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Jonathan Barone-University of Washington

“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

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  1. Dollars Dollars as Marrying Real and In-Game Progress as Points Jonathan Barone Center for Game Science University of Washington

  2. What stakeholders want: Users playing because they enjoy the game Measurable benefit

  3. Reality: Users playing because they have to ???

  4. About CGS • We make scientific discovery and math education games Treefrog Treasure Foldit • then use those games for research. • Ultimate goal: expert-level knowledge from games • centerforgamescience.org

  5. Intro: DNA

  6. Overview • What reality-anchored scoring systems can do for serious games • How to design and implement such a system

  7. What’s in a score? Super Hexagon score formula: t Civilization 4 score formula:

  8. Serious games and score • How do serious games use score? Performance Evaluation Engagement

  9. Serious games and score • How do serious games use score? Engagement AND performance evaluation

  10. Inaccurate/arbitrary scoring Days? Days? You scored 6,230 points! “No.” Days? “Okay.” So, weeks or months later: Weeks? B- Days?

  11. Well-correlated scoring Work Instant (Little later)

  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. Should we bother? yep 66% 33%

  14. Acceptable abstraction Scientists Players B- 66% 100%

  15. Prototype, iterate • You know the drill. • One catveat: involve a domain expert from the start.

  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. 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. Outro: DNA

  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. 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 jbarone@cs.washington.edu Centerforgamescience.org

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