Expertise and Engagement: Re-Designing Citizen Science Games With Players' Minds in Mind

Many studies have already shown that games can be a useful tool to make boring or difficult tasks more engaging. However, with serious game design being a relatively nascent field, such experiences can still be hard to learn and not very motivating. In this paper, we explore the use of learning and motivation frameworks to improve player experience in the well-known citizen science game Foldit. Using Cognitive Load Theory (CLT) and Self Determination Theory (SDT), we developed six interface and mechanical changes to the tutorial levels in Foldit designed to increase engagement and retention. We tested these features with new players of Foldit and collected both behavioral data, using game metrics, and prior experience data, using self-report measures. This study offers three major contributions: (1) we document the process of operationalizing CLT and SDT as new game features, a unique methodology not used in game design previously; (2) the user interface, specifically the level selection screen, significantly impacts how players progress through the game; and (3) a player's expertise, whether from prior domain knowledge or prior gaming experience, increases their engagement. We discuss both implications of these findings as well as how these implementations can generalize to other designs.