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titleOverview

The admissions forecaster attempts to give an indication of what student numbers will be per department based on current application figures, and the likelihood that those applications will become UF based on the behaviour of applicants in previous yearsUnconditional Firm (UF). A conversion to UF rate is calculated for each route owner / student mix / application status / intake type (DL, HEP, IPC, Standard) combination based on conversion of applications to Unconditional Firm from that combination last year.  For example, a single rate is calculated for All Archaeology, UG standard applicants who on this day last year had an application status of CF.  If 50% of them became UF then we assume it is assumed that any Archaeology UG applicants with a status of CF today also have a 50% chance of becoming UF.  These rates are recalculated on a daily basis so that we are always considering what applicants did at exactly the same point in the cycle last year

Although, having said a single rate is calculated, we actually calculate 4 rates are actually calculated, but in each case the same logic applies:

  • Conversion to UF at end of cycle (last year)
  • Conversion to UF at end of cycle (3 year average)
  • Conversion to registered on 1st December (last year)
  • Conversion to registered on 1st December (3 year average)

We also estimate future applications based on what percentage of applications for a route owner / student mix / intake type had not yet been received this time last year and again of those not yet received, what percentage became UF.  This has a large impact on PG estimates

Using these rates we then add together all the UF estimates from each application status estimate to give an estimated total UF for that route owner.

Warning
titleLimitations and assumptions

The logic used for the admissions forecaster has a number of limitations that may affect the validity of the results it predicts:

  • It assumes that the past is a good predictor of the future and indeed that students this year will behave as students in previous years have behaved.  As external factors change this may not be the case
  • There is no adjustment for initiatives that may affect these rates, e.g. a successful marketing initiative last year affected the numbers because students were applying earlier than in previous years, but the algorithm assumed that the same rate of future applications applied.  Likewise the early UF offer scheme may have an effect that won't be covered
  •  The level of granularity does not take account of the differences that may appear between programmes.  For example, a department may offer some programmes that are easy to recruit to and others that are not, but a single rate is calculated for them all which may over-inflate the estimate for some programmes at the same time as under-estimating for others.

AS AT 18/04/2019 IT HAS NOT YET BEEN POSSIBLE TO TEST THE RESULTS. SNAPSHOTS ARE BEING TAKEN FOR 2019 AND AT THE END OF THE RECRUITMENT CYCLE ANALYSIS SHOULD BE POSSIBLE TO SEE WHEN AND IF THE PREDICTION BECAME ACCURATE. UNTIL THEN, THIS WORK SHOULD BE SEEN AS ANOTHER INDICATOR OF WHAT MIGHT HAPPEN RATHER THAN A STRONG PREDICTIONAs of 01/04/2020 extensive testing has been carried out on the accuracy of the the model utilised by the admissions forecaster. It has reaffirmed that the accuracy of the model is hugely dependent on the similarity of the current application year and the year which is used as the basis for the conversions rates. Attempts have been made to mitigate this, by allowing the user to choose the year they wish to use as the basis for the conversion rates, however change to the applications landscape will still have a significant affect on the accuracy of the model and this should always be kept in mind when quoting the figures produced by this report. If the the current application year and the year which is used as the basis for the conversions rates are similar then the model should produce a forecast of final numbers with a 5% margin of error from early May. The forecaster works best for UG and PGT cohorts and is less reliable for PGR cohorts.

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titleData refresh schedule
This workbook is refreshed daily at 6am and 3pm until the the December snapshot is taken, at which point the workbook is switched to show static data from the snapshot.8am
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titleWorkbook tabs

The workbook has the following tabs:

Above and below target scatterplot

This tab gives a graphical representation of how far away from target FTE, enrolment FTE is by department and student mix, with the potential to filter the results to show all departments, all departments within a faculty or a single department. Each department is represented by a single mark on the scatter plot with a downward arrow if the enrolments are below target, and upward arrow if enrolments are above target and a dot if the two are equal. The dotted line in each panel shows where the marks would sit if enrolments were equal to target. Therefore the distance of the mark from this line indicates the distance from target.

Faculty Overview

The faculty overview tab shows the progress of enrolments against target for a single Faculty. The top panel shows the numbers summed for the faculty broken down by student mix and the bottom panel shows the same numbers but additionally broken down by programme owner. In each chart the grey bar shows the target FTE, whilst the coloured bar shows the FTE of enrolled students. The student enrolment bars are coloured according their percentage difference from target. The yellow / orange bars show where enrolments are below target and the blue bars show where enrolments are above target. The darker the colour the further away from target.

