As school leaders will already know, improving attendance rates in schools can be very difficult. Most schools have an attendance rate of between 95 and 96 percent. Getting an improvement of just one percentage point in the attendance rate requires a range of different interventions, many of them tailored to certain types of pupils. Plus, the attendance percentage is one of the most difficult statistics to calculate. In this article, we’ll have a look at why this is so, and how Power BI can help:

Example page from School Analytics – attendance trends by year, term, month and day.

Why is the percentage attendance for a school so difficult to calculate?

1) It can be difficult to define

For example, should a school measure the attendance percentage over the last 12 months or the academic year to date. If we measure the academic year to date, then the percentage attendance figure can fluctuate wildly over the first few months of the academic year, before settling down in the second half of the autumn term.

2) Many factors have an affect on the attendace rate

Attendance percentage is a complex statistic that is affected by many different variables. So looking at the percentage attendance across the whole school population seldom tells us anything actionable. Instead we need to drill down into the data, and find groups of pupils and even individual pupils, for whom we can tailor an action plan or intervention.

3) The underlying numbers are big, very big!

The underlying numbers behind each percentage value are calculated from millions of individual marks. A typical secondary school will record perhaps a half a million individual marks every year – and given than many schools look at a three year trend – and you will understand why analysing attendance marks in Excel isn’t really an option.

An example page from School Analytics – exploring reasons for absence.

How Power BI can help you analyse your percentage attendance statistics

1) Analyse percentage attendance across different periods of time

Power BI can be configured to calculate the percentage attendance figure for each day, week, month, term and academic year. It can show you the statistics for this year against the statistics for the same period last year and the year before. Answer questions like: how does the attendance this term compare with attendance last term? How does the attendace this week compare with the attendace last week, or the same week last year? Power BI can calculate the percentage attendace for these differing time periods very quickly.

2) Easily filter vulnerable groups

Analysing the percentage attendance for vulnerable groups is easy using Power BI. No matter at what level you are analysing percentage attendance, Power BI makes it easy to drill down to see the percentages for disadvantaged children, SEN children, boys, girls, EAL, FSM, PP and by ethnicity. You can filter each page to show the attendance percentage for each vulnerable group. Each graph on the page allows you to drill down and see the names of the pupils behind the statistics, plus a photogrph of the pupil. So you can quickly relate a broad statistics to an individual pupil and see the context of the pupil in terms of any barriers to learning or external influences on that pupil’s attendance.

3) Discover the underlying causes of absence

Power BI also allows us to analyse why those pupils are are missing school so often. As a school, you can analyse each mark and categorise the mark into authorised and unauthorsed absence. From this, we can analyse deeper by looking at what type of authorised or unauthorised absence. How many absences were for unauthorised holidays? What percentage were for illness? How many exclusions generated an absence?

School Analytics and Power BI

Interested in using Power BI to analyse you school data? School Analytics provides a fast, cost effective way of integrating your pupil data with Power BI. School Analytics is a preconfigured Power BI report that works with your pupil database (SIMS and most other pupil databases). It’s ideal for primary and secondary schools that need to improve their data analysis. More details at