Introduction
Background
On March 11, 2020, the World Health Organization declared the novel coronavirus, covid-19, a pandemic. By the end of the month, populations across the United States and the globe began entering lockdown and social distancing. As a result, many businesses, institutions, and organizations worldwide reduced occupancy or closed their doors to control the spread of the virus. Schools worldwide decided whether to continue in-person learning or adopt distance learning practices. This project will review the status of school closures by the country during the 2020-2021 covid-19 pandemic and what characteristics distinguished those who adopted the closures or not.
Research Questions
This project will answer the following research questions:
* How did school closures and re-openings unfold over the covid-19 pandemic years 2020 - 2021?
* Which of the following characteristics: geographic location, country income level, student-to-teacher ratio, or access to distance learning modalities, were indicated in school closure adoption?
Data
Source and Scope
The data for this project was procured from the UNESCO Institute of Statistics data on COVID-19 Education Response (sourced below). The data set includes the daily school status for 210 countries/ territories from 2/16/2020 to 3/31/2022. This results in 162,750 observations over 775 days. The data also provides information on the approximate number of enrolled students and teachers in pre-primary to secondary school, regional names (as defined by the Sustainable Development Goals regional groups), country income level (as defined by the World Bank income groups), access to distance learning technologies (i.e., radio, TV, and online modalities), and the total number of weeks fully closed and partially open.
Data Cleaning and Manipulation
To tidy the data, I started by splitting column names that contained both the abbreviation and full regional name and removing columns with duplicate information (i.e., Regional Type 1, Region 1, Regional Type 2, and Region Type 3). I then rearranged the columns into a clean draft and added a ratio of enrolled students per teacher.
Show code
#read in file
unesco<-read_xlsx("UNESCO_school_closures_database.xlsx")
#Tidy Data
unesco[c('Reg2 Type', 'Regional Name')]<- str_split_fixed(unesco$`Region 2`, ': ', 2)
unesco[c('Reg3 Type', 'Income Level')]<- str_split_fixed(unesco$`Region 3`, ': ', 2)
#remove unnecessary columns and rearrange columns
unesco_fin<-unesco[c('Date','Country ID', 'Country', 'Regional Name', 'Income Level',
'Status', 'Enrolment (Pre-Primary to Upper Secondary)',
'Teachers (Pre-Primary to Upper Secondary)',
'School Age Population (Pre-Primary to Upper Secondary)',
'Distance learning modalities (TV)',
'Distance learning modalities (Radio)',
'Distance learning modalities (Global)',
'Distance learning modalities (Online)', 'Weeks fully closed',
'Weeks partially open')]
#add ratio of enrolled students to teachers
unesco_fin$Enrol_Teacher_Ratio <- unesco_fin$`Enrolment (Pre-Primary to Upper Secondary)`/ unesco_fin$`Teachers (Pre-Primary to Upper Secondary)`
The dimension (number of rows by columns), column names and sample of the data set is listed below.
Show code
dim(unesco_fin)
[1] 162750 16Show code
colnames(unesco_fin)
[1] "Date"
[2] "Country ID"
[3] "Country"
[4] "Regional Name"
[5] "Income Level"
[6] "Status"
[7] "Enrolment (Pre-Primary to Upper Secondary)"
[8] "Teachers (Pre-Primary to Upper Secondary)"
[9] "School Age Population (Pre-Primary to Upper Secondary)"
[10] "Distance learning modalities (TV)"
[11] "Distance learning modalities (Radio)"
[12] "Distance learning modalities (Global)"
[13] "Distance learning modalities (Online)"
[14] "Weeks fully closed"
[15] "Weeks partially open"
[16] "Enrol_Teacher_Ratio" Show code
unesco_fin
# A tibble: 162,750 x 16
Date `Country ID` Country `Regional Name`
<dttm> <chr> <chr> <chr>
1 2020-02-16 00:00:00 ABW Aruba Latin America and the Car~
2 2020-02-17 00:00:00 ABW Aruba Latin America and the Car~
3 2020-02-18 00:00:00 ABW Aruba Latin America and the Car~
4 2020-02-19 00:00:00 ABW Aruba Latin America and the Car~
5 2020-02-20 00:00:00 ABW Aruba Latin America and the Car~
6 2020-02-21 00:00:00 ABW Aruba Latin America and the Car~
7 2020-02-22 00:00:00 ABW Aruba Latin America and the Car~
8 2020-02-23 00:00:00 ABW Aruba Latin America and the Car~
9 2020-02-24 00:00:00 ABW Aruba Latin America and the Car~
10 2020-02-25 00:00:00 ABW Aruba Latin America and the Car~
# ... with 162,740 more rows, and 12 more variables:
# `Income Level` <chr>, Status <chr>,
# `Enrolment (Pre-Primary to Upper Secondary)` <dbl>,
# `Teachers (Pre-Primary to Upper Secondary)` <dbl>,
# `School Age Population (Pre-Primary to Upper Secondary)` <dbl>,
# `Distance learning modalities (TV)` <chr>,
# `Distance learning modalities (Radio)` <chr>, ...To view static information such as country income level, student-to-teacher ratio, access to distance learning, and geographic information, I created a separate data frame that included one observation per country, removing the date and status columns. This reduced the data set from 162,750 observations to 210.
