Open AccessTowards an Approach Based on Adjusted Genetic Algorithms to Improve the Quantity of Existing Data in the Context of Social Learning
Author(s) -
Sonia Souabi,
Asmaâ Retbi,
Mohammed Khalidi Idrissi,
Samir Bennani
Publication year - 2021
Publication title -
international journal of emerging technologies in learning/international journal: emerging technologies in learning
Language(s) - English
Resource type - Journals
eISSN - 1868-8799
pISSN - 1863-0383
DOI - 10.3991/ijet.v16i09.20685
Subject(s) - computer science , scarcity , context (archaeology) , genetic algorithm , machine learning , artificial intelligence , scale (ratio) , test (biology) , database , test data , data mining , software engineering , paleontology , physics , quantum mechanics , economics , biology , microeconomics
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.