Journal of Korea Game Society (한국게임학회 논문지)

Korea Game Society (한국게임학회)
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An Analysis of Player Types using Data Clustering in Gamification

데이터 클러스터링을 활용한 게이미피케이션 환경에서의 플레이어 유형 분석

  • Park, Sungjin (Dept. of System Management Engineering, Kangwon National University) ;
  • Kang, Bumsoo (Dept. of System Management Engineering, Kangwon National University) ;
  • Kim, Sungsoo (Dept. of System Management Engineering, Kangwon National University) ;
  • Kim, Sangkyun (Dept. of System Management Engineering, Kangwon National University)
  • 박성진 (강원대학교 시스템경영공학과) ;
  • 강범수 (강원대학교 시스템경영공학과) ;
  • 김성수 (강원대학교 시스템경영공학과) ;
  • 김상균 (강원대학교 시스템경영공학과)
  • Received : 2017.11.10
  • Accepted : 2017.12.19
  • Published : 2017.12.20
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Abstract

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.

본 연구의 목적은 데이터 클러스터링을 활용해 기존의 플레이어 유형 이론을 비교하고 검증하는 것이다. 연구 진행을 위해 A 대학교 2016년 2학기에 진행된 초대형 강의 수강생의 결과 데이터 235개를 활용했다. 본 연구에서는 K-평균(Means)과 적절한 클러스터 수를 결정하기 위해 실루엣(Silhouette) 평가기법을 적용했다. 적용한 플레이어 유형은 바틀의 2차원, 3차원 플레이어 유형, Ferro의 5 가지 유형, 브레인헥스이다. 연구결과에 따르면, 바틀의 2차원 플레이어 유형이 데이터 클러스터링 관점에서 가장 적합한 것으로 나타났다. 각 플레이어 유형 별 특성분포도 해석했다. 본 연구결과는 게이미피케이션을 적용하거나 개발 프로세스를 연구할 때 사용되는 플레이어 분석 부분에 영향을 미칠 것으로 예상된다.

Keywords

References

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