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An Empirical Study to Predict Student Performance Using Information of the Virtual Learning Environment

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Book cover Higher Education Learning Methodologies and Technologies Online (HELMeTO 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1779))

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Abstract

This study aims to explore a new feature model and a set of machine learning classifiers to predict student performance by monitoring his/her activities on the Virtual Learning Environment (VLE). The features are used for training the classifier to predict the final result of each student. The proposed model is evaluated by using a dataset built at the Open University of London. The results show good performance (80% of accuracy) of the proposed approach compared to other similar studies.

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Author information

Authors and Affiliations

  1. Department of Engineering, University of Sannio Benevento, Benevento, Italy

    Lerina Aversano, Mario Luca Bernardi, Martina Iammarino, Debora Montano & Chiara Verdone

  2. UnitelmaSapienza University, Rome, Italy

    Marta Cimitile

Authors
  1. Lerina Aversano
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  2. Mario Luca Bernardi
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  3. Marta Cimitile
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  4. Martina Iammarino
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  5. Debora Montano
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  6. Chiara Verdone
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Corresponding author

Correspondence to Lerina Aversano .

Editor information

Editors and Affiliations

  1. National Research Council of Italy, Institute for Educational Technology, Palermo, Italy

    Giovanni Fulantelli

  2. Universidad Internacional de La Rioja, Logroño, Spain

    Daniel Burgos

  3. University of Bari, Bari, Italy

    Gabriella Casalino

  4. Unitelma Sapienza University, Rome, Italy

    Marta Cimitile

  5. University of Palermo, Palermo, Italy

    Giosuè Lo Bosco

  6. National Research Council of Italy, Institute for Educational Technology, Palermo, Italy

    Davide Taibi

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Aversano, L., Bernardi, M.L., Cimitile, M., Iammarino, M., Montano, D., Verdone, C. (2023). An Empirical Study to Predict Student Performance Using Information of the Virtual Learning Environment. In: Fulantelli, G., Burgos, D., Casalino, G., Cimitile, M., Lo Bosco, G., Taibi, D. (eds) Higher Education Learning Methodologies and Technologies Online. HELMeTO 2022. Communications in Computer and Information Science, vol 1779. Springer, Cham. https://doi.org/10.1007/978-3-031-29800-4_41

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  • DOI: https://doi.org/10.1007/978-3-031-29800-4_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-29799-1

  • Online ISBN: 978-3-031-29800-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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