Extraction of Relevant Terms and Learning Outcomes from Online Courses

Isabel Guitart, Jordi Conesa, David Baneres, Joaquim Moré, Jordi Duran, David Gañan


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 (NLP) techniques to analyze the course’s materials and discover what concepts are taught, their relevancy in the course and their alignment with the learning outcomes of the course. The contribution of the paper is a semi-automatic system that allows obtaining a better understanding of courses. A validation experiment on a master of the Open University of Catalonia is presented in order to show the quality of the results. The system can be used to analyze the suitability of course’s materials and to enrich and contextualize other analytical processes.


Information retrieval; analytics; learning analytics; natural language processing; eLearning; learning outcomes discovery

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Copyright (c) 2017 Isabel Guitart, Jordi Conesa, David Baneres, Joaquim Moré, Jordi Duran, David Gañan

International Journal of Emerging Technologies in Learning (iJET) – eISSN: 1863-0383
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