Game learning analytics has a great potential to provide insight and improve the use of games in different educational situations. However, it is necessary to clearly establish what the learner’s requirements are and to set realistic expectations about the learning process and outcomes. Application of game learning analytics requires pedagogically informed policies that settle the learning goals and relate them to analysis and visualization; and a supporting infrastructure that provides the mechanism on top of which it is executed. Both concerns can be addressed separated: on the one hand, there is a Learning Analytics Model (LAM) which describes how the analysis is carried out, interpreted as learning, and presented to stakeholders; and on the other hand, an underlying analytics system that concentrates on performance, security, flexibility and generality. An important advantage of this separation is that it allows LAM authors to concentrate on their area of expertise, limiting their exposition to the actual mechanism used underneath. However, LAMs built for a single game fail to account for the frequent case where games and their analytics are aggregated into larger, overarching plots, games or courses. This work describes an extension to an existing game learning analytics system, used in RAGE and BEACONING H2020 projects, which manages multilevel analytics through improvements to both policy and mechanism; and introduces meta-Learning Analytic Models, which characterize learning in hierarchical structures.