Student Enrolment Forecasting

The Student Enrolment Forecaster is a tool that uses historic recruitment and enrolments data, finds trends, and applies those trends to the current recruitment cycle.

In a given recruitment cycle the number of applications, offers made and firm acceptances are compared to the number of enrolments per entity (faculty/programme/etc.), and a conversion rate is calculated. This conversion rate can then by applied to our current recruitment cycle to produce an enrolment forecast.

This tool forecasts enrolments to the 1st December, to account for the withdrawals that occur during the first few months of the academic year.

** Please note that it is the responsibility of the user to determine which conversion rates and years are the most appropriate to use for the population they are forecasting for.  This tool is not a magic wand, and only applies historic trends to current figures.  It cannot take into consideration and account for any changes that take place during the current recruitment cycle.  Outputs should be used in context.

The tool forecasts main scheme applications only.  Applications that come through clearing, adjustement or RPA are excluded.

HEP, IPC and pre-sessional students are also not included in any calculations or predictions.

Standard and deferred applications are included, however in-year and January starts are not.

All documents referred to below can be found in the following folder - https://drive.google.com/drive/u/0/folders/1BlFB7qGmaW2S9yVyCkTg2bD6uEXMorNr

• Data Dictionary.xlsx
  • Data Filters → details the exclusions and inclusions (including those discussed in section 2.0 above)
  • Department | Data Input → lists field names for the department level data, along with the data types, original data source and original names, a calculation (where relevant), and a description
  • Department | Calculations → contains all parameters and calculated fields for the department level data used in the Tableau workbook, along with a calculation and definition
  • Programme | Data Input → lists field names for the programme level data, along with the data types, original data source and original names, a calculation (where relevant), and a description
  • Programme | Calculations → contains all parameters and calculated fields for the programme level data used in the Tableau workbook, along with a calculation and definition
  • https://docs.google.com/spreadsheets/d/1Hu1STXFdCx45JUO4HOpuHd654p8faG4F/edit?usp=drive_link&ouid=108891095127708120027&rtpof=true&sd=true
• Targets 2024-5.xlsx
  • Target data for each department
  • https://docs.google.com/spreadsheets/d/1WFMaOe1PaG44ty1imMAK3viUvRCabqUK/edit?usp=drive_link
  • Original source data:
    • Arts & Humanities | https://docs.google.com/spreadsheets/d/1E2W0MIQ7_yz_3ZnKGshIMVE_E6xWeAC2Es8y6QkQgf4/edit#gid=0
    • Social Sciences | https://docs.google.com/spreadsheets/d/1tmoa6nvB1dr3izaPiPsL8E63U1C1RI6teXVQ41TPjnY/edit#gid=0
    • Science | https://docs.google.com/spreadsheets/d/1REHDOQ6j1On3gtg6BPzDlSs4dTbMFnw0dv1_TuhdeVU/edit#gid=0

Enrolments: The headcount of students enrolled on 1^st December.

Comparative cycle: The matching point in previous cycles.  If the date is 10^th April 2023 and we want to compare the number of applications to previous cycles, we will count any applications we received from the start of the previous cycle up to 10^th April in the previous cycle.

Application cycle: changes each year, but this year is 

Conversion rate:  What percentage of a group were present in another population.  For example, how many applicants went on to enrol on a course.

Targets:  Targets can refer to several more specific definitions, please refer to the April Reforecasting Targets document linked in section 2.0 above to see which definition applies to which departmental target.

5.1 Types of Forecasts

Six conversion rates are calculated:

• Applications to enrolments
• Offers to enrolments
• Firm acceptances to enrolments (conditional + unconditional firms)
• Conditional firms to enrolments
• Unconditional firms to enrolments
• Registrations to enrolments

Forecasts can be calculated either forecasting from our current position or by projecting our end of cycle position and forecasting from that.

From current position

• How many applications do we currently have?
• Apply the historic conversion rate to our current position to calculate the forecast

From projected end of cycle position

• In a previous cycle, what was the growth between this comparative point and the end of that cycle?
• Apply that % growth to our current number of applications to get the projected end of cycle position
• Apply the historic conversion rate to that projected position to calculate the forecast

Year Averages

• 2 year average → the previous two complete cycles
• 3 year average → the previous three complete cycles
• 4 year average → the previous four complete cycles
• 5 year average → the previous five complete cycles

For example in the 2024/5 recruitment cycle, the 3 year average will consist of 2023/4, 2022/3 and 2021/2.

For each conversion rate (e.g. applications to enrolments, firms to enrolments) a forecast could be produced 18 different ways (detailed in the table below).  This means that overall in the forecaster there are 108 different ways to produce a forecast.

5.2 Conversion Rate & Enrolment Forecast (from projected end of cycle position)

The way in which the above forecasts are calculated is shown in the flowchart below.

5.3 Conversion Rate & Enrolment Forecast (from projected end of cycle position)

(all numbers below are made up examples, and not based on any true figures)

As an example, let's consider the following scenario.  The Archaeology department within Arts & Humanities received 312 applications from overseas students applying to start a postgraduate course in 2022/3.  From those applications they offered on 243 and received firm acceptances from 99 prospective students.  On 1st December 2022, Archaeology had 61 students.

Step 1:  Calculate the conversion rate of applications to enrolments

• 61 enrolments / 312 applicants * 100 =  19.6%

Step 2:  Calculate the percentage change between last year's comparable and end of cycle position

All of those figures are the total number for the whole recruitment cycle.  If we want a forecast part-way through the recruitment cycle, we need to take into account how the number of applications grew between this point in the cycle and the end of cycle.  In February 2022 Archaeology had only received 268 applications.

• ((312 - 268)/268)*100 = 16.4%

Step 3:  Project this year's end of cycle position

So far this recruitment cycle, Archaeology has received 295 applications.  To calculate our projected end of cycle position, we multiply our current number of applications this cycle by 1 + the percentage growth.

• 295 * 1.164 = 340 applications

Step 4:  Forecast enrolments

Using the projected end of cycle position from step 3 and the conversion rate from step 1, we can now produce an enrolment forecast.

• 340 * 19.6% = 67 enrolments

5.4 Enrolment Shortfalls & Offers Required

The enrolment forecasts generated are compared to a target figure (where a target has been provided).  If there is a projected shortfall - where the enrolment forecast is less than the target - we can calculate how many additional offers we would have to give in order to rectify the enrolments shortfall.

Step 1:  Calculate the enrolment shortfall

For this calculation, do the enrolment forecast minus the target for that entity.  It is done this way round so that negatives are clear in the Tableau report.

• 67 - 75 = 8 enrolments

Step 2:  Calculate how many offers are required

Using the offers to enrolments conversion rate, we can work out how many offers we need to make (offers to enrolments conversion rate calculated using the figures above).

• 8 / 0.251 = 32 offers

All calculated fields (enrolment forecasts, conversion rates, etc.) are calculated at the lowest possible level of aggregation.  For the department dashboards, this means:

• Department dashboards, per: faculty, department, level of study, home/overseas, year
• Programme dashboards, per: faculty, department, programme, level of study, home/overseas, year

The tabular dashboards break this down, and the forecasts can be viewed at this lowest level.