Teaching Staff Allocation Model

This use case explains how to model teaching staff loads, allocation and cost for private and vocational education providers in Model Reef.

You will:

• Represent teaching roles and teams across campuses and programs.

• Build drivers for contact hours, preparation and assessment effort.

• Allocate teaching effort and cost to programs and courses.

• Connect staff planning to enrolments, capacity and program profitability.

Model Reef is not a timetable generator or HR system. It models teaching effort, allocation and cost at planning level rather than individual class schedules.

When to use this pattern

Use this pattern when:

• Teaching staff cost is one of the largest expense lines.

• You need to align hiring and teaching loads with enrolment and program plans.

• You want to understand the cost to deliver each program or group of courses.

• You are exploring different delivery or staffing models.

It integrates with:

• Student Enrolment and Intake Forecasts

• Course or Program Profitability

• Campus or Program Consolidation

• Build a Staffing Cost Model

Architecture overview

Teaching staff allocation modelling uses the following building blocks:

1

Structure

• Branches for campuses or academic units.

• Staff variables for teaching roles.

2

Workload drivers

• Contact hours per course or program.

• Preparation, marking and administration effort.

• Class sizes and group numbers.

3

Allocation and cost

• Allocation of staff effort to programs.

• Salary and on cost per role.

• Use of permanent, part time and casual staff.

4

Capacity and planning

• Maximum feasible loads per role.

• Headcount and cost required under different enrolment scenarios.

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Step 1: Define teaching roles and units

Start by listing the main teaching roles, for example:

• Professors or senior lecturers.

• Lecturers or trainers.

• Tutors or teaching assistants.

• Clinical or workplace supervisors for vocational programs.

Decide how to group academic units, for example by faculty or department, then create Staff variables such as:

• Staff - Lecturers - Nursing - Campus A.

• Staff - Tutors - Business - Campus B.

• Staff - Clinical Supervisors - Health - Campus A.

These variables will hold headcount, cost and timing for teaching roles.

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Step 2: Create workload drivers for teaching effort

In the Data Library, create drivers for each program or course group, such as:

• Contact Hours per Student per Course.

• Number of Courses per Program per Term.

• Typical Class Size.

• Preparation and Marking Hours per Contact Hour.

• Additional coordination or administration hours per course.

From Student Enrolment and Intake Forecasts, you already have student numbers per program and campus. Use these to derive:

• Total Contact Hours = Students × Contact Hours per Student.

• Number of Classes or Groups = Students ÷ Class Size.

• Total Teaching Hours = Contact Hours + Preparation and Marking Hours + Coordination Hours.

These series represent the total teaching effort required by program or course group.

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Step 3: Allocate teaching effort to roles and staff groups

Define how teaching effort is shared between roles, for example:

• Percentage of contact hours delivered by lecturers versus tutors.

• Clinical supervision portion delivered by supervisors versus workplace mentors.

Create allocation drivers such as:

• Share of teaching hours by role for each program.

Then calculate required hours per role and program and use these to inform Staff variables. You can use formulas that link Staff variables to the required hours and convert them to FTE using working hours per FTE.

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Step 4: Build staff cost and timing

For each Staff variable, set:

• Headcount per period (either directly or via FTE drivers).

• Salary and benefits per FTE.

• On cost percentages for pension, payroll taxes and other charges.

• Start and end dates for roles.

• Payment frequency and delay for cash timing.

Where you use casual or sessional staff, you can represent them either as separate Staff variables with different salary and loadings, or as Opex variables linked to teaching hours via pay rates per hour.

This generates the teaching staff cost lines in P&L and corresponding cashflows.

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Step 5: Attribute teaching cost to programs and courses

Use either of these approaches, or a mix:

1

Direct staff per program

Where staff are predominantly assigned to one program, map Staff variables directly to that program's P&L.

2

Central staff with allocation

Maintain staff in faculty or campus branches and create allocation Opex variables that push cost into program level cost variables based on teaching hours or student load.

In both cases, the objective is that course or program profitability views show a fair share of teaching cost for each offering.

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Step 6: Use scenarios for staffing, delivery and mode strategies

Clone the base model into scenario models to explore:

• Different class size and teaching load policies.

• Shifts between face to face and online or blended delivery.

• Increased use of sessional staff.

• Centralisation of certain courses across campuses.

• Investing in additional teaching resources for priority programs.

In each scenario, adjust:

• Workload and class size drivers.

• Allocation of teaching effort across roles.

• Headcount and salary levels.

• Use of casual versus permanent staff.

Compare scenarios using:

• Teaching staff cost by program, campus and role.

• Capacity and load indicators for staff.

• Course and program margin when combined with revenue and direct cost models.

• Overall EBITDA and cash impact.

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Check your work

• Teaching loads and class sizes match academic and industrial norms.

• Staff cost outputs reconcile with HR and finance data for recent periods.

• Program level staff cost shares align with internal expectations.

• Scenario outcomes are realistic and useful for academic and finance planning.

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Troubleshooting

Required staff numbers look too high or low

Check workload, class size and working hours per FTE assumptions and ensure they are consistent across programs and roles.

Course profitability shifts unexpectedly when staff assumptions change

Verify that allocation rules are correct and not double counting or omitting staff cost for some programs.

Model is too complex with many small courses

Group smaller courses into course clusters or modules and apply average workloads for planning purposes rather than modelling each course separately.

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Related guides

• Build a Variance Analysis Model

• Build an Acquisition Case vs Base Case

• Staff Variables: what is, rules, what it affects

• Drivers Syntax