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Unpacking Skills at the Cradle: A Machine Learning Approach to Construct Infant Skill Measures

Project description

Investing in Early Childhood Skills

Early childhood development (ECD) interventions designed to improve nutrition and create stimulating environments for young children can measurably improve the quality of life in the longer term when children reach adulthood. Promising research findings of the long-term benefits of ECD interventions have led to calls to develop large-scale programmes that integrate ECD interventions into existing public service infrastructure. Despite this call to action, recent evidence looking at the impact of ECD interventions over a shorter time horizon finds initial programme effects to fade-out. To study this puzzling persistence and fade-out pattern typically observed for ECD interventions, the SKILL project aims to add new evidence of medium-term effects. In addition, the project will use advances in the field of machine learning to develop a new way of measuring infant skills and identify which skills should be invested in by public services.

Field of science

  • /natural sciences/computer and information sciences/artificial intelligence/machine learning

Call for proposal

H2020-MSCA-IF-2018
See other projects for this call

Funding Scheme

MSCA-IF-EF-ST - Standard EF

Coordinator

QUEEN MARY UNIVERSITY OF LONDON
Address
327 Mile End Road
E1 4NS London
United Kingdom
Activity type
Higher or Secondary Education Establishments
EU contribution
€ 212 933,76