and 2016 which covered 35 countries in total (23 in EMGA and 9 in EGRA). A number of OIC

countries participated in EdData II though very few participated in both EGRA and EGMA.

However, many countries have implemented EGRA and EGMA in the context of other national

projects.

In contrast to TIMSS, PIRLS, PISA, EGRA and EGMA, data on literacy rate is readily available for

a wide range of countries though it is only a crude measure of quality of learning outcome. Since

literacy information is regularly collected by OIC member countries, it is available for almost all

countries and provides a broad measure of the learning outcomes of a country’s education

system. However, being self-assessed, this may not align with trends in learning outcomes.

Input-based quality indicators such as are STR and proportion of trained teachers are also

widely available for OIC countries. A school or education system is considered to be high-quality

if it has more resources per child.

Another useful source of information on the input quality is the OECD’s Teaching and Learning

International Survey (TALIS) which contains detailed data on the quality of lower secondary

(mainstream) school teachers and leaders. In each country, about 200 schools were sampled

and in each school, 20 teachers and 1 school leader were interviewed. However, OIC countries

are poorly represented in this survey. In TALIS 2013, the 34 countries and economies covered

included only 2 OICmember states -- Malaysia and UAE.

While the number of countries covered

in TALIS 2018 increased to 50, the share of OIC member states among participating countries

remained largely the same. While Saudi Arabia and Kazakhstan joined United Arab Emirates and

Turkey, Malaysia dropped out after participating in 2008 and 2013 rounds

. Therefore, TALIS

data has not been used for statistical analysis.

Since the OIC and most other developing countries face a multitude of problems in education

service delivery, particularly in terms of access as well as quality, it is difficult to compare

achievements across countries. One solution is to develop a unified measurement framework

that integrates schooling and learning shortfalls. Such integrated framework encompasses a

range of schooling, learning and education deprivation measures (Datt and Wang 2017).

Equally, one can use a composite statistical measure of “access to literacy” and “access to

numeracy” by combining information on educational quantity and educational quality. Some

attempts have been made to combine household data (e.g. Demographic and Health Survey) on

grade completion with survey data (e.g. Southern and Eastern African Consortium for

Monitoring Educational Quality or SACMEQ) on learning outcomes for 11 African countries:

Kenya, Lesotho, Malawi, Mozambique, Namibia, South Africa, Swaziland, Tanzania, Uganda,

Zambia, and Zimbabwe (Spaull and Taylor, 2015). However, such measurement framework and

composite indicators are yet to be fully standardized, tested and adopted by international bodies

There is also a school based survey called the “Snapshot of School Management Effectiveness” (SSME),

developed with support from the USAID. The SSME was designed to capture indicators of effective schools that

have been identified by researchers as important for student learning. The SSME also collects information on

student and household characteristics, basic school inputs (e.g., school infrastructure, pedagogical materials,

teacher and head teacher characteristics), and classroom teaching and learning processes (e.g., instructional

content, student teacher interaction, and assessment techniques). In addition, selected EGRA and EGMA

components are often combinedwith the SSME to produce information on learning outcomes in reading, writing,

and arithmetic (Mulcahy-Dunn, Dick, Crouch, and Newton, 2016).

Turkey only participated in 2008 round; se