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Competence-based recommender systems: a systematic literature review.

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Title: Competence-based recommender systems: a systematic literature review.
Authors: Yago, Hector, Clemente, Julia, Rodriguez, Daniel
Source: Behaviour & Information Technology. Oct/Nov2018, Vol. 37 Issue 10/11, p958-977. 20p. 2 Diagrams, 5 Charts, 3 Graphs, 1 Map.
Subjects: Outcome-based education, Information services, Internet, Learning strategies, Research funding, World Wide Web, Data mining, Systematic reviews, National competency-based educational tests
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. [ABSTRACT FROM AUTHOR]
Copyright of Behaviour & Information Technology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Competence-based recommender systems: a systematic literature review.
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  Data: <searchLink fieldCode="AR" term="%22Yago%2C+Hector%22">Yago, Hector</searchLink><br /><searchLink fieldCode="AR" term="%22Clemente%2C+Julia%22">Clemente, Julia</searchLink><br /><searchLink fieldCode="AR" term="%22Rodriguez%2C+Daniel%22">Rodriguez, Daniel</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22Behaviour+%26+Information+Technology%22">Behaviour & Information Technology</searchLink>. Oct/Nov2018, Vol. 37 Issue 10/11, p958-977. 20p. 2 Diagrams, 5 Charts, 3 Graphs, 1 Map.
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  Data: <searchLink fieldCode="DE" term="%22Outcome-based+education%22">Outcome-based education</searchLink><br /><searchLink fieldCode="DE" term="%22Information+services%22">Information services</searchLink><br /><searchLink fieldCode="DE" term="%22Internet%22">Internet</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+strategies%22">Learning strategies</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22World+Wide+Web%22">World Wide Web</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Systematic+reviews%22">Systematic reviews</searchLink><br /><searchLink fieldCode="DE" term="%22National+competency-based+educational+tests%22">National competency-based educational tests</searchLink>
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Behaviour & Information Technology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/0144929X.2018.1496276
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        Text: English
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              Text: Oct/Nov2018
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