Research on online interactions during a learning situation to better understand users' practices and to provide them with quality-oriented features, resources and services is attracting a large community. As a result, the interest for sharing educational data sets that translate the interactions of users with e-learning systems has become a hot topic today. However, the current systems aggregating social and usage data about their users suffer from a series of weaknesses. In particular, they lack a common information model that would allow for exchanges of interaction data at a large scale. To tackle this issue, we propose in this paper a generic model able to federate heterogeneous context metadata and to facilitate their share and reuse. This framework has been successfully applied to several data sets provided by the research community, and thus gives access to a big data set that could help researchers to increase efficiency of existing learning analytics technics, and promote research and development of new algorithms and services on top of these data.