Towards Data-Driven Learning Paths to Develop Computational Thinking with Scratch

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.