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

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.

A Pilot Study on Teacher-Facing Real-Time Classroom Game Dashboards

Educational games are an increasingly popular teaching tool in modern classrooms. However, the development of complementary tools for teachers facilitating classroom gameplay is lacking. We present the results of a participatory design process for a teacher-facing, real-time game data dashboard. This two-phase process included a workshop to elicit teachers' requirements for such a tool, and a pilot study of our dashboard prototype. We analyze post-gameplay survey and interview data to understand teachers' experiences with the tool in terms of evidence of co-design, feasibility, and effectiveness. Our results indicate the participatory design yielded a tool both useful for and usable by teachers within the context of a real class gameplay session. We advocate for the continued development of data-driven teacher tools to improve the effectiveness of games deployed in the classroom.