Exploring Differences Between Hybrid GPT-Human and Human-Created Qualitative Codebooks in an Educational Game
摘要
This study explores the ability of GPT-4 working together with humans to generate a codebook to analyze scientific observations from middle school learners in the What-if Hypothetical Implementations in Minecraft (WHIMC) project. It compares this Hybrid codebook to one fully developed by Humans using a variety of techniques to evaluate how the codes developed by each approach relate to one another and to external measures of student interest. Results show that the Hybrid GPT-Human codes consist of broader categories that align more consistently with the external interest metrics, whereas the Human codes offer finer-grained insights into specific student behaviors. However, the complementary insights offered by each suggest that combining both approaches could improve our understanding of student engagement and inform more effective strategies in educational game design and intervention.