Classroom Interactional Quality: Profiles of Individual Teacher-Child Relationship and Global Teaching Quality in Head Start

• In Event: Improving Quality and Access to Early Care and Education in the United States

Abstract

According to a developmental systems framework, transactional processes that occur between teachers and children shape children’s development in preschool (Pianta et al., 2016). We bring together what are often considered two separate aspects of preschool classroom quality – dyadic teacher-child relationships and classroom-level teacher support, which reflects the quality of interactions across all children and teachers in the classroom. Evidence linking these two aspects of classroom quality to school readiness is mixed (Burchinal et al., 2010; Hatfield et al, 2016; Howes et al., 2008; Rosada et al., 2020), as they are usually considered separately. Individual children experience these two aspects of classroom environments simultaneously (Mortensen & Barnett, 2014), thus it is critical to examine how these experiences are clustered together at the individual level. Characteristics of the child and teacher likely influence these transactional classroom processes (Sabol et al., 2017). Using a child-centered analysis, we identified: 1) profiles of interactional quality that characterize the experiences of children attending Head Start; and 2) child and teacher characteristics associated with the profiles.

Method. Data are from the Head Start CARES dataset, which is representative of the national Head Start population. The analytic sample included 290 classrooms and 2,114 four-year-old children. Individual teacher-child relationship quality was measured by teacher reports on the Student-Teacher Relationship Scale (Pianta, 2001), which includes two dimensions: Closeness and Conflict. Classroom interactional quality was observed using the Emotional and Instructional Support scales from the CLASS (Pianta et al., 2008). Teachers reported their emotional exhaustion (Maslach et al., 1996), psychological distress (Kessler et al., 2002), education, and years of experience. We used multi-level Latent Profile Analysis (ML-LPA) to estimate profiles of interactional quality at the child level (accounting for nesting at the classroom level), indicated by classroom Emotional and Instructional Support and individual teacher-child Closeness and Conflict.

Results & Conclusions. A six profile solution emerged as the best fitting and most conceptually compelling model (see Figure 1). The profiles differed on Conflict, Emotional Support, and Instructional Support. The largest profile was characterized by positive STRS scores (low conflict, high closeness), moderate Emotional Support Scores, and Low Instructional Support scores. The smallest profiles were those with high CLASS scores despite the emphasis on global interactions in professional development initiatives. Girls were less likely than boys to be in a profile characterized by high conflict and moderate to low CLASS scores (see Table 1). Children in the high conflict profiles had teachers reporting the highest levels of emotional exhaustion and psychological distress; no differences were found by teacher education or experience (see Table 1). Overall, the results indicate considerable heterogeneity in the experiences of individual children that would be lost in independent examinations of individual or global quality, or in variable-centered approaches to measuring classroom interactional quality. We fill a critical gap in understanding the actual experiences of children attending Head Start. The findings highlight the role of teacher wellbeing in efforts to improve classroom quality; other characteristics typically considered to be indicators of quality such as teacher education were similar across profiles.

Authors

• Katherine Paschall, Child Trends -Maryland
  Presenting Author

• Melissa A. Barnett, University of Arizona
  Non-Presenting Author

• Ann M Mastergeorge, Texas Tech University
  Non-Presenting Author

• Maria Belinda Vasquez, University of Arizona
  Non-Presenting Author

• Xiaomin Li, University of Arizona
  Non-Presenting Author

• Victoria Cooper, University of Arizona
  Non-Presenting Author