With the advent of e-learning, there is a strong demand for tools that help to cre- ate 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 scenar- io. We also introduce a more generic training and testing method for text seg- mentation algorithms based on a Latent Dirichlet Allocation (LDA) topic model. In addition we introduce a scalable ran- dom tex...

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