Linked Educational Online Courses to Provide Personalized Learning

  • Heitor BarrosEmail author
  • Jonathas Magalhães
  • Társis Marinho
  • Marlos Silva
  • Michel Miranda
  • Evandro Costa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10633)


The emergence of MOOCs enabled students from around the world engage in courses taught by professors from leading universities. However, the relatively low completion rates of MOOC participants has been a central criticism in the popular discourse. Some studies point to up to 90% evasion in some courses. The lack of knowledge in relation to course prerequisites (background gaps) is one of the reasons that reduce the completion rate. To alleviate this problem, this paper proposes the use of a Linked Courses structure to provide support to students. In this proposal, before starting a course, the background gaps of each student are identified and a personalized set of support courses is recommended to help him. Results obtained so far indicate the effectiveness of this approach.


  1. 1.
    Brinton, C., Rill, R., Ha, S., Chiang, M., Smith, R., Ju, W.: Individualization for education at scale: MIIC design and preliminaryevaluation. IEEE Trans. Learn. Technol. PP(99), 1 (2014)Google Scholar
  2. 2.
    Chen, C.-M.: Intelligent web-based learning system with personalized learning path guidance. Comput. Educ. 51(2), 787–814 (2008)CrossRefGoogle Scholar
  3. 3.
    Costa, E., Silva, P., Magalhães, J., Silva, M.: An open and inspectable learner modelingwith a negotiation mechanism to solve cognitive conflicts in an intelligent tutoring system. In: Proceedings of the 2nd Workshop on Personalization Approaches for Learning Environments (PALE 2012). CEUR Workshop Proceedings, vol. 872. (2012)Google Scholar
  4. 4.
    Fabio, R.A., Antonietti, A.: Effects of hypermedia instruction on declarative, conditional and procedural knowledge in ADHD students. Res. Dev. Disabil. 33(6), 2028–2039 (2012)CrossRefGoogle Scholar
  5. 5.
    Henning, P.A., et al.: Personalized web learning: merging open educational resources into adaptive courses for higher education. Personal. Approach. Learn. Environ. 55, 55–62 (2014). ISSN: 1613-0073Google Scholar
  6. 6.
    Kizilcec, R.F., Piech, C., Schneider, E.: Deconstructing disengagement: analyzing learner subpopulations in massive open online courses. In: Proceedings of the Third International Conference on Learning Analytics and Knowledge, pp. 170–179. ACM (2013)Google Scholar
  7. 7.
    Lin, C.F., Yeh, Y.-C., Hung, Y.H., Chang, R.I.: Data mining for providing a personalized learning path in creativity: an application of decision trees. Comput. Educ. 68, 199–210 (2013)CrossRefGoogle Scholar
  8. 8.
    Ozpolat, E., Akar, G.B.: Automatic detection of learning styles for an e-learning system. Comput. Educ. 53(2), 355–367 (2009)CrossRefGoogle Scholar
  9. 9.
    Pappano, L.: The Rise of MOOCs. The New York Times Magazine, September 2013Google Scholar
  10. 10.
    Zapata-Rivera, J.-D., Greer, J.E.: Interacting with inspectable Bayesian student models. Int. J. Artif. Intell. Educ. 14(2), 127–163 (2004)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Heitor Barros
    • 1
    Email author
  • Jonathas Magalhães
    • 2
  • Társis Marinho
    • 2
  • Marlos Silva
    • 2
  • Michel Miranda
    • 3
  • Evandro Costa
    • 3
  1. 1.Instituto Federal de Brasília - IFBBrasíliaBrazil
  2. 2.Federal University of Campina Grande - UFCGCampina GrandeBrazil
  3. 3.Federal Univeristy of Alagoas - UFALMaceióBrazil

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