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Knowledge-Driven Wikipedia Article Recommendation for Electronic Textbooks

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Addressing Global Challenges and Quality Education (EC-TEL 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12315))

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Abstract

In this paper, we introduce an approach that combines automatic domain knowledge modeling, student modeling, and content recommendation approaches to recommend relevant Wikipedia articles for students working with online electronic textbooks.

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References

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Authors and Affiliations

  1. University of Pittsburgh, Pittsburgh, PA, 15260, USA

    Behnam Rahdari, Peter Brusilovsky, Khushboo Thaker & Jordan Barria-Pineda

Authors
  1. Behnam Rahdari
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  2. Peter Brusilovsky
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  3. Khushboo Thaker
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  4. Jordan Barria-Pineda
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Corresponding authors

Correspondence to Behnam Rahdari , Peter Brusilovsky , Khushboo Thaker or Jordan Barria-Pineda .

Editor information

Editors and Affiliations

  1. Universidad Carlos III de Madrid, Leganés (Madrid), Spain

    Carlos Alario-Hoyos

  2. Tallinn University, Tallinn, Estonia

    María Jesús Rodríguez-Triana

  3. Open University Netherlands, Heerlen, The Netherlands

    Maren Scheffel

  4. Trinity College Dublin, Dublin, Ireland

    Inmaculada Arnedillo-Sánchez

  5. Graz University of Technology and Know-Center, Graz, Austria

    Sebastian Maximilian Dennerlein

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Rahdari, B., Brusilovsky, P., Thaker, K., Barria-Pineda, J. (2020). Knowledge-Driven Wikipedia Article Recommendation for Electronic Textbooks. In: Alario-Hoyos, C., Rodríguez-Triana, M.J., Scheffel, M., Arnedillo-Sánchez, I., Dennerlein, S.M. (eds) Addressing Global Challenges and Quality Education. EC-TEL 2020. Lecture Notes in Computer Science(), vol 12315. Springer, Cham. https://doi.org/10.1007/978-3-030-57717-9_28

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  • DOI: https://doi.org/10.1007/978-3-030-57717-9_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-57716-2

  • Online ISBN: 978-3-030-57717-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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