This study aims to provide an overview regarding the use of recommender systems in education through a systematic review and a bibliometric analysis. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was followed and a total of 1,622 related documents from Scopus and WoS are examined from 2001 to 2022. The study goes over the literature and describes personalized learning, artificial intelligence (AI) in education as well as recommender systems and educational recommender systems. Besides descriptive statistics about the document collection, the result analysis involves the citation, sources, authors, affiliations, countries, and document information and categories of the related articles. The thematic evolution of the topic throughout the years is also examined. Based on the results, the recency and significance of recommender systems and their potentials in the educational domain were evident. Their ability to take into account learners' unique traits, experiences, skills, and preferences was highlighted. Recommender systems emerged as a learning tool that can empower learners, improve education quality and learning outcomes, increase learners' motivation, engagement, achievements, and satisfaction, and enable learners to be in charge of their own learning. Finally, recommender systems arose as an effective educational tool that can promote and improve adaptive learning and personalized learning.
Lampropoulos, G. (2023). Recommender systems in education: A literature review and bibliometric analysis. Advances in Mobile Learning Educational Research, 3(2), 829–850. https://doi.org/10.25082/amler.2023.02.011
This study aims to provide an overview regarding the use of recommender systems in education through a systematic review and a bibliometric analysis. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was followed and a total of 1,622 related documents from Scopus and WoS are examined from 2001 to 2022. The study goes over the literature and describes personalized learning, artificial intelligence (AI) in education as well as recommender systems and educational recommender systems. Besides descriptive statistics about the document collection, the result analysis involves the citation, sources, authors, affiliations, countries, and document information and categories of the related articles. The thematic evolution of the topic throughout the years is also examined. Based on the results, the recency and significance of recommender systems and their potentials in the educational domain were evident. Their ability to take into account learners’ unique traits, experiences, skills, and preferences was highlighted. Recommender systems emerged as a learning tool that can empower learners, improve education quality and learning outcomes, increase learners’ motivation, engagement, achievements, and satisfaction, and enable learners to be in charge of their own learning. Finally, recommender systems arose as an effective educational tool that can promote and improve adaptive learning and personalized learning.
@article{lampropoulos_2023_recommender,
author = {Lampropoulos, Georgios},
doi = {10.25082/amler.2023.02.011},
journal = {Advances in Mobile Learning Educational Research},
number = {2},
pages = {829--850},
title = {Recommender systems in education: A literature review and bibliometric analysis},
url = {https://doi.org/10.25082/AMLER.2023.02.011},
volume = {3},
year = {2023}
}
