COCO: Semantic-Enriched Collection of Online Courses at Scale with Experimental Use Cases

  • Danilo Dessì
  • Gianni Fenu
  • Mirko Marras
  • Diego Reforgiato Recupero
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 746)


With the proliferation in number and scale of online courses, several challenges have emerged in supporting stakeholders during their delivery and fruition. Machine Learning and Semantic Analysis can add value to the underlying online environments in order to overcome a subset of such challenges (e.g. classification, retrieval, and recommendation). However, conducting reproducible experiments in these applications is still an open problem due to the lack of available datasets in Technology-Enhanced Learning (TEL), mostly small and local. In this paper, we propose COCO, a novel semantic-enriched collection including over 43 K online courses at scale, 16 K instructors and 2,5 M learners who provided 4,5 M ratings and 1,2 M comments in total. This outruns existing TEL datasets in terms of scale, completeness, and comprehensiveness. Besides describing the collection procedure and the dataset structure, we depict and analyze two potential use cases as meaningful examples of the large variety of multi-disciplinary studies made possible by having COCO.


Dataset Online courses Classification Recommendation 



Danilo Dessì and Mirko Marras acknowledge Sardinia Regional Government for the financial support of their PhD scholarship (P.O.R. Sardegna F.S.E. Operational Programme of the Autonomous Region of Sardinia, European Social Fund 2014–2020, Axis III “Education and Training”, Specific Goal 10.5).


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Danilo Dessì
    • 1
  • Gianni Fenu
    • 1
  • Mirko Marras
    • 1
  • Diego Reforgiato Recupero
    • 1
  1. 1.Department of Mathematics and Computer ScienceUniversity of CagliariCagliariItaly

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