There is great potential for adapting Virtual Reality (VR) exergames based on a user's affective state. However, physical activity and VR interfere with physiological sensors, making affect recognition challenging. We conducted a study (n=72) in which users experienced four emotion inducing VR exergaming environments (happiness, sadness, stress and calmness) at three different levels of exertion (low, medium, high). We collected physiological measures through pupillometry, electrodermal activity, heart rate, and facial tracking, as well as subjective affect ratings. Our validated virtual environments, data, and analyses are openly available. We found that the level of exertion influences the way affect can be recognised, as well as affect itself. Furthermore, our results highlight the importance of data cleaning to account for environmental and interpersonal factors interfering with physiological measures. The results shed light on the relationships between physiological measures and affective states and inform design choices about sensors and data cleaning approaches for affective VR.
doi.org/10.1145/3613904.3642611
Today, millions worldwide play popular location-based games (LBGs) such as Pokémon GO. LBGs are designed to be played outdoors, and past research has shown that they can incentivize players to travel to nature. To further explore this nature-connection, we investigated via a mixed-methods approach the connections between engagement with LBGs, inspiration and environmental awareness as follows. First, we identified relevant gamification features in Study 1. Based on the insights, we built a survey that we sent to Pokémon GO players (N=311) in Study 2. The results showed that (a) social networking features, reminders, and virtual objects were the most relevant gamification features to explain inspired by playing Pokémon GO and that (b) inspired to outdoor engagement partially mediated the relationship between inspired by playing Pokémon GO and environmental awareness. These results warrant further investigations into whether LBGs could motivate pro-environment attitudes and inspire people to care for nature.
doi.org/10.1145/3613904.3642786
Previous work demonstrated that esports players often leverage insights from other players and communities to learn and improve. However, little research examined social learning in esports, over time, in granular detail. Understanding the role of others in the esports learning process has implications for the design of computational support systems that can help esports players learn and make the games more accessible. Therefore, we perform an exploration of this topic using Co-Regulated Learning as a theoretical lens. In doing so, we hope to enrich existing knowledge on social learning in esports, provide insights for the future development of computational support, and a road-map for future work. Through an interview study of an esports community consisting of 14, college-aged, male players, we uncovered 10 themes regarding how Co-Regulated learning occurs within their teams. Based on these, we discuss three main takeaways and their implications for future research and development.
doi.org/10.1145/3613904.3642249
Computer mice are widely used today as the primary input device in competitive video games. If a player exhibits more wrist rotation than other players when moving the mouse laterally, the player is said to have stronger wrist-aiming habits. Despite strong public interest, there has been no affordable technique to quantify the extent of a player's wrist-aiming habits and no scientific investigation into how the habits affect player performance and workload. We present a reliable and affordable technique to quantify the extent of a player's wrist-aiming habits using a mouse equipped with two optical sensors (i.e., a dual-sensor mouse). In two user studies, we demonstrate the reliability of the technique and examine the relationship between wrist-aiming habits and player performance or workload. In summary, player expertise and mouse sensitivity significantly impacted wrist-aiming habits; the extent of wrist-aiming showed a positive correlation with upper limb workload.
doi.org/10.1145/3613904.3642797
The aim of this work is to explore the forms of toxic behaviour that players encounter in competitive multiplayer real-time strategy (RTS) games. To this end, we carried out ethnographic observations and player interviews within the popular RTS game StarCraft II, and approached the data inductively, leading us to discover ten categories of toxic behaviour. While the harmfulness of toxic actions can be obtained as a product of severity and frequency, players' assessment of the severity of toxic behaviors was contextualized by, (1) directly observed; (2) background; and (3) extraneous factors. Following our empirical findings, we derive a conceptual model for differentiating toxicity from mildly annoying and more severe behaviors. The discovered view of toxicity challenges the prevailing paradigm of treating players' toxic behavior as a monolithic construct with a linear intensity spectrum. Instead, we advocate for a granular approach that acknowledges the underlying dynamics behind negative online behaviors.
doi.org/10.1145/3613904.3642137