Use of Felder Silverman Learning Style Model in Technology Enhanced Learning
Abstract
Technology enhancement and the pandemic catalyzed substantial transformations in the Education arena. This forces researchers to investigate the new norms of teaching and learning further. This study investigates the literature on the Felder Silverman Learning Style Model (FSLSM) from 2019 to 2023. In literature, FSLSM is used in static and automatic learner identification. FSLSM is used to identify learning styles and provide personalized learning environments. Personalized learning environments are the most demanding needs of learners today. Therefore, the research in this area is still growing and challenging. FSLSM is used to provide personalized content and learning objects and interfaces. Further, this study investigates the relationship between the learning style and learning outcomes using the static FSLSM for non-STEM students in the blended learning context. The finding reveals majority of the learners are sensing, active, visual, and sequential learners. There are no strong verbal and strong reflective learners. Further, most of the learners are weak in all the dimensions. This study conducted one-way ANOVA to check the hypothesis and failed to reject the null hypothesis. This study concluded there is no significant relationship between learning style and the learning outcomes in the non-STEM background in the blended learning environment.
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No learning resources are hidden; personalization affects prioritization and recommendations rather than access.
“Importantly, no learning resources are hidden; personalization affects prioritization and recommendations rather than access [21,22].”
From AI for All: Adaptive, Accessible, and Inclusive Learning Experiences in the Age of Intelligent LMSs (2026) · cites this paper as [22]
Cites
The PRISMA 2020 statement: An updated guideline for reporting systematic reviews
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., et al. (2021) · BMJ · doi:10.1136/bmj.n71 · Cited by 108,283 · see citing works
Cao, W. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1212056 · Cited by 94 · cited 1× · see citing works
“The meta-analysis confirms BL outcomes, perceptions and achievements differ among the countries[29], for example in China and the USA BL fails to improve learner's engagement in academic activities[29].”
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AI for All: Adaptive, Accessible, and Inclusive Learning Experiences in the Age of Intelligent LMSs
Angeioplastis, A., Konstantakis, M., Aliprantis, J., Ordoumpozanis, K., Varsamis, D., & Tsimpiris, A. (2026) · Information · doi:10.3390/info17020216 · Cited by 3 · see citing works
- Authors
- Faathima Fayaza, M. S., & Ahangama, S.
- Affiliations
- South Eastern University of Sri Lanka,Oluvil,Sri Lanka
- University of Moratuwa (LK)
- ORCID
- Ahangama, Supunmali
- Venue
- 2024 International Research Conference on Smart Computing and Systems Engineering (SCSE)
- Publisher
- IEEE
- Published
- 2024-04-04
- Pages
- 1-6
- OpenAlex
- W4399529010
- Cited by
- 4
- References
- 2
- Citations by year
- 2025: 4
- FWCI
- 6.15
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Research Conference on Smart Computing and Systems Engineering (SCSE) (publishedVersion)
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Learning Styles and Cognitive Differences
- Social Sciences › Education › Online and Blended Learning
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neuroscience, Education and Cognitive Function
- Keywords
- Felder-Silverman learning styles, learning styles, technology-enhanced learning, personalized learning environments, learning outcomes, blended learning, non-STEM students, learning objects, COVID-19 pandemic impact on education
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