%0 Thesis %1 oberdorfer2021better %A Oberdörfer, Sebastian %D 2021 %K getit myown oberdoerfer %R 10.25972/OPUS-21970 %T Better Learning with Gaming: Knowledge Encoding and Knowledge Learning Using Gamification %U https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/21970 %X Computer games are highly immersive, engaging, and motivating learning environments. By providing a tutorial at the start of a new game, players learn the basics of the game’s underlying principles as well as practice how to successfully play the game. During the actual gameplay, players repetitively apply this knowledge, thus improving it due to repeti- tion. Computer games also challenge players with a constant stream of new challenges which increase in difficulty over time. As a result, com- puter games even require players to transfer their knowledge to master these new challenges. A computer game consists of several game me- chanics. Game mechanics are the rules of a computer game and encode the game’s underlying principles. They create the virtual environments, generate a game’s challenges and allow players to interact with the game. Game mechanics also can encode real world knowledge. This knowledge may be acquired by players via gameplay. However, the actual process of knowledge encoding and knowledge learning using game mechanics has not been thoroughly defined, yet. This thesis therefore proposes a the- oretical model to define the knowledge learning using game mechanics: the Gamified Knowledge Encoding. The model is applied to design a seri- ous game for affine transformations, i.e., GEtiT, and to predict the learning outcome of playing a computer game that encodes orbital mechanics in its game mechanics, i.e., Kerbal Space Program. To assess the effects of different visualization technologies on the overall learning outcome, GEtiT visualizes the gameplay in desktop-3D and immersive virtual reality. The model’s applicability for effective game design as well as GEtiT’s overall design are evaluated in a usability study. The learning outcome of play- ing GEtiT and Kerbal Space Program is assessed in four additional user studies. The studies’ results validate the use of the Gamified Knowledge Encoding for the purpose of developing effective serious games and to predict the learning outcome of existing serious games. GEtiT and Ker- bal Space Program yield a similar training effect but a higher motivation to tackle the assignments in comparison to a traditional learning method. In conclusion, this thesis expands the understanding of using game me- chanics for an effective learning of knowledge. The presented results are of high importance for researches, educators, and developers as they also provide guidelines for the development of effective serious games.

@phdthesis{oberdorfer2021better, abstract = {Computer games are highly immersive, engaging, and motivating learning environments. By providing a tutorial at the start of a new game, players learn the basics of the game’s underlying principles as well as practice how to successfully play the game. During the actual gameplay, players repetitively apply this knowledge, thus improving it due to repeti- tion. Computer games also challenge players with a constant stream of new challenges which increase in difficulty over time. As a result, com- puter games even require players to transfer their knowledge to master these new challenges. A computer game consists of several game me- chanics. Game mechanics are the rules of a computer game and encode the game’s underlying principles. They create the virtual environments, generate a game’s challenges and allow players to interact with the game. Game mechanics also can encode real world knowledge. This knowledge may be acquired by players via gameplay. However, the actual process of knowledge encoding and knowledge learning using game mechanics has not been thoroughly defined, yet. This thesis therefore proposes a the- oretical model to define the knowledge learning using game mechanics: the Gamified Knowledge Encoding. The model is applied to design a seri- ous game for affine transformations, i.e., GEtiT, and to predict the learning outcome of playing a computer game that encodes orbital mechanics in its game mechanics, i.e., Kerbal Space Program. To assess the effects of different visualization technologies on the overall learning outcome, GEtiT visualizes the gameplay in desktop-3D and immersive virtual reality. The model’s applicability for effective game design as well as GEtiT’s overall design are evaluated in a usability study. The learning outcome of play- ing GEtiT and Kerbal Space Program is assessed in four additional user studies. The studies’ results validate the use of the Gamified Knowledge Encoding for the purpose of developing effective serious games and to predict the learning outcome of existing serious games. GEtiT and Ker- bal Space Program yield a similar training effect but a higher motivation to tackle the assignments in comparison to a traditional learning method. In conclusion, this thesis expands the understanding of using game me- chanics for an effective learning of knowledge. The presented results are of high importance for researches, educators, and developers as they also provide guidelines for the development of effective serious games.}, added-at = {2021-11-08T16:52:20.000+0100}, author = {Oberdörfer, Sebastian}, biburl = {https://www.bibsonomy.org/bibtex/21cff898c8d56d4581afbc2e89b936ab9/sp-jmu}, doi = {10.25972/OPUS-21970}, interhash = {e4d196d13c38fb25a94233e281afb796}, intrahash = {1cff898c8d56d4581afbc2e89b936ab9}, keywords = {getit myown oberdoerfer}, timestamp = {2023-09-12T12:15:29.000+0200}, title = {Better Learning with Gaming: Knowledge Encoding and Knowledge Learning Using Gamification}, url = {https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/21970}, year = 2021 }