A Framework for Applying Sequential Data Analytics to Design Personalized Digital Game-Based Learning for Computing Education

Abstract

<p></p><p>In this study, we have proposed and implemented a sequential data analytics (SDA)-driven methodological framework to design adaptivity for digital game-based learning (DGBL). The goal of this framework is to facilitate children’s personalized learning experiences for K–5 computing education. Although DGBL experiences can be beneficial, young children need personalized learning support because they are likely to experience cognitive challenges in computational thinking (CT) development and learning transfer. We implemented the educational game Penguin Go to test our methodological framework to detect children’s optimal learning interaction patterns. Specifically, using SDA, we identified children’s diverse gameplay patterns and inferred their learning states related to CT. To better understand children’s gameplay performance and CT development in context, we used qualitative data as triangulation. We discuss adaptivity design based on the children’s gameplay challenges indicated by their gameplay sequence patterns. This study shows that SDA can inform what in-game support is necessary to foster student learning and when to deliver such support in gameplay. The study findings suggest design guidelines regarding the integration of the proposed SDA framework.<br></p>