School timetabling

Data preparation before generation

The teacher, subject, room, class, and cycle data to clean before generation starts.

Juho Isola, Smootables founder

What data must be ready before generation? Planners need clean teacher, subject, resource, class, and cycle data. Missing fields, duplicates, and wrong room types often surface later as solver failures, even though the real problem is dirty input data.

Hand-wrangled data can hide double-bookings until too late. One sourced example describes a teacher discovering in September that they are timetabled in two rooms at once, with part-time staff a common cause. These guides cover planner process and decisions, not a product comparison. To evaluate software capabilities, see automatic school timetabling software.

Key takeaways

  • Prepare teacher qualifications, maximum weekly hours, part-time availability, and preferred free periods.
  • Prepare subject requirements such as periods per week, double periods, special rooms, and combinations.
  • Audit rooms, labs, shared equipment, classes, periods per day, cycle length, and breaks.
  • Clean duplicates, missing fields, and wrong room types before they look like solver failures.

What teacher data should be cleaned?

Teacher data needs more than names. Prepare qualifications, maximum weekly hours, part-time availability, and preferred free periods. Part-time availability needs particular care because it is a common cause of double-booking when data is managed by hand.

What data should be gathered?

Gather data in groups so each owner can check their part.

  • Teacher information: qualifications, maximum weekly hours, part-time availability, and preferred free periods
  • Subject requirements: periods per week, double periods, special room needs, and combinations
  • Resource audit: rooms, labs, and shared equipment
  • Class information needed for lesson placement
  • Structural parameters: periods per day, cycle length, and breaks
  • Known data issues such as duplicates, missing fields, and wrong room types

How should planners clean data before a run?

Clean the input before asking the solver to explain placement failures.

  1. Gather teacher, subject, resource, class, and cycle data.
  2. Remove duplicates and fill missing required fields.
  3. Check room types against the lessons that need them.
  4. Review part-time availability against the teaching cycle.
  5. Run static validation for missing values, duplicates, and unstaffed lessons.
  6. Fix the input data before treating the issue as a solver problem.

How do subject and resource data interact?

Subject requirements create demand for rooms and equipment. Double periods, special rooms, and lesson combinations reduce the number of usable periods. If a room type is wrong or missing, the fix is cleaner resource data.

What are signs that data is not ready?

These signs should send the planner back to preparation.

  • The same teacher appears with more than one spelling or identifier
  • Lessons are missing staff, subject, class, or room data
  • Room types do not match the lessons assigned to them
  • Part-time availability is unclear or not mapped to the cycle
  • Periods per day, cycle length, or breaks are still changing
  • Double-bookings are discovered by people rather than validation

Questions planners ask during data preparation

Which teacher data matters?

Qualifications, maximum weekly hours, part-time availability, and preferred free periods all affect where lessons can be placed.

Which subject data should be checked?

Check periods per week, double periods, special room needs, and combinations.

Why do data issues look like solver failures?

A duplicate, missing field, or wrong room type can block placement. The solver reports the block, but the planner must fix the input data.

More guides on this topic

See how Smootables fits your school

Book a walkthrough and we will map Smootables to your planning, workload, and timetabling process.