Department Overview

The department overview tab offers, for each department / programme owner the option to drill down into the data to see, by programme how the numbers for the department have been calculated. The top panel shows a summary for the whole department. Each square is coloured depending on the percentage difference from target for the department / student mix combination Clicking on any student mix row in that top panel will show a breakdown for that department / student mix by route below. As in the Faculty Overview, the grey bars show the target FTE, whilst the coloured bars show the FTE of enrolled students. The student enrolment bars are coloured according their percentage difference from target. The yellow / orange bars show where enrolments are below target and the blue bars show where enrolments are above target. The darker the colour the further away from target.

All Departments

This tab gives a university level overview of the progress of student enrolments against targets. The totals are shown in the top panel, followed by a breakdown by programme owner below. Each square is coloured depending on the percentage difference from target for the department / student mix combination.

The results can be shown as a total number for all years of study selected, or broken down by those years of study using the 'Split by year of study?' filter.UG

This tab shows targets and predictions by route owner for UG admissions. The first column shows the prediction against the target that has been planned in RAMP for the upcoming academic year. The second column shows the surplus or shortfall that would occur against the target if the prediction turns out to be correct. The third column shows, where a route owner took students from clearing in the previous academic year, the FTE of those students. The fourth, and final, column adds the number achieved in clearing last year to the number of UF's predicted by the model together and compares that to the target to see whether there will be a surplus or a deficit if the prediction turns out to be correct and the same number of students are taken in clearing.

The sheet also shows a total for all route owners at the top. But, it should be noted that a shortfall in one department is not offset by a surplus in another, and where a department reaches target they may not take as many in clearing this year, so the final overall figure is unlikely to be correct

Hovering over any bar in the chart will bring up a table that shows the detail of the prediction by current applications status

UG Table

This is a tabular version of the data on the UG sheet which can be downloaded by crosstab to Excel

PG

This tab shows targets and predictions by route owner for PG admissions. The first column shows the prediction against the target that has been planned in RAMP for the upcoming academic year. The second column shows the surplus or shortfall that would occur against the target if the prediction turns out to be correct. The next two columns are a repeat of the first two columns but with the inclusion of an estimate of how many future applications are likely to be received.

Again, the sheet also shows a total for all route owners at the top. But, it should be noted that a shortfall in one department is not offset by a surplus in another, so the final overall figure is unlikely to be correct.

Hovering over any bar in the chart will bring up a table that shows the detail of the prediction by current applications status

PG Table

This is a tabular version of the data on the PG sheet which can be downloaded by crosstab to Excel


Department Summary

The department summary tab shows all PG and UG predictions for a single route owner on a single page. That is it consolidates what is shown on the UG and PG tabs into a by department view. Clicking on any listed student mix will display a breakdown by programme at the bottom of the page

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titleFile Locations

SQL Files

apps_conversion_predictions_v.sql

apps_conv_rates_1_year_v.sql

apps_conv_rates_1_year_reg_v.sql

apps_conv_rates_3_years_reg_v.sql

apps_conv_rates_3_years_v.sql

Tableau File

Q:\DISCOVER\Student\Workbooks\Published\Applicant Tracking\Tableau\Admissions Forecaster v0.7 PLANDISC.twbx

Snapshot

a daily snapshot is taken of apps_conversion_predictions_v and inserted into apps_conv_predict_snap by the stored procedure mv_refresh_pkg.apps_predictions_snap which is called by the scheduled job append_apps_predictions that runs daily at 5am






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titleFilters

The workbook makes use of the following filters:

Filter NameFilter Options
FacultyDL FlagThis filter allows you to restrict the results shown to a single faculty or (where applicable) show data for all facultiesDepartmentfor the inclusion / exclusion of applicants and targets based on whether or not they are York distance learning applicants.
HEP FlagThis filter allows you to restrict the results shown to a single department or (where applicable) multiple / all departments
Include pending?If No is selected, then only enrolment records from SITS where the registration status equates to either 'Registered' or 'Hull Registered' will be included. Selecting Yes will additionally include student enrolment records from SITS where the registration status is pending
Routes includedfor the inclusion / exclusion of applicants and targets based on whether or not they are Higher Education Partners (HEP) applicants.
IPC Flag

This filter allows for the inclusion / exclusion of groups of registrations / targets that have been identified as necessary inclusions / exclusions for reporting purposes. The groups are defined as follows:

