What we are learning | What we are reading: Data for Decisions – Can technology be used to improve data collection, analysis, and planning to improve learning outcomes?


This is Part 2 of a six-part blog series inspired by discussions with partners working in the EdTech space who recognize they do not have the information they need to make informed decisions and recommendations. So, we put together a series summarising what we have learned so far in each of our focus areas: Data for Decisions, Digital Personalised Learning (DPL), Girls’ Education & Technology, Participation & Messaging, and Teacher Continuous Professional Development. We provide some key lessons, an outline of our ongoing work, and some additional resources (written by us and by our partners) for readers who want to delve deeper. This blog focuses on Data for Decisions. 

Right from the start, we have to dispense with the idea that the provision of education data automatically translates into more equitable and effective decisions. It does not. 

In reality, a perennial gap has emerged between the supply (too much, too fragmented, too little) of education data and the use of this information in decisions. This is especially compounded in low- and middle-income countries. This gap can and does lead to inefficiencies in resource allocation and service delivery, and disproportionately impacts the most marginalised learners.

Our goal is to change that. We specifically work with governments and development partners to strengthen data-driven decision-making. Recent investments in technology have increased the potential for more regular and reliable data collection, robust data analysis, and curated data visualisation in low- and middle-income countries. We must build a bridge across the gap between this kind of more/better data and decision making. To do that, we have to get the right data story to the right people at the right time. If realised, this opportunity can improve all aspects of policy and planning at the district, regional, national, and international levels.

To this end, we have developed a portfolio of research, technical assistance, and global public goods. Examples of our work include:

What are some of the lessons from our work?
  • The provision of large volumes of education data — and, especially, fragmented or duplicated education data — can hinder decision-making as much as a lack of education data
  • Education management information systems should provide a single source of truth with no repetition of data points (e.g., duplicated school identification numbers, multiple records for the same teacher or student)
  • Dynamic school-led data management can lead to a virtuous cycle of learning and improvement if school leaders accept the short-term costs of a higher administrative workload
  • The sustainability of school-led data management systems depends, in part, on government support for the decisions of school leaders
  • The development of education data systems must go beyond technology to focus on issues such as creating a culture of data-driven decision-making and building capacity across the entire system 
What should you read next?
  1. What Matters Most for Education Management Information Systems: A Framework Paper (World Bank)
  2. Advancing Data-Driven Decision-Making for School Improvement: Findings from the One Tablet Per School User Testing Programme in Sierra Leone (EdTech Hub with the Ministry of Basic and Senior Secondary Education in Sierra Leone and Leh Wi Lan)
  3. 6 key insights into the data and information education leaders want most (Brookings)
  4. Meeting the data challenge in education (Global Partnership for Education)
  5. Use of learning assessment data in education policymaking (UNESCO)
  6. Lessons learnt from education data mapping in Africa: Workshop summary and synthesis (EdTech Hub with Unlocking Data, Zizi Afrique and ESSA)
  7. Learning from experience: A post-Covid-19 data architecture for a resilient education data ecosystem in Sierra Leone (Fab Inc with EdTech Hub)
  8. Mapping the education data ecosystem in Sierra Leone (EdTech Hub with the Ministry of Basic and Senior Secondary Education, Sierra Leone)
  9. Using EdTech to Support Learning Remotely in the Early Years. Rapid Literature Review of Evidence from the Global Response to Covid-19. (EdTech Hub Helpdesk Request)
  10. Monitoring Distance Education: A Brief to Support Decision-Making in Bangladesh and Other Low- and Lower-Middle-Income Countries (EdTech Hub)

Coming Next: Our reading recommendations for one of our five focus areas – Digital Personalised Learning. And you can always find more in our Evidence Library.

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