By Dr. Irem Korucu, Dr. Behnaz Ghoraani, Dr. Christy Timm Fulkerson
Keywords: Self-regulation, multimodal sensing, naturalistic assessment, data science, early childhood development
Our Research
Self-regulation develops rapidly during early childhood and plays an important role in children’s academic achievement, social competence, health, and well-being. However, researchers often rely on laboratory-based tasks or adult reports to measure these skills, which may not capture how children regulate their behavior, attention, and emotions during everyday classroom experiences. This project addresses this gap by using wearable sensing technologies, classroom audio, and artificial intelligence (AI)-based methods to study children’s self-regulation in naturalistic preschool settings. By combining behavioral, movement, and linguistic data, the project aims to develop scalable and ecologically valid tools for understanding how everyday classroom experiences support children’s self-regulation, language development, and early learning.
Our Strategy
Students will develop skills in data management, coding and computational analysis, AI and natural language processing, data visualization, behavioral research, critical thinking, scientific communication, interdisciplinary collaboration, and ethical research involving children and AI. You will learn how to:
Collect and organize wearable sensor, classroom audio, and developmental assessment data.
Code and analyze children’s movement and classroom interactions, including linguistic features identified through transcripts.
Develop and apply data science and AI methods for signal processing, natural language processing, visualization, and pattern detection.
Present findings through research posters, presentations, publications, and interdisciplinary team projects.
Our Impact
- Develop innovative, ecologically valid ways to measure preschool children’s self-regulation in real-world classroom settings.
- Identify behavioral and linguistic features of classroom interactions that may support children’s self-regulation, language, and early learning.
- Develop AI and data science tools for analyzing wearable movement data and naturalistic classroom language.
- Create an interdisciplinary research and training environment that prepares students to apply AI and data science to developmental and educational questions.
- Together, these efforts will advance our understanding of children’s self-regulation while developing innovative tools and interdisciplinary student expertise at the intersection of developmental science, education, and technology.
Our Team
Dr. Irem Korucu, Department of Psychology
Dr. Behnaz Ghoraani, Department of Electrical Engineering and Computer Science
Dr. Christy Timm Fulkerson, Department of Communication Sciences and Disorders