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Comparison of Vector Space Representations of Documents for the Task of Information Retrieval of Massive Open Online Courses

  • Julius KleninEmail author
  • Dmitry Botov
  • Yuri Dmitrin
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 789)

Abstract

One of the important issues, arising in development of educational courses is maintaining relevance for the intended receivers of the course. In general, it requires developers of such courses to use and borrow some elements presented in similar content developed by others. This form of collaboration allows for the integration of experience and points of view of multiple authors, which tends to result in better, more relevant content. This article addresses the question of searching for relevant massive open online courses (MOOC) using a course programme document as a query. As a novel solution to this task we propose the application of language modelling. Presented results of the experiment, comparing several most popular models of vector space representation of text documents, such as the classical weighting scheme TF-IDF, Latent Semantic Indexing, topic modeling in the form of Latent Dirichlet Allocation, popular modern neural net language models word2vec and paragraph vectors. The experiment is carried out on the corpus of courses in Russian, collected from several popular MOOC-platforms. The effectiveness of the proposed model is evaluated taking into account opinions of university professors.

Keywords

Vector space model Educational course programme Document modelling Information retrieval Word embedding Mooc-platform Educational data mining 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Information Technologies InstituteChelyabinsk State UniversityChelyabinskRussian Federation

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