Using Power BI to analyse Higher Education Female STEM enrolments

By Roger Light, Data & Analytics Consultant, Altis Consulting UK

Introduction

Here is a straightforward look at some data that’s highly relevant to the Information Technology (IT) space. It is widely accepted that there is a significant gender disparity in IT and one of the causes of this is the relative lack of female graduates in science, technology, engineering and maths (STEM). Of course you don’t need a STEM degree to flourish in IT but graduate recruitment is a significant entry point and herein lies a problem that can be illustrated with a little help from the UK Higher Education Statistics Agency (HESA).

On their website, HESA publish some numbers that can be made to show the problem clearly. The table below, which shows enrolments for the 2020/21 academic year, is rather dry but this is understandable; its goal is not analysis or storytelling, it simply needs to describe the data for those potentially interested in working with it. 

Fig 1: https://www.hesa.ac.uk/data-and-analysis/students/table-46

We can, given just one report page, bring that data to life and focus on the issue that concerns us. The interactive Power BI report below has a few simple tooltips as you move the mouse over the visuals, but no drill-throughs to further analysis.

The report is divided into three main areas, first a set of high-level comparisons on the left, showing the percentage of female enrolments against males, with a 50% benchmark line. Second is the central two graphs drilling down the comparisons to HESA’s more detailed subject levels. They also include a grey bar to show all enrolments for the subject, providing an indication of which subjects are most consequential in their impact on the overall gender proportion.

Over on the right we have a simple count of the number of the enrolments, which is then broken out by gender, and then further break outs by study mode and level. Finally at the bottom left are links to a description of how I defined STEM, and some definitions of terms supplied by HESA.

If we want to follow the story of computing we can start by clicking the Technology label on the STEM Sub-Group chart. This filters the rest of the report and, in particular gives us this view of Level 2 Subject areas:

Fig 2: Technology enrolments at Subject Level 2

Clearly, computing is driving the gender disparity in Technology, with a lower percentage of females and much higher enrolment count overall. If we want to see precise figures, we can move the mouse over each subject area and view the tooltips:

Fig 3: Tooltips for Computing and Materials & Technology

Clicking on the Computing subject area on this visual filters the report again and provides a clear picture of the level 3 subject areas in the visual below:

Fig 4: Computing enrolments at Subject Level 3

Female enrolment is relatively low in every area of Computing but with 82,000 enrolments overall, the impact of Computer Science is far greater than the other subjects. Checking the tooltip for this visual shows that females make up just 19.9% of Computer Science enrolments:

Fig 5: Computer Science enrolments tooltip

Finally, if we want to drill just a little further, we can use the Study Mode and Study Level visuals on the right. Holding the CTRL key to retain the “Computing” filter, we can click the “Full-Time” label for Study Mode and the “Undergraduate” label for Study Level. This combination filters the report to full-time, undergraduate enrolments for Computing. Checking the Subject Level 3 tooltip for Computer Science one more time shows a further drop in female enrolment proportion to just 15.7%. Such is the current scale of the challenge facing universities and industry recruiters.

Fig 6: Computer Science enrolments filtered to full-time undergraduate

Using this report, a similar story can be traced for Engineering, and a contrasting one for Medical Sciences. If we were to allow drill-throughs to other pages with more detail, we would be able to show even more. For instance, we could analyse the 5,000 enrolments where gender was supplied as Other. Designing a report layout, choosing appropriate visuals and realising the design in a tool such as Power BI is matter of knowledge and practice. Connect with us if you’d like to learn more about how we can help you be successful with Power BI and Data Visualisation.

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