趣相关的个性化学习。