In this book, leading researchers and implementation experts from an array of disciplines provide evidencebased, cost-effective, and actionable strategies for delivering quality early childhood education (ECE) at scale in low- and middle-income countries (LMICs).
Over the past decade, neuroscientists, developmental and cognitive psychologists, economists, and education researchers have amassed evidence to inform ECE program design. Yet much of this evidence has not been readily accessible to policy makers and practitioners, and potential synergies from crossdisciplinary considerations have not been realized.
Quality Early Learning: Nurturing Children's Potential
synthesizes the evidence across disciplines and charts a forward course for quality ECE. The volume includes:
- Overview, From Evidence to Effective Policies: How to Invest in Early Childhood Education to Nurture Children's Potential, by Magdalena Bendini, Amanda E. Devercelli, Elaine Ding, Melissa Kelly, and Adelle Pushparatnam
- Chapter 1, Learning in the Early Years, by Elizabeth Spelke and Kristin Shutts
- Chapter 2, Pedagogy and Curricula Content: Building Foundational Skills and Knowledge,by David Whitebread and Yasmin Sitabkhan
Chapter 3, Building an Effective Early Childhood Education Workforce, by Nirmala Rao, Emma Pearson, Benjamin Piper, and Carrie Lau
Chapter 4, Creating Early Childhood Education Environments That Promote Early Learning,by Cynthia Adlerstein and Alejandra Cortázar
Chapter 5, The Role of Management, Leadership, and Monitoring in Producing Quality Learning Outcomes in Early Childhood Education, by Iram Siraj, Violeta Arancibia, and Juan Barón
Chapter 6, Toward Quality Early Learning: Systems for Success, by Sharon Lynn Kagan and Caitlin M. Dermody
This volume covers the latest evidence on how young children learn most effectively and on how ECE programs can foster children's natural ability and motivation to learn. The volume offers guidance for policy makers on policy design and implementation, including what elements of ECE to prioritize in resource- and capacity-constrained settings in LMICs.