Clustering Students Based on Gamification User Types and Learning Styles
Emre Arslan Atilla Özkaymak Nesrin Özdener Dönmez
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.