Sensing Technology and Children’s Communication and Language Development & Learning
Children’s early interactions and experiences with caregivers, practitioners, and their friends foster important communication and language skills linked to other essential child outcomes, including social-emotional development and cognition skills. It is important for caregivers and practitioners to learn how to create environments that enrich and enhance young children’s communication and language skills. Sensing technologies can help us gain new insights about communication and language opportunities in everyday contexts (see Figure A) – and importantly, better ensure children are experiencing the 3R’s of Early Learning ™, which are fundamental to their development.
Figure A. Heat map of adult words a Preschooler with a language delay heard over 1 Day
Figure published in Irvin, D.W., Crutchfield, S.A., Greenwood, C.R., Simpson, R.L., Sangwan, A., & Hansen, J.H.L. (2017). Exploring classroom behavioral imaging: Moving closer to effective and data-based early childhood inclusion planning. Advances in Neurodevelopmental Disorders, 1(2), 95-104.
Some of the groundbreaking initiatives currently taking place include using:
(1) advanced key word and phrase spotting algorithms in the classroom, museums, and home to capture the quality of talk and interactions young children with or at-risk for disabilities experience (e.g., Wh-questions or social-emotional words and phrases teachers use), and
(2) a Real Time Location System (RTLS) tool (see Figure B) coupled with speech processing algorithms to fundamentally improve our understanding of how peer social networks influence communication and language development in preschool settings – allowing us to determine if young children who have more interactions with peers with higher language skills, in turn, demonstrate gains in language skills over the course of a school year.
Center Priorities
Engaging Partners
We are partnering with early childhood practitioners and teachers, leaders, and caregivers, locally and nationally, to use sensing technologies to better understand children’s early communication and language learning and development in early care and education centers, home settings, and community settings.
Making an Impact
Sensing technologies have been applied to early childhood contexts across the United States, including Florida, Kansas, Missouri, and Ohio. The use of sensing technologies could increase our application of support for children’s interactions with practitioners, caregivers, and peers across different routines/activities throughout the day.
Impact
- Sensing technology can provide a more granular view of children’s home, community, and school language learning environments (both within and across these contexts).
- These novel tools could one day inform practice and intervention efficacy, as well as help better identify language environments that can support children’s development.
Our Work in Language Development & Learning with Sensing Technology
Collaborative Research: Using Sensing Technology and Automated Speech Recognition to Capture Teacher Language Interactions in Diverse Pre-K Classrooms
This project will use existing sensing technology tools to record teachers’ talk with children and teacher-child proximity to determine when and with whom teacher language interactions occur. This project will benefit society by offering a granular view of the language learning environments of young children, with the eventual goal of sharing concrete, actionable data with teachers about how to support children.
Read the NSF abstract here
Funding Agency: National Science Foundation
Award Number: 2341383
Project Period: 6/13/2024 – 6/30/2027
Award Amount: $109,799
Key Personnel:
- Elizabeth Hadley, PI, University of South Florida
- John Hansen, PI, University of Texas at Dallas
- Dwight Irvin, PI, University of Florida
Collaborative Research: Social-Emotional Analysis of the Language Environment (SEAL): Key Word & Phrase Spotting in Early Childhood Care Settings
This project develops a tool for conducting a 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 and their use of language, including words and phrases, within everyday interactions that tap into social-emotional learning.
Read the NSF abstract here
Funding Agency: National Science Foundation, subaward from the University of Kansas
Award Number: 2235041
Project Period: 6/15/2023 – 7/31/2025
Award Amount: $42,000
Key Personnel:
- Kathryn Bigelow, PI, University of Kansas
- John Hansen, PI, University of Texas at Dallas
- Dwight Irvin, Site PI/Co-PI, University of Florida
Longitudinal Peer Social Networks and Early Language Development: Transforming Understanding of Critical Features of Young Children’s Classroom Experiences
This study is designed to fundamentally improve our understanding of how peer social networks, in particular the language skills of those within a child’s social network, influences language development in preschool settings. This multi-disciplinary team examines language development and peer interactions using longitudinal social network analyses for 30 preschool classrooms and 500 children. State-of-the-art sensing systems involving location trackers and voice-activated recorders provide continuous data on who children interact with and the precise nature of peer-to-peer talk to examine the nature of peer language networks for their influence on language development over time. We examine individual differences in children’s peer language networks, the effects of these networks on growth, and also important moderators of the relations between peer language networks and language growth as a function of child-level characteristics. Results of this study are likely to transform understanding of peer-to-peer dynamics in preschool classrooms and support teachers’ use of practices to leverage the role of peers in shaping early language development.
Funding Agency: The Spencer Foundation, subaward from The Ohio State University
Award Number: 202200007
Project Period: 2/1/2023 – 9/30/2025
Award Amount: $ 112,328
Key Personnel:
- Laura Justice, PI, The Ohio State University
- Dwight Irvin, Site PI/Co-PI, University of Florida
- Daniel Messinger, Co-PI, University of Maimi
- Jay Buzhardt, Co-PI, University of Kansas
Collaborative Research: CSL-MultiAD: Assessing Collaborative STEM Learning through Rich Information Flow based on Multi-Sensor Audio Diarization
This project uses existing wearable technology and advanced speech
recognition/diarization algorithms to monitor child engagement over time during science in the classroom, home, and community-based settings. Existing algorithms are refined to analyze who is speaking and when and identify key words of interest for science topics and learning assessment.
Read the NSF abstract here
Funding Agency: National Science Foundation, Cyberlearning & Future Learning Technologies
Award Number: 2330366
Project Period: 2/1/2023 – 7/1/2025
Award Amount: $112,255
Key Personnel:
- John Hansen, PI, University of Texas at Dallas
- Dwight Irvin, PI, University of Florida
- Jay Buzhardt, Co-PI, University of Kansas