An increasing number of schools and districts are seeking to focus on data-driven instruction to inform and improve their teachers’ practice. Although schools have become quite adept at gathering data in the last ten years, many are still not where they need to be in terms of effectively interpreting the data and utilizing it in teaching, particularly to differentiate instruction. Data-driven instruction requires a paradigm shift from focusing on process to pedagogy focused on achieving student results. Teachers and support staff who are new to focused analysis of student outcomes data are not prepared to adopt data-driven practices without extensive professional development. However, schools and districts who have instituted effective data-driven programs are seeing significant improvements in student achievement when they incorporate a data-driven instruction model.
So, what is data-driven instruction?
Data-driven instruction is based on data-driven decision making in the classroom. There are five major components of data-driven teaching: reliable baseline data, S.M.A.R.T. instructional goals, regular and frequent formative assessment, professional learning communities (PLCs), and targeted interventions. Once these components are in place, teachers can make informed instructional choices that are specifically directed toward improving student achievement.
What kind of data is best?
No single assessment is the best one. A variety of tests can and should be used. However, all tests should be statistically reliable and valid. Further, they must be aligned to the standards and skills that students are expected to learn. Thus, all tests (state summative assessments, formative assessments, and unit tests) must assess specific standards or subsets of standards.
What to do with the data?
Each teacher must develop a class overview that summarizes every student’s results in terms of the applicable standards for the course. The overview must have specific information about grade-level expectations (GLEs) and specific examples from test items that students answered incorrectly. The purpose of the overview is to identify strands in which students are proficient and in which they had difficulty.
Armed with the overview, teachers can target areas where students are struggling. Content areas are sorted into three categories: strengths, challenges, and critical needs. With these categories, teachers can drill down and select instruction that prioritizes specific needs, beginning with critical needs first.
This overview provides a cursory outline of a few of the principles of data-driven instruction and the practices that inform it. In order to implement this type of instruction in schools, teachers and their supervisors need extensive, ongoing professional development to prepare them for such a dramatic change to their practice.
Data-driven instruction is inherently both results-oriented and reflective. Teachers must continuously ask, “What evidence do I have that my instruction is working?” Data-driven teaching is predicated on the premise that if teachers constantly analyze what they do and adjust their practice in response, student learning will improve. By focusing initially on manageable, rapid improvements and then building upon those improvements through an ongoing process of reflection about classroom instruction and student learning outcomes, teachers who use data effectively to drive their instruction will make significant impacts on their students’ achievement. Further, when teachers participate in PLCs and collaboratively identify and implement strategic, effective instructional interventions, their students will thrive.
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