This project explores how multidimensional bio-psychological measures are used to understand the cognitive aspects of student learning in STEM (Science, Technology, Engineering and Math) focused educational games. Furthermore, we seek to articulate a method for how learning events can be automatically analyzed using these tools. Given the complexity and difficulty of finding externalized markers of learning as it happens, it is evident that more robust measures could benefit this process. The work reported here, with funding from National Science Foundation grant (NSF DRL-1417456), aims to incorporate more diverse measures of behavior and physiology in order to create a more complete assessment of learning and cognition in a game based environment. Tools used in this project include eye tracking systems, heart rate sensors, as well as tools for detecting electrodermal activity (EDA), temperature and movement data. Findings indicated both the utility of more varied measures as well as the need for more precise tools for synchronization of diverse data streams.