Cited by 253Recommender Systems and TechniquesIntelligent Tutoring Systems and Adaptive LearningOnline Learning and Analytics

A systematic literature review on adaptive content recommenders in personalized learning environments from 2015 to 2020

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Raj, Nisha S., Renumol, V. G.·2022·Journal of Computers in Education

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Crossref · 10.1007/s40692-021-00199-4

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87 of 87 references verified0 citation errors · 0 not checkable

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  1. IEEE-LTSC, IEEE P1484.12.1-2002/Cor 1/D14. Draft standard for learning object metadata — corrigendum 1: corrigenda for 1484.12.1 LOM (learning object metadata), IEEE Learning Technology Standards Committee, 2010.

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Authors
Raj, N. S., & Renumol, V. G.
Affiliations
Cochin University of Science and Technology (IN)
Corresponding author
Raj, Nisha S.
Venue
Journal of Computers in Education
Published
2021-08-11
Volume
9
Issue
1
Pages
113-148
ISSN
  • 2197-9987 (ISSN-L)
  • 2197-9995
OpenAlex
W3189219694
Cited by
253
References
87
Citations by year
2026: 31 · 2025: 73 · 2024: 90 · 2023: 37 · 2022: 22
FWCI
5.68
Type
journal article
Language
en
Open access
Locations
  • Journal of Computers in Education (publishedVersion)
  • PubMed Central (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
Keywords
personalized learning environments, recommender systems, learner attributes, learning styles, learner preferences, hybrid recommendation, collaborative filtering, content-based filtering, ontological approach, recommendation evaluation metrics, student satisfaction, usability evaluation

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