Take-home Exercise 1

Published

January 12, 2024

Creating data visualisation beyond default

Setting the Scene

OECD education director Andreas Schleicher shared in a BBC article that “Singapore managed to achieve excellence without wide differences between children from wealthy and disadvantaged families.” (2016) Furthermore, several Singapore’s Minister for Education also started an “every school a good school” slogan. The general public, however, strongly belief that there are still disparities that exist, especially between the elite schools and neighborhood school, between students from families with higher socioeconomic status and those with relatively lower socioeconomic status and immigration and non-immigration families.

The Task

The 2022 Programme for International Student Assessment (PISA) data was released on December 5, 2022. PISA global education survey every three years to assess the education systems worldwide through testing 15 year old students in the subjects of mathematics, reading, and science.

In this take-home exercise, you are required to use appropriate Exploratory Data Analysis (EDA) methods and ggplot2 functions to reveal:

the distribution of Singapore students’ performance in mathematics, reading, and science, and
the relationship between these performances with schools, gender and socioeconomic status of the students.

The Data

The PISA 2022 database contains the full set of responses from individual students, school principals and parents. There are a total of five data files and their contents are as follows:

Student questionnaire data file
School questionnaire data file
Teacher questionnaire data file
Cognitive item data file
Questionnaire timing data file

These data files are in SAS and SPSS formats. For the purpose of this assignment, you are required to use the Student questionnaire data file only. However, you are encouraged to download the other files for future needs.

Besides the data files, you will find a collection of complementary materials such as questionnaires, codebooks, compendia and the rescaled indices for trend analyses in this page too.

To learn more about PISA 2022 survey, you are encouraged to consult PISA 2022 Technical Report

The Designing Tool

The data should be processed by using appropriate tidyverse family of packages and the statistical graphics must be prepared using ggplot2 and its extensions.

The Write-up

The write-up of the take-home exercise should include but not limited to the followings:

A reproducible description of the procedures used to prepare the analytical visualisation. Please refer to the senior submission I shared below.
A write-up of not more than 150 words to describe and discuss the patterns reveal by each EDA visualisation prepared.

Submission Instructions

This is an individual assignment. You are required to work on the take-home exercises and prepare submission individually.

Important

The specific submission instructions are as follows:

The analytical visualisation must be prepared by using R and appropriate R packages.
Limit your submission to not more than five EDA visualisation.
The write-up of the take-home exercise must be in Quarto html document format. You are required to publish the write-up on Netlify.

Submission date

Your completed take-home exercise is due on 21st January 2024, by 11:59pm mid-night.