Video Games Classification with Game Experience and Hierarchy of Needs

Abstract

A new game classification system needs to be developed, which not only describes the similarities and differences between video games but also does not need to use complex categories that could easily confuse the public. Such system needs to be more general to be able to categorize existing games and bear the brunt of new games. The objective and uniform description is also a prerequisite for the system, which will not merely fluctuate due to individual bias. To achieve these, we should use existing games to find representative features that could demonstrate different patterns between games rather than subjectively evaluating the similarity. To improve the generality of such patterns and reduce the cognitive cost of public, the number of categories must be reasonable. In this research, to reasonable and effectively find such patterns, we introduced two theories for extracting features from existing games. One is from a modern edition of Maslow’s hierarchy of needs, and the other is from the factors of a mainstream questionnaire of measuring player experience. Such techniques are then used to generate an original questionnaire, which is applied to collect specific features of different games, instead of merely measuring player experience or describing player needs. After conducting a survey among game researchers and professional game developers, to validate the questionnaire’s factors and items, approaches of statistical analysis are needed to be introduced. Based on these results, we first need to subjectively evaluate whether the questionnaire could be used to categorize games, especially those examples that genre theory cannot handle well. Finally, we need to introduce machine learning algorithms to objectively evaluate the feasibility of our classification system.