Data-Driven Instructions

Data-driven instruction is a means of gathering data through Informal Assessments, such as observations, interviews to informal tasks and performance measures, and from Formal Assessments, which include the design of a school-wide initiative by documenting the children's immediate instructional needs and measuring student’s performances. According to Patricia Cunningham & Richard Allington, Data-driven instruction view assessment as an important step in the instructional cycle; Assessment is not grading – although assessment can help you determine and support the grades you give. Assessment is collecting and analyzing data to make a decision about how children are performing and growing.

What is Data Driven Instruction?

"Data driven instruction…" is something we have been hearing for a long time. But what does that really mean in the everyday life of a teacher and his/her classroom? In this age of testing, it is often difficult to keep the notion of using data to make decisions about what is being taught.

When we see the word 'data', we like to think of formative assessments and student work. The kind of assessments that drive instruction in regular everyday classroom life are more formative (think “to inform”), such as observations, anecdotal notes, student writing and responses to reading.

How does it work?

It all begins with the data – taking inventories and assessments, observing reading and writing behaviors, studying writing samples and listening to student talk. When we take notes, ask questions in a conference, lean in while a student is reading independently at guided reading, take a running record or utilize a more formal assessment such as the Developmental Reading Assessment (DRA) or Fountas and Pinnell Benchmark Assessment System, we are gathering data. Learning how to take the data and record it in a meaningful way is the beginning of the cycle.

Next, as practitioners we must take stock in what we have found out. This analysis of the data is an important step in the process. What is this data telling us? We must look for patterns, as well as compare the notes we have taken with writing samples and other assessments. We need to decide what are the strengths and needs of individuals, small groups of students and the entire class. Sometimes it helps to work with others at your grade level to analyze the data.

Once we have analyzed our data and created our understandings, it is time to make informed instructional decisions. These decisions are guided by the following questions:

  1. What literacy practice(s) will I utilize to teach to these needs?
  2. What sort of grouping will allow for the best opportunity for the students to learn what it is I see as a need?
  3. Will I teach these strategies to the whole class, in a small guided group or in an individual conference?
  4. Which method and grouping will be the most effective and efficient? What specific objective(s) will I be teaching?

Answering these questions will help inform instructional decisions and will influence lesson planning.

Then we create our instructional plan for the unit/month/week/day and specific lessons. Utilizing templates and planning sheets are helpful ways to keep the process of planning organized and fluid. When we think through what we need to teach in an organized way, we are more likely to scaffold our students towards successful learning in our classrooms.

It's important now to reflect on what you have taught. Did you observe evidence of student learning through your checks for understanding, and through direct application in student work? What did you hear and see students doing in their reading and writing? Now it is time to begin the analysis again.

Remember, using data to drive instruction is an ongoing process. It will lead to responsive and differentiated teaching and learning for your students.

Now let us see how Wesley sister school teachers explain the benefits of carrying out data driven instruction at classroom.

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