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IES Grant

Title: Moving Beyond the Average: Building a Comprehensive Model of Classroom Quality That Incorporates Children's Individual Experiences
Center: NCER Year: 2020
Principal Investigator: Soliday Hong, Sandra Awardee: University of North Carolina, Chapel Hill
Program: Early Learning Programs and Policies      [Program Details]
Award Period: 2 years (08/01/2020 - 07/31/2022) Award Amount: $594,427
Type: Exploration Award Number: R305A200308

Co-Principal Investigator: Sabol, Terri

Purpose: The goal of this project is to explore how individual children's engagement varies within classrooms and the degree to which variation in child engagement is associated with children's outcomes. Specifically, the research team aims to (i) evaluate the degree to which the average engagement of children in academic content and experiences with teachers varies within children and within classrooms, (ii) examine relations to children's outcomes and how this varies by activity setting, and (iii) determine if these associations vary based on children's demographics. Achieving these aims will help promote the understanding on the extent to which children's individual engagement, above and beyond average engagement, may serve as a malleable target for early childhood education interventions.

Project Activities: Researchers will examine the relationship between individual children's engagement within classrooms and child outcomes. A series of secondary data analyses will be completed to unpack these relationships.

Products: The products of this research will include information about the relationship between engagement and child outcomes that can be used to inform early childhood education. The findings from the research will be shared in conference proceedings and peer-reviewed publications.

Structured Abstract

Setting: This study draws from three datasets that represent children's pre-K experiences in varied locations across the country: the National Center for Early Development and Learning (NCEDL) Multi-State Study of Pre-Kindergarten, the State-Wide Early Education Programs Study (SWEEP), and the Los Angeles Exploring Children's Early Learning Settings dataset (LAExCELS.

Sample: All three studies included families from middle- and low-income socioeconomic contexts, as well as racial, ethnic, and linguistic diversity. A total of 701 programs and classrooms and 2,962 children are included in the Multi-State and SWEEP datasets and 163 preschool programs and 204 children are included in the LAExCELS dataset.

Factors: The malleable factor being explored in this project is individual child engagement in academic content and experiences with teachers.

Research Design and Methods: All three studies included direct classroom observations, time-sampling of child engagement and child assessments. Researchers will use secondary data analyses to examine if children's average engagement, variability in engagement, and cross-content interactions in center-based preschool settings predict child outcomes. They will also examine the extent to which this relation differs (using interaction terms) based on dual-language learner status and racial/ethnic groups.

Key Measures: An intensive time-sampling measure of individual and group-level engagement, the Emerging Academics Snapshot, is available in all three datasets. The measure is comprised of academic content codes (e.g., math, oral language, social studies, etc.), children's engagement with teachers (e.g., scaffolded and didactic approaches and social integration), and activity settings (e.g., whole group, free-choice, etc.). Simultaneous time-sampled measurement of the complexity and reciprocity of teacher-child interactions was conducted using the Adult Involvement Scale. Child outcomes include measures of language, literacy, math, social skills, and executive function.

Data Analytic Strategy: Researchers will use hierarchical linear regression models to carry out the planned secondary data analyses.