• DocumentCode
    251918
  • Title

    Adaptive Approach for Gamification Optimization

  • Author

    Codish, David ; Ravid, Gilad

  • Author_Institution
    Ind. Eng. & Manage., Ben-Gurion Univ. of the Negev, Beer-Sheba, Israel
  • fYear
    2014
  • fDate
    8-11 Dec. 2014
  • Firstpage
    609
  • Lastpage
    610
  • Abstract
    Gamification solutions today are mostly applied as a single strategy to products and processes (one size fits all approach), thus the engagement and playfulness levels achieved from them are sub-optimal and possibly even negative. Ensuring users are fully engaged in gamification requires that designers understand what motivates users and how different game elements, or combination of elements, address these needs. Measuring engagement through Gamification Analytics (GA) and more importantly being able to act based on this data and modify the implementation in a timely manner if needed, is an important capability that is mostly missing today, and is addresses in the framework proposed in this paper. Since different people are motivated differently, an ideal gamification implementation should be able to perform on-line GA and induce the above modifications, at the user level, taking into account his demographics and personality and creating a personalized gamification experience within the overall gamification solution. We additionally propose a cloud based, open-source database for sharing different game mechanics and dynamics structures.
  • Keywords
    cloud computing; database management systems; game theory; optimisation; cloud based database; dynamics structure; game element; game mechanics; gamification analytics; gamification implementation; gamification optimization; gamification solution; online GA; open-source database; personalized gamification experience; playfulness level; Cloud computing; Conferences; Adaptive Gamification; Gamification; Gamification Analytics; Personality;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Utility and Cloud Computing (UCC), 2014 IEEE/ACM 7th International Conference on
  • Conference_Location
    London
  • Type

    conf

  • DOI
    10.1109/UCC.2014.94
  • Filename
    7027561