Using machine learning to model early parent-child communication

Abdellah Fourtassi

Following the seminal work of Piaget, the traditional approach in cognitive development has focused on studying the structure of children’s knowledge in controlled situations (e.g., laboratory experiments). While this approach allows for precise inference about how children behave in certain tasks, it cannot provide an understanding of the social context within which knowledge emerges. In fact, it has been known, at least since Vygotsky, that children acquire new skills and concepts with the help of more competent members of society who scaffold the children’s learning, allowing them to attain skills that are just beyond their current abilities. In fact, much of the children’s abstract knowledge about the world, it has been argued, is mediated through discussions with their parents/caregivers. In this talk, I explain how new advances in Data Science, especially in Natural Language Processing (NLP), allow us to account for what and how information is talked about with children by their parents through language.

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