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Predicting Academic Performance of Students from the Assessment Submission in Virtual Learning Environment

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Research and Innovation Forum 2022 (RIIFORUM 2022)

Part of the book series: Springer Proceedings in Complexity ((SPCOM))

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Abstract

The growth of online learning platforms has transformed the educational world, and the research community is now more interested than ever in discovering optimal ways to exploit online education repositories, including students’ interactions with such platforms. Early prediction of student academic success has been a prevalent study issue, and multiple studies have articulated several measures and predictors for such analysis. In this work, we hand-engineer numerous factors relevant to a student’s first evaluation in a course using data obtained from the Open University in the United Kingdom. A series of typical machine learning methods runs an array of trials on two feature sets, incorporating demographics with interactions and assessment data and ignoring demographics. The outcomes are examined to find key characteristics and determinants of student success. Such research is expected to aid in developing appropriate educational policies for an active layer of student support and intervention.

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Author information

Authors and Affiliations

  1. Information Technology University, 346-B, Ferozepur Road, Lahore, Pakistan

    Hajra Waheed, Ifra Nisar, Mehr-un-Nisa Khalid & Ali Shahid

  2. King Abdulaziz University, Jeddah, Saudi Arabia

    Naif Radi Aljohani

  3. Manchester Metropolitan University, Manchester, M15 6BH, United Kingdom

    Saeed-Ul Hassan

  4. Staffordshire University, Staffordshire, ST4 2DE, United Kingdom

    Raheel Nawaz

Authors
  1. Hajra Waheed
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  2. Ifra Nisar
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  3. Mehr-un-Nisa Khalid
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  4. Ali Shahid
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  5. Naif Radi Aljohani
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  6. Saeed-Ul Hassan
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  7. Raheel Nawaz
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Corresponding author

Correspondence to Hajra Waheed .

Editor information

Editors and Affiliations

  1. SGH Warsaw School of Economics, Warsaw, Poland

    Anna Visvizi

  2. University of Salerno, Fisciano, Italy

    Orlando Troisi

  3. University of Salerno, Fisciano, Italy

    Mara Grimaldi

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Waheed, H. et al. (2023). Predicting Academic Performance of Students from the Assessment Submission in Virtual Learning Environment. In: Visvizi, A., Troisi, O., Grimaldi, M. (eds) Research and Innovation Forum 2022. RIIFORUM 2022. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-031-19560-0_33

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  • DOI: https://doi.org/10.1007/978-3-031-19560-0_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19559-4

  • Online ISBN: 978-3-031-19560-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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