New perspective of learning objects in e-learning system
Abstract
Purpose Together, learning objects (LOs) and e-pedagogical practices have the potential to improve the performance of e-learning systems in several ways. They can make e-learning more personalised and adaptable, providing students with a more engaging and effective learning experience. Design/methodology/approach The development of LOs and e-pedagogical practices have significantly influenced and changed the performance of e-learning systems. LOs are self-contained, reusable units of instructional content that create instructional materials, such as online courses, tutorials and assessments. They provide a flexible and modular approach to designing and delivering e-learning content, allowing educators to easily customise and adapt their materials to the needs of their students. e-pedagogical practices refer to the use of technology to enhance and support the teaching and learning process. They include strategies such as online collaboration, gamification and adaptive learning to improve student engagement, motivation and achievement. Findings To achieve this objective, this study consists of two main phases. First, the authors extract metadata from LOs using latent semantic analysis algorithms, which are considered a strong tool in web-mining exploration techniques. Second, they identify LOs according to a particular form of similarity using fuzzy c-means (FCM) algorithms. To improve classification accuracy, the FCM is used as a clustering algorithm. Originality/value Finally, in order to assess the effectiveness of LOs with FCM, a series of experimental studies using a real-world dataset are conducted. The results of this study indicate that the proposed approach exceeds the traditional approach and produces good results.
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In [30], latent semantic analysis and fuzzy C-means were used to extract metadata from learning objects and identify learning objects based on similarity.
“LSA is used to extract metadata from learning objects (LOs).LSA is a technique that finds patterns in relationships between terms and concepts in unstructured data, mainly used for analyzing relationships in a set of documents.Fuzzy C-means (FCM) algorithms: FCM is used to identify learning objects based on a specific form of similarity.”
From Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review (2023) · cites this paper as [30]
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Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review
Gligorea, I., Cioca, M., Oancea, R., Gorski, A.-T., Gorski, H., & Tudorache, P. (2023) · Education Sciences · doi:10.3390/educsci13121216 · Cited by 980 · see citing works
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- Authors
- Amane, M., Aissaoui, K., & Berrada, M.
- Affiliations
- Sidi Mohamed Ben Abdellah University (MA)
- Mohamed I University (MA)
- Venue
- The International Journal of Information and Learning Technology
- Publisher
- Emerald
- Published
- 2023-05-19
- Volume
- 40
- Issue
- 3
- Pages
- 269-279
- ISSN
- 2056-4880 (ISSN-L)
- OpenAlex
- W4376881311
- Cited by
- 10
- References
- 34
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 2 · 2023: 4
- FWCI
- 2.28
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Information and Learning Technology (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Open Education and E-Learning
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Information Systems › Educational Technology and E-Learning
- Keywords
- learning objects, e-learning systems, learning object metadata, latent semantic analysis, fuzzy c-means clustering, personalized learning, adaptive learning, gamification, online collaboration
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