An Analytic and Psychometric Evaluation of Dynamic Game Adaption for Increasing Session-Level Retention in Casual Games


This paper shows how game analytics will be used to dynamically adapt casual game environments in order to extend session-level retention. Our technique involves using game analytics to create an abstracted game analytic area to make the matter tractable. We then model player retention in this area and use these models to form guided changes to game analytics so as to bring about a targeted distribution of game states that will, in turn, influence player behavior. Experiments performed showed that the adaptive versions of 2 different casual games, Scrabblesque and Sidequest: The sport, were ready to better work a target distribution of game states whereas also significantly reducing the quitting rate compared to the nonadaptive version of the games. We showed that these gains weren't coming back at the value of player experience by performing a psychometric evaluation in that we tend to measured player intrinsic motivation and engagement with the sport environments. In each cases, we showed that players taking part in the adaptive version of the games reported higher intrinsic motivation and engagement scores than players enjoying the nonadaptive version of the games.

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