RT Journal Article
T1 Improving evidence-based assessment of players using serious games
A1 Alonso Fernández, Cristina
A1 Freire Morán, Manuel
A1 Martínez Ortiz, Iván
A1 Fernández Manjón, Baltasar
AB Serious games are highly interactive systems which can therefore capture large amounts of player interaction data. This data can be analyzed to provide a deep insight into the effect of the game on its players. However, traditional techniques to assess players of serious games make little use of interaction data, relying instead on costly external questionnaires. We propose an evidence-based process to improve the assessment of players by using their interaction data. The process first combines player interaction data and traditional questionnaires to derive and refine game learning analytics variables, which can then be used to predict the effects of the game on its players. Once the game is validated, and suitable prediction models have been built, the prediction models can be used in large-scale deployments to assess players solely based on their interactions, without the need for external questionnaires. We briefly describe two case studies where this combination of traditional questionnaires and data mining techniques has been successfully applied. The evidence-based assessment process proposed radically simplifies the deployment and application of serious games in real class settings.
SN 0736-5853
YR 2021
FD 2021
LK https://hdl.handle.net/20.500.14352/101233
UL https://hdl.handle.net/20.500.14352/101233
LA eng
NO Alonso-Fernández, Cristina, et al. «Improving Evidence-Based Assessment of Players Using Serious Games». Telematics and Informatics, vol. 60, julio de 2021, p. 101583. https://doi.org/10.1016/j.tele.2021.101583.
NO European Commission
NO Comunidad de Madrid
DS Docta Complutense
RD 31 jul 2025