Optimizing a gamified design through reinforcement learning : a case study in stack overflow
| Autor Principal: | |
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| Otros autores o Colaboradores: | , |
| Formato: | Capítulo de libro |
| Lengua: | inglés |
| Temas: | |
| Acceso en línea: | http://dx.doi.org/10.1007/978-3-030-84825-5_7 Consultar en el Cátalogo |
| Resumen: | Gamification can be used to foster participation in knowledge sharing communities. While designing and assessing the potential impact of a gamification design in such a context, it is important to avoid work disruption and negative side effects. A gamification optimization approach implemented with deep reinforcement learning based on play-testing approaches helps prevent possible disruptive configuration and has the capability to adapt to different communities or gamification targets. In this research, a case of study for this approach is presented running over the Stack Overflow Q&A community. The approach detects the best configuration for a Contribution, Reinforcement, and Dissemination (CRD) gamification strategy using Stack Overflow historical data in a year. The results show that the approach funds proper gamification strategy configurations. Moreover, those configurations are robust enough to be applied along the time unseen periods. |
| Notas: | Formato de archivo PDF. -- Este documento es producción intelectual de la Facultad de Informática - UNLP (Colección BIPA/Biblioteca) |
| Descripción Física: | 1 archivo (358,4 kB) |
| DOI: | 10.1007/978-3-030-84825-5_7 |