Visualizing event sequence game data to understand player’s skill growth through behavior complexity

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

[1]  James T. Enns,et al.  High-speed visual estimation using preattentive processing , 1996, TCHI.

[2]  Cynthia A. Brewer,et al.  ColorBrewer.org: An Online Tool for Selecting Colour Schemes for Maps , 2003 .

[3]  Ye Zhao,et al.  Visualizing Hidden Themes of Taxi Movement with Semantic Transformation , 2014, 2014 IEEE Pacific Visualization Symposium.

[4]  Jarke J. van Wijk,et al.  Exploring Multivariate Event Sequences Using Rules, Aggregations, and Selections , 2018, IEEE Transactions on Visualization and Computer Graphics.

[5]  Mira Dontcheva,et al.  MatrixWave: Visual Comparison of Event Sequence Data , 2015, CHI.

[6]  Yang Wang,et al.  Patterns and Sequences: Interactive Exploration of Clickstreams to Understand Common Visitor Paths , 2017, IEEE Transactions on Visualization and Computer Graphics.

[7]  Kyung-Joong Kim,et al.  Interpreting behaviors of mobile game players from in-game data and context logs , 2015, 2015 IEEE Conference on Computational Intelligence and Games (CIG).

[8]  Guenter Wallner Sequential Analysis of Player Behavior , 2015, CHI PLAY.

[9]  BEN MEDLER,et al.  Analytics of Play : Using Information Visualization and Gameplay Practices for Visualizing Video Game Data , 2011 .

[10]  Ben Shneiderman,et al.  LifeFlow: visualizing an overview of event sequences , 2011, CHI.

[11]  Jan L. Plass,et al.  Visualizing log-file data from a game using timed word trees , 2018, Inf. Vis..

[12]  Emanuel Zgraggen,et al.  (s|qu)eries: Visual Regular Expressions for Querying and Exploring Event Sequences , 2015, CHI.

[13]  R. Caillois,et al.  Man, Play and Games , 1958 .

[14]  Ben Shneiderman,et al.  Cohort Comparison of Event Sequences with Balanced Integration of Visual Analytics and Statistics , 2015, IUI.

[15]  Michael John,et al.  Data cracker: developing a visual game analytic tool for analyzing online gameplay , 2011, CHI.

[16]  Donald Craig,et al.  An EHR interface for viewing and accessing patient health events from collaborative sources , 2011, 2011 International Conference on Collaboration Technologies and Systems (CTS).

[17]  Wei Li,et al.  Toward Visualizing Subjective Uncertainty: A Conceptual Framework Addressing Perceived Uncertainty through Action Redundancy , 2018, EuroRV³@EuroVis.

[18]  Danielle Albers Szafir,et al.  Modeling Color Difference for Visualization Design , 2018, IEEE Transactions on Visualization and Computer Graphics.

[19]  Gilbert Ritschard,et al.  Analyzing and Visualizing State Sequences in R with TraMineR , 2011 .

[20]  Mike Sips,et al.  Understanding a Sequence of Sequences: Visual Exploration of Categorical States in Lake Sediment Cores , 2018, IEEE Transactions on Visualization and Computer Graphics.

[21]  Günter Wallner,et al.  Visualization-based analysis of gameplay data - A review of literature , 2013, Entertain. Comput..

[22]  E. Fussell,et al.  Measuring the Early Adult Life Course in Mexico: An Application of the Entropy Index , 2005 .

[23]  Yuanzhe Chen,et al.  Sequence Synopsis: Optimize Visual Summary of Temporal Event Data , 2018, IEEE Transactions on Visualization and Computer Graphics.

[24]  Quan Li,et al.  A Visual Analytics Approach for Understanding Reasons behind Snowballing and Comeback in MOBA Games , 2017, IEEE Transactions on Visualization and Computer Graphics.

[25]  E. Vogel,et al.  Visual working memory capacity: from psychophysics and neurobiology to individual differences , 2013, Trends in Cognitive Sciences.

[26]  Aart C. Liefbroer,et al.  De-standardization of Family-Life Trajectories of Young Adults: A Cross-National Comparison Using Sequence Analysis , 2007 .

[27]  Robert Biddle,et al.  Agile Development Iterations and UI Design , 2007, Agile 2007 (AGILE 2007).

[28]  Ben Medler,et al.  Player Dossiers: Analyzing Gameplay Data as a Reward , 2011, Game Stud..

[29]  Juan Manuel Dodero,et al.  An architecture for skill assessment in serious games based on Event Sequence Analysis , 2017, TEEM.

[30]  Ajay S. Vinze,et al.  Event sequence modeling of IT adoption in healthcare , 2013, Decis. Support Syst..

[31]  Christopher D. Shaw,et al.  Visualizing and understanding players' behavior in video games: discovering patterns and supporting aggregation and comparison , 2011, Sandbox '11.

[32]  Min Chen,et al.  Data, Information, and Knowledge in Visualization , 2009, IEEE Computer Graphics and Applications.