CATalytics

PROBLEM & MOTIVATION

According to a survey of more than 14,000 international students in Australia and the UK, 77% of students listed both university and subject rankings as “very important” when deciding a place to study. As such, given the importance of these rankings in decision-making, we were inclined to provide an interactive outlet to aid students in their decision-making process.

In addition, most rankings systems online are in a tabular format. This makes it difficult for anyone to truly visualize the information being displayed and how various factors interact with each other. Thus, our team aims to create a dynamic visual that not only provides interactivity between factors but also reveal hidden patterns of associations that tabular formats might miss out.

Who can use our visual?

Pre-university selection for students
University students planning to go for exchange

What can be derived from our visual?

Effect of factors on ranking of university
Location of universities
Trends of school make-up
Overall ranking of universities
Multi-Comparisons between universities based on factors

SELECTED DATASET

Our project will be based on the Times Higher Education World University Rankings. (Extracted from: https://www.kaggle.com/mylesoneill/world-university-rankings)

Our project will analyze the following attributes from the dataset:

Label	Description
world_rank	world rank for the university. Contains rank ranges and equal ranks (eg. =94 and 201-250)
university_name	name of university
country	country of each university
teaching	university score for teaching (the learning environment)
international	university score international outlook (staff, students, research)
research	university score for research (volume, income and reputation
citations	university score for citations (research influence)
income	university score for industry income (knowledge transfer)
total_score	total score for university, used to determine rank
num_students	number of students at the university
student_staff_ratio	number of students divided by number of staff
international_students	percentage of students who are international
female_male_ratio	female student to Male student ratio
year	year of the ranking (2011 to 2016 included)

Performance indicators were grouped into five areas:

Performance Indicator	Description
Teaching	The learning environment
Research	Volume, income and reputation
Citations	Research influence
International outlook	Staff, students and research
Industry income	Knowledge transfer

Note: Universities were excluded from the rankings if (i) they do not teach undergraduates or (ii) research output amounted to fewer than 200 articles per year over a five year period (2010 to 2014).

Breakdown of Performance Indicators

Source: https://www.timeshighereducation.com/news/ranking-methodology-2016

APPROACH

After referencing to multiple previous works that display university rankings, we decided to focus only on The Times Higher Education World University Rankings The rationale is that the dataset provided is robust enough to tell a good story, and that we cannot feasibly combine data from multiple sources without having missing or irrelevant values. Our other choices and sources which we eliminated were:

Example	1) World University Rankings 2015-2016 We started with the web app provided by the official Times Higher Education website itself and we realised it provided good filtering systems. Hoever, the app does not visualize its data comprehensively. This reference helps us to know which filtering categories are important.
Example	2) Caleydo LineUp: Visual Analysis of Multi-Attribute Rankings Next, we wanted to value add to an existing visualisation for university rankings and its factors. This one uses bar chart and LineUp so even though they are informative, we wanted to have something else to show in addition to what the visuals currently display.
\|\| 3) Which universities do good science? This work from Kaggle itself shows us the benchmark and inspiration for which variables are used to compare and gain insights from. The work features mostly scatterplots and even a correlogram. We have then decided to adopt a more multidimensional approach to visualizing the data and decided on glyphs and ternary plots.

STORYBOARD

The pictures below serve as a mock-up to the proposed application.

Screen	Description
	Screen 1 - Analysing a single university/multiple universities according to performance indicator The first button "Factors" will bring users to this page. Users will first select whether they want to look into the performance indicators of a single university or multiple universities by clicking on the respective radio button Users can choose the year(s) to display as well as the university/universities to be displayed If a single university is selected, the map will zoom into the country of the university. A tooltip pop-up will appear showing the rank, country and number of students and international students of the university. The female-male ratio and the staff-student ratio will be shown as bar graphs. The star glyph will show the respective positions of the performance indicators, with overlapping graphs showing either the performance indicators of a single university across years, or the performance indicators across universities in one year.
	Screen 2 - Analysing the trend of university make-up through the years The second button "Trend" will bring users to this page. Users select the rank type of the universities. Proposed types include: Selecting specific countries Top 10 universities Top 100 universities etc Users can drag the arrow to view the resulting ternary plot across the years The ternary plot will animate to show the respective data points in the respective years The map will also show which country/countries the university/universities is/are from
	Screen 3 - Viewing the ranking of universities The third button "Ranking Table" will bring users to this page. Users will be able to see the ranking of the universities Users can filter by the respective elements