%0 Conference Paper %1 oberdorfer2018gamified %A Oberdörfer, Sebastian %A Latoschik, Marc Erich %B Proceedings of the 10th International Conference on Virtual Worlds and Games for Serious Applications (VS Games 2018) %D 2018 %K myown oberdoerfer %P 1-2 %R 10.1109/VS-Games.2018.8493425 %T Gamified Knowledge Encoding: Knowledge Training Using Game Mechanics %U https://downloads.hci.informatik.uni-wuerzburg.de/2018-vsgames-gke-preprint.pdf %X 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.

@inproceedings{oberdorfer2018gamified, 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.}, added-at = {2018-07-10T15:17:55.000+0200}, author = {Oberdörfer, Sebastian and Latoschik, Marc Erich}, biburl = {https://www.bibsonomy.org/bibtex/2e86b7f0e02cbbfa2cdc7b93ea38dd0a0/hci-uwb}, booktitle = {Proceedings of the 10th International Conference on Virtual Worlds and Games for Serious Applications (VS Games 2018)}, doi = {10.1109/VS-Games.2018.8493425}, interhash = {4cf022fb6d598f73dbea90abb05f1ec2}, intrahash = {e86b7f0e02cbbfa2cdc7b93ea38dd0a0}, keywords = {myown oberdoerfer}, month = {September}, note = {Best Poster Award 🏆}, organization = {IEEE}, pages = {1-2}, timestamp = {2023-02-21T13:05:02.000+0100}, title = {Gamified Knowledge Encoding: Knowledge Training Using Game Mechanics}, url = {https://downloads.hci.informatik.uni-wuerzburg.de/2018-vsgames-gke-preprint.pdf}, year = 2018 }