The approach to disaggregated education data links diagnosis, accountable implementation and evidence of effect to verification that the result is sustained.
A proper review of disaggregated education data should establish the intended outcome before selecting controls or indicators. For the matter, follow-up should determine whether the change is embedded in ordinary operations and whether it has created new risks or unequal effects. For disaggregated education data, the basis for selection, authority for exceptions and timing of reassessment should remain traceable.
Application to disaggregated education data
In examining disaggregated education data: a documented improvement approach, an average may improve while a material group experiences no improvement or a worse outcome. For disaggregated education data, disaggregation should follow a defined public-interest question and should protect confidentiality where small numbers could identify individuals.
A narrow control over the matter may create false assurance. In the present context, admission without adequate support, completion measures detached from learning and aggregate gains concealing excluded groups may produce acceptable aggregate reporting while individual learners remain exposed to material disadvantage.
For disaggregated education data, readily available material should not define the enquiry if it cannot answer the relevant decision question. For disaggregated education data, the most relevant material is likely to include evaluation of interventions, clearly defined access and completion indicators, records of barriers and support, and learner feedback and complaints.
Controls for disaggregated education data
Review of disaggregated education data should examine results by relevant learner, programme, location and delivery characteristics; compare both levels and rates of change; and test whether observed gaps persist after differences in coverage and prior conditions are considered.
For decisions concerning disaggregated education data, traceability is necessary for accountable decision-making and fair correction. For the corrective action, the responsible body should be able to identify the evidence considered, the judgement made, the person or body authorised to make it and the action that followed.
- Could missing data be unequal?
- Are group definitions stable?
- Are sample sizes adequate?
- Which groups are concealed by the aggregate?
- Which disparity requires action first?
Review of disaggregated education data
For disaggregated education data, education systems should examine not only who enters education, but who can participate effectively, progress and complete with the intended learning outcomes.
Interpretation of the intended improvement should avoid two errors: treating a formal commitment as proof of effect, and treating one adverse case as proof that every part of the system has failed. For the intended improvement, equal treatment does not necessarily produce equitable access or outcomes. In the context of disaggregated education data, different support may be justified where it addresses a documented barrier without changing the required educational outcome.
For decisions concerning disaggregated education data, a clear objective, proportionate evidential basis and account of affected learners are required.