Integrating learning styles and adaptive e-learning system: Current developments, problems and opportunities
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Adaptation based on a combination of learning style and information level yields significantly better learning gains.
“According to the findings of (Akbulut & Cardak, 2012;Alshammari & Qtaish, 2019;Alzain et al., 2018a, b;Shi et al., 2013;Truong, 2016), adaptation based on a combination of learning style, and information level yields significantly better learning gains.”
From Adaptive e-learning environment based on learning styles and its impact on development students' engagement (2021) · cites this paper as Truong, 2016
The scholarly literature on learning styles has few strong experimental findings, and few findings on the effect of adapting instruction to learning style.
“The extensive scholarly literature on learning styles is distributed with few strong experimental findings (Truong, 2016), and a few findings on the effect of adapting instruction to learning style.”
From Adaptive e-learning environment based on learning styles and its impact on development students' engagement (2021) · cites this paper as Truong, 2016
Cites
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Data mining for credit card fraud: a comparative study
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E-Learning personalization based on hybrid recommendation strategy and learning style identification
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Akbulut, Y., & Cardak, C. S. (2012) · Computers & Education · doi:10.1016/j.compedu.2011.10.008 · Cited by 269 · see citing works
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El-Sabagh, H. A. (2021) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-021-00289-4 · Cited by 566 · see citing works
A systematic literature review of personalized learning terms
Shemshack, A., & Spector, J. M. (2020) · Smart Learning Environments · doi:10.1186/s40561-020-00140-9 · Cited by 539 · see citing works
“Truong (2016) stated that providing the same content to students with different qualifications and personal traits and having different interests and needs is not Fig.”
BackgroundMartin, F., Chen, Y., Moore, R. L., & Westine, C. D. (2020) · Educational Technology Research and Development · doi:10.1007/s11423-020-09793-2 · Cited by 306 · see citing works
Artificial intelligence for precision education in radiology
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Artificial intelligence-enabled adaptive learning platforms: A review
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AI-based learning style detection in adaptive learning systems: a systematic literature review
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- Authors
- Truong, H. M.
- Affiliations
- Corvinno Technology Transfer (HU)
- Corresponding author
- Truong, Huong May
- ORCID
- Truong, Huong May
- Venue
- Computers in Human Behavior
- Publisher
- Elsevier BV
- Published
- 2016-02
- Volume
- 55
- Pages
- 1185-1193
- ISSN
- 0747-5632 (ISSN-L)
- OpenAlex
- W1993708070
- Cited by
- 531
- References
- 4
- Citations by year
- 2026: 13 · 2025: 27 · 2024: 57 · 2023: 67 · 2022: 41 · 2021: 56 · 2020: 85 · 2019: 63 · 2018: 58 · 2017: 40 · 2016: 20 · 2015: 3 · 2013: 1
- FWCI
- 101.90
- Type
- journal article
- Language
- en
- Open access
- Licence
- Locations
- Computers in Human Behavior (publishedVersion)
- IE Repository (submittedVersion, cc-by-nc-nd)
- Funders
- European Commission
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Learning Styles and Cognitive Differences
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- learning styles, adaptive e-learning systems, learning style identification, learning style theory, adaptive hypermedia, personalized learning
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