Clustering Students Based on Gamification User Types and Learning Styles

Emre Arslan, Atilla Özkaymak, Nesrin Özdener Dönmez
10/22/2023
cs.LG

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Abstract

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.

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Cite this paper

@article{arslan2023clustering,
  title  = {Clustering Students Based on Gamification User Types and Learning Styles},
  author = {Emre Arslan and Atilla Özkaymak and Nesrin Özdener Dönmez},
  year   = {2023},
  eprint = {2310.14430},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.14430v1}
}

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