Teacher Tip / Data-Driven Instruction

The field of education has always had the capacity to produce a tremendous amount of data, perhaps more than any other industry. In fact, teachers are bombarded with data on a daily basis, from attendance records to grades to assessment results. Whatever the case may be, teachers are expected to have the ability to successfully and effectively analyze all of the data and then synthesize that information when making instructional decisions based on the individual needs of their students. It’s a lot for anyone to handle, let alone a busy teacher!

What is data-driven instruction?

Also referred to as data-driven instruction and inquiry (DDI), when applied to the field of education, DDI is at its core a philosophy that many schools and districts use to improve student learning throughout the year. It is a thoughtful decision-making process that is designed to improve student learning. When approached precisely and systematically, DDI should focus on one simple question — are our students learning? With the recent push of standards-based instruction, students are being asked to closely read more rigorous and complex texts in order to explain their reasoning using evidence. DDI is no different. It asks teachers to do exactly the same thing, only they need to take the process one step further and use the evidence to drive their instruction. By doing so, a powerful ideal is created that serves to foster academic excellence across the board, for students and teachers alike.

What does the DDI process involve?

The data-driven decision-making process involves patterns — lots and lots of patterns. While the process is iterative in nature, it goes through a pattern whereby data is collected and analyzed. Teachers then use the results to make informed instructional decisions. These decisions help them better plan their lessons so that the needs of all learners are met. When instruction is complete, teachers need to reflect on how well (or poorly) the process went and what, if anything, can be done to improve instruction the next time. When this process is done well, it has the capacity to have a tremendous impact on students’ academic, emotional, and social gains and successes. Remember — using data to make pedagogical decisions leads to responsive and differentiated instruction that reaches all learners! Take a look at the following graphic. It shows the process in detail, as well as highlights its iterative nature.

Why is DDI important to teaching and learning?

At the heart of teacher effectiveness is the teacher’s ability to “dig down” into each of their student’s strengths and weaknesses. One of the best ways to accomplish this feat is to use data, lots of data. All of the research points to the fact that using data to make instructional decisions can lead to greater academic gains for students. Data-driven instruction has long been a focus for many schools and districts across the nation, particularly since teacher accountability has been recently connected to student achievement. The challenge of connecting data to appropriate instructional strategies and resources becomes less problematic once teachers understand how to effectively and efficiently use the DDI process so that they are able to specifically target the learning needs of all of their students.

Without a solid foundation in the data-driven decision-making process, data is just data for data’s sake, meaning that unless teachers learn how to interpret and use data properly to drive instruction, the data is meaningless. DDI is important to teaching and learning because it puts the “why,” “when,” and “how” back into teachers’ decisions. By doing so, teachers are better equipped to empower their students with truly effective strategies that are designed to target specific gaps in learning.

A picture may be worth a thousand words, but in terms of education, data is king because it is able to provide so much rich information that teachers can then use to drive their instruction. By analyzing data, teachers not only are provided with a snapshot of what students know at a particular moment in time, but they also are able to determine what students don’t know and what needs to be done fill these gaps within a certain timeframe. Once teachers understand how to analyze and interpret data appropriately, they can then make important decisions which are designed to positively affect students’ learning outcomes.

Authored by: Kristin LeBeau, M.A. Content Development Specialist Professional Development Institute

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