Abstract:
Education is one of the basic humans needs it is referred to as a country's backbone.
Nowadays, Smart education technology has made the entire world smarter by the day. In
just a few months, The Novel coronavirus (covid-19) has changed the world. As a result,
the entire world is no longer relevant. In the context of Bangladesh, continuing one's studies
while dealing with the pandemic is a new challenge. The role of education in our daily life
is immense and in this pandemic time, there has been an unprecedented change in the field
of education. The online education system has made education a novelty when the whole
world has come to a standstill. This paper aims to find out the types of issues that a student
may encounter in an online class during the covid-19 situation in Bangladesh. In this covid19 situation in Bangladesh, students continue their activities online and a student needs to
analyze the types of issues that a student may encounter in an online class. In this paper,
we used a machine-learning algorithm to and Data have been analyzed by basic
questionnaires. To predict this problem, the accuracy was 74 percent given by Random
forest by selecting all feature and 66 percent given by Xgb Boost by selecting Limited
Feature. The motive of physical class and online class are almost similar to each other and
it is very difficult to compare each class value. We used seven algorithms to run our model
and the algorithm is KNN, Naive bias, decision tree, random forest, Xgb boost, SVM, and
neural network. We have made use of Random forest classification algorithms, correlation
heatmap, and univariant selection to select necessary data and to remove the unnecessary
data that are not related to predicting students’ stress levels in online classes that have been
discussed in a separate section of our paper.