Show code
#create single row per country data set
unesco_short<-unique(unesco_fin[c("Country", "Country ID", "Regional Name", "Income Level",
"Enrolment (Pre-Primary to Upper Secondary)",
"Teachers (Pre-Primary to Upper Secondary)",
'Enrol_Teacher_Ratio',
"School Age Population (Pre-Primary to Upper Secondary)",
"Distance learning modalities (TV)",
'Distance learning modalities (Radio)',
"Distance learning modalities (Global)",
"Distance learning modalities (Online)",
"Weeks fully closed", "Weeks partially open")])
unesco_short[unesco_short==""]<-NA
dim(unesco_short)
[1] 210 14Show code
colnames(unesco_short)
[1] "Country"
[2] "Country ID"
[3] "Regional Name"
[4] "Income Level"
[5] "Enrolment (Pre-Primary to Upper Secondary)"
[6] "Teachers (Pre-Primary to Upper Secondary)"
[7] "Enrol_Teacher_Ratio"
[8] "School Age Population (Pre-Primary to Upper Secondary)"
[9] "Distance learning modalities (TV)"
[10] "Distance learning modalities (Radio)"
[11] "Distance learning modalities (Global)"
[12] "Distance learning modalities (Online)"
[13] "Weeks fully closed"
[14] "Weeks partially open" Show code
unesco_short
# A tibble: 210 x 14
Country `Country ID` `Regional Name` `Income Level`
<chr> <chr> <chr> <chr>
1 Aruba ABW Latin America and~ High income
2 Afghanistan AFG Asia (Central and~ Low income
3 Angola AGO Africa (Sub-Sahar~ Lower middle ~
4 Anguilla AIA Latin America and~ <NA>
5 Albania ALB Northern America ~ Upper middle ~
6 Andorra AND Northern America ~ High income
7 United Arab Emirates ARE Western Asia and ~ High income
8 Argentina ARG Latin America and~ Upper middle ~
9 Armenia ARM Western Asia and ~ Upper middle ~
10 Antigua and Barbuda ATG Latin America and~ High income
# ... with 200 more rows, and 10 more variables:
# `Enrolment (Pre-Primary to Upper Secondary)` <dbl>,
# `Teachers (Pre-Primary to Upper Secondary)` <dbl>,
# Enrol_Teacher_Ratio <dbl>,
# `School Age Population (Pre-Primary to Upper Secondary)` <dbl>,
# `Distance learning modalities (TV)` <chr>,
# `Distance learning modalities (Radio)` <chr>, ...Visualizations
School Closures and Reopenings
Q1: How did school closures and re-openings unfold over the pandemic years of 2020 - 2021?
The chart below summarizes the daily school status updates: “academic break,” “fully open,” “partially open,” and “closed due to covid-19” for each country from 2/16/2020 - 3/31/2022, followed by the total number of observations in each category.
