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

Abstract

In the current era, multiple disciplines struggle with the scarcity of data, particularly in the area of e-learning and social learning. In order to test their approaches 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 algorithms 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 database. In this case, testing machine learning and recommendation system approaches will be more practical and relevant. The test is carried out on two databases to prove the efficiency of genetic algorithms and to compare the structure 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.