With the advent of e-learning, there is a strong demand for tools that help to create e-learning courses in an automatic or semi-automatic way. While resources for new courses are often freely available, they are generally not properly structured into easy to handle units. In this paper, we investigate how state of the art text segmentation algorithms can be applied to automatically transform unstructured text into coherent pieces appropriate for e-learning courses. The feasibility to course generation is validated on a test corpus specifically tailored to this scenario. We also introduce a more generic training and testing method for text segmentation algorithms based on a Latent Dirichlet Allocation (LDA) topic model. In addition we introduce a scalable random text segmentation algorithm, in order to establish lower and upper bounds to be able to evaluate segmentation results on a common basis.