2024-06-24
Impact of COVID-19 on Students’ Education, Social Life, and Mental Health
INTRODUCTION
The COVID-19 pandemic has turned our lives upside down in ways we never imagined. Students, in particular, have faced a whirlwind of changes. Suddenly, their classrooms moved online, social gatherings became virtual, and the stress levels shot up. It’s clear that these drastic shifts have deeply affected their education, social lives, and mental health. To help address these challenges, it’s important to take a closer look at how students are coping and what impact these changes have had on their daily lives.
KEY AREAS
1.Education: -How has the shift to online education affected students’ learning experiences? -What are students’ perceptions of online class effectiveness and their ability to adapt to this new mode of learning?
2.Social Life: -How has the pandemic affected students’ social interactions and relationships? -Are students feeling more or less connected to their family, close friends, and relatives during the lockdown?
3.Mental Health: -How has the mental health of students been affected during the pandemic? -What stress-relief methods are students using, and how effective are they?
Objectives
By analyzing survey data from students, this project aims to provide insights into these questions and help educational institutions, policymakers, and mental health professionals address the challenges faced by students during the pandemic. The specific objectives are:
1.To analyze the distribution and effectiveness of various stress-relief methods used by students.
2.To examine the impact of online classes on students’ educational experiences and satisfaction.
3.To assess the changes in social interactions and the sense of connectedness among students.
4.To explore the correlation between different activities and students’ stress levels.
5.To evaluate changes in students’ weight and health issues during the lockdown.
Data Source
The data for this project is sourced from a survey conducted among students, capturing various aspects of their lives during the COVID-19 lockdown. The survey includes questions about their education, social interactions, mental health, and lifestyle changes. This source was found on kaggle.
Methodology
The project will follow these steps to achieve the objectives:
1.Data Wrangling and Cleaning:
-Load the dataset and inspect the data for missing values and inconsistencies.
-Clean the data by handling missing values and ensuring the data types are appropriate for analysis.
2.Exploratory Data Analysis (EDA):
-Perform descriptive statistics to understand the basic characteristics of the data.
-Visualize the distribution of key variables such as stress levels, online class ratings, and time spent on various activities.
3.Data Visualization:
-Create various plots to visualize the data, including histograms, count plots, scatter plots, and heatmaps.
-Use these visualizations to identify patterns and correlations in the data.
4.Analysis and Interpretation:
-Analyze the data to draw meaningful conclusions about the impact of COVID-19 on students’ education, social life, and mental health.
-Summarize the findings and provide recommendations based on the analysis.
By following this structured approach, the project aims to provide a comprehensive understanding of the challenges and changes experienced by students during the COVID-19 pandemic.
Load Libraries and Data
library(tidyverse) library(corrplot) # Load the dataset file_path <- './COVID-19 Survey Student Responses.csv' data <- read_csv(file_path) # Display the first few rows of the dataset head(data)
## # A tibble: 6 × 19 ## ID `Region of residence` `Age of Subject` `Time spent on Online Class` ## <chr> <chr> <dbl> <dbl> ## 1 R1 Delhi-NCR 21 2 ## 2 R2 Delhi-NCR 21 0 ## 3 R3 Delhi-NCR 20 7 ## 4 R4 Delhi-NCR 20 3 ## 5 R5 Delhi-NCR 21 3 ## 6 R6 Delhi-NCR 21 0 ## # ℹ 15 more variables: `Rating of Online Class experience` <chr>, ## # `Medium for online class` <chr>, `Time spent on self study` <dbl>, ## # `Time spent on fitness` <dbl>, `Time spent on sleep` <dbl>, ## # `Time spent on social media` <dbl>, `Prefered social media platform` <chr>, ## # `Time spent on TV` <chr>, `Number of meals per day` <dbl>, ## # `Change in your weight` <chr>, `Health issue during lockdown` <chr>, ## # `Stress busters` <chr>, `Time utilized` <chr>, …
Check for Missing Values and Descriptive Statistics
# Check for missing values missing_values <- colSums(is.na(data)) print(missing_values)
## ID ## 0 ## Region of residence ## 0 ## Age of Subject ## 0 ## Time spent on Online Class ## 0 ## Rating of Online Class experience ## 24 ## Medium for online class ## 51 ## Time spent on self study ## 0 ## Time spent on fitness ## 0 ## Time spent on sleep ## 0 ## Time spent on social media ## 0 ## Prefered social media platform ## 0 ## Time spent on TV ## 2 ## Number of meals per day ## 0 ## Change in your weight ## 0 ## Health issue during lockdown ## 0 ## Stress busters ## 0 ## Time utilized ## 0 ## Do you find yourself more connected with your family, close friends , relatives ? ## 0 ## What you miss the most ## 0
# Handle missing values (e.g., drop rows with missing values) data <- drop_na(data) # Descriptive statistics summary(data)
## ID Region of residence Age of Subject ## Length:1129 Length:1129 Min. : 7.00 ## Class :character Class :character 1st Qu.:17.00 ## Mode :character Mode :character Median :20.00 ## Mean :20.12 ## 3rd Qu.:21.00 ## Max. :59.00 ## Time spent on Online Class Rating of Online Class experience ## Min. : 0.000 Length:1129 ## 1st Qu.: 2.000 Class :character ## Median : 3.000 Mode :character ## Mean : 3.303 ## 3rd Qu.: 5.000 ## Max. :10.000 ## Medium for online class Time spent on self study Time spent on fitness ## Length:1129 Min. : 0.000 Min. :0.0000 ## Class :character 1st Qu.: 2.000 1st Qu.:0.0000 ## Mode :character Median : 2.000 Median :1.0000 ## Mean : 2.916 Mean :0.7632 ## 3rd Qu.: 4.000 3rd Qu.:1.0000 ## Max. :18.000 Max. :5.0000 ## Time spent on sleep Time spent on social media Prefered social media platform ## Min. : 4.00 Min. : 0.000 Length:1129 ## 1st Qu.: 7.00 1st Qu.: 1.000 Class :character ## Median : 8.00 Median : 2.000 Mode :character ## Mean : 7.85 Mean : 2.356 ## 3rd Qu.: 8.50 3rd Qu.: 3.000 ## Max. :15.00 Max. :10.000 ## Time spent on TV Number of meals per day Change in your weight ## Length:1129 Min. :1.000 Length:1129 ## Class :character 1st Qu.:2.000 Class :character ## Mode :character Median :3.000 Mode :character ## Mean :2.925 ## 3rd Qu.:3.000 ## Max. :8.000 ## Health issue during lockdown Stress busters Time utilized ## Length:1129 Length:1129 Length:1129 ## Class :character Class :character Class :character ## Mode :character Mode :character Mode :character ## ## ## ## Do you find yourself more connected with your family, close friends , relatives ? ## Length:1129 ## Class :character ## Mode :character ## ## ## ## What you miss the most ## Length:1129 ## Class :character ## Mode :character ## ## ##
Distribution Analysis: Stress Busters
Impact of Online Classes on Students
Impact on Social Interactions
Distribution of Academic Performance (Time spent on self study)
Correlation Analysis: Heatmap
Stress Levels and Activities
Change in Weight during Lockdown
