Competence-based recommender systems: a systematic literature review. Issue 10 (2nd November 2018)

• Record Type: Journal Article
• Authors: Yago, Hector; Clemente, Julia; Rodriguez, Daniel
• Is Part Of: Behaviour & information technology. Volume 37:Issue 10/11(2018)
• Page Start: 958
• Page End: 977
• Publication Date: 2018-11-02
• Subjects: Recommender system -- systematic literature review -- competence-based learning -- adaptive learning -- emerging challenges; Electronic data processing -- Periodicals; Human engineering -- Periodicals; Information technology -- Periodicals; 303.4833
• DOI: 10.1080/0144929X.2018.1496276
• Languages: English
• ISSNs: 0144-929X

Abstract

ABSTRACT: Competence-based learning is increasingly widespread in many institutions since it provides flexibility, facilitates the self-learning and brings the academic and professional worlds closer together. Thus, the competence-based recommender systems emerged taking the advantages of competences to offer suggestions (performance of a learning experience, assistance of an expert or recommendation of a learning resource) to the user (learner or instructor). The objective of this work is to conduct a new Systematic Literature Review (SLR) concerning competence-based recommender systems to analyse in relation to their nature and assessment of competences an others key factors that provide more flexible and exhaustive recommendations. To do so, a SLR research methodology was followed in which 25 competence-based recommender systems related to learning or instruction environments were classified according to multiple criteria. We evaluate the role of competences in these proposals and enumerate the emerging challenges. Also a critical analysis of current proposals is carried out to determine their strengths and weakness. Finally, future research paths to be explored are grouped around two main axes closely interlinked; first about the typical challenges related to recommender systems and second, concerning ambitious emerging challenges.