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Exploiting Time in Adaptive Learning from Educational Data

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Bridges and Mediation in Higher Distance Education (HELMeTO 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1344))

Abstract

Virtual Learning Environments (VLEs) are web platforms where educational content is delivered, along with tools to support individual study. Logs that record how students interact with the platform are collected daily, so automated methods can be used to extract useful knowledge from these data. All stakeholders involved in the learning activities of the VLEs, especially students and teachers, can benefit from the insights derived from the educational data and valuable information can be extracted using machine learning algorithms. Usually, educational data are examined as stationary data using conventional batch methods. However, these data are non-stationary by nature and could be better treated as data streams. This paper reports the results of a classification study in which Random Forests, applied in both batch and adaptive mode, are used to build a model for predicting student exam failure/success. In addition, an analysis of the most important features is performed to detect the most discriminating attributes related to the student’s result. Experiments conducted on a subset of the Open University Learning Analytics (OULAD) dataset demonstrate the reliability of the adaptive version of Random Forest in accurately classifying the evolving educational data.

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Notes

  1. 1.

    Open University website: http://www.open.ac.uk.

  2. 2.

    Freely available data from Open University: https://analyse.kmi.open.ac.uk/open_dataset#data.

  3. 3.

    Student oriented subset of the Open University Learning Analytics dataset: https://zenodo.org/record/4264397#.X60DEkJKj8E.

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Acknowledgements

Gabriella Casalino acknowledges funding from the Italian Ministry of Education, University and Research through the European PON project AIM (Attraction and International Mobility), nr. 1852414, activity 2, line 1. All authors are members of the INdAM GNCS research group.

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Authors and Affiliations

  1. Computer Science Department, University of Bari “Aldo Moro”, 70125, Bari, Italy

    Gabriella Casalino, Giovanna Castellano & Gennaro Vessio

Authors
  1. Gabriella Casalino
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  2. Giovanna Castellano
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  3. Gennaro Vessio
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Corresponding author

Correspondence to Gabriella Casalino .

Editor information

Editors and Affiliations

  1. Giustino Fortunato University, Benevento, Italy

    Laura Sara Agrati

  2. Universidad Internacional de La Rioja, Logroño, La Rioja, Spain

    Daniel Burgos

  3. University of Pisa, Pisa, Italy

    Pietro Ducange

  4. University of Foggia, Foggia, Italy

    Pierpaolo Limone

  5. University of Bari Aldo Moro, Bari, Italy

    Loredana Perla

  6. eCampus University, Novedrate, Como, Italy

    Pietro Picerno

  7. eCampus University, Novedrate, Como, Italy

    Paolo Raviolo

  8. Open University in the Netherlands, Heerlen, The Netherlands

    Christian M. Stracke

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Casalino, G., Castellano, G., Vessio, G. (2021). Exploiting Time in Adaptive Learning from Educational Data. In: Agrati, L.S., et al. Bridges and Mediation in Higher Distance Education. HELMeTO 2020. Communications in Computer and Information Science, vol 1344. Springer, Cham. https://doi.org/10.1007/978-3-030-67435-9_1

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  • DOI: https://doi.org/10.1007/978-3-030-67435-9_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-67434-2

  • Online ISBN: 978-3-030-67435-9

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