Hybrid Recommender Strategy in Learning: An Experimental Investigation
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Abstract
Purpose—finding ways of improving learning in a formal higher education context.
Design/methodology/approach—in the proposed model we will consider extending traditional content management systems, giving learners the possibility to add new materials and to rate them, and a hybrid strategy that combines technical recommendations with some profile-based filtering to offer adaptive and suitable sequencing learning content to learners.
Findings—the experiment shows that our recommendation techniques are able to reflect the learners’ interests.
Research limitations/implications—it’s necessary to demonstrate the contributions of these kinds of solutions to the learners’ success.
Practical implications—theoretical and practical framework for future research in the field was developed.
Originality/Value—the main contributions are the extended Learning Management
System and the hybrid Recommender System, which implements a new proposal to evaluate learners’ similarities.
Research type: research paper.
Design/methodology/approach—in the proposed model we will consider extending traditional content management systems, giving learners the possibility to add new materials and to rate them, and a hybrid strategy that combines technical recommendations with some profile-based filtering to offer adaptive and suitable sequencing learning content to learners.
Findings—the experiment shows that our recommendation techniques are able to reflect the learners’ interests.
Research limitations/implications—it’s necessary to demonstrate the contributions of these kinds of solutions to the learners’ success.
Practical implications—theoretical and practical framework for future research in the field was developed.
Originality/Value—the main contributions are the extended Learning Management
System and the hybrid Recommender System, which implements a new proposal to evaluate learners’ similarities.
Research type: research paper.
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