Personalized gamification explores user models to tailor gamification designs to mitigate cases wherein the one-size-fits-all approach ineffectively improves learning outcomes. The tailoring process should simultaneously consider user and contextual characteristics (e.g., activity to be done and geographic location), which leads to several…

The development of large language models offers new possibilities for enhancing adaptive scaffolding of student learning in game-based learning environments. In this work, we present a novel framework for automatic plan generation that utilizes text-based representations of students' actions within a game-based learning environment, Crystal…

Understanding students' multi-party epistemic and topic based-dialogue contributions, or how students present knowledge in group-based chat interactions during collaborative game-based learning, offers valuable insights into group dynamics and learning processes. However, manually annotating these contributions is labor-intensive and challenging.…

This study explores the potential of the large language model GPT-4 as an automated tool for qualitative data analysis by educational researchers, exploring which techniques are most successful for different types of constructs. Specifically, we assess three different prompt engineering strategies -- Zero-shot, Few-shot, and Fewshot with…