Improving the use of learning data for educational planning in sub-Saharan Africa

Written on 01 Sep 22 by Ieva Raudonyte
Student assessment
Use of data



IIEP-UNESCO releases a comparative analysis on how to improve the use of learning data for educational planning in sub-Saharan Africa.

How do countries in sub-Saharan Africa use data from large-scale learning assessments in different phases of the educational planning cycle? What facilitates and impedes the use of the data? How can governments and development partners sustain and improve the use of learning data?

The new IIEP-UNESCO publication compares data from The Gambia, Ghana, Guinea, Namibia, Senegal, and Zambia to answer these questions. It explores the complex dynamics of the use of learning data, examining among other factors, the interactions among the different actors.

This analysis demonstrates that the implementation of learning assessments alone is not sufficient to trigger the use of learning data to inform educational planning. We may be tempted to think that if good quality data are available, there is a good chance that education planners will actively consider them, and that the data inform their activities accordingly. However, the path that leads from data generation to informed planning is much less straightforward, especially in contexts with limited financial and human resources.

The study demonstrates that the use of assessment data in the education planning cycle remains somewhat limited, with data only sporadically informing planning processes. Learning assessment data most often inform the monitoring and evaluation of the education sector plans (ESP) as well as education sector analyses (ESA). However, it is difficult to establish a clear link between learning assessment results and ESP preparation.

This publication discusses multiple barriers and conducive conditions for the use of learning data, with a particular focus on the:

  • importance of developing national capacities to produce and use data,
  • key role of international partners supporting learning assessment systems and the impact of their activities,
  • importance of national leadership and ownership in national assessment systems,
  • often missing collaboration among relevant actors at central and decentralised levels,
  • importance of regulatory frameworks and institutional arrangements for improved use of learning data.

Who should consult this publication?

This publication provides useful guidance for leadership in ministries of education, national assessment teams, and international partners supporting learning assessments including:

For ministry of education leadership

  • Clearly define the goals of assessments and plan for the use of data when developing the assessments.
  • Gear the system towards national leadership and ownership.
  • Commit to the development of national capacities in the production and use of learning data at different administrative levels.

For national assessment teams

  • Facilitate the understanding of learning data for different actors by adapting the content and dissemination of products.
  • Link dissemination activities with constructive feedback loops.
  • Involve other actors in the management of learning assessments and look for synergies with other information sources.
  • Adjust assessment cycles to regular planning and budgeting activities.

For international partners

  • Invest in capacities and transfer expertise, discourage outsourcing of learning assessment activities.
  • Ensure that your support of learning assessments is in line with priorities defined within ESPs and other national strategic documents.
  • Rely as far as possible on existing national assessments rather than creating new ones.

See also:

A three-part online learning series on the use of learning assessment data in sub-Saharan Africa gathers experts from around the world to discuss the following topics:

  1. Use of learning data in the education planning cycle: modalities and obstacles.
  2. Policies and institutional settings for the use of learning assessment data.
  3. Exploring actors’ interactions and the use of learning data.
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