Show code
#timeline of global school status
unesco_fin %>%
group_by(`Country`)%>%
ggplot(aes(Date,color = `Status`, stat= "identity"))+
scale_color_brewer(palette="Set1")+
geom_freqpoly(size = 1.5)+
labs(title = "Daily Global School Status during 2020 -2022",
caption = "Observations aggregate the daily status for 210 territories per month/timeframe.")+
ylab("Number of Observations")
Show code
#total number of observations by global school status
table(unesco_fin$Status)
Academic break Closed due to COVID-19 Fully open
36534 29038 66554
Partially open
30624 The percentage of each school status category by total observations are:
* Academic break- 22.45%
* Fully open- 40.89%
* Partially open- 18.82%
* Closed due to COVID-19- 17.84%
We see schools were primarily fully open approximately 41% of the time or on seasonal academic break 22% during these two years. School closures due to covid account for approximately 18% of the observations, while time partially open accounts for 19%. This suggests that schools experienced more time under normal operations (63%) than disrupted by the pandemic (37%).
Early and Remaining School Closures
Show code
# A tibble: 20 x 4
Date Country `Regional Name` Status
<dttm> <chr> <chr> <chr>
1 2020-02-16 00:00:00 Mongolia Asia (Eastern ~ Close~
2 2020-02-17 00:00:00 Mongolia Asia (Eastern ~ Close~
3 2020-02-18 00:00:00 Mongolia Asia (Eastern ~ Close~
4 2020-02-19 00:00:00 Mongolia Asia (Eastern ~ Close~
5 2020-02-20 00:00:00 Mongolia Asia (Eastern ~ Close~
6 2020-02-21 00:00:00 China Asia (Eastern ~ Close~
7 2020-02-21 00:00:00 Mongolia Asia (Eastern ~ Close~
8 2020-02-22 00:00:00 China Asia (Eastern ~ Close~
9 2020-02-22 00:00:00 Mongolia Asia (Eastern ~ Close~
10 2020-02-23 00:00:00 China Asia (Eastern ~ Close~
11 2020-02-23 00:00:00 Mongolia Asia (Eastern ~ Close~
12 2020-02-24 00:00:00 China Asia (Eastern ~ Close~
13 2020-02-24 00:00:00 Mongolia Asia (Eastern ~ Close~
14 2020-02-24 00:00:00 San Marino Northern Ameri~ Close~
15 2020-02-25 00:00:00 China Asia (Eastern ~ Close~
16 2020-02-25 00:00:00 Mongolia Asia (Eastern ~ Close~
17 2020-02-25 00:00:00 San Marino Northern Ameri~ Close~
18 2020-02-26 00:00:00 Bahrain Western Asia a~ Close~
19 2020-02-26 00:00:00 China Asia (Eastern ~ Close~
20 2020-02-26 00:00:00 Iran (Islamic Republic ~ Asia (Central ~ Close~Show code
# A tibble: 4 x 4
Date Country `Regional Name` Status
<dttm> <chr> <chr> <chr>
1 2022-03-31 00:00:00 Honduras Latin America and the Ca~ Close~
2 2022-03-31 00:00:00 Philippines Asia (Eastern and South-~ Close~
3 2022-03-31 00:00:00 Solomon Islands Oceania Close~
4 2022-03-31 00:00:00 Vanuatu Oceania Close~At the pandemic’s start, school closures originated in Asia. The first recorded country to close schools due to Covid-19 was Mongolia on 2/16/2020, followed by China 5 days later on 2/21. Three more countries followed in February, San Marino on 2/24 and Bahrain and Iran on 2/26. By the end of March, 189 countries recorded a closure due to the pandemic. On the last day of the study, 3/31/2022, the following countries were listed as closed due to Covid-19: Honduras, the Philippines, Solomon Islands, and Vanuatu.
