Leveraging Cluster Analysis to Understand Educational Game Player Experiences and Support Design
This work addresses the need for inexpensive, automated player analysis in small educational game studios, though it is incremental as it applies existing clustering best practices to a specific domain.
The paper tackled the problem of educational game designers needing automated methods to categorize player interactions from large-scale testing, presenting a reusable clustering process that analyzed a real-time strategy game to derive actionable insights for design improvements.
The ability for an educational game designer to understand their audience's play styles and resulting experience is an essential tool for improving their game's design. As a game is subjected to large-scale player testing, the designers require inexpensive, automated methods for categorizing patterns of player-game interactions. In this paper we present a simple, reusable process using best practices for data clustering, feasible for use within a small educational game studio. We utilize the method to analyze a real-time strategy game, processing game telemetry data to determine categories of players based on their in-game actions, the feedback they received, and their progress through the game. An interpretive analysis of these clusters results in actionable insights for the game's designers.