Research Article
Student’s Perception towards Mobile learning using Interned Enabled Mobile devices during COVID-19
@ARTICLE{10.4108/eai.16-9-2021.170958, author={Pooja Gupta and Vimal Kumar and Vikash Yadav}, title={Student’s Perception towards Mobile learning using Interned Enabled Mobile devices during COVID-19}, journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems}, volume={8}, number={29}, publisher={EAI}, journal_a={INIS}, year={2021}, month={9}, keywords={Mobile learning, COVID-19, 5G technology, Adoption, Machine learning algorithm}, doi={10.4108/eai.16-9-2021.170958} }- Pooja Gupta
Vimal Kumar
Vikash Yadav
Year: 2021
Student’s Perception towards Mobile learning using Interned Enabled Mobile devices during COVID-19
INIS
EAI
DOI: 10.4108/eai.16-9-2021.170958
Abstract
INTRODUCTION: The novel corona disease disrupted education all around the world. This shifted people to mobile learning in real time wireless classroom from the physical face-to-face classroom.
OBJECTIVE: Mobile learning has been present for years but the use of mobile learning is more in the current scenario due to COVID-19. However, people’s acceptance of mobile learning education at institutions is still low. Thus, this research seeks to understand the student’s perspective by analysing constructs hypothesized in the proposed hybrid model.
METHOD: Data is collected using a survey from an Indian institute of the Meerut region with a total of 1022 students.
RESULT: Data analysis and research findings showed that Random Forest and K-Nearest Neighbour Algorithms outperforms than other classifiers in predicting the dependent variables with better accuracy rate, precision, and recall value in this study.
CONCLUSION: The research findings will help the designers and software development to design learning applications considering the perspective of students with respect to 5G technology.
Copyright © 2021 Pooja Gupta et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
