Learning Performance Styles in Gamified College Classes Using Data Clustering

This study aimed to investigate the efficacy of learning gamification in developing sustainable educational environments. To this end, gamified class data were analyzed to identify students' learning performance patterns. The study sample comprised 369 data points collected across four point domains: Activity, Game, Project, and Exam Points, which students obtained in their gamified college courses conducted between 2016 and 2019. A K-means data clustering algorithm and silhouette analysis were utilized to evaluate student performances and determine differential learning styles in gamified environments. Cluster analysis revealed three types of learning patterns centered on performance, mastery, and avoidance. Based on our findings, we propose suggestions regarding class design for instructors considering using gamification strategies to support a sustainable educational environment. We also highlight the scope for future research in both in-person and online gamified learning.