Factors Related to Data-Based Decision-Making: Examining Experience, Professional Development, and the Mediating Effect of Confidence on Teacher Graph Literacy

In tiered instructional systems (Response to Intervention [RTI]/Multitier System of Supports [MTSS]) that rely on ongoing assessment of students at risk of experiencing academic difficulties, the ability to make informed decisions using student data is critical for student learning. Prior research has demonstrated that, on average, teachers have difficulty analyzing and interpreting student progress-monitoring (PM) data presented graphically (i.e., graph literacy). This study examines the impact that teacher training, experience, and confidence have on teacher graph literacy, using structural equation modeling. Data were gathered from a nationally representative sample of 309 teachers and included latent variables related to their experience (e.g., years teaching, years working with RTI), training (e.g., hours of data-based decision-making [DBDM] professional development), and confidence (e.g., confidence in interpreting data, confidence in determining student response) as well as data-based decision-making skills on a graph literacy assessment. Findings indicate that latent experience and confidence factors predicted graph literacy but training did not. Furthermore, training increased teacher confidence but experience did not. Finally, confidence did not mediate the effect of experience or training on graph literacy.