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A Systematic Review of Deep Learning Approaches to Educational Data Mining

Author

Listed:
  • Antonio Hernández-Blanco
  • Boris Herrera-Flores
  • David Tomás
  • Borja Navarro-Colorado

Abstract

Educational Data Mining (EDM) is a research field that focuses on the application of data mining, machine learning, and statistical methods to detect patterns in large collections of educational data. Different machine learning techniques have been applied in this field over the years, but it has been recently that Deep Learning has gained increasing attention in the educational domain. Deep Learning is a machine learning method based on neural network architectures with multiple layers of processing units, which has been successfully applied to a broad set of problems in the areas of image recognition and natural language processing. This paper surveys the research carried out in Deep Learning techniques applied to EDM, from its origins to the present day. The main goals of this study are to identify the EDM tasks that have benefited from Deep Learning and those that are pending to be explored, to describe the main datasets used, to provide an overview of the key concepts, main architectures, and configurations of Deep Learning and its applications to EDM, and to discuss current state-of-the-art and future directions on this area of research.

Suggested Citation

  • Antonio Hernández-Blanco & Boris Herrera-Flores & David Tomás & Borja Navarro-Colorado, 2019. "A Systematic Review of Deep Learning Approaches to Educational Data Mining," Complexity, Hindawi, vol. 2019, pages 1-22, May.
    • Handle: RePEc:hin:complx:1306039
    DOI: 10.1155/2019/1306039
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    References listed on IDEAS

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    1. Bolton, Roger E. & Jackson, Randall W. & West, Guy R., 1989. "Introduction," Socio-Economic Planning Sciences, Elsevier, vol. 23(5), pages 237-240.
    2. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
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    Cited by:

    1. Renza Campagni & Donatella Merlini & Maria Cecilia Verri, 2022. "Analysing Computer Science Courses over Time," Data, MDPI, vol. 7(2), pages 1-15, January.
    2. Aras Bozkurt & Abdulkadir Karadeniz & David Baneres & Ana Elena Guerrero-Roldán & M. Elena Rodríguez, 2021. "Artificial Intelligence and Reflections from Educational Landscape: A Review of AI Studies in Half a Century," Sustainability, MDPI, vol. 13(2), pages 1-16, January.
    3. Tieyuan Liu & Chang Wang & Liang Chang & Tianlong Gu, 2022. "Predicting High-Risk Students Using Learning Behavior," Mathematics, MDPI, vol. 10(14), pages 1-15, July.
    4. Kaknjo Arnela & Turulja Lejla, 2025. "The KDD Process in Big Data Analytics: A Theoretical Approach to Taxpayer Non-Compliance Analysis," Journal of Forensic Accounting Profession, Sciendo, vol. 5(1), pages 16-42.
    5. Hülya Yürekli & Öyküm Esra Yiğit & Okan Bulut & Min Lu & Ersoy Öz, 2022. "Exploring Factors That Affected Student Well-Being during the COVID-19 Pandemic: A Comparison of Data-Mining Approaches," IJERPH, MDPI, vol. 19(18), pages 1-16, September.

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