To obtain a more holistic view on how players improve, our understanding of player’s and skill and skill growth should consider not only the score obtained, which only measures the attempt’s outcome, but also the implicit strategy choice, execution and refinement that are not directly measurable. It is common to see repeated trials and strategy adjustments based on the latest outcomes in many video games. More interestingly, the complexity usually varies by each attempt a play takes. Based on the context of the game, this complexity can be interpreted as an abstract factor determined by the heterogeneity of players inputs. For instance, the complexity of actions, which is tricky to define and usually visually judged, can be used to reflect intrinsic behavior patterns at each state of the play. But visualization of these primitive values is not always sufficient to illustrate the more underlying behavior pattern. Game data usually take the form of an array of primitive factors in the game (gold, action, kill for instance). The visual approach is also known to lower the barrier of data analysis and thus exploit domain knowledge for informed judgment (Chen et al. To obtain a structural understanding of the data, visual analysis is often utilized to leverage the visual channel to enable fast and flexible exploration into players’ behavior patterns (Wallner and Kriglstein 2013). This makes game logs valuable assets to understand the player thinking preferences as well as skill learning processes. The inputs during game play are results of individual thinking to a constantly changing situation. Like many other casual activities, video game is a platform that allows diverse interactions. In summary, this paper illustrates a visualization approach to enable analysis into the subtleties of behavior complexity in video games. Evaluation with expert users shows that the system effectively reduced their time and effort in finding interesting subgroups and gave them unexplored angles of behavior complexity to contemplate player’s skill growth. Contextual information can be explored by switching the view modes to see potential links between complexity and raw attributes. Specialized glyph system (Strategy Signature) is implemented to find strategy differences easily with simple visual cues. To establish a novel perspective into the patterns not only in action choices but also in behavior complexity, we designed an interactive, customized line chart to track how complexity and performance change at each stage of skill acquisition. In this paper, we present a visualization system to help learning expert to understand how actions, timing and the resulting strategy change with regard to the solution complexity. For puzzle-based games where solutions are usually defined by their action sequences, player behavior can also be studied by their solution complexity. Analysis of game data is used to study player behavior.
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