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

J. Pérez, M. Castro, E. Awad, G. López

Knowledge-Based Systems Vol. 286, pp. 111440-1 - 111440-10


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.

Resumen divulgativo:

Esta investigación propone una arquitectura de simulador para generar datos sintéticos en juegos serios, utilizando modelos cognitivos y redes bayesianas. El objetivo es mejorar el modelado de datos y acelerar desarrollos, lo que se demuestra mediante un estudio de caso sobre un juego de ciberacoso.

Palabras Clave: Synthetic data; Serious games; Cyberbullying; Item response theory; Bayesian network; Hierarchical Bayesian model; Computational social science

Índice de impacto JCR y cuartil WoS: 8,800 - Q1 (2022)

Referencia DOI: DOI icon

Publicado en papel: Febrero 2024.

Publicado on-line: Enero 2024.

J. Pérez, M. Castro, E. Awad, G. López, Generation of probabilistic synthetic data for serious games: a case study on cyberbullying. Knowledge-Based Systems. Vol. 286, pp. 111440-1 - 111440-10, Febrero 2024. [Online: Enero 2024]

    Líneas de investigación:
  • Juegos serios, juegos formativos
  • Análisis de datos