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Teacher workload

Fair distribution of teacher workload

Equity as an explicit allocation goal: what to balance, what data helps, and why fairness is optimised rather than guaranteed.

Juho Isola, Smootables founder

Fair distribution means equity of allocation is considered explicitly, on the record, rather than assumed. It does not mean identical timetables; it means load and extra working days are shared as well as the school's constraints allow, and that someone checked.

In solver terms, fairness is a quality goal: soft constraints balance load across staff, optimised against everything else rather than guaranteed. That framing matters, because it puts caps and fairness in the right order.

This guide covers the review and the data that makes it possible. The hard ceilings that come first are in teacher overload.

Key takeaways

  • Equity of allocation is an explicit review step, not a by-product of feasibility.
  • Caps are hard and come first; fairness is optimised inside them.
  • The must-teach / can-teach preference scale widens the space fair allocation needs.
  • Balance what people experience: gaps, spread of duties, and extra working days, not just totals.

What fairness covers

The review compares what teachers experience, not only what they total: teaching load against the baseline, extra working days, spread of unpopular slots, and the gap pattern in each week. Equity of allocation of duties is the sourced framing, and duties is deliberately wider than lessons.

Keep the comparison inside like groups where contracts differ; a part-timer's pro-rated week is not unfair against a full-timer's, and the baseline already encodes that.

Data that makes balancing possible

Fairness reviews fail on missing data more than on bad intent.

  • Workload baseline per teacher, pro-rated and team-adjusted
  • The must-teach / can-teach scale from each head of department
  • Extra working days and duties per teacher, this cycle and last
  • Gap and spread patterns from the current draft grid
  • Constraints that legitimately skew allocation (specialisms, sites, patterns)
  • Last cycle's fairness findings and what moved

The fairness review

Run it on the draft, while the grid can still move.

  1. Confirm every hard cap holds; fairness starts inside legality.
  2. Compare load, duties, and extra days across comparable staff.
  3. Read the draft grid for concentrated gaps and unpopular slots.
  4. Where the spread looks skewed, check whether a binding constraint explains it.
  5. Adjust weights or allocation where there is room; record where there is not.
  6. Publish what was balanced and what could not be, with reasons.

How to do this in Smootables: weights that balance the week

Fairness weights start at 0: balancing is a deliberate policy choice, not a default.

Two of the six solver weights carry the fairness goals this guide describes: Teacher Gaps penalises idle periods between lessons, and Teacher Daily Balance spreads a teacher's lessons more evenly across days. Both run 0 to 200 and both start at 0, so fairness optimisation is off until you weight it deliberately, which matches the review-first approach here.

The evidence side comes from the workload panel: per-teacher assigned hours against maximums, per period and across the year, which is the comparison data the review needs. Weights optimise the spread; the panel shows what the spread actually is.

Caps first, fairness second

The order is load-bearing. Hard caps are contractual and binary; fairness is comparative and gradual. Run the fairness review only on drafts where every cap already holds, so a fairness improvement can never be bought with an illegal load.

The weight mechanics behind the balancing are covered in soft constraints, and the gap weight has a recipe walkthrough in minimising teacher gaps. For what fairness means commercially when evaluating tools, the comparison page is teacher workload planning.

Questions planners ask about fair distribution

Everyone is under cap; is the allocation fair?

Not necessarily. Caps bound individual load; fairness compares across people. A department can be fully legal while the same two teachers absorb every late period and cover slot. That comparison is the review this guide describes.

What does "must teach / can teach" change?

It is the sourced preference scale from heads of department, and it changes the solution space. Must-teach entries pin allocation; can-teach entries give the balancer room. More honest can-teach flexibility makes fair allocation easier to find.

What if fairness cannot improve without breaking something?

Then the current spread is the best the constraints allow, and the honest output is that statement plus the constraint that binds. Fairness findings that cannot move become inputs to next cycle's staffing, not silent disappointments.

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