Abstract
The Data2Game project investigates how the efficacy of computerized training games can be enhanced by tailoring training scenarios to the individual player. The research is centered around three research innovations: (1) techniques for the automated modelling of players’ affective states, based on exhibited social signals, (2) techniques for the automated generation of in-game narratives tailored to the learning needs of the player, and (3) validated studies on the relation of the player behavior and game properties to learning performance. This paper describes the integration of the main results into a joint prototype.
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Acknowledgments
Main credits go to the first three authors, who contributed equally to the paper. All project members deserve credits for making this project successful. And of course, our acknowledgments include the co-funding from NWO (Project number 055.16.114).
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Steinrücke, J. et al. (2021). Data2Game: Towards an Integrated Demonstrator. In: Ahram, T.Z., Falcão, C.S. (eds) Advances in Usability, User Experience, Wearable and Assistive Technology. AHFE 2021. Lecture Notes in Networks and Systems, vol 275. Springer, Cham. https://doi.org/10.1007/978-3-030-80091-8_28
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