Positioning in learning networks is a process that assists learners in finding a starting point and an efficient route in the network that will foster their competence building. In order to avoid labor-intense routines in the network we explore computational approaches to services such as positioning that are based on the contents of the learning network and the behavior of those participating in it, rather than in predefined procedures and (meta-) data. We present a content-based approach to positioning that uses latent semantic analysis to compare learner portfolios to contents offered in the learning network. Initial results indicate the feasibility of the approach, but a number of important caveats are discussed.