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Enhancing E-Learning Systems with Personalized Recommendation Based on Collaborative Tagging Techniques

Klašnja-MIlićević, Aleksandra; Ivanović, Mirjana; Vesin, Boban; Budimac, Zoran
Peer reviewed, Journal article
Published version
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Åpne
VesinAppliedIntelligenceFINAL.pdf (3.954Mb)
Permanent lenke
http://hdl.handle.net/11250/2452410
Utgivelsesdato
2017
Metadata
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  • Institutt for datateknologi og informatikk [4881]
  • Publikasjoner fra CRIStin - NTNU [26591]
Originalversjon
10.1007/s10489-017-1051-8
Sammendrag
Personalization of the e-learning systems according to the learner’s needs and knowledge level presents the key element in a learning process. E-learning systems with personalized recommendations should adapt the learning experience according to the goals of the individual learner. Aiming to facilitate personalization of a learning content, various kinds of techniques can be applied. Collaborative and social tagging techniques could be useful for enhancing recommendation of learning resources. In this paper, we analyze the suitability of different techniques for applying tag-based recommendations in e-learning environments. The most appropriate model ranking, based on tensor factorization technique, has been modified to gain the most efficient recommendation results. We propose reducing tag space with clustering technique based on learning style model, in order to improve execution time and decrease memory requirements, while preserving the quality of the recommendations. Such reduced model for providing tag-based recommendations has been used and evaluated in a programming tutoring system.
Utgiver
Springer
Tidsskrift
Applied intelligence (Boston)

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