# A New Measure for Serious Games Evaluation: Gaming Educational Balanced (GEB) Model
## 4 Conclusions  
A new metric, the Gaming Educational Balanced Model (GEB), has been proposed in this study to evaluate serious games. The theory is based on the MDA framework, adding the User Experience, to define the main gameplay sections. Likewise, the educational overview has been derived from the contents of 4 PEG theory. However, each section of the GEB model has been adapted in the light of the latest research on serious games design [3; 5; 15; 16; 18; 19; 21; 22; 23]. These innovations on the necessary adaptability of the games to the learning pace of the players are incorporated in the metric. Finally, drawing from 4 PEG theory, the importance of balancing the learning and the fun content has been customized in the GEB model, as well as the distribution of such content throughout the game experience. As a novelty, a value system was created for this rubric in which the characteristics of gameplay and educational content were both worth 40%. The remaining 20% corresponded to the balance between fun and learning. Compared to the previous model, some points have been removed to make more general sections that cover every element in the game. The evaluation is made easier when the exact aspects are described and the same scores established for these sections [20; 25; 28; 29]. The Game Overview subsections are Mechanics, Dynamics, Aesthetics and Technology and User Experience. The Educational Overview has been divided into Instructional strategies and Motivational design. This last subsection counts triple, because it has been divided into five educational features related to the learning adaptions. Lastly, Overall Balance splits the score between the distribution of Learning and Fun balance and Can Learn vs. Must Learn educational contents. The rubric has also been applied to three indie games designed to raise awareness of various mental illnesses, in order to validate the GEB model. Each experience has been assessed by an author of this article and by 12 volunteers from the University of Burgos community. The results of both evaluations have shown that the GEB metric can be used \begin{table}
\begin{tabular}{c c c} \hline \hline
**Machine Learning Technique** & **Accuracy (\%)** & **ROC Area** \\ \hline Naïve-Bayes & 27.78 & 0.389 \\ KNN & 61.1 & 0.722 \\ Decision Trees & 58.33 & 0.762 \\ Multilayer Perceptron & 75.00 & 0.983 \\ Random Forest & 75.00 & 0.924 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy and ROC area for the machine-learning models. to determine the educational strengths and weaknesses of a serious game. However, the outcomes also showed that certain personal circumstances of the evaluator may affect the ratings. Therefore, the ideal user of this metric will be a player with knowledge and interest in the topics that are covered. Another possible option was to try each game with several people to mitigate these variable factors, as this paper has shown [27; 108]. A statistical and machine-learning validation has been performed on the results of the games evaluation to assure that those concepts are clearly explained in the GEBs metric questions and users are able to distinguish them. This evaluation includes a violins plot analysis, a Pearson's correlation analysis, a Principal Components Analysis and an evaluation of the information and hidden patterns contained in the survey results by means of machine learning classifiers. The result of the analysis shows that the variables included in the GEB model and metric have been properly explained, and the users can distinguish a clear difference when evaluating them in different games and the GEB survey provides an individual fingerprint for each game that machine learning techniques can clearly inference. It must be taken into account that the GEB model is a measure aimed at the most serious games that have assimilated the most important and general variables to assess its design. Therefore, in the evaluation of different games, factors such as the target audience or the topic must be considered.