{"title":"Gamified Knowledge Encoding: Knowledge Training Using Game Mechanics","authors":"S. Oberdörfer, Marc Erich Latoschik","doi":"10.1109/VS-Games.2018.8493425","DOIUrl":null,"url":null,"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.","PeriodicalId":264923,"journal":{"name":"2018 10th International Conference on Virtual Worlds and Games for Serious Applications (VS-Games)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"22","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 10th International Conference on Virtual Worlds and Games for Serious Applications (VS-Games)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VS-Games.2018.8493425","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 22
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.