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Predicting Student Performance in Distance Higher Education Using Active Learning

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Book cover Engineering Applications of Neural Networks (EANN 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 744))

Abstract

Students’ performance prediction in higher education has been identified as one of the most important research problems in machine learning. Educational data mining constitutes an important branch of machine learning trying to effectively analyze students’ academic behavior and predict their performance. Over recent years, several machine learning methods have been effectively used in the educational field with remarkable results, and especially supervised classification methods. The early identification of in case fail students is of utmost importance for the academic staff and the universities. In this paper, we investigate the effectiveness of active learning methodologies in predicting students’ performance in distance higher education. As far as we are aware of there exists no study dealing with the implementation of active learning methodologies in the educational field. Several experiments take place in our research comparing the accuracy measures of familiar active learners and demonstrating their efficiency by the exploitation of a small labeled dataset together with a large pool of unlabeled data.

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Author information

Authors and Affiliations

  1. Educational Software Development Laboratory (ESDLab), Department of Mathematics, University of Patras, Patras, Greece

    Georgios Kostopoulos & Sotiris Kotsiantis

  2. Department of Electrical and Computer Engineering, School of Engineering, Democritus University of Thrace, University Campus, Xanthi, Greece

    George Gravvanis

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

    Anastasia-Dimitra Lipitakis

  4. Hellenic Open University, Parodos Aristotelous 18, 26335, Patras, Greece

    Sotiris Kotsiantis & George Gravvanis

Authors
  1. Georgios Kostopoulos
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  2. Anastasia-Dimitra Lipitakis
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  3. Sotiris Kotsiantis
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  4. George Gravvanis
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Corresponding author

Correspondence to Georgios Kostopoulos .

Editor information

Editors and Affiliations

  1. Politecnico di Milano, Milan, Italy

    Giacomo Boracchi

  2. Democritus University of Thrace, University Campus, Xanthi, Greece

    Lazaros Iliadis

  3. School of Computing Science and Digital Media, Robert Gordon University, Aberdeen, United Kingdom

    Chrisina Jayne

  4. Univesity of Ioannina, Ioannina, Greece

    Aristidis Likas

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Kostopoulos, G., Lipitakis, AD., Kotsiantis, S., Gravvanis, G. (2017). Predicting Student Performance in Distance Higher Education Using Active Learning. In: Boracchi, G., Iliadis, L., Jayne, C., Likas, A. (eds) Engineering Applications of Neural Networks. EANN 2017. Communications in Computer and Information Science, vol 744. Springer, Cham. https://doi.org/10.1007/978-3-319-65172-9_7

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  • DOI: https://doi.org/10.1007/978-3-319-65172-9_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65171-2

  • Online ISBN: 978-3-319-65172-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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