Within the field of game analytics, visualization has been widely instrumentalized to provide an effective graphical interface for analysts to understand player experiences, preferences, and behaviors. However, it is often difficult to decipher strategies or intent with vast amounts of granular events that are rapidly accumulating. Most current visualization approaches focus on aggregated data, utilizing fundamental charts, heatmaps, and scatterplots to conduct statistical analyses, neglecting sequential information across player behavior. In this paper, we focus on sequential visualization approaches, proposing a set of requirements and an initial version of a visualization system developed based on a participatory, iterative co-design approach, collaborating with stakeholders of different games. This co-creative development demonstrates what features analysts need to reason about process visualization, which informs game analytics research. We further discuss a technical realization incorporating the resulting requirements, e.g., manipulation, filtering, abstraction, and segmentation, as well as the underlying algorithms used to realize them.

Adaptation, or ability and willingness to consider an alternative approach, is a critical component of learning through reflection, especially in educational games, where there are often multiple avenues to success. As a domain, educational games have shown increased interest in using retrospective visualizations to promote and support reflection. Such visualizations, which can facilitate comparison with peer data, may also have an impact on adaptation in educational games. This has, however, not been empirically examined within the domain. In this work, we examine how comparison with other players’ data influenced adaptation, a part of reflection, in the context of a game that teaches parallel programming. Our results indicate that comparison with peers does significantly impact willingness to try a different approach, but suggest that there may also be other ways. We discuss what these results mean for future use of retrospective visualizations in educational games and present opportunities for future work.

Identifying explainable player strategies and decision patterns that give insights into player behavior is one of the most difficult tasks for game analytics, yet yields great informative potential for various purposes. Industrial stakeholders can capture player experience and infer issues or feedback on design, content, and game balancing - while players themselves might want to leverage this technique to contrast their style of play to other players, fostering self-regulated learning. On top of that, in the case of educational games, the identification of learning strategies (as well as the discovery of popular erroneous strategies) could even elevate their potential to successfully communicate educational concepts. To advance this field, we investigate how the spatial map of a game contributes to identifying player strategies and emphasize the importance of the appropriate level of abstraction to capture strategical decisions. Using visualizations and expert domain knowledge about spatial abstraction, we illustrate how the partitioning into affordance zones can reveal patterns and strategies in gameplay. To showcase the generalizability of our methodology, we investigate two case studies for the distinct genres of educational games (Parallel) and MMORPGs (Guild Wars 2). The two case studies unveil insightful strategies between different player sets of interest – only possible by the apt level of spatial abstraction.

Within the field of games, visualization of player log data is becoming an important method for its utility in providing an intuitive and informative way to understand players' experience, which is thus often used by game analytics personnel and game user researchers. Moreover, even players themselves show increased interest in using analytics to quantify and self-improve their performances. Among other types of visualizations, node-edge graphs have proven to be capable of revealing the process of individual and aggregated players, allowing analysts to discover play patterns that can inform game design. However, visualization of player traces often has several disadvantages. First, displaying players' process data does not trivially scale, as tendentially high variance often leads to complex and abstruse graphs. Second, when aggregating all players, individual variations are often overlooked. For example, data from minorities (e.g., casual players or players who played the game very differently than others) are often treated as outliers or noise. In this paper, we present an iterative segmentation approach that allows analysts to interact with the visualization and group players into different subcategories through meta-data or behavioral patterns. Using this approach, analysts can bypass complicated visualizations while protecting significant unique information.

Emotion regulation and coping strategies are key to resilience, problem solving and eventually well-being in everyday life, but investigating or influencing these without adjustable and ecologically valid environments still poses a major challenge. With respect to video games, stressful events can frequently appear and, depending on the individual’s emotion regulation, lead to frustrating experiences, dissatisfied players or even churn – or to a sense of accomplishment, mastery or positive tension. In the greater endeavor of establishing games for studying, controlling and reinforcing resilience, we developed an alternate reality game that unifies the advantages of a fully customizable game environment with the close connection to real-life interactions. To estimate how emotion regulation strategies could be initiated as well as investigated, this work first induces different kinds of established stressors (time pressure, social encounters and being unchangeably stuck) into ordinary gameplay, quantifies the players’ physiological and psychological responses and addresses the participants’ strategies on dealing with these situations. In this paper we present results of the study showing that the induced stressors were effective in constituting stressful situations comparable to real-life experiences; and each of these conditions resulted in different types of physiological and perceived responses and behaviors, e.g. continuous stress in social encounters versus instantaneous stress when notified about time pressure; or frustrating experiences when being forcibly stuck versus stress that participants underwent but were able to work against. With this platform to study how people cope with stress, instituted within a multi-modal mixed-methods evaluation, we contribute to games beyond entertainment towards educating resilience.

To reduce frustration while performing no-risk tasks (e.g. in training and games) for BCI users, we propose increasing their perceived level of control through fabricated input - system-generated positive task outcomes. Two surrogate BCI studies injected fabricated input creating additional positive task outcomes to a 50% baseline. Users' perceived control increased significantly compared to the 50% baseline. In turn, frustration levels decreased. Fabricated input worked equally well in a game story context that provided an emotional stake in the protagonist's success and a simpler task lacking such incentives. People's number of input attempts during the tasks determined perceived control more than our controlled ratios of positive to negative task outcomes. Delays between users' input attempts and subsequent fabricated inputs further moderated their perceived control.

Many successful games rely heavily on data analytics to understand players and inform design. Popular methodologies focus on machine learning and statistical analysis of aggregated data. While effective in extracting information regarding player action, much of the context regarding when and how those actions occurred is lost. Qualitative methods allow researchers to examine context and derive meaningful explanations about the goals and motivations behind player behavior, but are difficult to scale. In this paper, we build on previous work by combining two existing methodologies: Interactive Behavior Analytics (IBA) [2] and sequence analysis (SA), in order to create a novel, mixed methods, human-in-the-loop data analysis methodology that uses behavioral labels and visualizations to allow analysts to examine player behavior in a way that is context sensitive, scalable, and generalizable. We present the methodology along with a case study demonstrating how it can be used to analyze behavioral patterns of teamwork in the popular multiplayer game Defense of the Ancients 2 (DotA 2).