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Investigating Influence of Demographic Factors on Study Recommenders

  • Michal Huptych
  • Martin Hlosta
  • Zdenek Zdrahal
  • Jakub Kocvara
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10948)

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.

Keywords

Personalised learning Educational recommender systems 

References

  1. 1.
    Bouchet, F., Labarthe, H., Yacef, K., Bachelet, R.: Comparing peer recommendation strategies in a MOOC. In: Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization, pp. 129–134. ACM (2017)Google Scholar
  2. 2.
    Drachsler, H., Verbert, K., Santos, O.C., Manouselis, N.: Panorama of recommender systems to support learning. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 421–451. Springer, Boston, MA (2015).  https://doi.org/10.1007/978-1-4899-7637-6_12CrossRefGoogle Scholar
  3. 3.
    Ghauth, K.I., Abdullah, N.A.: The effect of incorporating good learners’ ratings in e-learning content-based recommender system. J. Educ. Technol. Soc. 14(2), 248 (2011)Google Scholar
  4. 4.
    Huptych, M., Bohuslavek, M., Hlosta, M., Zdrahal, Z.: Measures for recommendations based on past students’ activity. In: LAK 2017 Proceedings of the 7th International Learning Analytics & Knowledge Conference on - LAK 2017, pp. 404–408 (2017)Google Scholar
  5. 5.
    Kerkiri, T., Manitsaris, A., Mavridis, I.: How e-learning systems may benefit from ontologies and recommendation methods to efficiently personalise resources. Int. J. Knowl. Learn. 5(3–4), 347–370 (2009)CrossRefGoogle Scholar
  6. 6.
    Nabizadeh, A.H., Mário Jorge, A., Paulo Leal, J.: Rutico: Recommending successful learning paths under time constraints. In: Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization, pp. 153–158. UMAP 2017. ACM, New York (2017)Google Scholar
  7. 7.
    Wen-Shung Tai, D., Wu, H.J., Li, P.H.: Effective e-learning recommendation system based on self-organizing maps and association mining. Electron. Libr. 26(3), 329–344 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Michal Huptych
    • 1
    • 2
  • Martin Hlosta
    • 1
  • Zdenek Zdrahal
    • 1
    • 2
  • Jakub Kocvara
    • 1
  1. 1.Knowledge Media InstituteThe Open UniversityMilton KeynesUK
  2. 2.CIIRCCzech Technical University in PraguePragueCzech Republic

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