Exploring the use of open-source LLMs for Game Learning Analytics: an empirical study - Filho, Defala; Honda, Fabrizio; Pires, Fernanda; Pessoa, Marcela (Anais do Simpósio Brasileiro de Informática na Educação (SBIE), 2025)
Game Learning Analytics (GLA) is essential for analyzing player behavior and identifying their learning progress through data collection and analysis. However, data modeling for GLA is a complex and challenging process. Large Language Models (LLMs) can be an alternative, especially opensource models with no usage limitations. This study investigates how the models “Gemma 2 (9B),” “Qwen 2.5 (14B),” “LLaMA 3.1 Instruct (8B),” and “Phi4 Mini (3.8B)” are capable of modeling data and filling out the data capture template from the GLBoard model. Seven GLA experts evaluated each model’s responses and identified the “Qwen” model as the most satisfactory, highlighting the potential of open-source LLMs in GLA activities.