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

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

Motivated by a growing body of research implying large private and social returns to investments in children at an early age, there has been interest among policy makers in cost-effective programs to improve early-life environments. The returns of these programs are likely to be particularly high in low and middle-income countries where it has been estimated that 43% of children under 5 are at risk of developing cognitive delays that can hinder further skill development. Although several correlates of poverty contribute to this risk, a major factor appears to be that children often lack sufficiently stimulating environments during critical periods of brain development. Due to high levels of neuroplasticity, stimulation during this period it is thought to be critical for longer term cognitive and psychosocial development. One approach that has proven effective in improving child development outcomes is interventions that support caregivers to engage in cognitively stimulating activities with their children. Evidence from multiple randomized evaluations across several countries shows that parenting interventions can significantly improve cognitive of young children in the short run. Less well understood is how these short-term cognitive and language skill improvements for children can be sustained over time to translate into improved outcomes at school, work, health and overall wellbeing. In a first project, the research fellow aimed to track back children that were part of a parenting intervention in a rural area of China’s Shaanxi province to study whether there were any sustained improvements in parenting behavior and child outcomes after the initial program ended. The original parenting intervention took place between November 2014 and April 2015 and consisted of weekly home-visits to families with children that were around 3 years old at the start of the intervention. During the weekly home-visits parenting trainers trained parents to interact more effectively with their offspring through cognitively stimulating activities using a structured educational curriculum. This initial parenting intervention was successful and improved parenting practices and cognitive skills of young children for the families enrolled in the program. In order to study whether there were any persistent improvements in the lives of these families our enumerators went back two and half years after the initial program ended to trace all families and collected data on parenting behavior and child outcomes. The study found that the initial parenting intervention has persistent benefits for cognitive skills development of children. Parents of treatment children are found to spend considerably more time with their offspring. Beyond parental investments in the home, the study found that the parenting intervention also changed how parents think about the decision to enroll their children in school. Children from original treatment villages were enrolled earlier and in better quality preschools. The study further found that the changes in parents schooling decision reflect an increase in valuation of school quality relative to other attributes like distance and tuition fees. The second project aimed to develop better measures to track children’s skill development over time as they age. In collaboration with Stanford’s Centre on China’s Economy and Institutions (SCCEI), the research fellow set up a research project titled: “Big Data on Little People: Towards Better Measurement of ECD Data” with as its main aim to develop and validate a shortened version of the Bayley Scales of Infant and Toddler Development (Bayley) test. Although this test is considered the golden standard in the literature when it comes to the measurement of early skills, it is expensive and time consuming to administer. Given the high prevalence of cognitive developmental delay in rural China and other low-income settings, there is a pressing need for shorter assessment tools suitable at identifying cognitive delays on a large scale and in low-income settings.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз