The Role Of Machine Learning Techniques In Exploration Of Impacts Of E-Learning During Covid-19 Pandemic: A Comparative Analysis Of Models | [International Journal of Membrane Science and Technology • 2023]

Author(s):

1. Baljit Saini: Department of Computer Engineering, K.D. Polytechnic,Patan-384265, Gujarat,

2. Sanjay Bansal: Rajasthan Vidyapeeth Technology College, Udaipur-313001, Rajasthan.

3. Bhupinder Chaudhary: Department of Hospital Management and Hospice Studies, Jamia Millia Islamia, New Delhi

4. Kinjal Jani: Department of Computer Engineering, K.D. Polytechnic,Patan-384265, Gujarat,

Abstract:

The coronavirus, which originated in Wuhan (China), has expanded to both developed and poor nations, with the developed nations like America, Italy, etc. currently suffering the most from its effects. Lockdown has had the greatest negative economic effects. This pandemic also affected the education system, so it is clear that education has been impacted by the COVID 19 outbreak. The occurrence prompted educators to consider different teaching strategies during the confinement. Thus, it opens the door for online learning, Learning has entered the digital realm in the current circumstances. where students and academic experts are virtually connected. E-learning is very easy to use and comprehend. This study will assist in determining the students' interest in online learning, as well as their preferred learning styles, platforms, and methods of material delivery both during and after the COVID-19 Pandemic. To discover more about how e-learning has helped during this pandemic, this study was conducted for technical education students as well as those who attend colleges and universities in Gujrat State. In this study, various machine learning models were evaluated to find out the best model or technique to analyse impact of e-learning during this pandemic and the student's interest towards the e-learning. The proposed model also compared with the previously implemented models and achieved the higher accuracy i.e. 88.56%, 91.45% for training and testing respectively.

Page(s): 1911-1919

DOI: 10.15379/ijmst.v10i1.3689

Published: Journal: International Journal of Membrane Science and Technology, Volume: 10, Issue: 1, Year: 2023

Keywords:

Elearning , Naïve Bayes , Logistic regression , decision tree , kNearest Neighbour

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