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Investigating Influence of Demographic Factors on Study Recommenders

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Artificial Intelligence in Education (AIED 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10948))

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Abstract

Recommender systems in e-learning platforms, can utilise various data about learners in order to provide them with the next best material to study. We build on our previous work, which defines the recommendations in terms of two measures (i.e. relevance and effort) calculated from data of successful students in the previous runs of the courses. In this paper we investigate the impact of students’ socio-demographic factors and analyse how these factors improved the recommendation. It has been shown that education and age were found to have a significant impact on engagement with materials.

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References

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Author information

Authors and Affiliations

  1. Knowledge Media Institute, The Open University, Milton Keynes, UK

    Michal Huptych, Martin Hlosta, Zdenek Zdrahal & Jakub Kocvara

  2. CIIRC, Czech Technical University in Prague, Prague, Czech Republic

    Michal Huptych & Zdenek Zdrahal

Authors
  1. Michal Huptych
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  2. Martin Hlosta
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  3. Zdenek Zdrahal
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  4. Jakub Kocvara
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Corresponding author

Correspondence to Martin Hlosta .

Editor information

Editors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, PA, USA

    Carolyn Penstein Rosé

  2. University of Technology, Sydney, NSW, Australia

    Roberto MartĂ­nez-Maldonado

  3. University of Duisburg-Essen , Duisburg, Germany

    H. Ulrich Hoppe

  4. UCL Institute of Education, London, UK

    Rose Luckin

  5. UCL Institute of Education, London, UK

    Manolis Mavrikis

  6. UCL Institute of Education, London, UK

    Kaska Porayska-Pomsta

  7. Carnegie Mellon University, Pittsburgh, PA, USA

    Bruce McLaren

  8. University of Sussex, Brighton, UK

    Benedict du Boulay

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Huptych, M., Hlosta, M., Zdrahal, Z., Kocvara, J. (2018). Investigating Influence of Demographic Factors on Study Recommenders. In: Penstein Rosé, C., et al. Artificial Intelligence in Education. AIED 2018. Lecture Notes in Computer Science(), vol 10948. Springer, Cham. https://doi.org/10.1007/978-3-319-93846-2_27

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  • DOI: https://doi.org/10.1007/978-3-319-93846-2_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93845-5

  • Online ISBN: 978-3-319-93846-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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