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'''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-informed decision-making|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.
== History ==
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== Implications ==
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The primary implication for school districts is in ensuring high quality and relevant data is gathered and available. Beyond creating systems to gather and share the data, the school district must provide the expertise, in the form of data expert personnel and/or the access to professional development resources to ensure school building leaders are able to access and use the data (Swan and Mazur, 2011).
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While the literature shows the vital importance of the role of the district in setting the stage for data driven instruction, more of the work of connecting student performance to classroom practices happens at the school and classroom level.
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Schools have a major role in establishing the conditions for data-driven instruction to flourish. Heppen, et al. indicate a need for a clear and consistent focus on using data and a data-rich environment to support teachers’ efforts to use data to drive instruction. When the leadership creates and maintains an environment which promotes collaboration and clearly communicates the urgency to improve student learning, teachers feel supported to engage in data use. The additional scaffold of modeling the use of data at the school level increases teachers’ expertise in the use of data (2010).
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Data-driven instruction is created and implemented in the classroom. Teachers have the most direct link between student performance and classroom practices. Through the use of data, teachers can make decisions about what and how to teach including how to use time in class, interventions for students who are not meeting standards, customizing lessons based on real-time information, adapting teaching practice to align to student needs, and making changes to pace, scope and sequence (Hamilton, et al., 2009).
To be able to engage in data-driven instruction, teachers must first develop the knowledge, skills, and dispositions required. Working in a school culture and climate in which data-driven instruction is valued and supported, teachers have the ability to increase student achievement and potentially reduce the achievement gap. Additionally, teachers must have access to learning opportunities or professional development which helps them understand the pedagogical framework and technical skills required to obtain, analyze, and use information about students to make instructional decisions (Furlong-Gordon, 2009).
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A significant new growth in data-driven instruction is in having students shape their lessons using data about their own progress. Younger learners who are able to self-report regarding grades and other assessments can experience high levels of achievement and progress within instruction (Hattie, 2012). The strategies that students use to evaluate their own learning vary in effectiveness. In a meta-analysis, Dunlosky, Rawson, Marsh, Nathan & Willingham ranked ten learning strategies based on the projected impact each would have on achievement:
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