The aim of this study is clustering students according to their gamification user types and learning styles with the purpose of providing instructors with a new perspective of grouping students in case of clustering which cannot be done by hand when there are multiple scales in data. The data used consists of 251 students who were enrolled at a Turkish state university. When grouping students, K-means algorithm has been utilized as clustering algorithm. As for determining the gamification user types and learning styles of students, Gamification User Type Hexad Scale and Grasha-Riechmann Student Learning Style Scale have been used respectively. Silhouette coefficient is utilized as clustering quality measure. After fitting the algorithm in several ways, highest Silhouette coefficient obtained was 0.12 meaning that results are neutral but not satisfactory. All the statistical operations and data visualizations were made using Python programming language.

本研究旨在使用游戏化用户类型和学习风格对学生进行聚类，以便为教师提供一种将学生分组的新视角，并解决在数据中存在多个尺度时无法手动进行聚类的问题。使用的数据包括一所土耳其州立大学的251名学生。在对学生进行分组时，采用了K-means聚类算法。为确定学生的游戏化用户类型和学习风格，分别使用了Gamification User Type Hexad Scale和Grasha-Riechmann学生学习风格量表。采用Silhouette系数作为聚类质量度量。经过多次算法拟合，得到的最高Silhouette系数为0.12，意味着结果是中性但令人不满意的。所有的统计操作和数据可视化都是使用Python编程语言完成的。

基于游戏化用户类型和学习风格的学生聚类