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

摘要

Background. Although the effectiveness of game-based learning (GBL) is wellsupported,rnmuch less is known about the process underlying it. Nevertheless,rndeveloping a mental model that matches the game system, which in turnrnmodels a real-world system, is a promising proposed process.rnAim. This article explores the first steps in model matching: identifying thernentities and (complex) relations in a game system.rnMethod. Participants (N = 30) played the analog game DOMINION and completed arnmulti-step mental model mapping exercise. Categories of entities in mentalrnmodel maps were inductively identified with grounded theory coding, whilerncomplex relations in mental model maps were identified via content analysis.rnResults. Participants described formal game entities, player actions, sociality,rnlearning processes, and subjective experience in their mental model maps.rnParticipants identified very few complex relations—and no feedback loops—inrntheir mental model maps.rnConclusions. Games—and analog games specifically—provide a breadth of resourcesrnfor model matching and GBL. Through gameplay, learners come to affixrnconceptual meanings to material objects, a process dubbed lamination.