A gamification design framework recommender system
Author
Vecerek, Attila
Term
4. term
Publication year
2019
Submitted on
2019-06-06
Pages
103
Abstract
Gamification er brugen af spildesign-elementer i sammenhænge, der ikke er spil. Der findes mange gamification-designrammer (frameworks), som tilbyder metoder og retningslinjer, men det kan være svært især for mindre erfarne praktikere at vælge den mest passende ramme til et givent projekt. Dette speciale undersøger, hvordan projektkrav kan kobles til gamification-designrammer. Med Design Science Research Methodology – en struktureret tilgang til at udvikle og evaluere praktiske værktøjer – blev der udviklet et anbefalingssystem for et begrænset sæt klassificerede rammer. En gruppe gamification-praktikere validerede værktøjet med fokus på dets brugervenlighed og relevansen af dets anbefalinger. Studiet introducerer begreberne optimale og suboptimale rammer og identificerer tre hovedfaktorer til sammenligning: funktionsdækning (feature-completeness), domænespecificitet og målgruppespecificitet. Det blev også udforsket, om forskellige rammer kan kombineres til én samlet, optimal ramme. Valideringen viste, at der er behov for mere forskning i klassificering af gamification-rammer for at forbedre anbefalingernes relevans. Systemet blev implementeret og er tilgængeligt for gamification-praktikere.
Gamification is the use of game design elements in contexts that are not games. There are many gamification design frameworks that offer methods and guidelines, but choosing the right one for a project can be difficult, especially for less experienced practitioners. This thesis examines how project requirements can be mapped to gamification design frameworks. Using Design Science Research Methodology—a structured approach to building and evaluating practical tools—it developed a recommender system for a limited set of classified frameworks. A group of gamification practitioners validated the tool, focusing on its usability and the relevance of its recommendations. The study introduces the terms optimal and suboptimal frameworks and identifies three main comparison factors: feature-completeness, domain specificity, and target specificity. It also explored whether different frameworks could be combined into a single optimal framework. The validation showed that more research on framework classification is needed to further improve the relevance of the recommendations. The system was deployed and is available to gamification practitioners.
[This abstract was generated with the help of AI]
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