dc.contributor.author | Klašnja-MIlićević, Aleksandra | |
dc.contributor.author | Ivanović, Mirjana | |
dc.contributor.author | Vesin, Boban | |
dc.contributor.author | Budimac, Zoran | |
dc.date.accessioned | 2017-08-30T11:57:20Z | |
dc.date.available | 2017-08-30T11:57:20Z | |
dc.date.created | 2017-08-02T11:19:57Z | |
dc.date.issued | 2017 | |
dc.identifier.issn | 0924-669X | |
dc.identifier.uri | http://hdl.handle.net/11250/2452410 | |
dc.description.abstract | 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. | |
dc.language.iso | eng | |
dc.publisher | Springer | |
dc.title | Enhancing E-Learning Systems with Personalized Recommendation Based on Collaborative Tagging Techniques | |
dc.type | Peer reviewed | |
dc.type | Journal article | |
dc.description.version | publishedVersion | |
dc.source.journal | Applied intelligence (Boston) | |
dc.identifier.doi | 10.1007/s10489-017-1051-8 | |
dc.identifier.cristin | 1483788 | |
dc.description.localcode | This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. | |
cristin.unitcode | 194,63,10,0 | |
cristin.unitname | Institutt for datateknikk og informasjonsvitenskap | |
cristin.ispublished | false | |
cristin.fulltext | original | |
cristin.qualitycode | 2 | |