You've been logged out of GDC Vault since the maximum users allowed for this account has been reached. To access Members Only content on GDC Vault, please log out of GDC Vault from the computer which last accessed this account.

Click here to find out about GDC Vault Membership options for more users.

close

Session Name:

What Makes Us Tick: Inferring Players' Motivation from Gameplay Behavior to Foster Long-Term Engagement

Overview:

The talk will showcase a novel method to map player features from out-of-game to player behavior within a game. The out-of-game feature in this specific case is player motivation described by the Ubisoft Perceived Experience Questionnaire (UPEQ). First of all it was necessary to collect gameplay in a very granular manner including all possible activities that players can engage with. Additionally it was necessary to ask the same players to report their levels of competence, autonomy, relatedness and presence using UPEQ. Survey responses were processed in an ordinal fashion. Preference learning methods, based on support vector machines, were used to infer the mapping between gameplay and the 4 motivation factors. Our key findings suggest that gameplay features are strong predictors of player motivation as the obtained models reach accuracy of near certainty, from 93% up to 97% on unseen players.

Did you know free users get access to 30% of content from the last 2 years?


Get your team full access to the most up to date GDC content

  • GDC Summer 2020
  • Alessandro Canossa
  • Modl.ai
  • free content
  • Programming
  • Programming