Show code
#facet grid of school status timeline
unesco_fin %>%
ggplot(aes(Date,color = `Status`))+
geom_freqpoly(size = 1)+
scale_color_brewer(palette="Set1")+
facet_wrap(vars(`Regional Name`), scales = 'free_y',
labeller = labeller(`Regional Name` = c(
"Africa (Sub-Saharan)" = "Sub-Saharan Africa",
"Asia (Central and Southern)" = "Central & South Asia",
"Asia (Eastern and South-eastern)" = "Eastern & S.E. Asia",
"Latin America and the Caribbean" = "L. America & Caribb.",
"Northern America and Europe" = "N. America & Europe",
"Oceania" = "Oceania",
"Western Asia and Northern Africa" = " W. Asia & N. Africa")))+
guides(x = guide_axis(angle= 45))+
labs(title = "Daily School Status by Region")+
ylab("Number of Observations")
Each region had a spike in closures in March to approximately July 2020. After this date, there are distinct regional trends. In Sub-Saharan Africa and Oceania, regions primarily alternated between fully open and academic break after July 2020, highlighting limited disruption in those following months. Northern America and Europe saw a similar return to fully open after July 2020 but increased in partial openings from January to July 2021. In Western Asia and Northern Africa, schools returned primarily to partial openings after July 2020, with fully open status not surpassing partial openings until July 2021. Finally, in Latin American/ Caribbean, Central/Southern Asia, and Eastern/South-eastern Asia, regions varied in partial opening and fully open, with peaks of partially open around September 2021. The following chart shows the extent of school status changes within each region.
Show code
unesco_fin$Status <- factor(unesco_fin$Status, levels=c("Academic break", "Fully open","Partially open", "Closed due to COVID-19"))
unesco_fin %>%
ggplot(aes(`Regional Name`, fill=`Status`))+
geom_bar(position = "fill")+
guides(x = guide_axis(angle= 30))+
scale_fill_brewer(palette = "Paired")+
labs(title = "School Status Percentage by Region", caption= "Green areas represent school disruption. Blue areas represent normal operations.")+
ylab("Percent of Total Observations")
This chart shows a representation of school status changes by region. Observations in green (both light and dark) display the percentage of time a region experienced disruption, while values in blue (both light and dark) show typical school statuses of fully open or on regular academic break. The Oceania region had the least disruption, followed by Northern America and Europe and Sub-Saharan Africa. Latin America and the Caribbean had the highest level of disruption (over 50%), followed by Eastern and South-eastern Asia. This suggests that geographical location may have a role in school closure status.
The next set of graphs show observations exclusively for schools Closed due to COVID-19 by region.
Show code
#facet grid of school status timeline
unesco_fin %>%
filter(`Status` == "Closed due to COVID-19")%>%
ggplot(aes(Date,color = `Status`))+
geom_freqpoly(size = 1)+
facet_wrap(vars(`Regional Name`), scales = 'free_y', dir="v",
labeller = labeller(`Regional Name` = c(
"Africa (Sub-Saharan)" = "Sub-Saharan Africa",
"Asia (Central and Southern)" = "Central & South Asia",
"Asia (Eastern and South-eastern)" = "Eastern & S.E. Asia",
"Latin America and the Caribbean" = "L. America & Caribb.",
"Northern America and Europe" = "N. America & Europe",
"Oceania" = "Oceania",
"Western Asia and Northern Africa" = " W. Asia & N. Africa")))+
guides(x = guide_axis(angle= 45))+
labs(title = "Daily School Closures due to Covid-19 by Region")
Show code
#Descriptive stats
#average number of weeks fully closed
unesco_short %>%
group_by(`Regional Name`) %>%
select(starts_with("Weeks fully")) %>%
summarize_all(mean, na.rm = TRUE)
# A tibble: 7 x 2
`Regional Name` `Weeks fully closed`
<chr> <dbl>
1 Africa (Sub-Saharan) 18.1
2 Asia (Central and Southern) 24.4
3 Asia (Eastern and South-eastern) 24.4
4 Latin America and the Caribbean 29.6
5 Northern America and Europe 12.4
6 Oceania 7.12
7 Western Asia and Northern Africa 24.6 School closures peaked during the first four months of the pandemic, beginning in March with a significant decline in July. Time fully closed varied by region, with Latin America and the Caribbean the highest at 29.6 weeks and Oceania the lowest at seven weeks.
Over the next 18 months, many territories increasingly returned to open, alternating with pre-planned academic breaks and partial openings. Dips and peaks appeared primarily caused by academic schedules, although peaks in cases and/or deaths in the region could also be an attributing factor. In both cases, this suggests regional trends as an area of interest and possible determining factor in school closure status.
