Can data reveal learning in computing? Insights from an empirical study in Game Learning Analytics - Martins, Waldecir; Honda, Fabrizio; Fernandes, David; Pires, Fernanda; Pessoa, Marcela (Anais do Simpósio Brasileiro de Educação em Computação (EDUCOMP); 2026: Anais do VI Simpósio Brasileiro de Educação em ComputaçãoDO - 10.5753/educomp.2026.18682, 2026)
Computer Science Education has taken on a fundamental role in the Brazilian educational context, especially after its mandatory inclusion in Basic Education. However, its concepts are complex, even for students in higher education. In this scenario, educational games emerge as a promising strategy to mitigate this complexity, especially when combined with Game Learning Analytics (GLA), which enables the analysis of the player’s journey to identify evidence of learning. This work consists of an empirical study in GLA, aimed at analyzing interaction data (logs) that we collected through GLBoard from students using the educational game “OptimaCorporation”. The study involved eight Computer Science students, and the results indicate that interaction data can reveal signs of learning, but they require complementary tools.