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

Autor:	Swanson, Luke, Gagnon, David, Scianna, Jennifer, McCloskey, John, Spevacek, Nicholas, Slater, Stefan, Harpstead, Erik
Rok vydání:	2022
Předmět:	Computer Science - Human-Computer Interaction Computer Science - Machine Learning Computer Science - Multimedia
Druh dokumentu:	Working Paper
Popis:	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. Comment: Presented at Games, Learning & Society (GLS) 2022 Conference. Irving, CA
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2210.09911 Zobrazit plný text záznamu View this record from Arxiv