Abstract: The COVID-19 pandemic has led to the closure of educational establishments all around the world. To keep academic activities alive, most educational organizations have switched to online learning platforms. Since, problems about e-learning readiness, design, and efficacy remain unanswered, particularly in developing countries like India, where technological barriers such as device compatibility and network availability represent a severe issue. Studies suggest that digital learning can be as successful as traditional education that requires appearance, but learners for online training, especially in adapting different learning methods to online mode is very much crucial. Few studies have examined the satisfaction of e-learning[1][2]. According to the data, students' reaction to online teaching depends on their ability to use online tools, their ability to technically access e-learning materials, and their teacher's style of different learning activities. In this paper, we have clearly analyzed, examined and predict the impact of online education system by using different Machine Learning classifier and ensemble algorithms. We have collected some real time data to show some insight reviews on the satisfactory level of e-learners after pandemic.
Keywords: Algorithms, Machine Learning, Online Education, Predictive Analysis.
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DOI:
10.17148/IJARCCE.2022.11536
