Paper published in a book (Scientific congresses, symposiums and conference proceedings)
A generalizable performance evaluation model of driving games via risk-weighted trajectories
Flemming, Rory; Schmück, Emmanuel; Mussack, Dominic et al.
2019In Proceedings of The 12th International Conference on Educational Data Mining (EDM 2019)
Peer reviewed
 

Files


Full Text
flemming2019.pdf
Author postprint (484.57 kB)
Request a copy

All documents in ORBilu are protected by a user license.

Send to



Details



Abstract :
[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.
Disciplines :
Social & behavioral sciences, psychology: Multidisciplinary, general & others
Author, co-author :
Flemming, Rory;  University of Minnesota > Department of Psychology
Schmück, Emmanuel ;  University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Behavioural and Cognitive Sciences (DBCS)
Mussack, Dominic ;  University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Behavioural and Cognitive Sciences (DBCS)
Cardoso-Leite, Pedro ;  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
External co-authors :
yes
Language :
English
Title :
A generalizable performance evaluation model of driving games via risk-weighted trajectories
Publication date :
2019
Event name :
12th International Conference on Educational Data Mining (EDM 2019)
Event date :
from 02/07/2019 to 05/07/2019
Audience :
International
Main work title :
Proceedings of The 12th International Conference on Educational Data Mining (EDM 2019)
Pages :
551
Peer reviewed :
Peer reviewed
FnR Project :
FNR11242114 - Scientifically Validated Digital Learning Environments, 2016 (01/06/2017-31/01/2023) - Pedro Cardoso-leite
Funders :
FNR - Fonds National de la Recherche [LU]
Commentary :
548
Available on ORBilu :
since 03 March 2021

Statistics


Number of views
45 (6 by Unilu)
Number of downloads
0 (0 by Unilu)

Scopus citations®
 
0
Scopus citations®
without self-citations
0

Bibliography


Similar publications



Contact ORBilu