Gamified Knowledge Encoding: Knowledge Training Using Game Mechanics

Abstract

Game mechanics (GMs) encode a game's rules, underlying principles and overall knowledge. During the gameplay, players practice this knowledge due to repetition and compile mental models for it. Mental models allow for a training transfer from a training context to a different context. Hence, as GMs can encode any knowledge, they can also encode specific learning contents as their rules and be used for an effective transfer-oriented knowledge training. In this article, we propose the Gamified Knowledge Encoding model (GKE) that not only describes a direct knowledge encoding of a specific learning content in GMs, but also defines their training effects. Ultimately, the GKE can be used as an underlying guideline to develop well-tailored game-based training environments.