Informations officielles
Avis de l’ICEQC et analyses fondées sur des sources concernant les données probantes, les politiques et les instruments réglementaires en matière d’éducation.
10 résultats affichés sur 10
1061 articles publiés
Quality improvement method
This practice note explains how turning education policy into implementation should be scoped, implemented and verified, with closure dependent on demonstrated effect.
Quality improvement method
Internal review of access and re-engagement measures for out-of-school children and youth — defined outcomes, controlled implementation, residual risk and independent verification.
Quality improvement method
Testing whether improvements in institutional planning for global higher education trends are sustained — baseline, responsible action, outcome evidence and verification.
Quality improvement method
Work on institutional planning in response to higher education trends is structured around a defined baseline, accountable action, outcome evidence and verification before closure.
Quality improvement method
Review of equity monitoring distinguishes completed activity from verified improvement and keeps unresolved action open to further examination.
Quality improvement method
Translating refreshed participation data into controlled improvement — cause analysis, assigned action, evidence of effect and confirmation of sustained change.
Quality improvement method
Corrective action where disaggregated education data remain incomplete — cause analysis, assigned action, evidence of effect and confirmation of sustained change.
Quality improvement method
The article treats quality controls for revised administrative education data as a controlled process requiring clear ownership, outcome evidence and review of residual risk.
Quality improvement method
Using updated education indicators to reset institutional risk priorities — defined outcomes, controlled implementation, residual risk and independent verification.
Quality improvement method
This practice note explains how institutional AI governance should be scoped, implemented and verified, with closure dependent on demonstrated effect.
