Exploring Data-Driven Approaches to Visualize Learners' Engagement Profile in Immersive Gameful Experiences

Abstract

This paper presents a design approach to process, visualize, and analyze use of behavioral log data collected from the Watershed Explorers: Industrial History immersive virtual reality (VR) environment. We managed 1,142 quantitative behavioral log records generated by 18 players interacting within the environment. Time-stamped log data was extracted via REDCap and organized chronologically based on action types associated with different VR learning experience features. The raw quantitative log data was further processed into four activity categories—engagement with videos, images, glossaries, and narration—allowing for a macro-level visualization of the time spent on each activity type by all players. We demonstrate how individual players’ behavioral engagement profiles can be visualized to replicate their gameplay trajectories across different elements (Narration, Photo, Video, and Glossary). These insights pave the way for future design implications, particularly in curating personalized immersive VR experiences. Implications and design considerations for future research, including aligning engagement profiles with survey data and diversifying the types of behavioral engagement log data to enrich player engagement profiles, are also discussed.