Parents’ views on education around the world

Parents' views on education around the world

December 2018

Summary

The Varkey Foundation launched the Global Parents’ Survey between December 2017 and January 2018. 27,500 parents in 29 countries were asked a series of questions about the hopes, fears and aspirations they had for their children’s education. The detailed answers for individual questions from each country have been published.

In this Data Byte, we explore response patterns for selected questions to determine clusters of countries providing similar answers.

What does the chart show?

For the present analysis, countries were characterized based on five question metrics:

  • The proportion of parents rating the quality of teaching at their child’s school as good
  • The proportion of parents helping their child with their education
  • The proportion of parents thinking that their child’s school prepares them well for the world of 2030 and beyond
  • How important parents think that it is for their child to attend university in order to achieve the most in life (average score, ranging from 1 - not important at all - to 10 - extremely important)
  • The proportion of parents optimistic about their child’s future

The chart is interactive. You can zoom in and out and rotate the graph to explore the three dimensions by clicking and dragging. Hover in the top right corner and several options will appear. Click “Reset” to return to the initial view of the graph. The exact values on the three axes can be found by hovering on the point.

Cluster analysis was used to determine groups of countries with similar answers. Principal Component Analysis was used to extract response patterns and reduce dimensions from five (for the five metrics) to three. The graph shows the country projections on the first three principal components. The colour of the point corresponds to the cluster they belong to and the size of the point corresponds to the maximum of the three principal component values. This allows to quickly differentiate countries which do not match any response pattern (ie. close to zero on all three principal components - small points) from the rest.

scatter3d-Varkey

Why is the chart interesting?

The first principal component was the most important response pattern overall. It explained 67% of variance on its own. Countries with high negative scores had a low opinion of their child's school overall (ie. lower school ability to prepare the child for the future, lower school teaching quality) and tended to have low engagement as well. They were less optimistic about the child’s future in particular. These were South Korea, Japan, Russia, Germany and France. Countries with high positive scores had the opposite pattern. These were India, Indonesia, Kenya and the United States.

Countries with high negative scores on the second principal component thought that the school teaching quality was low but had high engagement. In particular, they thought that it was very important for their child to attend university in order to achieve the most in life. Countries with high positive scores had the opposite pattern. Russia scored relatively low while Finland, the United Kingdom and France scored relatively high.

Countries with high negative scores on the third principal component thought that the school had low teaching quality but was able to prepare the child well for the future nonetheless. Countries with high positive scores had the opposite pattern. Italy, Poland, Brazil and France scored relatively low while Germany, India, Indonesia and Finland scored relatively high.

Countries scoring the lowest on the first principal component (ie.South Korea, Japan, Russia, Germany and France) formed one cluster. All remaining countries belonged to the other cluster. However, France, Poland and Italy, on the edge of the clusters and scoring low on the third principal component, did not fit as well in their respective groups as the other countries.

Further information

In 2018, the Global Education Census was carried out by Cambridge Assessment International Education to explore the experiences of teachers and students around the world. Results can be found here.

PAM cluster analysis was used to determine groups of countries with similar answers. The optimum number of clusters was determined using the silhouette method. The optimum number of principal components was determined using the scree test method. The first three principal components explained 91% of variance of the original variables, so little information was lost.

Research Matters 26

Research Matters

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