Abstract:
The Covid-19 pandemic has disrupted educational activities across Bangladesh, resulting in the
closure of educational institutes, affecting nearly four crore students' daily learning activities.
Besides, the economic downturn brought on by the pandemic is impacting students and their
families. Access to educational opportunities is increasingly difficult for students from low-income
families, and the rising poverty rate adds to their woes. According to various projections,
Bangladesh's poverty rate is estimated to be around 35-40%, compared to a pre-pandemic rate of
around 20%. Poverty has a disproportionate impact on lower-income families, where survival
takes precedence over schooling. Otherwise, most of the students are having trouble with
understanding the new online platform. Not only that, the poor students cannot afford a laptop to
attend classes. Internet issues are also a massive problem, as most of the students have moved from
cities to villages to their hometowns, they cannot attend the classes because of low internet
connectivity. Those who are attending classes online and came familiar with the online platform,
most of them have become addicted to the internet. As a result, they are hampering their eyesight
and other physical problems. We have applied seven traditional ML algorithms. They are- KNN,
Naïve Bayes, Decision Tree, Support Vector Machine (SVM), Neural Network algorithm, Random
Forest, and AdaBoost. The best accuracy was predicted by the neural network, which was 85.45%
