This study explores the development of a game-based creativity assessment model using biometric and real-time data. By integrating game learning analytics (GLA) with neuroscience approaches such as functional near-infrared spectroscopy (fNIRS), the goal of this research is to provide a comprehensive, process-oriented approach to creativity. The study emphasizes the importance of social and affective dimensions of creativity, moving beyond traditional cognitive-focused models. By analyzing log data, social network interactions, and biometric feedback, the study highlights how these dimensions influence creative outcomes. The results demonstrate the effectiveness of using GLA to provide personalized and adaptive feedback, fostering a deeper understanding of creative processes and improving educational practices.