Educational games can act as excellent learning environments, where learners play and learn at the same time. However, typically, once a game has been developed, it is launched and then maybe evaluated for learning effectiveness but details on how learners actually use the game as well as how they play and learn in the game are rarely investigated. In addition, which groups of learners are more attracted or less attracted by the game is seldom looked at. However, such investigations are essential to ensure that the game is used in the way it was intended, that the game is fun and provides learning opportunities at the same time, that learners can benefit the most from the game and to make the game interesting for many different groups of players. In this paper, we introduce a learning analytics approach that builds learner profiles based on learners’ characteristics and behaviour in the educational game OMEGA+. The approach is rather generic and can be easily adapted to other educational games. By using the proposed learning analytics approach, clusters of learners are built that provide insights into how learners use the game, how they play and how they learn. In addition, when considering demographic attributes when analysing the clusters, insights can be gained on which groups of learners are more and which groups are less attracted to the game.