Data-Driven Teaching Practices: Rhetoric and Reality
Much has already been written on the U.S. obsession with standardized test scores. Add to the obsession the passionate belief that policymakers who gather, digest, and use a vast array of numbers can reshape teaching practices.
I refer to data-driven instruction–a way of making teaching less subjective, more objective, less experience-based, more scientific. Ultimately, a reform that will make teaching systematic and effective. Standardized test scores, dropout figures, percentages of non-native speakers proficient in English–are collected, disaggregated by ethnicity and school grade, and analyzed. Then with access to data warehouses, staff can obtain electronic packets of student performance data that can be used to make instructional decisions to increase academic performance. Data-driven instruction, advocates say, is scientific and consistent with how successful businesses have used data for decades in making decisions that increased their productivity.
Not a new idea. Teachers had always assessed learning informally before state- and district-designed tests. Teachers accumulated information from pop quizzes, class discussions, observing students in pairs and small groups, and individual conferences. Based on these data, teachers revised lessons. Teachers leaned heavily on their experience with students and the incremental learning they had accumulated from teaching 180 days, year after year.
In the 1990s and, especially after No Child Left Behind became law, the electronic gathering of data, disaggregating information by groups and individuals, and then applying lessons learned from the analysis to teaching became a top Data-Driven Teaching Practices: Rhetoric and Reality | Larry Cuban on School Reform and Classroom Practice:
