Echo: Analyzing Gameplay Sessions by Reconstructing Them From Recorded Data

Games user research (GUR) is centered on ensuring games deliver the experience that their designers intended. GUR researchers frequently make use of playtesting to evaluate games. This often requires watching back hours of video footage after the session to ensure that they did not miss anything important. Analytics have been used to help improve this process, providing visualizations of the underlying gameplay data. Yet, many of these game analytics tools provide static visualizations which do not accurately capture the dynamic aspects of modern video games. To address this problem, we have created Echo, a tool that uses gameplay data to reconstruct the original session with in-game assets, instead of abstracting them away. Echo has been designed to help bridge the gap between static gameplay data representation and video footage, with the goal of providing the best of both. A user study revealed that participants found Echo less frustrating to use compared to videos for gameplay analysis and also ranked it higher for efficiency, among others. It revealed that participants felt less cognitive load when using Echo as well. Qualitative results were also promising as participants employed several distinct workflows while using Echo. We received numerous suggestions for building upon the current state of the tool, including support for multiple viewports, live annotations, and visible gameplay metrics.