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Generation of probabilistic synthetic data for serious games: a case study on cyberbullying

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Fecha
2024-02-28
Autor
Pérez Sánchez, Jaime
Castro Ponce, Mario
Awad, Edmond
López López, Gregorio
Estado
info:eu-repo/semantics/publishedVersion
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Resumen
 
 
Synthetic data generation has been a growing area of research in recent years. However, its potential applications in serious games have yet to be thoroughly explored. Advances in this field could anticipate data modeling and analysis, as well as speed up the development process. To fill this gap in the literature, we propose a simulator architecture for generating probabilistic synthetic data for decision-based serious games. This architecture is designed to be versatile and modular so that it can be used by other researchers on similar problems (e.g., multiple choice exams, political surveys, any type of questionnaire). To simulate the interaction of synthetic players with the game, we use a cognitive testing model based on the Item Response Theory framework. We also show how probabilistic graphical models (in particular, Bayesian networks) can introduce expert knowledge and external data into the simulation. Finally, we apply the proposed architecture and methods in the case of a serious game focused on cyberbullying. We perform Bayesian inference experiments using a hierarchical model to demonstrate the identifiability and robustness of the generated data.
 
URI
https:doi.org10.1016j.knosys.2024.111440
Generation of probabilistic synthetic data for serious games: a case study on cyberbullying
Tipo de Actividad
Artículos en revistas
ISSN
0950-7051
Materias/ categorías / ODS
Instituto de Investigación Tecnológica (IIT)
Palabras Clave

Synthetic data; Serious games; Cyberbullying; Item response theory; Bayesian network; Hierarchical Bayesian model; Computational social science
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Repositorio de la Universidad Pontificia Comillas copyright © 2015  Desarrollado con DSpace Software
Contacto | Sugerencias