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Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions

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Abstract

With the development of sophisticated e-learning environments, personalization is becoming an important feature in e-learning systems due to the differences in background, goals, capabilities and personalities of the large numbers of learners. Personalization can achieve using different type of recommendation techniques. This paper presents an overview of the most important requirements and challenges for designing a recommender system in e-learning environments. The aim of this paper is to present the various limitations of the current generation of recommendation techniques and possible extensions with model for tagging activities and tag-based recommender systems, which can apply to e-learning environments in order to provide better recommendation capabilities.

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Notes

  1. http://www.flickr.com.

  2. http://www.del.icio.us.

  3. http://www.citeulike.org.

  4. http://www.connotea.org.

  5. http://www.43things.com.

  6. http://movielens.org.

  7. https://netflix.com/.

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Acknowledgments

Ministry of Education, Science and Technological Development of Serbia supported the presented research, through project: “Intelligent techniques and their integration into wide-spectrum decision support” (Project No. 174023).

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Authors and Affiliations

  1. Faculty of Sciences, University of Novi Sad, Novi Sad, Serbia

    Aleksandra Klašnja-Milićević & Mirjana Ivanović

  2. Katholische Universität Eichstätt-Ingolstadt, Ostenstraße 26, 85072, Eichstätt, Deutschland

    Alexandros Nanopoulos

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  1. Aleksandra Klašnja-Milićević
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  2. Mirjana Ivanović
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  3. Alexandros Nanopoulos
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Correspondence to Aleksandra Klašnja-Milićević.

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Klašnja-Milićević, A., Ivanović, M. & Nanopoulos, A. Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions. Artif Intell Rev 44, 571–604 (2015). https://doi.org/10.1007/s10462-015-9440-z

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