INSIGHT: Two types of data are considered in data-driven teaching.
- First, teachers analyze data about student performance on specific content standards that were planned, taught, and assessed.
- Second, teachers analyze and apply data about teaching processes with proven track records for improving student learning on the same content standards.
EXAMPLE: Data about student performance are generated by classroom, district, state, national, and international tests and assessments. These data let teachers know how well students performed on content standards taught during instruction (criterion-referenced data). And sometimes they compare student performance against national norms (norm-referenced data). These data do not inform teachers about how to teach -- that happens when data about teaching processes are analyzed.
Data about teaching processes are generated in research studies where teaching processes are independent variables and student learning is the dependent variable. Less rigorous data about teaching are observed by teachers watching other teachers during classroom instruction, viewed by teachers in video episodes of classroom teaching, and shared by teachers reflecting about their teaching practices. Data-driven teaching strives to answer the question:
What teaching practices lead to improved student learning on specific content standards?
INSIGHT: When teachers analyze national and state testing program data with their classroom data, they discover insights that remain hidden from teachers who continue to use traditional analyses.
EXAMPLE: By analyzing data about specific content standards and indicators rather than grade level equivalents, teachers get closer to the underlying causes of student performance. This leads to improvements in teaching practices that are aligned with both state and local standards and student needs.
