  Personalized gamification explores knowledge about the users to tailor
gamification designs to improve one-size-fits-all gamification. The tailoring
process should simultaneously consider user and contextual characteristics
(e.g., activity to be done and geographic location), which leads to several
occasions to tailor. Consequently, tools for automating gamification
personalization are needed. The problems that emerge are that which of those
characteristics are relevant and how to do such tailoring are open questions,
and that the required automating tools are lacking. We tackled these problems
in two steps. First, we conducted an exploratory study, collecting
participants' opinions on the game elements they consider the most useful for
different learning activity types (LAT) via survey. Then, we modeled opinions
through conditional decision trees to address the aforementioned tailoring
process. Second, as a product from the first step, we implemented a recommender
system that suggests personalized gamification designs (which game elements to
use), addressing the problem of automating gamification personalization. Our
findings i) present empirical evidence that LAT, geographic locations, and
other user characteristics affect users' preferences, ii) enable defining
gamification designs tailored to user and contextual features simultaneously,
and iii) provide technological aid for those interested in designing
personalized gamification. The main implications are that demographics,
game-related characteristics, geographic location, and LAT to be done, as well
as the interaction between different kinds of information (user and contextual
characteristics), should be considered in defining gamification designs and
that personalizing gamification designs can be improved with aid from our
recommender system.