Characteristics of Closed Locations
Q2: Which of the following characteristics: geographic location, country income level, student-to-teacher ratio, or access to distance learning modalities, were indicated in school closure adoption?
Geography appear to play a role in the type and length of school closure status. However, is that variable impacted by country income level, student-to-teacher ratio, or access to distance learning technology? This next section will attempt to answer that question.
The Average Number of Weeks Fully Closed
Show code
#descriptive stats of weeks fully closed
unesco_fin%>%
summarise(
mean.closed= mean(`Weeks fully closed`, na.rm=TRUE),
median.closed= median(`Weeks fully closed`, na.rm=TRUE),
IQR.closed= IQR(`Weeks fully closed`, na.rm=TRUE),
sd.closed= sd(`Weeks fully closed`),
var.closed= var(`Weeks fully closed`))
# A tibble: 1 x 5
mean.closed median.closed IQR.closed sd.closed var.closed
<dbl> <dbl> <dbl> <dbl> <dbl>
1 19.7 16 17 14.6 214.Next, I compared the average number of weeks closed to the actual weeks fully closed to determine closure levels of:
* High levels are greater than or equal to 20 weeks (higher than the mean value)
* Average levels between 16 and 20 weeks (between the median and mean value).
* Low levels are less than 16 weeks (less than the median value)
Count of School Closures by Level and Region
Show code
#create column with mean of weeks fully closed
unesco_short2<-unesco_short%>%
mutate(`Closure_Level`=`Weeks fully closed`)
#recode mean.closed with closure level
unesco_short2<-unesco_short2%>%
mutate(Closure_Level = case_when(
Closure_Level >= 20 ~ "High",
Closure_Level >= 16 & Closure_Level < 20 ~ "Average",
Closure_Level < 16 & Closure_Level > 0~ "Low",
Closure_Level < 1 ~ "None"))
#cross-tabulation of region by closure level
xtabs(~`Regional Name` + `Closure_Level`, unesco_short2)
Closure_Level
Regional Name Average High Low None
Africa (Sub-Saharan) 7 17 23 1
Asia (Central and Southern) 0 8 4 2
Asia (Eastern and South-eastern) 1 8 7 0
Latin America and the Caribbean 4 29 7 1
Northern America and Europe 10 9 26 5
Oceania 0 1 13 3
Western Asia and Northern Africa 9 10 5 0Show code
#reorder closure level
unesco_short2$Closure_Level <- factor(unesco_short2$Closure_Level, levels=c("None", "Low", "Average","High"))
#Regional Closure Level
unesco_short2 %>%
ggplot(aes(`Regional Name`, fill=`Closure_Level`))+
geom_bar(position = "fill")+
guides(x = guide_axis(angle= 30))+
scale_fill_brewer(palette = "Paired")+
labs(title = "Level of School Closure by Region")+
ylab("Percent of Total Observations")
As mentioned previously, Oceania and Northern America and Europe experienced low school closures. However, some regions have comparable high and low numbers of weeks closed, such as Central and Southern Asia and Eastern and South-eastern Asia. Alternatively, some regions had primarily lengthy closures, such as Latin America and the Caribbean and Western Asia and Northern Africa.
Country Income Level
The following table shows income level distribution based on the World Bank country income groups. Note that there are six countries where no data was captured: Anguilla, Cook Islands, Montserrat, Niue, Svalbard, and Tokelau. The below cross-tabulation and graph display country count by income level and regional name.
