Playing and Learning Together in a Multiplayer Data Science Education Game World

• In Event: AERA Poster Session Saturday 9:45 am
  In Poster Session: Amplifying Participants' Perspectives to Enhance Learning Environments

Abstract

This study examines how middle school students coordinated roles and used tools to make strategic decisions in Blind, a multiplayer educational game world designed to support data science learning. Informed by Activity Theory, we used video analysis and network mapping to analyze team-based gameplay, including students’ verbal interactions and tool use. Our analysis traced how players shifted roles within teams, moving from guided collaboration to self-initiated strategies and from teacher-supported tasks to emergent decisions shaped by gameplay conditions. Findings highlight how tool demonstrations and gameplay data supported adaptive decisions and how community interactions supported subverting traditional game rules in pursuit of gameplay goals. These insights inform the design of collaborative and competitive digital learning environments.

Authors

• Tianyu Ma, University of Miami

• Jennifer Beth Kahn, University of Miami