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

arXiv: https://arxiv.org/abs/2210.09911 · HTML: https://mlsanity.com/paper/2210.09911/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 0 of its 11 references that are in the map.
- First posted on arXiv 18 Oct 2022.

## Abstract (arXiv)

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.