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Pregled bibliografske jedinice broj: 763678

Recommender System and Web 2.0 Tools to Enhance a Blended Learning Model


Hoić-Božić, Natasa; Holenko Dlab, Martina; Mornar, Vedran
Recommender System and Web 2.0 Tools to Enhance a Blended Learning Model // IEEE transactions on education, 59 (2016), 1; 39-44 doi:10.1109/TE.2015.2427116 (međunarodna recenzija, članak, znanstveni)


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Naslov
Recommender System and Web 2.0 Tools to Enhance a Blended Learning Model

Autori
Hoić-Božić, Natasa ; Holenko Dlab, Martina ; Mornar, Vedran

Izvornik
IEEE transactions on education (0018-9359) 59 (2016), 1; 39-44

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Blended learning model; collaborative learning; recommender systems; Web 2.0

Sažetak
Blended learning models that combine face-to- face and online learning are of great importance in modern higher education. However, their development should be in line with the recent changes in e-learning that emphasize a student- centered approach and use tools available on the Web to support the learning process. This paper presents research on implementing a contemporary blended learning model within the e-course “Hypermedia Supported Education”. The blended model developed combines an LMS (learning management system), a set of Web 2.0 tools and the E-Learning Activities Recommender System (ELARS) to enhance personalized online learning. As well as incorporating various technologies, the model combines a number of pedagogical approaches, focusing on collaborative and problem-based learning, to ensure the achievement of the course learning outcomes. The results of the comparative study showed the effectiveness of the proposed model in that students who performed personalized collaborative e learning activities achieved better course results. These findings encourage the further application of the model to other computer science courses.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Informacijske i komunikacijske znanosti



POVEZANOST RADA


Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb,
Fakultet informatike i digitalnih tehnologija, Rijeka

Poveznice na cjeloviti tekst rada:

doi ieeexplore.ieee.org ieeexplore.ieee.org

Citiraj ovu publikaciju:

Hoić-Božić, Natasa; Holenko Dlab, Martina; Mornar, Vedran
Recommender System and Web 2.0 Tools to Enhance a Blended Learning Model // IEEE transactions on education, 59 (2016), 1; 39-44 doi:10.1109/TE.2015.2427116 (međunarodna recenzija, članak, znanstveni)
Hoić-Božić, N., Holenko Dlab, M. & Mornar, V. (2016) Recommender System and Web 2.0 Tools to Enhance a Blended Learning Model. IEEE transactions on education, 59 (1), 39-44 doi:10.1109/TE.2015.2427116.
@article{article, author = {Hoi\'{c}-Bo\v{z}i\'{c}, Natasa and Holenko Dlab, Martina and Mornar, Vedran}, year = {2016}, pages = {39-44}, DOI = {10.1109/TE.2015.2427116}, keywords = {Blended learning model, collaborative learning, recommender systems, Web 2.0}, journal = {IEEE transactions on education}, doi = {10.1109/TE.2015.2427116}, volume = {59}, number = {1}, issn = {0018-9359}, title = {Recommender System and Web 2.0 Tools to Enhance a Blended Learning Model}, keyword = {Blended learning model, collaborative learning, recommender systems, Web 2.0} }
@article{article, author = {Hoi\'{c}-Bo\v{z}i\'{c}, Natasa and Holenko Dlab, Martina and Mornar, Vedran}, year = {2016}, pages = {39-44}, DOI = {10.1109/TE.2015.2427116}, keywords = {Blended learning model, collaborative learning, recommender systems, Web 2.0}, journal = {IEEE transactions on education}, doi = {10.1109/TE.2015.2427116}, volume = {59}, number = {1}, issn = {0018-9359}, title = {Recommender System and Web 2.0 Tools to Enhance a Blended Learning Model}, keyword = {Blended learning model, collaborative learning, recommender systems, Web 2.0} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


Uključenost u ostale bibliografske baze podataka::


  • INSPEC
  • Scopus


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