Hybrid Recommender Strategy in Learning: An Experimental Investigation

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.

Keywords

• recommender strategy
• personalized recommender systems
• collaborative filtering
• collaborative formal learning
• sequencing
• learner profile
• technology enhanced learning