A systematic literature review on adaptive content recommenders in personalized learning environments from 2015 to 2020
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The ratings and satisfaction levels provided by the learner are considered direct feedback, while the scores are considered indirect feedback on AL systems usability.
“The ratings and satisfaction levels provided by the learner are considered direct feedback, while the scores are considered indirect feedback on AL systems usability (Raj & Renumol, 2022).”
From Using an adaptive learning tool to improve student performance and satisfaction in online and face-to-face education for a more personalized approach (2024) · cites this paper as Raj & Renumol, 2022
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<scp>AI</scp>‐driven adaptive learning for sustainable educational transformation
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“The ratings and satisfaction levels provided by the learner are considered direct feedback, while the scores are considered indirect feedback on AL systems usability (Raj & Renumol, 2022).”
BackgroundA systematic literature review on personalised learning in the higher education context
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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
- PMC
- PMC8357108
- 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
- bronze
- doi.org
- Licence
- 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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