Abstract
Analysis of game data is used to study player behavior. For puzzle-based games where solutions are usually defined by their action sequences, player behavior can also be studied by their solution complexity. 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. 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. Specialized glyph system (Strategy Signature) is implemented to find strategy differences easily with simple visual cues. Contextual information can be explored by switching the view modes to see potential links between complexity and raw attributes. 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. In summary, this paper illustrates a visualization approach to enable analysis into the subtleties of behavior complexity in video games.Graphical abstract
Highlights
Like many other casual activities, video game is a platform that allows diverse interactions
For puzzle-based games where solutions are usually defined by their action sequences, player behavior can be studied by their solution complexity
Given by the raw data being in event sequence format, we found the application area of the two methods can be extended to other fields where reasoning with the diversity and complexity of repetitive solution is meaningful
Summary
Like many other casual activities, video game is a platform that allows diverse interactions. Game data usually take the form of an array of primitive factors in the game (gold, action, kill for instance) Visualization of these primitive values is not always sufficient to illustrate the more underlying behavior pattern. Through the changes in measurable scores and player actions, behavior complexity varies and differs from each individual attempt at executing a particular strategy. Over time, these data show how strategies are chosen, executed and refined, and encoded with appropriate visual signals, the inherent complexity produced by players may lead to a new angle to study their behaviors as they improve through time. Little existing literature has covered a visualization approach to facilitate such perspective, i.e., the integration of behavior complexity and effective visualization design
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