Student’s Perception towards Mobile learning using Interned Enabled Mobile devices during COVID-19

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.

Keywords

• mobile learning
• covid-19
• 5g technology
• adoption
• machine learning algorithm