Strengthening e-Education in India using Machine Learning

TL;DR

This paper explores how machine learning can enhance e-education in India by enabling personalized recommendations and teaching methods, aiming to improve literacy and educational access across the country.

Contribution

It proposes using genetic algorithms for personalized course recommendations and teaching strategies, demonstrating their effectiveness through implementation and experiments.

Findings

Genetic algorithms improve personalized course recommendations.

Machine learning enhances customized teaching methodologies.

Implementation results verify the proposed methods' viability.

Abstract

e-Education has developed as one of the most encouraging territories. The Indian Government is investing all amounts of energy to improve education among the residents of the nation. School and graduate understudies are focused on, however the stage is being created for all the residents seeking to learn. Without a doubt, the objective is to build the quantity of literates with advanced education. To accomplish the equivalent, propels in Data and Correspondence innovation are being utilized in the education division, which has cleared route for e-Training in India as well. To help educators in concentrating more on more current viewpoints, their excess work can be disposed of utilizing Machine Learning (ML). Difference to programming, ML deals with information and answers to create rules. In the event that Machine Learning is tackled effectively, it can setup the training division and contribute essentially to the development of the country. Hence, the work presented in this paper fortifies e-Education in India utilizing Machine Learning. For the most part, three concerns are focused to be tended to: Personalized recommendation of course and Customized teaching methodology. The work proposes utilizing developmental methodology of hereditary calculations for improving conventional procedures. Implementation and experiments presented in the paper verify the viability of proposed calculations.