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Jonathan Barone-University of Washington


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“Dollars as Points: Marrying Real and In-Game Progress”

Serious game creators want good play to create measurable real-world benefit. Players want games to provide positive feedback for good play. Learn strategies to satisfy both of these requirements in a harmonious, efficient way, and how to identify warning signs that your game may be missing the mark.

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Jonathan Barone-University of Washington

  1. 1. Dollars as Points Marrying Real and In-Game Progress Jonathan Barone Center for Game Science University of Washington
  2. 2. What stakeholders want: Users playing because they enjoy the game Measurable benefit
  3. 3. Reality: Users playing because they have to ???
  4. 4. About CGS • We make scientific discovery and math education games • then use those games for research. • Ultimate goal: expert-level knowledge from games • Foldit Treefrog Treasure
  5. 5. Intro: DNA
  6. 6. Overview • What reality-anchored scoring systems can do for serious games • How to design and implement such a system
  7. 7. What’s in a score? Super Hexagon score formula: t Civilization 4 score formula:
  8. 8. Serious games and score • How do serious games use score? Engagement Performance Evaluation
  9. 9. Serious games and score • How do serious games use score? Engagement AND performance evaluation
  10. 10. Inaccurate/arbitrary scoring You scored 6,230 points! B - “No.” “Okay.” Days? Days? Days? Weeks? Days? So, weeks or months later:
  11. 11. Well-correlated scoring Instant (Little later) Work
  12. 12. Designing a scoring system • Is a score that reflects real metrics feasible and practical? • How much flexibility do we have? • Prototype/iterate. • Does it work for the players? • Does it work for the partners?
  13. 13. Should we bother? 66% 33% yep
  14. 14. Acceptable abstraction Scientists Players 100% 66% B-
  15. 15. Prototype, iterate • You know the drill. • One catveat: involve a domain expert from the start.
  16. 16. Does it work for players? • Qualitative, non-leading questions: – Do they understand the concepts? – Is it motivating them? • A/B test if possible • Hopefully:
  17. 17. Does it work for partners? • Quantitative, statistically significant data: – Compare to control group. – Show transfer to real life. – Compare to value of non-game methods. • Hopefully:
  18. 18. Outro: DNA
  19. 19. Conclusion • Scoring needs to suit the players. • For use as a real metric, it needs to suit the partners, too. • It’s critical for the designer to understand the field and constraints. • Qualitative evidence from players, quantitative evidence to partners.
  20. 20. Acknowledgements • The DNA team: Brian Britigan, Matt Burns, Seth Cooper, Rowan Copley, Barbara Krug, Sundipta Rao, Zoran Popovic, Georg Seelig, and Eric Winfree • • Screenshots credited to: Terry Cavanagh, Firaxis, Green-Eye Visualization