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The Variability of the Reasons for Student Dropout in Distance Learning and the Prediction of Dropout-Prone Students

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Machine Learning Paradigms

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 158))

Abstract

The adult education that is provided by Universities that use distance learning methods is without doubt inseparable from high dropout rates, frequently higher than those in conventional Universities. Dropping out in a University that provides distance education is caused by professional, academic, health and family and personal reasons. Limiting dropout is crucial and therefore, the aptitude to predict students’ dropping out could be very useful. We try to identify the most appropriate comprehensive learning algorithm using the most informative attributes for the prediction of students’ dropout. Additionally, we have explored the reasons of dropping out in order to examine on a large scale whether they are affected over time and study these changes. The data used was provided by the Student Registry of the Hellenic Open University and additional data was collected by an interview-based survey. It was found that the most informative attributes are the student gender, the participation at the first face to face meeting and the marks on the first two written assignments. A web-based application, which is based on these attributes and can automatically recognize students with high probability of dropping out, was constructed in order to help tutors detect students at risk even at the beginning of the academic year.

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Author information

Authors and Affiliations

  1. Department of Business Administration, Technological Educational Institute of Western Greece, 1 M. Alexandrou str. Koukouli, 263 34, Patras, Greece

    Christos Pierrakeas & Giannis Koutsonikos

  2. Department of Informatics and Telematics, Harokopio University of Athens, Kallithea, Greece

    Anastasia-Dimitra Lipitakis

  3. Department of Mathematics, University of Patras, 265 04, Rio, Greece

    Sotiris Kotsiantis

  4. Department of Computer Engineering & Informatics, University of Patras, 265 04, Rio, Greece

    Michalis Xenos

  5. Department of Electrical and Computer Engineering, Democritus University of Thrace, University Campus, Kimmeria, 67 100, Xanthi, Greece

    George A. Gravvanis

Authors
  1. Christos Pierrakeas
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  2. Giannis Koutsonikos
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  3. Anastasia-Dimitra Lipitakis
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  4. Sotiris Kotsiantis
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  5. Michalis Xenos
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  6. George A. Gravvanis
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Corresponding author

Correspondence to Sotiris Kotsiantis .

Editor information

Editors and Affiliations

  1. Department of Informatics, University of Piraeus, Piraeus, Greece

    Maria Virvou

  2. Department of Informatics, University of Piraeus, Piraeus, Greece

    Efthimios Alepis

  3. University of Piraeus, Pireas, Greece

    George A. Tsihrintzis

  4. Faculty of Science, Technology and Mathematics, University of Canberra, Canberra, SA, Australia

    Lakhmi C. Jain

Appendix

Appendix

The tool is available in the web page: http://www.math.upatras.gr/~sotos/tool1/.

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Pierrakeas, C., Koutsonikos, G., Lipitakis, AD., Kotsiantis, S., Xenos, M., Gravvanis, G.A. (2020). The Variability of the Reasons for Student Dropout in Distance Learning and the Prediction of Dropout-Prone Students. In: Virvou, M., Alepis, E., Tsihrintzis, G., Jain, L. (eds) Machine Learning Paradigms. Intelligent Systems Reference Library, vol 158. Springer, Cham. https://doi.org/10.1007/978-3-030-13743-4_6

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