With video games and Esports becoming ever-present, playing as long as possible to improve proficiency has become a common strategy among video game enthusiasts. This behavior does not only bear increased health and social risks, but is also connected to performance decline, which itself inhibits skill acquisition. To promote health and advance self-regulated learning, we investigate the effects of distributed practice on skill acquisition in an ecologically valid Esports task. As research about distributed practice in video games is restricted to simple games or short practice sessions, we explore the impact of distributed versus massed practice with an extended training period of 45 minutes. Using (n = 16) participants and a complex contemporary fighting game, we show that massed practice schedules do not provide any advantage over distributed ones - suggesting that practice time can be evenly distributed without improvement losses, which would alleviate health risks connected to longer sessions.

With the rise of popularity and societal importance of video games, trends of grinding play for excessive amounts of time to improve proficiency becomes more and more common. This bears not only physical and mental health risks, but has already shown to be detrimental for skill acquisition, defeating its purpose in the first place. For the sake of health promotion, well-being, and the advancement of self-regulated learning, we specifically investigate the impact of detailed feedback on player performance. As research about comparable activities in related domains suggest inconclusive implications for when feedback in video game learning should be presented, we particularly explore the impact of delivering immediate versus delayed feedback, during or after play. Using (n = 30) participants and the well-investigated Super Mario Bros., we communicate insights in favor for deploying feedback immediately when polishing one's video game related skills such as in speedrunning.

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.

Room-scale mapping facilitates natural locomotion in virtual reality (VR), but it creates a problem when encountering virtual walls. In traditional video games, player avatars can simply be prevented from moving through walls. This is not possible in VR with room-scale mapping due to the lack of physical boundaries. Game design is either limited by avoiding walls, or the players might ignore them, which endangers the immersion and the overall game experience. To prevent players from walking through walls, we propose a combination of auditory, visual, and vibrotactile feedback for wall collisions. This solution can be implemented with standard game engine features, does not require any additional hardware or sensors, and is independent of game concept and narrative. A between-group study with 46 participants showed that a large majority of players without the feedback did pass through virtual walls, while 87% of the participants with the feedback refrained from walking through walls. The study found no notable differences in game experience.