A New MOOCs’ Recommendation Framework based on LinkedIn Data

  • Kais Dai
  • Ana Fernández Vilas
  • Rebeca P. Díaz Redondo
Conference paper
Part of the Lecture Notes in Educational Technology book series (LNET)

Abstract

We propose a new framework for recommending Massive Open Online Courses (MOOCs) to lifelong learners. Our approach can be summarized in two steps: (1) recommending MOOCs to potential learners according to their curricular information by relying on their LinkedIn profiles, and (2) recommending topics of interest to MOOCs’ providers by considering the job market needs. We also provide some insights about MOOCs of our Coursera dataset, thus to be taken into account during the decision process.

Keywords

MOOCs Recommendation System Collaborative Filtering LinkedIn Coursera 

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References

  1. 1.
    RĂDOIU, D.: Organization and constraints of a recommender system for MOOCs. Scientific Bulletin of the “Petru Maior” University of Tîrgu Mure, 11, 51-59 (2014)Google Scholar
  2. 2.
    Dai, K., Nespereira, C. G., Vilas, A. F., & Redondo, R. P. D.: Scraping and Clustering Techniques for the Characterization of LinkedIn Profiles. In: proceedings of the Fourth International Conference on Information Technology Convergence & Services. 1-15 (2015)Google Scholar
  3. 3.
    Coursera, https://blog.coursera.org/post/142363925112 (last accessed: 04/06/2016)
  4. 4.
    Schafer, J. B., Frankowski, D., Herlocker, J., & Sen, S.: Collaborative filtering recommender systems. In The adaptive web. 291-324. Springer Berlin Heidelberg (2007)Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2017

Authors and Affiliations

  • Kais Dai
    • 1
  • Ana Fernández Vilas
    • 1
  • Rebeca P. Díaz Redondo
    • 1
  1. 1.Information & Computing Lab., AtlantTIC Research CenterUniversity of VigoVigoSpain

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