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As a research and innovation centre specializing in e-learning, the UOC's eLearn Center (eLC) continuously analyses global sources of knowledge to detect and reflect on trends and events that transform online higher education and lifelong learning around the world. As a result of this horizon scanning, regular reports on trends in the field of e-learning are published, such as last year's ​E-learning Research Report 2017 (​eLRR 17​), which requires its experts to carry out repetitive extraction, analysis, synthesis, codification and classification tasks on hundreds, and in some cases thousands, of bibliographic references. Given the systemic nature of this analysis process and the large volume of articles and other documentary sources needing regular reviewing, data analysis technologies and text analysis tools were thought to have the potential to adequately cover part of this laborious process.

Application of a text analytics model for the automatic discovery and classification of research trends in e-learning (the UOC case)