期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2021
卷号：12
期号：12
DOI：10.14569/IJACSA.2021.0121257
语种：English
出版社：Science and Information Society (SAI)
摘要：Today, due to their interactive, participatory and entertaining nature, the Serious Games set themselves apart from other learning methods used in teaching. Much progress has been made in the design techniques and methods of Serious Games, but little in their evaluation. In order to fill this gap, we had proposed in our previous work an evaluation tool capable of helping practitioners to evaluate Serious Games in different training contexts. This evaluation tool for Serious Games is designed around four dimensions, namely the pedagogical, technological, ludic and behavioral dimensions, which are measured by clearly defined criteria. During this process, it was highlighted that the human factor (evaluator) influences considerably the result of the weightings through the choice to weight the evaluation dimensions of the Serious Games. In order to reduce this influence during the evaluation process and to keep the correlation between the variables of our evaluation system, we present in this paper, an improvement of our evaluation tool by equipping it with an intelligent supervised self-learning algorithm allowing self-regulation of the weights according to the context of use of the Serious Game to be evaluated. Thanks to the experimental verification of the optimization results, the root mean square error and the coefficient of determination are 0.016 and 98.59 percent respectively, indicating that the model has high precision which guaranteed better predictive performance. A comparison was made between this intelligent model and the models presented in our previous work; the results obtained indicated the same order of the four dimensions, and this by reducing the influence of the human factor during the Multi-Output Support Vector Regression weighting process.
关键词：Serious game; evaluation tool; multi-output support vector regression
