COLLABORATIVE RESEARCH: Social-Emotional Analysis of the Language Environment (SEAL): Key Word & Phrase Spotting in Early Childhood Care Settings

o This project is jointly funded by the Developmental Science Program and the Established Program to Stimulate Competitive Research (EPSCoR).

Children’s success in school and in life depends on whether they have learned certain social and emotional skills in their early years. To build teachers’ capacity to promote social emotional learning, there is a need for tools that provide data-based feedback to teachers on talk that focuses on social-emotional learning. This project creates such a tool, the Social Emotional Analysis of the Language Environment (SEAL).

SEAL uses advanced speech processing algorithms to automatically capture a key element of teacher practice in early childhood education classrooms known to support social emotional learning: their use of language, and specifically, words and phrases within everyday interactions that tap into social emotional learning. This automatic measurement of teachers’ social-emotional talk from audio recordings in toddler classrooms could represent a transformative approach to help teachers better support children’s social-emotional development (e.g., understanding, managing, and expressing emotions).

In turn, such a tool could better ensure young children gain the skills that ensure they are ready for school, relationships, and life.

Project activities to demonstrate proof-of-concept for SEAL include: (1) developing a social and emotional word and phrase bank, informed by a national survey and focus groups with early childhood professionals and input from an expert advisory panel; (2) exploring teacher audio data to refine existing speech processing algorithms to detect classroom adult social emotional key words and phrases; and (3) examining initial validity of the word and phrase bank by comparing human transcription to a baseline algorithm to automatically detected speech data. This probe study serves to create evidence that a future fully formulated SEAL solution could yield data that will be easy to access and interpret and can be used by individual teachers, coaches, and early childhood programs to improve practices aimed at promoting child social emotional outcomes.

Eventually, like an automated monitor for counting steps, we envision SEAL could be used by teachers themselves to monitor classroom social emotional talk and build capacity to improve social emotional competence in young children.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.