Towards Predicting Hexad User Types from Smartphone Data

Maximilian Altmeyer; Pascal Lessel; Marc Schubhan; Antonio Krüger

In: Extended Abstracts of the 2019 Annual Symposium on Computer-Human Interaction in Play. ACM SIGCHI Annual Symposium On Computer-Human Interaction in Play (CHI Play-2019), October 22-25, Barcelona, Spain, ISBN 978-1-4503-6871-1/19/10, ACM, 10/2019.


Tailoring gamified systems has been shown to be appreciated and more effective than “one-size-fits-all” systems. A promising approach is using the Hexad user types model. However, obtaining the Hexad user type requires users to fill out a questionnaire, preventing an automated adaptation. Since smartphone data was shown to be linked to personality traits, which in turn were shown to be linked to the Hexad user types, we explore to what extent it can be used to predict the score of each user type. In our study (N=122) we found regression models, indicating that using smartphone data to predict user types is promising and may allow to tailor gamified systems without explicit user interaction.

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altmeyer_TowardsPredictingHexadUserTypesfromSmartphoneData.pdf (pdf, 787 KB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence