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A Deep Learning System to Help Students Build Concept Maps

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Learning Technologies and Systems (ICWL 2022, SETE 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13869))

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Abstract

Nowadays, overwhelmed as people are by the amount of information available, it becomes more and more difficult to build an adequate cognitive process of knowledge building and discovery, on any one knowledge domain. Therefore, having knowledge-building tools at disposition, especially in the age of schooling, is of great importance. Knowledge building occurs by linking new concepts to already learned ones, thus connecting concepts together by means of semantic links representing their relationship. To accomplish this task, most of learners use Concept Maps, that is graphic tools, particularly suitable, to organize, represent and share knowledge. In fact, a Concept Map can explicitly express the knowledge of a person or group, about a given domain of interest: from primary school to university, and to professional/vocational training, Concept Maps can stimulate and unveil the occurrence of the so-called meaningful learning, according to the Ausubel’s learning theory. In this paper we investigate the use of a deep learning-based architecture, called TransH, designed for Knowledge Graph Embedding, to support the process of Concept Maps building. This approach has not been yet investigated for this particular educational task. Some preliminary case studies are discussed, confirming the potential of this approach.

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Notes

  1. 1.

    https://graphed.igiresearch.com.

  2. 2.

    https://cmap.ihmc.us/cmaptools/.

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Author information

Authors and Affiliations

  1. Faculty of Economics, Universitas Mercatorum, Piazza Mattei, 10, Rome, Italy

    Francesco Pes & Filippo Sciarrone

  2. Diag-Dipartimento di Ingegneria Informatica Automatica e Gestionale, Sapienza University of Rome, Via Ariosto, 25, Rome, Italy

    Marco Temperini

Authors
  1. Francesco Pes
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  2. Filippo Sciarrone
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  3. Marco Temperini
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Corresponding author

Correspondence to Filippo Sciarrone .

Editor information

Editors and Affiliations

  1. Universidad de La Laguna, Tenerife, Spain

    Carina S. González-González

  2. Universidad Complutense de Madrid, Madrid, Madrid, Spain

    Baltasar Fernández-Manjón

  3. Durham University, Durham, UK

    Frederick Li

  4. Universidad de Salamanca, Salamanca, Spain

    Francisco José García-Peñalvo

  5. Mercatorum University, Rome, Italy

    Filippo Sciarrone

  6. Université de Caen Normandie, Caen, France

    Marc Spaniol

  7. University of Salamanca, Salamanca, Spain

    Alicia García-Holgado

  8. Universidad de La Laguna, Tenerife, Spain

    Manuel Area-Moreira

  9. University of Hagen, Hagen, Germany

    Matthias Hemmje

  10. South China Normal University, Guangzhou, China

    Tianyong Hao

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Pes, F., Sciarrone, F., Temperini, M. (2023). A Deep Learning System to Help Students Build Concept Maps. In: González-González, C.S., et al. Learning Technologies and Systems. ICWL SETE 2022 2022. Lecture Notes in Computer Science, vol 13869. Springer, Cham. https://doi.org/10.1007/978-3-031-33023-0_29

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  • DOI: https://doi.org/10.1007/978-3-031-33023-0_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-33022-3

  • Online ISBN: 978-3-031-33023-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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