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Cited by 75Recommender Systems and TechniquesOnline Learning and AnalyticsIntelligent Tutoring Systems and Adaptive Learning

On recommendation of learning objects using Felder–Silverman learning style model

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Nafea, Shaimaa M., Siewe, Francois, He, Ying·2019·IEEE Access
DOI 10.1109/access.2019.2935417
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Abstract

The e-learning recommender system in learning institutions is increasingly becoming the preferred mode of delivery, as it enables learning anytime, anywhere. However, delivering personalised course learning objects based on student's preferences is still a challenge. Current mainstream recommendation algorithms, such as the Collaborative Filtering (CF) and Content-Based Filtering (CBF), deal with only two types of entities, namely users and items with their ratings. However, these methods do not pay attention to student's preferences, such as learning styles, which are especially important for the accuracy of course learning objects prediction or recommendation. Moreover, several recommendation techniques experience cold-start and rating sparsity problems. To address the challenge of improving the quality of recommender systems, in this paper a novel recommender algorithm for machine learning is proposed, which combines students actual rating with their learning styles to recommend personalised course learning objects (LOs). Various recommendation techniques are considered in an experimental study investigating the best technique to use in predicting student ratings for e-learning recommender systems. We use the Felder-Silverman Learning Styles Model (FSLSM) to represent both the student learning styles and the learning object profiles. The predicted ratings are compared with the actual student ratings to determine the accuracy of the recommendation techniques, using the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) metrics. This approach has been experimented on 80 students for an online course created in the MOODLE Learning Management System. The results of the experiment show that the best recommendation technique is our proposed hybrid recommendation algorithm that combines the collaborative filtering and the content-based filtering techniques to enhance the accuracy of the predictions, and solves the cold-start and the rating sparsity problems using the FSLSM representations of the student learning styles and the learning object profiles.

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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 [21]

    The Introduction describes personalized top-n learning-object lists based on predicted ratings, supporting the claim that personalization affects recommendations and prioritization (page unavailable). The Dataset section says students rated learning objects, but neither it nor the retrieved passages says that all resources remain visible or accessible; that part is unsupported. (LLM, conf=0.83)

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Authors
Nafea, S. M., Siewe, F., & He, Y.
Affiliations
  • Arab Academy for Science, Technology, and Maritime Transport (EG)
  • De Montfort University (GB)
ORCID
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2019
Volume
7
Pages
163034-163048
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2019.2935417
OpenAlex
W2968445022
Cited by
75
References
3
Citations by year
2026: 6 · 2025: 12 · 2024: 14 · 2023: 18 · 2022: 13 · 2021: 9 · 2020: 3
FWCI
7.78
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Access (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • DMU Open Research Archive (De Montfort University) (submittedVersion)
Funders
  • Arab Academy for Science, Technology and Maritime Transport
  • De Montfort University
Topics
  • Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
Keywords
learning object recommendation, personalized learning, hybrid recommendation, collaborative filtering, content-based filtering, cold start, rating sparsity, student learning styles, Moodle

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