Introduction to Data-Informed Instruction (DII)

Teachers make decisions, one after another, from the moment they park their cars to the time they say goodbye to students. These decisions affect classroom management, student performance, and even job satisfaction. Teachers make effective instructional decisions by leveraging what they know to be successful, what they have learned or observed, and what they can determine from data. Data-informed instruction (DII) is the process of varying content, process, product and learning environment (sound familiar?) based on information garnered from student work and assessment. In this blog post, we will peek into the classrooms of four teachers who use DII to enhance student achievement and support students with diverse learning needs and backgrounds.

DII Case Studies

Let’s start with an early elementary classroom. Viewing DII through the lens of an early literacy teacher demonstrates the power of data to differentiate small-group instruction.. There are several ways to individualize reading instruction, and for this example we’ll focus on leveled reading systems. Students’ reading levels are data points determined by various types of reading assessments that are used to match students with leveled texts for guided and/or individual practice. It makes sense in terms of differentiation for students to access the reading passages at the level most likely to promote growth. In this example, differentiated literacy instruction is determined by assessment as a part of DII. 

Next, we move to a fifth-grade math lesson where a teacher is leading a lesson on proportions. With the entire group, he details some of the larger pieces of proportion understanding and targets the connection to division. As he advances through the lesson, he verbally quizzes the students who respond with dry erase boards. He instantly determines “who knows what” and, in so doing, obtains data about student knowledge of the connection between proportions and division. The teacher reteaches that content in the moment. The formative assessment created data, which drove the subsequent instructional decisions in this example of DII. 

Continuing on to a middle school science lesson, we witness a teacher going through her exit tickets where she checked for understanding on hypothesis creation. At the same time, the teacher asked for students to choose both their partners and mode for presenting their science fair projects. In this manner, she will vary the options in response to data collected on students’ preferences for partners and means of presenting information. The students will ultimately complete their project with a preferred partner in an agreed format. In this example of DII, the teacher made instructional decisions informed by data on students’ preferences and interests, providing the greatest opportunity for student motivation as a factor of academic achievement. 

Finally, a couple of high school history teachers discuss the potentials of teaming up in the upcoming unit on The Civil Rights Movement. They ultimately aim for students to debate the success of the movement; but first they want to know how they can vary the learning environment based on students’ WIDA English language proficiency scores from the prior year. This data will help inform instructional decisions that will remediate for language barriers while also promoting students’ English language development. The teachers decide to homogeneously organize classes by English proficiency for content instruction. One teacher will be responsible for teaching the high-level and mid-level proficiency English speakers, while the other will teach the content to the emerging-level proficiency English speakers. To promote an inclusive learning environment, students will come together in heterogeneous mixed groups to complete the debate. The teacher provides differentiated language supports and graphic organizers to help students of all proficiency levels equitably contribute to the classroom activity.

Decision-making dominates the professional’s life, and especially that of the teacher. How those decisions are made ultimately impact student outcomes. It only makes sense to garner data and have the results drive the decisions. Differentiation based on data is challenging, yet effective. Teachers continually, and naturally, gather data; it’s time to put that information to use in differentiation.

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Henri Moser, Ed.D.