Show code
#reorder closure level
unesco_short2$Closure_Level <- factor(unesco_short2$Closure_Level, levels=c("None", "Low", "Average","High"))
#count of countries closure level by income
unesco_short2 %>%
ggplot(aes(`Income Level`,`Closure_Level`, color= `Regional Name`))+
geom_point(position="jitter", size=2)+
guides(x = guide_axis(n.dodge = 2))+
scale_color_brewer(palette="Paired")+
theme_minimal()+
labs(title = "Level of School Closure by Income Level and Region")
Show code
#cross-tabulation of region by income
xtabs(~`Regional Name` + `Income Level`, unesco_short2)
Income Level
Regional Name High income Low income
Africa (Sub-Saharan) 2 22
Asia (Central and Southern) 0 2
Asia (Eastern and South-eastern) 4 1
Latin America and the Caribbean 14 1
Northern America and Europe 39 0
Oceania 4 0
Western Asia and Northern Africa 8 3
Income Level
Regional Name Lower middle income
Africa (Sub-Saharan) 19
Asia (Central and Southern) 8
Asia (Eastern and South-eastern) 7
Latin America and the Caribbean 4
Northern America and Europe 2
Oceania 5
Western Asia and Northern Africa 5
Income Level
Regional Name Upper middle income
Africa (Sub-Saharan) 5
Asia (Central and Southern) 4
Asia (Eastern and South-eastern) 4
Latin America and the Caribbean 20
Northern America and Europe 8
Oceania 5
Western Asia and Northern Africa 8Show code
#cross-tabulation of income by closure level
xtabs(~`Income Level`+ `Closure_Level`, unesco_short2)
Closure_Level
Income Level None Low Average High
High income 5 38 10 18
Low income 2 10 6 11
Lower middle income 1 21 7 21
Upper middle income 3 12 8 31Although most high-income countries (38 of 71) experience low downtime, upper-middle-income countries (31 of 54) had longer weeks closed than average. Low and lower-middle-income countries had comparable amounts of high and low levels of closures. Higher incomes may result in less time closed, but this becomes less definitive as lower-income countries adopt comparable levels of high and low school closures.
Enrolled Student to Teacher Ratio
Below is a summary of enrolled students to teachers for the entire data set. The average (mean) is approximately 20 students/ teacher across the globe. During the pandemic, social distancing and limited occupancy were adopted to slow the spread of the virus. Thus, we will see what effect this ratio has on the amount of time fully closed or partially open. Second, we will review how this ratio may be related to country income level and distance learning modality.
Show code
#summary of enrollment to teacher ratio
summary(unesco_fin$Enrol_Teacher_Ratio)
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
6.437 12.895 16.951 20.043 25.268 62.971 2325 Show code
#student ratio by closure level
#count of countries closure level by income
unesco_short2 %>%
ggplot(aes(`Closure_Level`, `Enrol_Teacher_Ratio`, fill= `Closure_Level`))+
geom_boxplot()+
scale_fill_brewer(palette="Set1")+
labs(title = "School Closure Level by Student to Teacher Ratio")
Student-to-teacher ratio does not appear as a significant determiner of school closure level.
Access to Distance Learning Technology
This section of plots summarizes the students’ access to distance learning modalities by country income level and the number of weeks fully closed.
Summary of distance learning technology access by country income level.
The plot shows the country’s income level distribution by types of distance learning modes. The aim is to uncover if there are trends in income and the type and number of modalities available.
Show code
#bar chart of countries by distance learning mods
unesco_short2%>%
ggplot(aes(`Income Level`, fill= `Distance learning modalities (Global)` ))+
geom_bar(position = "fill")+
scale_fill_brewer(palette = "Paired")+
labs(y= "Percentage of Countries", title = "Countries by Income Level and Distance Learning Modalities")+
guides(x = guide_axis(n.dodge = 2))
Here we see the most popular learning modalities across income levels are TV + Online + Radio followed by Online + Radio. Only low and lower-middle-income countries utilize the combination of TV + Radio as an addition mode and only low income countries use radio alone. High income countries have the highest use of online only. Interestingly both low and high income countries have over a quarter of their school population (25%) not utilizing any distance learning technology.
Distribution of Distance Learning Access by Weeks Fully Closed
Show code
#Weeks fully closed by distance learning modality
unesco_short2 %>%
mutate(`Distance learning modalities (Global)` = fct_reorder(`Distance learning modalities (Global)`, `Weeks fully closed`, .fun='median')) %>%
ggplot(aes(`Distance learning modalities (Global)`, `Weeks fully closed`,
fill=`Distance learning modalities (Global)`))+
geom_boxplot()+
scale_fill_brewer(palette="Paired")+
theme(legend.position="none")+
labs(title = "No. of Weeks Fully Closed by Distance Learning Modalities")+
guides(x = guide_axis(n.dodge = 2))
Overall, most of the distribution is between 10 - 40 weeks, with an average of around 20 weeks, irrespective of the technology. Locations with none had the fewest weeks fully closed, followed by those with radio only. Alternatively, those with TV+Online+Radio saw more extended school closures (especially among the outliers). This could suggest that those with online access and two or more modes, including online, experience longer weeks closed as distance learning technologies are available to teach and learn remotely.
