Data team conversations: a sensemaking framework of teachers’ collaborative use of student data

Abstract

The ability to use different types of student assessment data is central to current school improvement and a standard expectation of K-12 classroom teachers. While educators are encouraged to engage in different forms of inquiry to learn from students’ data to make instructional and school improvements, limited literature has investigated empirically how they make sense of the information when working collaboratively in grade-level or subject area teams. This paper examines the complex and multi-dimensional process of collaborative sensemaking through qualitative content analysis and Conversation Analysis to better understand teachers’ sensemaking process. Twenty-four transcripts of data-focused team meeting conversations and observational field notes revealed three major dimensions of their sensemaking model including: sources and quality of data, student characteristics and evaluation of instruction. The sensemaking model developed from this study provides a grounded framework for understanding how teachers’ evidence-based data-use in collaborative settings, though it is not intended to be generalisable.