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dc.contributor.authorNafea, Shaimaaen
dc.contributor.authorSiewe, Francoisen
dc.contributor.authorHe, Yingen
dc.date.accessioned2018-02-20T13:06:49Z
dc.date.available2018-02-20T13:06:49Z
dc.date.issued2018
dc.identifier.citationNafea, S., Siewe, F. and He, Y. (2018) ULEARN: Personalized course Learning Objects based on Hybrid Recommendation approach. 7th International Conference on Software and Information Engineering (ICSIE 2018), Cairo, May 2018.en
dc.identifier.urihttp://hdl.handle.net/2086/15247
dc.description.abstractAdaptive e-learning recommender system is observed one of the exciting research discipline in the education and teaching throughout the past few decades, since, the learning style is specific for each student In reality from the knowledge of his/ her learning style; matching teaching strategy with the most appropriate learning object is present to better return on learner academic level. This work focuses on the design of a personalized e-learning environment based on hybrid recommender system based on collaborative filtering and item content filtering as well as architecture of ULEARN system. ULEARN recommended adaptive teaching strategy by choosing and sequencing learning objects fitting with the learners’ learning styles. The proposed system can be used to rearrange learning object priority that matches student adaptive profile and teaching strategy in order to improve the quality of learning.en
dc.language.isoenen
dc.subjectCourse contenten
dc.subjectRecommender systemen
dc.subjectlearning objecten
dc.subjectlearner profileen
dc.subjectteaching strategyen
dc.titleULEARN: Personalized course Learning Objects based on Hybrid Recommendation approachen
dc.typeConferenceen
dc.researchgroupSoftware Technology Research Laboratory (STRL)en
dc.peerreviewedYesen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceCC-BY-NCen
dc.date.acceptance2018-02-18en
dc.researchinstituteCyber Technology Institute (CTI)en


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