@article{YCR18,
author = {Hector Yago and Julia Clemente and Daniel Rodriguez},
title = {Competence-based recommender systems: a systematic literature review},
journal = {Behaviour \& Information Technology},
volume = {37},
number = {10-11},
pages = {958--977},
year  = {2018},
publisher = {Taylor \& Francis},
doi = {10.1080/0144929X.2018.1496276},
URL = {https://doi.org/10.1080/0144929X.2018.1496276},
eprint = {https://doi.org/10.1080/0144929X.2018.1496276},
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.}
}