Details and Dynamics: Mental Models of Complex Systems in Game-Based Learning

Joe A. Wasserman, Jaime Banks

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Background. Although the effectiveness of game-based learning (GBL) is well-supported, much less is known about the process underlying it. Nevertheless, developing a mental model that matches the game system, which in turn models a real-world system, is a promising proposed process. Aim. This article explores the first steps in model matching: identifying the entities and (complex) relations in a game system. Method. Participants (N = 30) played the analog game DOMINION and completed a multi-step mental model mapping exercise. Categories of entities in mental model maps were inductively identified with grounded theory coding, while complex relations in mental model maps were identified via content analysis. Results. Participants described formal game entities, player actions, sociality, learning processes, and subjective experience in their mental model maps. Participants identified very few complex relations—and no feedback loops—in their mental model maps. Conclusions. Games—and analog games specifically—provide a breadth of resources for model matching and GBL. Through gameplay, learners come to affix conceptual meanings to material objects, a process dubbed lamination.

Original languageEnglish
Pages (from-to)603-624
Number of pages22
JournalSimulation and Gaming
Volume48
Issue number5
DOIs
StatePublished - Oct 1 2017

Keywords

  • GBL
  • analog games
  • complex systems
  • game-based learning
  • mental models
  • model matching

Fingerprint

Dive into the research topics of 'Details and Dynamics: Mental Models of Complex Systems in Game-Based Learning'. Together they form a unique fingerprint.

Cite this