• Journal of Korea Game Society
• /
• v.17 no.6
• /
• pp.77-88
• /
• 2017

The purpose of this study is to compare existing player type theories using data clustering. For the study, 235 result data of the gamified class in second semester of A university at 2016 used. This study applied K-means and Silhouette to decide the appropriate number of clusters. The player types applied in this study are Bartle's 2-D and 3-D player types, Ferro's five types, and BrainHex. According to the results, Bartle's 2D player type was found to be the best in perspective of data clustering. This study also analyzed the distribution of characteristics for each player types. The results of this study are expected to have an impact on player analysis, which is used in the application of gamification or in the development process.