Using video analysis and learning analytics to understand programming trajectories in data science activities with Scratch

Abstract

In this paper, we describe a new automated tool to analyze how students create their projects on Scratch 3.0, with the goal of understanding learning trajectories in a way that considers students' programming processes and practices, moving beyond the analysis of computational thinking concepts as evidence of learning. Drawing on a combination of qualitative video analysis and temporal learning analytics, we also present preliminary data from a pilot study that illustrates some possibilities afforded by this type of analytical tool. We expect that our tool can help researchers to better understand learning in the context of data visualization activities with block-based programming languages by shedding light on processes that are usually invisible and, thus, better support students in their diverse learning pathways.