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Personalized games should provide a better player experience than one-size-fits-all games. As a method for personalization, player type models have been discussed recently. Player type models would be useful tools in the personalization of games, if they have a relationship to the players’ experience of specific game mechanics. However, this relationship has never been empirically investigated. To close this gap, we examine whether player types—as a specific appearance of personality traits—can significantly and reliably predict player experience. We investigate the predictive power of two player types (Mastermind, Seeker) of the BrainHex player type model. Results of a field study (n = 51) with a mobile game prototype tailored to the two player types Mastermind and Seeker suggest that player type models still need improvement: Player type scores do not significantly predict player experience of according game mechanics. We discuss possible explanations and a way to design personalized games that adapt to users gaming preferences with player type models.

End-user programming concepts are increasingly employed in smart home research to address the growing complexity of controlling smart home environments. Different approaches and visual styles of end-user programming have been proposed and implemented within this context. Smart home control does however not only necessitate end-user programming but also understanding and modifying existing program structures. In this study, we compare three different approaches regarding their suitability for this application context with a specific focus on intelligibility and modification performance. We conducted an empirical study with 39 users performing three types of tasks (understanding, configuring, and programming), using three different approaches for end-user programming (form-filling, data-flow, and grid-canvas). The results of our study found no significant differences regarding the intelligibility of the three different implementations but clear differences in the subjective preference of users as well as configuration and programming performance.