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Making Education Count for Development

Data Collection and Availability in Six PISA for Development Countries

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This report reviews the collection, availability and quality of system-level data and metadata on education from countries participating in the PISA for Development project: Cambodia, Ecuador, Guatemala, Paraguay, Senegal and Zambia. PISA for Development aims to increase low income countries’ use of PISA assessments for monitoring progress towards national goals for improving education and for analysing the factors associated with student learning outcomes, particularly among poor and marginalised populations. The project also helps track progress towards the international education targets defined in the Education 2030 Framework for Action, which the international community adopted in 2015 as the strategy for achieving the Education Sustainable Development Goal (SDG).

The report suggests technically sound and viable options for improving data quality, completeness and international comparability in the six countries that are reviewed. It also provides insights into overcoming some of the challenges common to countries that participate in PISA for Development and to other middle income and low income countries.

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Methodology and tools for international education surveys

This chapter describes the methodology and tools used for international education surveys. The UIS developed an assessment tool better suited to the PISA for Development (PISA-D) context than the two frameworks generally used for the evaluation of countries’ education management information systems: the System approach for Better Education Results Education Management Information System (SABER-EMIS) and the Data Quality Assessment Framework (DQAF). The modified tool draws from the SABER and DQAF evaluation and scorings systems, but is adapted to metadata and aggregated data when necessary. The tool includes a concise rubric that evaluates 1) the quality of data based on three major components – coverage, time sensitivity and ownership of information; and 2) availability of data, which assesses the data’s transparency and openness via three types of user – internal users, external users and international organisations. For each component, the chapter details the status of the rubric at three levels of grading: latent, emerging and advanced.

English

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