Playtesting of games often relies on a mixed-methods approach to obtain more holistic insights about and, in turn, improve the player experience. However, triangulating the different data sources and visualizing them in an integrated manner such that they contextualize each other still proves challenging. Despite its potential value for gauging player behaviour, this area of research continues to be underexplored. In this paper, we propose a visualization approach that combines commonly tracked movement data with - from a visualization perspective rarely considered - gaze behaviour and emotional responses. We evaluated our approach through a qualitative expert study with five professional game developers. Our results show that both the individual visualization of gaze, emotions, and movement but especially their combination are valuable to understand and form hypotheses about player behaviour. At the same time, our results stress that careful attention needs to be paid to ensure that the visualization remains legible and does not obfuscate information.
Customised avatars are a powerful tool to increase identification, engagement and intrinsic motivation in digital games. We investigated the effects of customisation in a self-competitive VR exergame by modelling players and their previous performance in the game with customised avatars. In a first study we found that, similar to non-exertion games, customisation significantly increased identification and intrinsic motivation, as well as physical performance in the exergame. In a second study we identified a more complex relationship with the customisation style: idealised avatars increased wishful identification but decreased exergame performance compared to realistic avatars. In a third study, we found that 'enhancing' realistic avatars with idealised characteristics increased wishful identification, but did not have any adverse effects. We discuss the findings based on feedforward and self-determination theory, proposing notions of intrinsic identification (fostering a sense of self) and extrinsic identification (drawing away from the self) to explain the results.
Balancing games and producing content that remains interesting and challenging is a main cost factor in the design and maintenance of games. Dynamic difficulty adjustments (DDA) can successfully tune challenge levels to player abilities, but when implemented with classic heuristic parameter tuning (HPT) often turns out to be very noticeable, e.g. as "rubber-banding". Deep learning techniques can be employed for deep player behavior modeling (DPBM), enabling more complex adaptivity, but effects over frequent and longer-lasting game engagements, as well as how it compares to HPT has not been empirically investigated. We present a situated study of the effects of DDA via DPBM as compared to HPT on intrinsic motivation, perceived challenge and player motivation in a real-world MMORPG. The results indicate that DPBM can lead to significant improvements in intrinsic motivation and players prefer game experience episodes featuring DPBM over experience episodes with classic difficulty management.
Cognitive tasks are increasingly being gamified in an attempt to leverage the motivational power of games; however, they are sensitive to manipulation and literature is divided on how adding game elements affects participant performance and experience. We applied two popular gamification approaches (points/feedback and theme/narrative) to a typical cognitive task (the dot probe) and measured performance and experience in two studies (N1=287, N2=321). Similar to prior work, we confirm in Study1 that points increase reaction time and error rate, and positive affect. We replicated these results in Study2, and expanded our analysis to investigate participant experience. Our findings suggest that theme creates expectations of an interesting game, which gamified tasks fail to deliver, whereas points maintain enjoyment better throughout the task itself. Important for the development of gamified cognitive tasks, our findings suggest that novel approaches to gameful assessment may be better than the status quo.
Many online games suffer when players drop off due to lost connections or quitting prematurely, which leads to match terminations or game-play imbalances. While rule-based outcome evaluations or substitutions with bots are frequently used to mitigate such disruptions, these techniques are often perceived as unsatisfactory. Deep learning methods have successfully been used in deep player behavior modelling (DPBM) to produce non-player characters or bots which show more complex behavior patterns than those modelled using traditional AI techniques. Motivated by these findings, we present an investigation of the player-perceived awareness, believability and representativeness, when substituting disconnected players with DPBM agents in an online-multiplayer action game. Both quantitative and qualitative outcomes indicate that DPBM agent substitutes perform similarly to human players and that players were unable to detect substitutions. Notably, players were in fact able to detect substitution with agents driven by more traditional heuristics.