Towards an Approach Based on Adjusted Genetic Algorithms to Improve the Quantity of Existing Data in the Context of Social Learning

Authors

  • Sonia Souabi RIME TEAM-Networking, Modeling and e-Learning Team- MASI Laboratory- ENGINEERING.3S Research center-Mohammadia School of Engineers (EMI) MOHAMMED V UNIVERSITY IN RABAT MOROCCO
  • Asmaâ Retbi RIME TEAM-Networking, Modelling and e-Learning Team- MASI Laboratory- ENGINEERING.3S Research centerMohammadia School of Engineers (EMI)- MOHAMMED V UNIVERSITY IN RABAT MOROCCO
  • Mohammed Khalidi Idrissi RIME TEAM-Networking, Modelling and e-Learning Team- MASI Laboratory- ENGINEERING.3S Research centerMohammadia School of Engineers (EMI)- MOHAMMED V
  • Samir Bennani RIME TEAM-Networking, Modelling and e-Learning Team- MASI Laboratory- ENGINEERING.3S Research centerMohammadia School of Engineers (EMI)- MOHAMMED V

DOI:

https://doi.org/10.3991/ijet.v16i09.20685

Keywords:

Social Learning, Data Scarcity, Genetic Algorithms

Abstract


In the current era, multiple disciplines struggle with the scarcity of data, particu-larly in the area of e-learning and social learning. In order to test their ap-proaches and their recommendation systems, researchers need to ensure the availability of large databases. Nevertheless, it is sometimes challenging to find-out large scale databases, particularly in terms of education and e-learning. In this article, we outline a potential solution to this challenge intended to improve the quantity of an existing database. In this respect, we suggest genetic algo-rithms with some adjustments to enhance the size of an initial database as long as the generated data owns the same features and properties of the initial data-base. In this case, testing machine learning and recommendation system ap-proaches will be more practical and relevant. The test is carried out on two da-tabases to prove the efficiency of genetic algorithms and to compare the struc-ture of the initial databases with the generated databases. The result reveals that genetic algorithms can achieve a high performance to improve the quantity of existing data and to solve the problem of data scarcity.

Author Biographies

Sonia Souabi, RIME TEAM-Networking, Modeling and e-Learning Team- MASI Laboratory- ENGINEERING.3S Research center-Mohammadia School of Engineers (EMI) MOHAMMED V UNIVERSITY IN RABAT MOROCCO

Department of computer science

Asmaâ Retbi, RIME TEAM-Networking, Modelling and e-Learning Team- MASI Laboratory- ENGINEERING.3S Research centerMohammadia School of Engineers (EMI)- MOHAMMED V UNIVERSITY IN RABAT MOROCCO

Department of computer science

Mohammed Khalidi Idrissi, RIME TEAM-Networking, Modelling and e-Learning Team- MASI Laboratory- ENGINEERING.3S Research centerMohammadia School of Engineers (EMI)- MOHAMMED V

Department of computer science

Samir Bennani, RIME TEAM-Networking, Modelling and e-Learning Team- MASI Laboratory- ENGINEERING.3S Research centerMohammadia School of Engineers (EMI)- MOHAMMED V

Department of computer science

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Published

2021-05-04

How to Cite

Souabi, S., Retbi, A., Idrissi, M. K., & Bennani, S. (2021). Towards an Approach Based on Adjusted Genetic Algorithms to Improve the Quantity of Existing Data in the Context of Social Learning. International Journal of Emerging Technologies in Learning (iJET), 16(09), pp. 278–290. https://doi.org/10.3991/ijet.v16i09.20685

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Section

Papers