@article{RoepkeKoehlerDruryetal.2020,
  author    = {Roepke, Rene and K{\"o}hler, Klemens and Drury, Vincent and Schroeder, Ulrik and Wolf, Martin and Meyer, Ulrike},
  title     = {A pond full of phishing games - analysis of learning games for anti-phishing education},
  series = {Model-driven Simulation and Training Environments for Cybersecurity. MSTEC 2020},
  journal   = {Model-driven Simulation and Training Environments for Cybersecurity. MSTEC 2020},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-62433-0},
  doi       = {10.1007/978-3-030-62433-0_32020},
  pages     = {41 -- 60},
  year      = {2020},
  abstract  = {Game-based learning is a promising approach to anti-phishing education, as it fosters motivation and can help reduce the perceived difficulty of the educational material. Over the years, several prototypes for game-based applications have been proposed, that follow different approaches in content selection, presentation, and game mechanics. In this paper, a literature and product review of existing learning games is presented. Based on research papers and accessible applications, an in-depth analysis was conducted, encompassing target groups, educational contexts, learning goals based on Bloom's Revised Taxonomy, and learning content. As a result of this review, we created the publications on games (POG) data set for the domain of anti-phishing education. While there are games that can convey factual and conceptual knowledge, we find that most games are either unavailable, fail to convey procedural knowledge or lack technical depth. Thus, we identify potential areas of improvement for games suitable for end-users in informal learning contexts.},
  language  = {en}
}