Final Observation
Show code
#Sankey Network Plot
#read in sankey file
links2<-read_xlsx("sankey.xlsx", "links")
nodes2<-read_xlsx("sankey.xlsx", "nodes")
#create sankey diagram
sankeyNetwork(Links= links2, Nodes= nodes2, Source= "Source", Target= "Target", Value="Value", NodeID= "name",
LinkGroup = NULL, units = "",
colourScale = JS("d3.scaleOrdinal(d3.schemeCategory20);"), fontSize = 10,
fontFamily = NULL, nodeWidth = 15, nodePadding = 10, margin = NULL,
height = 600, width = 900, iterations = 32, sinksRight = TRUE)
Here we see the following level of school closures: high- over 20 weeks (82), average- 16 to 20 weeks (31), low- under 16 weeks (85), or none- did not close fully (12). There is a slim margin between the number of countries with low school closures compared to high (85 to 82). Of the 82 countries listed as having high closure levels, the following characteristics are present: access to online learning technology, including the largest group of 3 modes of distance learning (TV + Online + Radio) for 34 of 55 respondents.
Total number of weeks fully closed by region and country
The following graph provides a detailed visual representation of the distribution of weeks closed by country and region.
Show code
#Bar chart of number of weeks closed by country
allcountries_plot<-unesco_short %>%
mutate(`Country` = fct_reorder(`Country`, `Weeks fully closed`)) %>%
ggplot(aes(`Weeks fully closed`, Country, fill=`Regional Name`))+
geom_col()+
scale_fill_brewer(palette="Paired")+
labs(title = "No. of Weeks Fully Closed by Country and Region")
#save ggplot
aspect_ratio <- 2.5
height <- 7
ggsave("country_plot.jpeg", allcountries_plot, device= jpeg, height = 30 , width = 7 * aspect_ratio)
Show code
knitr::include_graphics("country_plot.jpeg")
(#fig:ggplot all countries)Bar chart of number of weeks closed by country
Conclusion
How did school closures unfold over the 2020-2021 covid-19 pandemic and what characteristics were evident in countries with high closure rates?
Globally, schools experienced more time under normal operations (63%) than disrupted by the pandemic (37%). The school statuses were distinguished by being fully open approximately 41% of the time, on seasonal academic break 22%, closed due to covid 18%, and partially open for 19%. After the globe spike between March and July 2020, school closures and re-openings trend regionally. In Sub-Saharan Africa and Oceania, regions primarily alternated between fully open and academic break after July 2020 highlighting limited disruption in those following months. Northern America and Europe saw a similar return to fully open after July 2020 but increased in partial openings from January - July 2021. In Western Asia and Northern Africa schools returned primarily to partial openings after July 2020 with fully open status surpassing partial openings in July 2021. Finally in Latin American/ Caribbean, Central/Southern Asia, and Eastern/South-eastern Asia, regions varied in partial openings and fully open with peaks of partially open around September 2021. This could have been attributed to regional academic calendars or localized spikes in covid variants.
Each region had a few exceptions, however, that significantly varied in extended closures and limited closing. Here we find access to two or more distance learning modalities, specifically if one is online, experience longer weeks closed during the Covid-19 pandemic. This could suggest that those with online access and two or more modes, including one online, are available to teach and learn remotely. High-income countries may result in less time closed even with no distance learning technology, but this becomes less definitive as lower-income countries adopt comparable levels of high and low school closures. Student-to-teacher ratio does not appear as a significant determiner of school closure level. Nonetheless, many countries were able to minimize school closures regardless of country income or student-to-teacher ratio as a global community. Future research could include adding post-secondary school closure status as this project was limited to pre-primary to secondary school.
Source
UNESCO map on school closures [https://en.unesco.org/covid19/educationresponse] and UIS, March 2022 [http://data.uis.unesco.org]
