Representation, Navigation and Exploration: a three layered approach on learning trajectories - Vieira, José Michel Fogaça; Zaina, Luciana Aparecida Martinez (Anais do Simpósio Brasileiro sobre Fatores Humanos em Sistemas Computacionais (IHC), 2020)
Learning trajectories are paths that students may follow in order to achieve their learning goals. Although the literature has addressed the subject, little has been done in the way of exploring how to visualize learning trajectories. In this paper, we present three forms of interactive learning trajectories visualizations linked to the context of computational thinking. As the interactions on visualizations involved different aspects, our proposal comprises three layers: the data representation, the reactions to the navigation and data exploration where more details of the data can be seen. Due to visualizations being tightly related to the context from which the data comes, we analyzed the data types available in Code.org, a well-known platform commonly used to teach computational thinking. To assess the three visualizations, we carried out usability and user experience evaluation with 23 Brazilian elementary schools teachers. The results revealed that the three visualizations achieved an average of 72% of overall understanding by the audience. Besides, our findings showed the visualizations were well accepted among the participants.We also found out that the user experience reported by the participants is in some way associated with the level of understanding of the visualizations.