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Enhancing the Early Prediction of Learners Performance in a Virtual Learning Environment

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New Trends in Information and Communications Technology Applications (NTICT 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2096))

  • 9 Accesses

Abstract

Educational institutions have widely adopted virtual learning environments (VLEs) in contemporary education. The limits of students’ location are no longer a problem because they can learn from anywhere and anytime based on this approach. Hence, by forecasting students’ performance in VLEs, educational institutions can enhance their online offerings and provide quality online learning content. This is not possible without taking into account various features that may have a great influence on students’ academic accomplishments. The present paper intends to predict students’ performance in an online platform. Four classifiers are utilized in the proposed model. This research integrates the whole dataset for science and social science modules. Moreover, to improve the model's prediction accuracy several steps are followed. First, new features are generated based on the available features namely, the total number of clicks before, the total number of clicks after, engagement, and average. Second, the model’s hyperparameters are adjusted using the random search optimizer, whereas a feature selection approach is performed to choose the maximum influential features. The experimental results showed that the prediction accuracy is significantly enhanced based on the procedure proposed in this research. The suggested model successfully provides an early prediction of students’ performance with an average accuracy of 84%. The outcomes of this research are discussed further to highlight its possible implications on theory and practice.

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Author information

Authors and Affiliations

  1. Department of Software, College of Information Technology, University of Babylon, Babil, Iraq

    Safa Ridha Albo Abdullah, Safa Ridha Albo Abdullah & Ahmed Al-Azawei

Authors
  1. Safa Ridha Albo Abdullah
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  2. Ahmed Al-Azawei
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Corresponding author

Correspondence to Safa Ridha Albo Abdullah .

Editor information

Editors and Affiliations

  1. University of Information Technology and Communications, Baghdad, Iraq

    Abbas M. Al-Bakry

  2. University of Information Technology and Communications, Baghdad, Iraq

    Mouayad A. Sahib

  3. University of Babylon, Babylon, Iraq

    Safaa O. Al-Mamory

  4. University of Information Technology and Communications, Baghdad, Iraq

    Jaafar A. Aldhaibani

  5. University of Information Technology and Communications, Baghdad, Iraq

    Ali N. Al-Shuwaili

  6. University of Information Technology and Communications, Baghdad, Iraq

    Haitham S. Hasan

  7. University of Information Technology and Communications, Baghdad, Iraq

    Rula A. Hamid

  8. University of Babylon, Babylon, Iraq

    Ali K. Idrees

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Abdullah, S.R.A., Al-Azawei, A. (2024). Enhancing the Early Prediction of Learners Performance in a Virtual Learning Environment. In: Al-Bakry, A.M., et al. New Trends in Information and Communications Technology Applications. NTICT 2023. Communications in Computer and Information Science, vol 2096. Springer, Cham. https://doi.org/10.1007/978-3-031-62814-6_18

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  • DOI: https://doi.org/10.1007/978-3-031-62814-6_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-62813-9

  • Online ISBN: 978-3-031-62814-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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