UCAS End of Cycle data – the Whitty effect and getting lost in the (Boston) Matrix

In February the good people at UCAS released their end of year report along with some lovely dashboards allowing people to look into the major trends around applications and accepts for the UK HE system. They also released their massive range of data sets which gave the VizHE folks something to do in lock down.

With their shiny dashboards UCAS had pretty much covered the basic summarisation of the data so we wanted to see what else we could extract that piqued our interest.

With the current world situation we talked through looking at subject trends from a position of what are the popular subjects and to what degree had they been influenced by the brilliant scientists and doctors who have been a large part of the collective effort in the past year or so.

We each followed separate lines of enquiries and came up with three fantastic vizzies – one using the Boston Matrix approach to review subject trends, one digging into the Whitty effect and one looking at the choices both within subject and ethnic group around clearing and adjustment.

The Boston matrix is a tool used to look at the portfolio of products to see how they are performing. It effectively places the products into four quadrants:

Star (High market growth, high market share)

Question Mark (High market growth, low market share)

Cash Cows (Low market growth, high market share)

Dogs (Low market growth, low market share)

The quadrants are created by plotting the change in market share against the market share for the current year based on the applications for each subject. It is possible to look at the data by different date ranges and by individual institution performance.

The visualisation is available here: https://public.tableau.com/views/UCASApplications-BostonMatrix/InstitutionBostonMatrix?:language=en&:display_count=y&:origin=viz_share_link

Jennie – The Chris Whitty Effect – Analysing the increase in UCAS applications for health and social care courses

I decided to focus on the increase in applications for health and social care courses coinciding with the COVID-19 pandemic, termed ‘The Chris Whitty Effect’ by WONKHE in their recent blog available here (Tracing the Chris Whitty effect in the 2020 end of cycle admissions subject data | Wonkhe).

Delving into the data more closely I could see straight away the increase in JACS subject area ‘Subjects allied to Medicine’ which has a big jump from 2019 to 2020 and it stands out amongst other JACS subject groups. The trend over time is also interesting for this subject; applications dropped in 2017 and 2018 coinciding with the change in removal of bursaries from 1st August 2017 for students taking pre-registration nursing, midwifery and allied health courses. This year though, main scheme applications (the main UCAS application scheme whereby students can apply for up to five institutions or course) are up by almost 9%.  Medicine and Dentistry has also seen a 5% increase in applications, but by far it is Subjects allied to Medicine that has seen the biggest increase.  When looking at total accepted places, again it is these two subject areas that are ranked on top, with a 17% increase in accepted places for Subjects allied to Medicine and a 9% increase for Medicine and Dentistry.  Conversely Non-European Languages and Literature has seen the biggest drop, with a 15% decrease in main scheme applications and an 11% decrease in total accepts, so it would be interesting to delve further into this data in future analysis.

Looking specifically at Subjects allied to Medicine I wanted to focus on individual providers to see if an increase in applications is also reflected in an increase in accepts. As I expected, the majority of providers that saw an increase in applications from 2019 to 2020 also saw an increase in total accepts. Of interest though are those providers for which applications increased but accepts either stayed static or even decreased. I wondered if this was due to caps on the numbers of places available at these providers so I looked at applications and accepts across all JACS subject areas for these particular providers.  Although I couldn’t see any obvious pattern here, some of these providers did see an increase in applications and accepts for certain subjects (see University of Bath).  This could mean that caps on places for Subjects allied to Medicine might be the issue, however more in-depth knowledge of courses at the provider would be needed to know for sure.

This time I experimented with using a long form dashboard which I haven’t used before.  I usually like to see everything all in one go, however I do think it works and helps tell the story.  I also played with the golden yellow colour across the dashboard, I don’t think it looks too bad, but let me know what you think 😊. If I had more time I would have liked to repeat the bottom chart for all providers in scatter plot to see if this pulls through any patterns when comparing across different subject areas.

The visualisation is available here: The Chris Whitty Effect – Analysing the increase in UCAS applications for health and social care courses – Jennie Holland | Tableau Public

Sasha – Widening Participation – Exploring changes in relationships of ethnic groups in UCAS application stages

I set out to investigate the difference between applications and acceptances recorded for each acceptance route against measures of widening participation in HE. The first challenge was to combine the files: there was one file containing applications, another acceptances and a set for each of the WP metrics. Starting with the ethnic group data I noticed that there was a huge difference in numbers; far more acceptances than applications. The crux being the applications file only contained main scheme applications whereas acceptance also included others (see below). Not a big issue as you can use this field to join the data. However what you end up with is only three acceptance routes: Firm, Insurance and Other, and not many insights to draw.

Back to the drawing board, I looked at the initial question that interested me. I wanted to look at the differences in groups of people going to university at the opposite end of the scale acceptance scale: adjustment and clearing. I dropped the combined file altogether and focused solely on acceptances. The below DNA chart shows the few groups going for adjustment and their subject choice by year. It also compares the popularity of these subjects when it comes to clearing.

The visualisation is available here: TBC