Abstract-Nowadays, universities (on-site and online) have a large competition in order to attract more students. In this panorama, learning analytics can be a very useful tool since it allows instructors (and university managers) to get a more thorough view of their context, to better understand the environment, and to identify potential improvements. In order to perform analytics efficiently, it is necessary to have as much information as possible about the instructional context. The paper proposes a novel approach to gather information from different aspects within courses. In particular, the approach applies natural language processing (

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The Universitat Oberta de Catalunya (Open University of Catalonia, UOC), is a public university based in Barcelona. The UOC is characterised by three main factors: (a) it is a virtual university based in an e-Learning model, (b) it is based in a strongly Spanish-Catalan bilingual region, and (c) students come from around the world, so that linguistic and cultural diversity is a crucial factor.Within this context, it becomes essential to meet the UOC's linguistic needs taking into account its particular characteristics. One of the tools created to this end is the adaptation of Apertium, a free/open-source rule-based machine translation platform, which can be found under http://apertium.uoc.edu/, customised to the translation needs of the institution in order to offer the best possible service to their user community.In order to continue adapting and adding value to the existing tool for generalisable large-scale applications, the UOC's translation system has recently implemented a semantic filter based on subject fields aimed at improving the translation quality and at better fitting the university needs. The paper will explain all the steps of this adaptive process, as well as a demonstration of the resulting tool: (a) the choice of the subject fields according to the university studies, (b) the design and implementation of the dictionaries used to extract the required information to filter and disambiguate homonym and polysemous terms, including source code in the dictionaries, and (c) the design and implementation of the corresponding web interface.

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