Assessing the Impact of Visual Design on the Interpretation of Aggregated Playtesting Data Visualization

Abstract

Making effective use of data generated from players interacting with games (often via playtesting to improve game quality) is a challenging task since the datasets are often mixed and very large. To address this, various visualization techniques have been introduced to help game developers cope with the data. However, there is a gap in research concerning the impact of different visual designs on the interpretation of gameplay data. In this paper, we propose four alternative visual designs of an aggregated visualization and assess how professional game developers interpret the data differently due to the changes in the visual designs. Our results provide an understanding and a supporting argument about the impact of the visual properties transparency and shading (both positive and negative) on the interpretation of the represented data. This is an important contribution to the field of Games User Research given the current move towards data-informed decision making and the popularity of data visualizations.