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Our Project
Project Pandemic aims at using Text Mining and sentiment analysis using R to address factors that played vital role in mental well-being of Master students in Germany during COVID-19 induced lockdown and online studies status quo. We conducted a survey for this purpose with a set of 29 Questions.
Our Story
We discovered that factors such as Financial constraint, online classes in a shared apartment, feeling of anxiousness have been common between students during lockdown semester.
In order for us to measure students' mental well-being, we analysed their concentration span during the semester with their online semester results. Students with bad concentration span experience worse semester results as compared to previous offline semesters.
We also found that students that are living in Shared Apartment and are taking online classes have been experiencing highest level of unhappiness.
In order to understand the sentiments of survey respondents, we visualised the most common words used by them in form of word cloud. For example, on left you see the word cloud of words used by students when asked about reason behind their concentration span being good OR bad OR worse. And you also see the over all sentiments of questions below it.
We have also shown the most common words used by students for other questions such as lack of motivation experienced during online classes, Effect of COVID-19 lockdown and Online classes on students' Masters timeline etc.
We were successful in finding that our model correctly predicts bad semester results for students with 87% accuracy.
The predictors we used for grade prediction were Loss of Job, Students location (Germany or elsewhere), Effect of COVID-19 on Masters degree timeline.
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