Hull based studentsIf the unit code is 9038 or the campus location for the enrolment record is Hull
Registrations without targetWhere there are enrolments against a particular route that has not had any intakes entered for it in RAMP
Routes with MTP Target (online not HEP)Records flagged as distance learning in either the MTP model or SITS, but this will not exclude online HEP programmes
Routes with MTP Target (campus based)Routes with a target in the MTP model that have not been flagged as distance learning
NHS CPDWhere the unit code is 0026 and the course code is either 'UHEACPD' or 'UHEAMOD'
POD (PGCAP/PFA)Where the course code starts with 'PYRK'
Visitingenrolments with a visiting mode of study in SITS
HEP ProgrammesRoutes where the route code starts with 'PMHEPS%' 
Lifelong LearningRoutes with a unit name of 'Centre for Lifelong Learning' or ones where the course code starts with 'UCLL' or is 'UCEDCED'
Year of StudyThe year of study that an enrolment is for. To see all new entrants, select 0 and 1 and additionally set New Entrant? (next filter) to 'Y'
New Entrant?This filter allows you to restrict the results to only show new entrants to the university. This is students on year 0 for a foundation course, or students on year 1 for other programmes. Select 'Y' to see just new entrants and both 'Y' and 'N' to see results for all students
IPC Flag

This filter allows for the inclusion / exclusion of targets and enrolments marked as 'IPC'. This is done via a tag in RAMP for targets and by the UDBF field on the SCJ record in SITS being set to IP2.

Selecting 'Y' in this filter includes records that are marked as IPC, selecting 'N' includes records that aren't marked as IPC and selecting both includes all records

Split by year of study?This is a filter option on the 'All Departments' tab that will show the results broken down by year of study if 'Yes' is selected and as a total across the selected years of study if 'No' is selected

applicants and targets based on the IPC flag the records are tagged with

Student Mix

This filter allows the selection of a single student mix to display results for. The UG tabs are restricted to UGH and UGO, whilst the PG tabs are restricted to PGTH, PGTO, PGRH and PGRO

Conversion rate to use (PG)

This filter can be used to select one of the following options of conversion rate from current application status to UF:

Conversion to UF at end of cycle (last year)Looks just at applicants on this day from last year and whether their final application status was UF
Conversion to UF at end of cycle (3 year average)Looks at applicants on this day from the last 3 year and whether their final application status was UF and calculates an average of those 3 years
Conversion to registered on 1st December (last year)Looks just at applicants on this day from last year and whether they were registered students at 1st December census date

Conversion to registered on 1st December (3 year average) **

Looks at applicants on this day from the last 3 year and whether they were registered students at 1st December census date and calculates an average of those 3 years
Conversion rate to use (UG)
Conversion to UF at end of cycle (last year)Looks just at applicants on this day from last year and whether their final application status was UF
Conversion to UF at end of cycle (3 year average)Looks at applicants on this day from the last 3 year and whether their final application status was UF and calculates an average of those 3 years
Conversion to registered on 1st December (last year) **Looks just at applicants on this day from last year and whether they were registered students at 1st December census date
Conversion to registered on 1st December (3 year average)Looks at applicants on this day from the last 3 year and whether they were registered students at 1st December census date and calculates an average of those 3 years

** Indicates the default setting for this parameter as specified by colleagues in SRA

Conversion Rate Year to use

(Only Applicable to 1 year Conversion Rates)

This filter can be used to select which of the previous three years should be used as the basis for the single year conversion rates. This has no effect if one of the 3 year average options is selected for the "Conversion Rate to Use" Filter. This affects both the UG and PG "Conversion Rate to Use" Filters.
Route Owner SelectionThis filter allows the display of different combinations of route owners, excluding, including or only showing HYMS and Health Sciences
Route OwnerThis filter allows the selection of a single route owner to display
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titleFuture work

The following work may be done in the future

ItemDescription
Reliability testingUse apps_conversion_predictions snapshots to assess the accuracy of the predictions against the actual admissions figures at the end of the current cycle. Ideally multiple cycles will be tested to gain a consistent picture
Include Accommodation FlagInformation about how soon an application is made for accommodation once the accommodation system is open may be a strong indicator of intention to come to the University. This could be built in as a predicting factor
CAS StatusCAS Status is another predictor of whether or not an application is intending to take up their offer at the university. This could also be built into the predictions
Remove hard coding of academic year in conversion rate viewsThe conversion rate views that use previous year average and last three years average is hard coded so need to be manually moved on.
Remove schema references in conversion rate viewsThe views need to use the same code to run on either planpre or plandisc.
SQL Files

Q:\EDW\SQL\York Data\Enrolments\Registration Monitoring

Tableau File
Q:\DISCOVER\Student\Workbooks\Published\Registration Monitoring\New registration monitor\Enrolment against targets monitor 2018.twbx
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titleTechnical Notes
details - view design

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