Reference : A generalizable performance evaluation model of driving games via risk-weighted traje...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Social & behavioral sciences, psychology : Multidisciplinary, general & others
http://hdl.handle.net/10993/46448
A generalizable performance evaluation model of driving games via risk-weighted trajectories
English
Flemming, Rory [University of Minnesota > Department of Psychology]
Schmück, Emmanuel mailto [University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Behavioural and Cognitive Sciences (DBCS)]
Mussack, Dominic mailto [University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Behavioural and Cognitive Sciences (DBCS)]
Cardoso-Leite, Pedro mailto [University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Behavioural and Cognitive Sciences (DBCS)]
Schrater, Paul [University of Minnesota > Department of Psychology]
2019
Proceedings of The 12th International Conference on Educational Data Mining (EDM 2019)
551
Yes
No
International
12th International Conference on Educational Data Mining (EDM 2019)
from 02/07/2019 to 05/07/2019
Montreal
[en] Efficient learning experiences require content to dynamically match a learner's skill; this assumes a fast and accurate assessment of the learner's skill and the ability to update content accordingly. Effective personalized learning therefore involves deriving a performance-predictive mapping between behavioral and environmental factors. Once learned, this relationship can be used to generate new content and to update skill estimates based on the learner's interactions in an adaptive system. To provide proof of concept: (1) We develop a fast-paced driving video game where the player skillfully navigates a cluttered environment comprising obstacles and collectibles. Game content is generated procedurally and player behavior is recorded in the game-this provides an ideal test-bed for a method aiming to learn such a performance-predictive mapping. (2) Using blurred occupancy maps of the game's segments, we generate risk-weighted trajectory profiles for each user and segment of the game. Here, we show that these profiles can be used in a regression model to predict in-game performance both within and between game segments. Additionally, these profiles themselves reveal a trade-off between in-game rewards and risks. Successful identification of predictive environmental units within the game provides insight into the mapping between environmental features and performance, while facilitating the process of procedurally generating new, appropriate content in our adaptive system. We show that rapidly assessed measures of risk are highly predictive of both driving performance and reward rate, providing proof-of-concept evidence for the feasibility of a personalized adaptive learning system for this game.
Fonds National de la Recherche - FnR
http://hdl.handle.net/10993/46448
548
FnR ; FNR11242114 > Pedro Cardoso-leite > DIGILEARN > Scientifically Validated Digital Learning Environments > 01/06/2017 > 31/05/2022 > 2016

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