Data-Driven Design for Educational Game: Leveraging Insights from Educational Game Reviews by the k-means Clustering

This study proposes a data-driven approach to educational game design, utilizing user feedback and reviews to identify key factors contributing to the success or failure of educational games. By analyzing reviews of popular educational games from Common Sense Media, we employed k-means clustering to group similar reviews and extract meaningful insights. The clustering process, validated through Silhouette Scores and Davies-Bouldin Index metrics, revealed distinct themes in user feedback, highlighting areas for potential enhancement in educational game design. Our results demonstrate that clustering reviews can effectively differentiate between positive and mixed feedback, providing actionable guidance for game developers. This study underscores the value of incorporating systematic user feedback analysis into the educational game design process, offering a pathway to creating more engaging and educationally effective games. The findings contribute to a more structured and evidence-based approach to educational game development, ultimately enhancing learning outcomes and user satisfaction