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Recommendation Systems for Education: Systematic Review

eVIDA Research Group, Faculty of Engineering, University of Deusto, 48007 Bilbao, Spain
Faculty of Engineering, Andres Bello Catholic University (UCAB), Ciudad Guayana 08050, Venezuela
Author to whom correspondence should be addressed.
Academic Editors: Georgios Kostopoulos and Sotiris Kotsiantis
Electronics 2021, 10(14), 1611;
Received: 10 May 2021 / Revised: 1 July 2021 / Accepted: 2 July 2021 / Published: 6 July 2021
(This article belongs to the Special Issue Machine Learning in Educational Data Mining)
Recommendation systems have emerged as a response to overload in terms of increased amounts of information online, which has become a problem for users regarding the time spent on their search and the amount of information retrieved by it. In the field of recommendation systems in education, the relevance of recommended educational resources will improve the student’s learning process, and hence the importance of being able to suitably and reliably ensure relevant, useful information. The purpose of this systematic review is to analyze the work undertaken on recommendation systems that support educational practices with a view to acquiring information related to the type of education and areas dealt with, the developmental approach used, and the elements recommended, as well as being able to detect any gaps in this area for future research work. A systematic review was carried out that included 98 articles from a total of 2937 found in main databases (IEEE, ACM, Scopus and WoS), about which it was able to be established that most are geared towards recommending educational resources for users of formal education, in which the main approaches used in recommendation systems are the collaborative approach, the content-based approach, and the hybrid approach, with a tendency to use machine learning in the last two years. Finally, possible future areas of research and development in this field are presented. View Full-Text
Keywords: systematic review; recommendation systems; education; machine learning systematic review; recommendation systems; education; machine learning
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MDPI and ACS Style

Urdaneta-Ponte, M.C.; Mendez-Zorrilla, A.; Oleagordia-Ruiz, I. Recommendation Systems for Education: Systematic Review. Electronics 2021, 10, 1611.

AMA Style

Urdaneta-Ponte MC, Mendez-Zorrilla A, Oleagordia-Ruiz I. Recommendation Systems for Education: Systematic Review. Electronics. 2021; 10(14):1611.

Chicago/Turabian Style

Urdaneta-Ponte, María C., Amaia Mendez-Zorrilla, and Ibon Oleagordia-Ruiz. 2021. "Recommendation Systems for Education: Systematic Review" Electronics 10, no. 14: 1611.

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