Data-driven instruction

Data-driven instruction is an educational approach that relies on information to inform teaching and learning. The idea refers to a method teachers use to improve instruction by looking at the information they have about their students. It takes place within the classroom, compared to data-driven decision making. Data-driven instruction works on two levels. One, it provides teachers the ability to be more responsive to students’ needs, and two, it allows students to be in charge of their own learning. Data-driven instruction can be understood through examination of its history, how it is used in the classroom, its attributes, and examples from teachers using this process.

Prior to the current emphasis on data and accountability in schools, some school leaders and education researchers focused on standards-based reform in education. From the idea of creating standards comes accountability, the idea that schools should report on their ability to meet the designated standards. Late in the last century and in the early 2000’s, an increased emphasis on accountability in public organizations made its way into the realm of education. With the passing of the No Child Left Behind (NCLB) Act in 2001 came laws requiring schools to provide information to the public concerning the quality of education provided to students. To be able to provide such data, states were mandated to create accountability measures and yearly assessments to gauge the effectiveness of schools in meeting those measures. Following NCLB, more recent legislation under the Race to the Top Act further pushed states to use data gathering and reporting to demonstrate school’s ability to meet the demands of the public. Embedded in both NCLB and the Race to the Top Act is an assumption that the collection and use of data can lead to increased student performance.

Data in the classroom is any information that is visible during instruction that could be used to inform teaching and learning. Types of data include quantitative and qualitative data, although quantitative data is most often used for data-driven instruction. Examples of quantitative data include test scores, results on a quiz, and levels of performance on a periodic assessment. Examples of qualitative data include field notes, student work/artifacts, interviews, focus groups, digital pictures, video, reflective journals.

Quantitative and qualitative data is generally captured through two forms of assessments: formative and summative. Formative assessment is the information that is revealed and shared during instruction and is actionable by the teacher or student. Paul Black and Dylan Wiliam offer examples of classroom assessment that is formative in nature, including student observations and discussions, understand pupils’ needs and challenges, and looking at student work. Conversely, summative assessments are designed to determine whether or not a student can transfer their learning to new contexts, as well as for accountability purposes. Formative assessment is the use of information made evident during instruction in order to improve student progress and performance. Summative assessments occur after teaching and learning occurred.

...
Wikipedia