The Balancing Act

Abstract

Purpose The purpose of this paper is to examine how data are used in classroom placement routines. The authors explore educators’ assumptions about the purposes of the classroom placement routine, detailing the ostensive (i.e. structure and template) and performative aspects of the routine itself, and the implications of data use for equity and leadership practices. Design/methodology/approach Using a multi-site case study involving in-depth interviews of teacher and school leaders and observations of meetings, the authors examined the role that data played in classroom placement routines in three elementary schools in the USA. Findings Findings show that educators across schools collected similar types of multi-dimensional data; however, analysis and decision-making processes varied based on their assumptions and goals. Assessing student needs holistically and balancing students across classes based on academic diversity, behavioral or socio-emotional needs, gender and teacher workload were consistent patterns. There was a distinct difference between collecting data and actually using it as a basis of decision making. Research limitations/implications These findings highlight the importance of using in-depth observations to understand data use in schools. Educators’ assumptions and philosophies about classroom placement contributed to the pattern of discussion and decisions made throughout the routines. Delving deeper into how data are used in specific routines and organizational contexts can illuminate how data use is socially constructed and enacted for equity. Practical implications Educators who guide school routines have the power to maintain taken-for-granted assumptions about students, or to create counter-narratives. Originality/value This study provides insights into classroom and student placement processes by emphasizing the social and interactional dimensions of data use as they unfold in practice. It also extends empirical knowledge about the purposes, dimensions, and uses of data-driven decision making models.