  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.
