Abstract
Recommendation engines are being increasingly deployed into the e-learning systems. This paper proposes a software architecture for recommending learning content and learning activities, which has been validated by means of a case study. The main goal of that architecture is to achieve better recommendations of learning content and learning activities not only in systems, but also in similar e-learning environments.
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Acknowledgment
The research reported here was funded under a project entitled “An innovative software platform for big data learning and gaming analytics for a user-centric adaptation of technology enhanced learning (APTITUDE)” - research projects on societal challenges – 2018 by the Bulgarian National Science Fund with contract №: KP-06OPR03/1 from 13.12.2018.
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Aleksieva-Petrova, A., Petrov, M. (2022). Recommendation Engine of Learning Contents and Activities Based on Learning Analytics. In: Auer, M.E., Tsiatsos, T. (eds) New Realities, Mobile Systems and Applications. IMCL 2021. Lecture Notes in Networks and Systems, vol 411. Springer, Cham. https://doi.org/10.1007/978-3-030-96296-8_33
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DOI: https://doi.org/10.1007/978-3-030-96296-8_33
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