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DataMiner Presentation 2011 05-24 v0.3
DataMiner Presentation 2011 05-24 v0.3
DataMiner Presentation 2011 05-24 v0.3
DataMiner Presentation 2011 05-24 v0.3
DataMiner Presentation 2011 05-24 v0.3
DataMiner Presentation 2011 05-24 v0.3
DataMiner Presentation 2011 05-24 v0.3
DataMiner Presentation 2011 05-24 v0.3
DataMiner Presentation 2011 05-24 v0.3
DataMiner Presentation 2011 05-24 v0.3
DataMiner Presentation 2011 05-24 v0.3
DataMiner Presentation 2011 05-24 v0.3
DataMiner Presentation 2011 05-24 v0.3
DataMiner Presentation 2011 05-24 v0.3
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DataMiner Presentation 2011 05-24 v0.3

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Presentation for the course of Human-Computer Interaction. …

Presentation for the course of Human-Computer Interaction.
Our final iteration of our social news application.

Published in: Education, Technology
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Transcript

  • 1. DataMiner
  • 2. DataMiner
    What is DataMiner?
    Our goal
    Demo
    Resultsiterations 1-3
    Functionalityafter iteration 3
    Iteration 4
    Conclusion
  • 3. What is DataMiner?
    DataMiner is a socialnewsgame.
    The user mines the underground forarticles. Mineralsrepresent different kinds of articles.
    The applicationlearnswhicharticles the user likes.
  • 4. Our goal
    Bringactuality in a playful way.
    We want toreachgroupswho’sprimaryinterestsaren’trelatedtonews.
    We want touse the game element topersuade these groupstoreadnews more often.
  • 5.
  • 6. Results
    Iteration 1 (9/4 – 24/4)
    2.45 clicks per article, keystrokesseverelyunderused
    Balanced ratings
    Some users never minedanarticle
    Users didn’tlike the graphics
    Some minor glitches
    Users askedfora legenda
  • 7. Results
    Iteration 2 (25/4 – 3/5)
    Some users found the help, othersdidn’t
    Only 9 users 
  • 8. Results
    Iteration 3 (4/5 – 9/5)
    On average 8.15 minutes foreachvisit
    Only 7 users 
  • 9. Iteration 3
    Availablefunctionality
    Miningarticles
    Rating articles
    Badges
    Legend
    Statistics
    Improvedgraphics
  • 10. Iteration 4 (10/5 – 23/5)
    New functionality
    Invite friends
  • 11. Iteration 4
    Goals & Methods
    Attract more users
    Measurenumber of users
    Google analytics
    Trytofindwhy users aren’tcoming
    Measureifall users canstill mine andratearticles
    Make the few users we have come back
    Google analytics
  • 12. Iteration 4
    Results
    29 users 
    41% comes back at leastonce
    Balancedratings
  • 13. Conclusion
    The application is easy touse!
    A reasonableamount of users come back toourapplication.
    Users find the applicationineffectiveforfindingarticles. (dueto bug)
    Most users don’tbecome more interested in news.
  • 14. Questions?

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