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Temporal Graph-Based CNNs (TG-CNNs) for Online Course Dropout Prediction

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Foundations of Intelligent Systems (ISMIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13515))

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Abstract

Due to the global pandemic, the use of online courses is increasing significantly; yet the rate of student dropout from online courses is rising. The Accessible Culture & Training Massive Open Online Course (ACT MOOC) dataset is comprised of a temporal sequence of student actions and subsequent dropout information. We introduce a novel approach based upon temporal graphs, which uses the sequence of (and time between) events to predict dropout. The dataset consists of 7,047 users, with a dropout rate of 57.7%. The Temporal Graph-Based Convolutional Neural Network (TG-CNN) models developed in this study are compared against baseline models and existing models in the literature. Performance is assessed using the AUC, accuracy, precision, recall, and F1 score. Our novel TG-CNN model achieves an AUC score of 0.797, which improves upon previous literature: JODIE 0.756, TGN \(+\) MeTA 0.794, TGN 0.777, and CoPE 0.762. Our model offers a novel and intuitive formulation of this problem, with state-of-the-art performance.

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Notes

  1. 1.

    Stanford Network Analysis Project - https://snap.stanford.edu/data/act-mooc.html.

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Acknowledgements

This work made use of the facilities of the N8 Centre of Excellence in Computationally Intensive Research provided and funded by the N8 research partnership and EPSRC (Grant No. EP/T022167/1). ZH is supported through funding by the EPSRC (Grant No. EP/S024336/1).

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Authors and Affiliations

  1. The University of Leeds, Leeds, UK

    Zoe Hancox & Samuel D. Relton

Authors
  1. Zoe Hancox
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  2. Samuel D. Relton
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Correspondence to Zoe Hancox .

Editor information

Editors and Affiliations

  1. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci

  2. Università della Calabria, Rende, Italy

    Sergio Flesca

  3. Università Federico II di Napoli, Naples, Italy

    Elio Masciari

  4. ICAR-CNR, Rende, Italy

    Giuseppe Manco

  5. University of North Carolina, Charlotte, NC, USA

    Zbigniew W. Raś

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Hancox, Z., Relton, S.D. (2022). Temporal Graph-Based CNNs (TG-CNNs) for Online Course Dropout Prediction. In: Ceci, M., Flesca, S., Masciari, E., Manco, G., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2022. Lecture Notes in Computer Science(), vol 13515. Springer, Cham. https://doi.org/10.1007/978-3-031-16564-1_34

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  • DOI: https://doi.org/10.1007/978-3-031-16564-1_34

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