Glyph: Visualization Tool for Understanding Problem Solving Strategies in Puzzle Games

Understanding player strategies is a key question when analyzing player behavior both for academic researchers and industry practitioners. For game designers and game user researchers, it is important to gauge the distance between intended strategies and emergent strategies; this comparison allows identification of glitches or undesirable behaviors. For academic researchers using games for serious purposes such as education, the strategies adopted by players are indicative of their cognitive progress in relation to serious goals, such as learning process. Current techniques and systems created to address these needs present a few drawbacks. Qualitative methods are difficult to scale upwards to include large number of players and are prone to subjective biases. Other approaches such as visualization and analytical tools are either designed to provide an aggregated overview of the data, losing the nuances of individual player behaviors, or, in the attempt of accounting for individual behavior, are not specifically designed to reduce the visual cognitive load. In this work, we propose a novel visualization technique that specifically addresses the tasks of comparing behavior sequences in order to capture an overview of the strategies enacted by players and at the same time examine individual player behaviors to identify differences and outliers. This approach allows users to form hypotheses about player strategies and verify them. We demonstrate the effectiveness of the technique through a case study: utilizing a prototype system to investigate data collected from a commercial educational puzzle game. While the prototype’s usability can be improved, initial testing results show that core features of the system proved useful to our potential users for understanding player strategies.

[1]  Katherine Isbister,et al.  Chapter 5 – Let the Game Tester Do the Talking: Think Aloud and Interviewing to Learn About the Game Experience , 2008 .

[2]  Maneesh Agrawala,et al.  Visualizing competitive behaviors in multi-user virtual environments , 2004, IEEE Visualization 2004.

[3]  Noah Wardrip-Fruin,et al.  First Person: New Media As Story, Performance, And Game , 2004 .

[4]  Mircea Pitici,et al.  The Music of Math Games , 2014 .

[5]  Alessandro Canossa,et al.  Analyzing spatial user behavior in computer games using geographic information systems , 2009, MindTrek '09.

[6]  Aniket Kittur,et al.  Kinetica: naturalistic multi-touch data visualization , 2014, CHI.

[7]  Alessandro Canossa,et al.  Towards gameplay analysis via gameplay metrics , 2009, MindTrek '09.

[8]  David Milam,et al.  Design patterns to guide player movement in 3D games , 2010, Sandbox '10.

[9]  N. Lavie Distracted and confused?: Selective attention under load , 2005, Trends in Cognitive Sciences.

[10]  Keith Cheverst,et al.  3D Space-Time Visualization of Player Behaviour in Pervasive Location-Based Games , 2008, Int. J. Comput. Games Technol..

[11]  Edward M. Reingold,et al.  Graph drawing by force‐directed placement , 1991, Softw. Pract. Exp..

[12]  Yun-En Liu,et al.  Gameplay analysis through state projection , 2010, FDG.

[13]  Jon Crowcroft,et al.  Avatar movement in World of Warcraft battlegrounds , 2009, 2009 8th Annual Workshop on Network and Systems Support for Games (NetGames).

[14]  Katherine Isbister,et al.  Game Usability: Advancing the Player Experience , 2008 .

[15]  Katherine Isbister,et al.  Game Usability - Advice from the Experts for Advancing the Player Experience , 2008 .

[16]  Günter Wallner,et al.  A spatiotemporal visualization approach for the analysis of gameplay data , 2012, CHI.

[17]  Zoran Popovic,et al.  Feature-based projections for effective playtrace analysis , 2011, FDG.

[18]  W. Gehring,et al.  More attention must be paid: The neurobiology of attentional effort , 2006, Brain Research Reviews.

[19]  Lori L. Scarlatos,et al.  Visualizations for the Assessment of Learning in Computer Games , 2011 .

[20]  Michael J. Fischer,et al.  The String-to-String Correction Problem , 1974, JACM.

[21]  Lennart E. Nacke,et al.  Biometric storyboards: visualising game user research data , 2012, CHI Extended Abstracts.

[22]  Bruce Phillips,et al.  Tracking real-time user experience (TRUE): a comprehensive instrumentation solution for complex systems , 2008, CHI.

[23]  Patrick J. F. Groenen,et al.  Modern Multidimensional Scaling: Theory and Applications , 2003 .

[24]  Manfred Tscheligi,et al.  Evaluating user experiences in games , 2008, CHI Extended Abstracts.

[25]  Donald J. Berndt,et al.  Using Dynamic Time Warping to Find Patterns in Time Series , 1994, KDD Workshop.

[26]  Ruck Thawonmas,et al.  Visualization of Online-Game Players Based on Their Action Behaviors , 2008, Int. J. Comput. Games Technol..

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

[28]  Clark Verbrugge,et al.  Measuring cooperative gameplay pacing in World of Warcraft , 2011, FDG.

[29]  John Dingliana,et al.  An empirical study on the impact of edge bundling on user comprehension of graphs , 2012, AVI.

[30]  Lorna Heaton,et al.  Design Research: Methods and Perspectives , 2006 .