IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0267138.html
   My bibliography  Save this article

A survival analysis based volatility and sparsity modeling network for student dropout prediction

Author

Listed:
  • Feng Pan
  • Bingyao Huang
  • Chunhong Zhang
  • Xinning Zhu
  • Zhenyu Wu
  • Moyu Zhang
  • Yang Ji
  • Zhanfei Ma
  • Zhengchen Li

Abstract

Student Dropout Prediction (SDP) is pivotal in mitigating withdrawals in Massive Open Online Courses. Previous studies generally modeled the SDP problem as a binary classification task, providing a single prediction outcome. Accordingly, some attempts introduce survival analysis methods to achieve continuous and consistent predictions over time. However, the volatility and sparsity of data always weaken the models’ performance. Prevailing solutions rely heavily on data pre-processing independent of predictive models, which are labor-intensive and may contaminate authentic data. This paper proposes a Survival Analysis based Volatility and Sparsity Modeling Network (SAVSNet) to address these issues in an end-to-end deep learning framework. Specifically, SAVSNet smooths the volatile time series by convolution network while preserving the original data information using Long-Short Term Memory Network (LSTM). Furthermore, we propose a Time-Missing-Aware LSTM unit to mitigate the impact of data sparsity by integrating informative missingness patterns into the model. A survival analysis loss function is adopted for parameter estimation, and the model outputs monotonically decreasing survival probabilities. In the experiments, we compare the proposed method with state-of-the-art methods in two real-world MOOC datasets, and the experiment results show the effectiveness of our proposed model.

Suggested Citation

  • Feng Pan & Bingyao Huang & Chunhong Zhang & Xinning Zhu & Zhenyu Wu & Moyu Zhang & Yang Ji & Zhanfei Ma & Zhengchen Li, 2022. "A survival analysis based volatility and sparsity modeling network for student dropout prediction," PLOS ONE, Public Library of Science, vol. 17(5), pages 1-22, May.
  • Handle: RePEc:plo:pone00:0267138
    DOI: 10.1371/journal.pone.0267138
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0267138
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0267138&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0267138?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Albert-László Barabási, 2005. "The origin of bursts and heavy tails in human dynamics," Nature, Nature, vol. 435(7039), pages 207-211, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Anzhi Sheng & Qi Su & Aming Li & Long Wang & Joshua B. Plotkin, 2023. "Constructing temporal networks with bursty activity patterns," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    2. He, Yifan & Zhao, Chen & Zeng, An, 2022. "Ranking locations in a city via the collective home-work relations in human mobility data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    3. Lu, Xi & Mo, Hongming & Deng, Yong, 2015. "An evidential opinion dynamics model based on heterogeneous social influential power," Chaos, Solitons & Fractals, Elsevier, vol. 73(C), pages 98-107.
    4. Wang, Cheng-Jun & Wu, Lingfei, 2016. "The scaling of attention networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 448(C), pages 196-204.
    5. Simon DeDeo, 2016. "Conflict and Computation on Wikipedia: A Finite-State Machine Analysis of Editor Interactions," Future Internet, MDPI, vol. 8(3), pages 1-23, July.
    6. repec:plo:pone00:0070854 is not listed on IDEAS
    7. Jing Yang & Yingwu Chen, 2011. "Fast Computing Betweenness Centrality with Virtual Nodes on Large Sparse Networks," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-5, July.
    8. Pablo Lara-Martínez & Bibiana Obregón-Quintana & C F Reyes-Manzano & Irene López-Rodríguez & Lev Guzmán-Vargas, 2022. "A multiplex analysis of phonological and orthographic networks," PLOS ONE, Public Library of Science, vol. 17(9), pages 1-19, September.
    9. Baltakys, Kęstutis & Kanniainen, Juho & Saramäki, Jari & Kivelä, Mikko, 2023. "Investor trade allocation patterns in stock markets," Journal of Economic Behavior & Organization, Elsevier, vol. 210(C), pages 191-209.
    10. Diao, Su-Meng & Liu, Yun & Zeng, Qing-An & Luo, Gui-Xun & Xiong, Fei, 2014. "A novel opinion dynamics model based on expanded observation ranges and individuals’ social influences in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 415(C), pages 220-228.
    11. Zhou, Bin & Xie, Jia-Rong & Yan, Xiao-Yong & Wang, Nianxin & Wang, Bing-Hong, 2017. "A model of task-deletion mechanism based on the priority queueing system of Barabási," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 415-421.
    12. Chen, Ning & Zhu, Xuzhen & Chen, Yanyan, 2019. "Information spreading on complex networks with general group distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 671-676.
    13. Zhenpeng Li & Xijin Tang & Zhenjie Hong, 2022. "Collective attention dynamic induced by novelty decay," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 95(8), pages 1-11, August.
    14. C Ben Gibson & Norbou Buchler & Blaine Hoffman & Claire-Genevieve La Fleur, 2019. "Participation shifts explain degree distributions in a human communications network," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-13, May.
    15. Koen Zwet & Ana I. Barros & Tom M. Engers & Peter M. A. Sloot, 2022. "Emergence of protests during the COVID-19 pandemic: quantitative models to explore the contributions of societal conditions," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-11, December.
    16. Huan-Kai Peng & Hao-Chih Lee & Jia-Yu Pan & Radu Marculescu, 2016. "Data-Driven Engineering of Social Dynamics: Pattern Matching and Profit Maximization," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-21, January.
    17. Qianqian Liu & Qun Wang, 2017. "A comparative study on uncooperative search models in survivor search and rescue," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 89(2), pages 843-857, November.
    18. Sur, Souvik & Ganguly, Niloy & Mukherjee, Animesh, 2015. "Attack tolerance of correlated time-varying social networks with well-defined communities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 420(C), pages 98-107.
    19. Kota Yamada & Atsunori Kanemura, 2020. "Simulating bout-and-pause patterns with reinforcement learning," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-21, November.
    20. Muaz Niazi & Amir Hussain, 2011. "Agent-based computing from multi-agent systems to agent-based models: a visual survey," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(2), pages 479-499, November.
    21. Bent Flyvbjerg & Alexander Budzier & Daniel Lunn, 2021. "Regression to the tail: Why the Olympics blow up," Environment and Planning A, , vol. 53(2), pages 233-260, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0267138. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.