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Modeling the Navigation on Enrolment Web Information Area of a University Using Machine Learning Techniques

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Advances in Artificial Intelligence (CAEPIA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11160))

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Abstract

This work analyses the navigation in the enrolment web information area of the University of the Basque Country. A complete data mining process shows that successful and failure navigation behaviors can be modeled using machine learning techniques. Unsupervised learning algorithms have been applied on two different domains: URLs visited by the users in each session (navigation sequence) and some interaction parameters extracted from the recorded click-stream (navigation style). Both domains have been used satisfactorily to model the behavior of success and failure navigation sessions achieving more than 78% of accuracy predicting success or failure sessions. Furthermore, the clustering based on the navigation style was able to identify the main characteristics of each type of session and to build a subsystem that enables to detect failure type sessions with high precision.

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Acknowledgment

This work has been funded by the following units: Firstly, by the University of the Basque Country UPV/EHU (PIF15/143 grant). Secondly, by the research group ADIAN that is supported by the Department of Education, Universities and Research of the Basque Government, (grant IT980-16). Finally, by the Ministry of Economy and Competitiveness of the Spanish Government, co-founded by the ERDF (eGovernAbility, TIN2014-52665-C2-1-R).

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Authors and Affiliations

  1. Faculty of Informatics, University of the Basque Country, 20018, Donostia, Spain

    Ainhoa Yera, Iñigo Perona, Olatz Arbelaitz & Javier Muguerza

Authors
  1. Ainhoa Yera
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  2. Iñigo Perona
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  3. Olatz Arbelaitz
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  4. Javier Muguerza
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Corresponding author

Correspondence to Ainhoa Yera .

Editor information

Editors and Affiliations

  1. Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Francisco Herrera

  2. Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Sergio Damas

  3. Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Rosana Montes

  4. Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Sergio Alonso

  5. Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Óscar Cordón

  6. Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

    Antonio González

  7. School of Engineering, Pablo de Olavide University, Seville, Spain

    Alicia Troncoso

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Yera, A., Perona, I., Arbelaitz, O., Muguerza, J. (2018). Modeling the Navigation on Enrolment Web Information Area of a University Using Machine Learning Techniques. In: Herrera, F., et al. Advances in Artificial Intelligence. CAEPIA 2018. Lecture Notes in Computer Science(), vol 11160. Springer, Cham. https://doi.org/10.1007/978-3-030-00374-6_29

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  • DOI: https://doi.org/10.1007/978-3-030-00374-6_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00373-9

  • Online ISBN: 978-3-030-00374-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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