Recommender Systems For Learning
Recommender Systems for Learning is a computational framework within personalized learning systems that specifically generates individualized educational recommendations by analyzing learners' behaviors, preferences, and knowledge states to optimize learning pathways and resource selection.
784
Publications
32.1K
Citations
2K
Authors
749
Institutions
Publications per year
2017–2026
421
784
Anna Y.Q. Huang, Owen H.T. Lu, Stephen J.H. Yang · Computers & Education · 2022 · 530 citations
Building a recommender agent for e-learning systems
2003 · 388 citations
1–5 of 784
2K
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749

Open University of the Netherlands
Heerlen, The Netherlands
11 h-index
32 publications
2.3K citations
National Cheng Kung University
Tainan City, Taiwan
11 h-index
30 publications
1.2K citations

Universidad Nacional de Educación a Distancia
Madrid, Spain
10 h-index
19 publications
834 citations
University of Pittsburgh
Pittsburgh, United States
9 h-index
13 publications
882 citations

Beijing Institute of Technology
Beijing, China
8 h-index
21 publications
3.1K citations

University of Saskatchewan
Saskatoon, Canada
8 h-index
13 publications
633 citations
Central China Normal University
Wuhan, China
8 h-index
33 publications
2.7K citations

The Open University
Milton Keynes, United Kingdom
7 h-index
14 publications
936 citations
1–8 of 749
21 publications · 1.2K citations
18
h-index
19 publications · 1.3K citations
16
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21 publications · 556 citations
16
h-index
15 publications · 814 citations
15
h-index
14 publications · 731 citations
14
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