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Maximizing reusability of learning objects through machine learning techniques
Meryem Amane1, Mounir Gouiouez2, Mohammed Berrada3
1Artificial Intelligence, Data Science and Emergent Systems Laboratory, Sidi Mohammed Ben Abdellah University, Fez, Morocco. meryem.amane@usmba.ac.ma.
Scientific Reports
|October 11, 2023
Summary
Machine learning enhances e-learning object reusability by improving categorization. This study uses feature selection and Euclidean distance metrics for efficient organization and superior results over traditional methods.
Area of Science:
- Computer Science
- Educational Technology
Background:
- E-learning systems benefit from reusable learning objects.
- Efficient categorization is crucial for managing these objects.
- Machine learning offers advanced solutions for object organization.
Purpose of the Study:
- To develop an efficient categorization system for learning objects.
- To enhance the reusability of learning objects in e-learning.
- To evaluate machine learning techniques for this purpose.
Main Methods:
- Metadata extraction from learning objects using web exploration algorithms.
- Feature selection techniques to reduce dimensionality and identify relevant metadata.
- Machine learning algorithms, specifically Euclidean distance metrics, for similarity-based categorization.
Main Results:
- The proposed machine learning approach effectively categorizes learning objects.
- Feature selection significantly reduces dataset dimensionality.
- Experimental results show the machine learning method outperforms traditional approaches.
Conclusions:
- Machine learning significantly improves learning object categorization and reusability.
- The developed system offers efficient and promising outcomes for e-learning.
- This approach facilitates better resource sharing and learner access.
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