Data for Decisions
What is Data for Decisions? Technology to advance data use and decision-making.
The challenge facing the education sector: Education systems often lack data, or struggle to apply what it tells them, when making choices about how to allocate resources or developing policy. This contributes to inefficiencies which tend to affect the most marginalised students and their learning disproportionately.
And so we ask: How can technology be used to improve data collection, analysis and planning to improve learning outcomes?
Our goal
Enhance data-driven decision-making
Improving educational outcomes is hampered by poor availability and use of data for effective decision-making. The spread of technology in low- and middle-income countries increases the potential for educational data to be collected in a reliable way, and can be used to improve all aspects of education programmes and policy at the district, regional, national and international levels.
The ways in which technology can be used to enhance data-driven decision-making will be applied as both a cross-cutting emphasis in the Hub’s work and as an explicit focus within specific studies.
Latest evidence
Topic Area Readouts
- Data for Decisions Readout
- 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?
Working Papers
EdTech Hub, Leh Wi Lan, the MBSSE and the TSC came together to test a proposed data management model in two phases. The first phase focused on 40 schools in Freetown while the second phase focused on 40 schools in Port Loko.
Notably, participating schools already had experience of using tablet based data systems through the Leh Wi Lan programme. This process aimed to generate insights on how and why school leaders engage with tablet-based data management systems to inform
the development of tools that better meet their needs.
This paper presents detailed analysis from the first phase of user testing for the MBSSE and TSC to use to refine the design of the programme. When conducting this analysis, we used a sequential mixed-methods approach to
understand the experiences of participating school leaders. The paper begins with background information on data-driven decision-making and the One Tablet Per School programme.
The subsequent sections outline the study’s methodological approach and summarise our findings before ending with recommendations for the next iteration of programme delivery
In September 2020, Sierra Leone’s Ministry of Basic and Senior Secondary Education (MBSSE) approached EdTech Hub to seek technical support with the design of the One Tablet Per School programme. A core component of the programme focuses on collecting dynamic school-level data — teacher registration, student enrolment, teacher and student attendance, Covid-19 cases — to inform programming and policy decisions.
In this context, EdTech Hub embedded a team member within the MBSSE’s Delivery Unit to support the development of this data system. The MBSSE established the Delivery Unit. in August 2020 to strengthen the = government’s implementation capacity and to monitor progress toward education policy priorities.
This report provides an initial self-assessment of the impact of our technical support after three months. EdTech Hub conducts these assessments to see how we can better support our partners and improve our overall approach to technical support. When preparing the report, we used outcome harvesting to identify observable and significant contributions that EdTech Hub has made to the development of the One Tablet Per School programme. The report begins with background information on education data systems
and the One Tablet Per School programme. The following sections provide an overview of our methodological approach, outline key findings from the assessment and discuss the impact of EdTech Hub’s technical support. The report concludes with practical considerations for the implementation of education data systems.
Reports
The aim of the workshop which took place on 30 June 2021 was to showcase experiences of mapping education data and discuss lessons learnt. After an explanation of the Unlocking Data community of practice, the event kicked off with three presentations:
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- John Mugo led a panel discussion, providing insights on catalysing an evidence
ecosystem for technical and vocational education and training (TVET) in Kenya. - Esme Kadzamira shared experiences from the implementation of the Malawi Open Data
for Education Systems Analysis (MODESA) project. - Laté Lawson gave an overview of our methodology for mapping education data sets.
- John Mugo led a panel discussion, providing insights on catalysing an evidence
After the presentations, there was a Q&A session followed by breakout group sessions to reflect on the presentations as well as discuss the current data initiatives that participants are involved in and what they are learning from them. This document provides a summary of the presentations as well as a synthesis of the discussions in the breakout groups.
Delivered in partnership with: Unlocking Data, Zizi and ESSA.
This study focuses on Korail, which is one of the largest slums in Dhaka and is located between the two affluent residential areas of Gulshan and Banani. We surveyed 476 students in Years 6 to 10, who were enrolled in secondary school prior to the Covid-19 pandemic. The survey was designed to obtain a picture regarding the state of technology ownership among Korail households, and the extent to which devices were utilised to help preserve learning continuity during the pandemic. The survey was also intended to probe what factors influenced student access to and use of technology.
This study sought to answer two research questions:
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- What is the current state of children’s educational technological devices in Korail slum to participate in education
- What are some of the underlying factors influencing the access children have to these devices?
Delivered in partnership with: Beyond Peace
This study explores the potential impact of interactive audio content for students and teachers delivered via Interactive Voice Response (IVR) in Ghana following the reopening of schools.
The content for the lessons was drawn from the Rising On Air (ROA) audio library, a 20-week programme developed by Rising Academies to support student learning over the radio during Covid-19 pandemic-related school closures.
Rising Academies’ 30 low-cost private primary schools, known as Omega schools, were included in a randomised controlled trial. Half of the schools were randomised to receive the student intervention and the other half to receive the teacher intervention. Of the total sample of 1,359 students, 719 students in Grades 4, 5 and 6 received daily audio lessons that focused on foundational numeracy skills. Of the total sample of 333 teachers, 160 teachers received weekly professional development sessions focused on the instruction of foundational reading. In the student intervention, no significant effect was found on students’ math skills and although the majority of students reported liking the intervention and wanting it to continue, engagement was a significant challenge.
Results from the teacher intervention indicated an improvement in teachers’ understanding of phonemic awareness, phonics, and morphology. Teachers’ beliefs about their ability to improve student learning in the areas of reading and engagement also increased, but the potential impact on student outcomes was not measured. Differences between the student and teacher interventions suggest some important considerations for future interventions delivered via IVR and highlight some of the challenges as well as potential opportunities for more effective low-tech solutions.
Delivered in partnership with: Rising Academy Network
Helpdesk Responses
This topic brief examines the literature on technology-based, remote approaches to supporting learning in the early years for children from birth to age five, identifying promising practices for using EdTech in early childhood education (ECE) in low- and middle-income countries (LMICs).
It draws on the nurturing care framework, Principles for Digital Development, and effective pedagogical practices for ECE.
This list curates resources on the use of EdTech to support the effective monitoring of educational outcomes, such as learning, reporting, and attendance.
Resources shared are both tools and initiatives that can be adapted to support effective educational monitoring.
