Social tagging strategy for enhancing e-learning experience

Success of e-learning systems depends on their capability to automatically retrieve and recommend relevant learning content according to the preferences of a specific learner. Learning experience and dynamic choice of educational material that is presented to learners can be enhanced using different...

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Vydané v:Computers and education Ročník 118; s. 166 - 181
Hlavní autori: Klašnja-Milićević, Aleksandra, Vesin, Boban, Ivanović, Mirjana
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.03.2018
Predmet:
Architectures for educational technology system
Human-computer interface
Intelligent tutoring systems
Programming and programming languages
Teaching/learning strategies
Teaching/learning strategies
Human-computer interface
Programming and programming languages
Intelligent tutoring systems
Architectures for educational technology system
ISSN:0360-1315, 1873-782X
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Popis
Shrnutí:Success of e-learning systems depends on their capability to automatically retrieve and recommend relevant learning content according to the preferences of a specific learner. Learning experience and dynamic choice of educational material that is presented to learners can be enhanced using different recommendation techniques. As popularity of collaborative tagging systems grows, users’ tags could provide useful information to improve recommender system algorithms in e-learning environments. In this paper, we present an approach for implementation of collaborative tagging techniques into online tutoring system. The implemented approach combines social tagging and sequential patterns mining for generating recommendations of learning resources to learners. Several experiments were carried out in order to verify usability of the proposed hybrid method within e-learning environment and analyze selected social tagging techniques. [Display omitted] •Approach for use of collaborative tagging in online tutoring system is proposed.•Hybrid recommendation method based on social tagging and sequential pattern mining.•The recommendation methods based on most popular tags are compared.•The experiments prove usability of the proposed hybrid method in e-learning.
ISSN:0360-1315
1873-782X
DOI:10.1016/j.compedu.2017.12.002