Title
Competence-Based Recommender Systems: A Systematic Literature Review
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.
Year
DOI
Venue
2018
10.1080/0144929X.2018.1496276
BEHAVIOUR & INFORMATION TECHNOLOGY
Keywords
Field
DocType
Recommender system, systematic literature review, competence-based learning, adaptive learning, emerging challenges
Recommender system,Competence (human resources),Multiple criteria,Systematic review,Computer science,Knowledge management,Learning experience,Learning resource,Research methodology,Adaptive learning
Journal
Volume
Issue
ISSN
37
10-11
0144-929X
Citations 
PageRank 
References 
0
0.34
28
Authors
3
Name
Order
Citations
PageRank
Héctor Yago Corral121.12
Julia Clemente2373.71
Daniel Rodríguez329427.65