Understandings and Applications of Data-Driven Decision Making by STEM Teacher Leaders

• In Event: Roundtable Session 4
  In Roundtable Session: Teacher Leader Learning Needs

Abstract

This study examined how STEM teacher leaders (TLs) in a professional development course conceptualized and used data-driven decision-making (DDDM). They used data to develop self-study projects (Samaras, 2011) addressing an issue in their school context. The study’s purposes: to examine TLs’ data use, decision-making (Ikemoto & Marsh, 2007), and epistemological stances about data use (Slavit, et al., 2012). This was a qualitative design, using instructional artifacts and interviews. TLs primarily used outcome data to identify their problems. TLs with stronger projects made more and varied decisions using data. The epistemological stance was using data to improve test scores. This study explored the DDDM process for teacher leaders in STEM, which contributes to the literature as little exists in this area.

Authors

• Lori C. Bland, George Mason University

• Toya Jones Frank, George Mason University