Leveraging Cluster Analysis to Understand Educational Game Player Experiences and Support Design

TL;DR

This paper presents a practical clustering approach to analyze player behaviors in educational games, providing designers with automated insights to enhance game development and player experience understanding.

Contribution

It introduces a simple, reusable clustering process tailored for small educational game studios to categorize player types based on telemetry data.

Findings

Identified distinct player clusters based on in-game actions and feedback.

Provided actionable insights for game design improvements.

Demonstrated feasibility of automated player analysis in real-time strategy games.

Abstract

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.