| Session Name: | What Makes Us Tick: Inferring Players' Motivation from Gameplay Behavior to Foster Long-Term Engagement |
| Speaker(s): | Alessandro Canossa |
| Company Name(s): | Modl.ai |
| Track / Format: | Programming |
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| 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. |
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