Abstract
This presentation explores the fine-grained processes of adult-child language interactions by leveraging advanced machine learning and natural language processing algorithms. Previous research has largely focused on the frequency of children’s exposure to language features, such as lexical diversity and syntactic complexity, using audio and video samples to predict language development. However, these approaches often overlook the complex semantic relationships and the influence of contextual and individual variability. By employing techniques such as semantic network analysis, probabilistic topic modeling, and sentiment analysis, this study aims to provide a more nuanced understanding of these interactions. The findings indicate that AI-driven methods can significantly enhance our comprehension of children’s language learning environments. They support the development of targeted interventions that adapt strategies to specific activity contexts and individual child characteristics, ultimately advocating for the effective fostering of high-quality language input to meet the diverse needs of young learners.
Speaker
Prof. Wonkyung Jang
Prof. Somin Park
About the Speaker
Dr. Wonkyung “Won” Jang, an assistant professor in the Department of Instructional Leadership and Academic Curriculum at The University of Oklahoma, specializes in early care and education (ECE) and data science. He explores how ECE stakeholders can embrace the power of “Big Data” to help children overcome developmental challenges, provide the simple joy of play, promote teacher well-being, and tackle pressing social justice challenges. Jang is particularly interested in uncovering how variations in individual children’s physical, social, and linguistic environments shape language and literacy development. He aims to apply this understanding to personalize children’s learning experiences within inclusive classroom settings, tailoring evidence-based practices to fulfill the local needs and circumstances of young learners. Employing machine learning and artificial intelligence techniques, Jang seeks to better capture individual differences in language development among children with diverse developmental, cultural, linguistic, and educational needs. Jang received his doctoral degree in Education from the University of North Carolina at Chapel Hill (UNC) in 2022. He earned a Master of Science degree in Statistics and a Graduate Certificate in Computational Linguistics en route to his Ph.D. For the past decade, Jang has worked as a teacher and a university-based researcher focused on ECE. He is the Principal Investigator of the AI Lab for Transforming Education for Young Children, leading federal and internal grants, and is a PI/Co-PI/Co-I of grants totaling $2,900,000.
Dr. Somin Park is an assistant professor at the Faculty of Education at the University of Hong Kong. Her research focuses on early language and literacy development during early childhood years. Her work investigates evidence-based practices for supporting early language and literacy development for monolingual- and emergent bilingual- children. Her work also examine what teacher characteristics and experiences support quality early language and literacy practices in the classrooms.
Time
9:30 - 11:00 AM
Location
ZOOM (https://hku.zoom.us/meeting/register/tJArc--sqTgqHd1c3Pd_3eWP0srrbxoeMiBt)
Chair
Prof. Lianjiang Jiang
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