When developing educational games we face the problem of finding the right design for making the learning activities as intrinsic to the game mechanics as possible. Nevertheless, in many cases it is not possible to fully integrate the learning content into the game play, resulting in the well known “chocolate-covered broccoli” game design.The long term goal of the work presented here is to determine whether a personalized selection of game mechanics for the playful part, the game mechanics around the learning part of the game, can improve the satisfaction of the player and therefore make the whole learning experience more enjoyable. The first step towards that goal is to obtain a model for the preferences of game mechanics for a particular type of game, and later use that model to guide the selection of game mechanics.In this paper, we present Enigma MNCN a treasure hunt for mobile devices designed for the National Museum of Natural Sciences of Spain and some experimental results intended to identify preferences for game mechanics in that type of game across demographic variables. The main finding of these experiments is that preferences in game mechanics get shadowed when combined with a mostly disliked learning mechanic.