Meta-learning: Can It Be Suitable to Automatise the KDD Process for the Educational Domain?

  • Marta Zorrilla
  • Diego García-Saiz
Conference paper

DOI: 10.1007/978-3-319-08729-0_28

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8537)
Cite this paper as:
Zorrilla M., García-Saiz D. (2014) Meta-learning: Can It Be Suitable to Automatise the KDD Process for the Educational Domain?. In: Kryszkiewicz M., Cornelis C., Ciucci D., Medina-Moreno J., Motoda H., Raś Z.W. (eds) Rough Sets and Intelligent Systems Paradigms. RSEISP 2014. Lecture Notes in Computer Science, vol 8537. Springer, Cham

Abstract

The use of e-learning platforms is practically generalised in all educational levels. Even more, virtual teaching is currently acquiring a great relevance never seen before. The information that these systems record is a wealthy source of information that once it is suitably analised, allows both, instructors and academic authorities to make more informed decisions. But, these individuals are not expert in data mining techniques, therefore they require tools which automatise the KDD process and, the same time, hide its complexity. In this paper, we show how meta-learning can be a suitable alternative for selecting the algorithm to be used in the KDD process, which will later be wrapped and deployed as a web service, making it easily accessible to the educational community. Our case study focuses on the student performance prediction from the activity performed by the students in courses hosted in Moodle platform.

Keywords

Meta-learning classification predicting student performance 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Marta Zorrilla
    • 1
  • Diego García-Saiz
    • 1
  1. 1.Department of Computer Science and ElectronicsUniversity of CantabriaSantanderSpain

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