Personalized gamification explores user models to tailor gamification designs to mitigate cases wherein the one-size-fits-all approach ineffectively improves learning outcomes. The tailoring process should simultaneously consider user and contextual characteristics (e.g., activity to be done and geographic location), which leads to several combinations to tailor. Consequently, tools for automating gamification personalization are needed. However, which of those characteristics are relevant and how to do such tailoring are open questions. Furthermore, 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 of 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 present empirical evidence that LAT, geographic locations, and other user characteristics affect users' preferences, enable defining gamification designs tailored to user and contextual features simultaneously, and 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.