Introduction and Overview
Other large-scale assessments are more difficult to classify because they do not fit neatly into any of the conventional categories (see annex 1A). Two examples are the Early Grade Reading Assessment (EGRA) and the Early Grade Mathematics Assessment (EGMA).1 EGRA was developed in 2006 as a simple, low-cost measure of pre- and early reading skills that governments, international development organizations, donors, or civil society could use in low-resource contexts (Gove and Cvelich 2011). EGMA followed a few years later as a measure of early mathematical or numeracy skills. EGRA tests letter recognition, phonemic awareness, ability to read simple words, and listening comprehension. EGMA tests number recognition, comparisons, and ordering sets of objects. Together, these two tools, and variations on them, have been administered in more than 50 countries and almost 100 languages. The EGRA toolkit provides a template that can be customized for a particular country using its alphabet, language, and texts. The EGMA toolkit is similar in nature. EGRA and EGMA are meant to be administered orally to children (usually enrolled in grades 1 to 3) in a one-to-one setting. This individual administration can make the data collection exercise more time intensive than a typical large-scale assessment, which would be administered using paper and pencil or digital devices in a group setting. In addition, unlike most large-scale assessments, the results from an EGRA or EGMA exercise are typically reported in terms of student performance on individual items or tasks rather than as an overall score. EGRA and EGMA have functioned best to generate data quickly on reading and math levels in low-resource environments and as baseline and follow-up tools for impact evaluations of targeted interventions to improve early reading and mathematics. They also can be a starting point on the journey to creating more-standardized, more-representative, large-scale assessment programs aligned with a defined curriculum. Another popular type of assessment is citizen-led assessment (CLA), which emerged in India in 2005 as a way to raise public awareness of low learning levels and to increase bottom-up accountability and action to improve the quality of education and learning.2 Thousands of volunteers traveled to rural districts and administered simple reading and math tests to children at home. The dismal results helped stimulate debate and prioritize learning in national policy in India. From this beginning, CLAs quickly expanded around the globe and are now also used in Kenya, Mali, Senegal, Tanzania, and Uganda, among others. Many government-led, large-scale assessment programs could learn useful lessons from CLAs in terms of how to work effectively with the media to accessibly disseminate assessment results to the public. In general, CLAs are administered in people’s homes, rather than in schools. As a result, they capture the learning levels not only of children attending school but also of those who have never enrolled or have dropped out. This approach is crucial to ensuring that no child is written off, particularly in countries with high dropout rates or where population subgroups are not enrolled in the education system at the same rate 1 https://www.eddataglobal.org/. 2 http://www.asercentre.org/.
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