Research on Data Use: A Framework and Analysis

The article describes a framework for organizing research on data use and provides insights on factors that influence the success of data use interventions [such as interim assessment].

  • Processes of data use are at the heart for the framework, including the process of noticing the data and patterns in them, interpreting the meaning of the data, and constructing implications for subsequent action. The model posits that these processes are highly influenced by educators’ beliefs, knowledge and motivation, and social interaction and negotiation with colleges.
  • Organizational and political context then influence these processes of data use. Critical aspects of such context include routines for data use, which in turn are influenced by access to data, leadership support, time to analyze and plan subsequent action, norms, and power relations among and between data users at different levels. 
  • Interventions to promote data use, including tools, comprehensive data initiatives and accountability policies are viewed as influencing both context dimensions and data use processes, which in turn influence potential data use outcomes of organizational change, and changes in practice and student learning.   
Content Comments 

Although the piece is primary directed at future research, the framework also is relevant for thinking about the categories of policies and practices that can support the productive use of interim assessment. Particularly important is the authors’ analysis of key features of interventions that affect their implementation. These include designed routines for engaging educators in data use; technological tools for accessing, storing and analyzing data; protocols and skilled facilitation to guide data interactions; professional development on data use mechanics and/or subject matter content; sanctions and rewards for using data, and systems of meaning, including classification systems, theories of action. Data use routines specify who comes together, when, and over what data to support data conversations


The authors note that studies investigating the relationship between interventions and student outcomes consistently show that (a) teachers' social interaction in data use routines and (b) individual teacher knowledge are key levers for promoting student learning. The authors hypothesize that these dimensions are important because they influence teachers' interpretive processes: what teachers notice in the data, how they interpret the data, and how they construct implications for action.