5 features of a monitoring, evaluation and learning system geared towards equity in education

A great accomplishment arising from the era following 1990’s World Declaration on Education for All in Jomtein, Thailand, is recognition of the gender gap in education, and the mandate for sex-disaggregated reporting from funders and multilateral agencies. Data on the dramatic access and outcome disparities between male and female learners created demand for programming focused on gender inequity. Twenty-seven years after Jomtien, there is a substantial amount of evidence on solutions that build gender equity in education, and on how education systems need to adapt to help girls and boys overcome gender-related institutional barriers.

The Education Equity Research Initiative, a collaborative partnership led by FHI 360 and Save the Children, seeks to create the same dynamic around other aspects of inequity in education – be it poverty, ethnic or racial disadvantage, migration status, or disability. As a community, we create frameworks, modules, and tools, so that little by little the reams of data that get produced include a consistent set of questions around equity of program participation and equity of outcomes.

An equity-oriented MEL system must not just produce data on scope and coverage, but allow for depth of understanding around who benefits and doesn’t, and offer actionable information on what to do about it.
My previous blog post speaks to the need to be deliberate in building a monitoring, evaluation and learning system that generates the data and analysis that help answer the question: are we improving education equity through our programming and policy? But how do we operationalize equity in education, in the context of education development programming? In Mainstreaming Equity in Education, a paper commissioned by the International Education Funders Group, our collaborative begins by recognizing that an equity-oriented monitoring, evaluation and learning (MEL) system around a program or set of interventions has an essential purpose not just to produce data on scope and coverage, but to allow for depth of understanding around who benefits and doesn’t, and offer actionable information on what to do about it. Here I outline five features that describe such a learning system.

  1. Consistency in inclusion of key dimensions. A commitment to equity means a commitment to consistency in measuring equity-relevant demographic and social characteristics. The Education Equity Research Initiative recommends that the “big 5” dimensions be included in all micro-level surveys: gender, ethnicity, socioeconomic status, disability, and migration status. The Practical Recommendations for Equity Analysis provide the modules that can be included in questionnaires to ensure comparability of such information. Disability modules are currently being piloted and will likely be adapted, but the set of tools developed by UNICEF will provide a strong foundation for making progress on this.
  2. Strong instruments for outcome measurement. An MEL system is only as good as the quality of the data it collects. For each outcome of interest, there should be a validated instrument that measures change with a high degree of reliability. A growing number of tools are available for measuring learning outcomes, and a range of instruments is now being developed for noncognitive skills. Beyond student learning assessment, instruments can include surveys that track employment or other life outcomes beyond schooling. A valid instrument with proven scales would make it possible to reliably separate group-level values and identify disparities, which is necessary for equity analysis.
  3. Comparison points. Even when a program is targeted at a subgroup characterized by equity dimensions, progress is difficult to gauge when no comparison point exists for groups that are not marked by the same characteristics. For some outcomes, population-based averages, or data from other program and policy monitoring may provide a valid comparison point. For others, having a control or comparison point within the program design will strengthen the quality of the evidence.
  4. Adequately powered samples. Equity analysis involves breaking down the data on the target populations in order to see disparities along a range of dimensions and subgroups. While not all dimensions of equity will always require separate descriptive and impact analysis, it is likely that intersections of such dimensions will become the focus of a program or policy intervention. We offer some guidance in the Practical Recommendations, and have begun work on an addendum that will further illustrate the approach to sampling for equity analysis.
  5. Longitudinal designs. As we note above, a crucial element in building equity is changing the trajectories that are predetermined by the constraints and barriers that individuals face due to their social or demographic characteristics. Designs that track learners over time and trace the magnitude of how a predicted path changes, as a result of a policy or program, allow program implementers and funders to see whether the program builds equity or diminishes it.

Setting up an MEL system that is geared towards equity, and generates evidence that helps advance the field, can seem a daunting proposition, particularly for smaller programs and initiatives. However, it can be a sequential process gradually building from the most essential low-hanging fruit – changes to existing tools and instruments. Even if large samples or longitudinal designs are beyond the reach of a program, consistently including key equity dimensions and categories of analysis in the instruments can be achieved with small, low-cost modifications that can make it possible for the program data to be pooled into larger analyses.

Generating high-quality data on education inputs, interventions, policies, and their impact on outcomes across equity dimensions allows the field as a whole to move closer to fulfilling the promise of achieving the SDGs.

In sum, while the above features will in many cases demand greater resources for MEL, it is important to maintain a field-building and evidence-building perspective on this process. Generating high-quality data on education inputs, interventions, policies, and their impact on outcomes across equity dimensions allows the field as a whole to move closer to fulfilling the promise of achieving the Sustainable Development Goals (SDGs).

Read more here: http://www.educationequity2030.org/resources-2/2017/3/8/practical-recommendations-for-equity-analysis-in-education

Photo credit: FHI 360

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