How to Use Data to Tailor Instruction

Today’s classrooms are more diverse than ever.
Each student has individual strengths, weaknesses, and interests—as well as social, behavioral, and cultural factors—that all impact their learning process. Teachers are put in the difficult position of trying to meet each student’s unique needs to ensure that all students succeed, regardless of their learning style.

How can teachers address the needs of all students at all levels, at the same time? The solution lies with student data, and using that data to personalize instruction.

What is data-driven instruction?
Unfortunately, many of today’s teachers and parents view standardized models of teaching as limiting and ineffective. See, for example, A Perspective on the Standardized Curriculum and Its Effect on Teaching and Learning1. Struggling students may be left behind, while advanced students may not be challenged sufficiently. The “one-size-fits-all” approach to education is outdated. Instead, learning must be strategically tailored to work for each student.

We live in a technocentric world where data informs nearly everything we do—from how we shop, to how we form relationships, to how we learn.

Data-driven instruction allows teachers to modify and improve their lesson plans to meet the needs of individual students by collecting and analyzing data. Teachers can then implement necessary changes using insights gained in the data assessment process.

Here’s a breakdown of the cycle of data-driven instruction:

  • Assessment. After presenting students with a lesson, teachers use assessment tools to gauge students’ understanding of the material.
  • Analysis. Results of assessments can help teachers determine areas of study where students struggle and excel, and at which stage in the learning process miscommunication may have occurred. Were concepts communicated clearly? Were assessment questions worded appropriately?
  • Adjustment. Insights from data analysis allow teachers to make the necessary adjustments to their teaching approach, from reteaching key concepts to the class as a whole, to targeting instruction to individual students who may have missed the mark.  

What kind of data is useful for teachers?
The key component of data-driven instruction is data—but what does that really mean? What kind of information should educators be analyzing in order to effectively tailor lesson plans to meet their students’ needs? And how should educators go about collecting that information?

Data extends far beyond test scores. In fact, a student’s academic background is only one piece of the puzzle. Overall, teachers can benefit from any information about the student—academic, social, behavioral, cultural—that can provide insights into how that student processes information.

Consider the following scenario: one of your students has recently relocated to the United States from El Salvador. He is unfamiliar with the language, and his parents are both new English language learners as well. His learning process is going to look much different than that of a native English-speaking student. These details about his life, in tandem with his academic history, inform the way you as a teacher approach his education.

English language learners, special education students, advanced learners—these students all learn and perceive the world around them in different ways. Evaluating their progress based on test scores alone won’t provide accurate results; teachers need secondary data to paint the full picture of a student’s learning process.

How can data be applied to literacy?
This process of data analysis and implementing changes in the teaching process can be applied to any area of education, but can be particularly useful with regards to literacy.

Teachers face various obstacles when assessing a student’s reading progress: reading comprehension, decoding, writing, and speaking skills all develop at varying paces. It is necessary for teachers to be able to accurately gauge which areas of literacy students struggle with, and determine the right next step for each individual student.

Fuel Education’s Big Universe digital literacy platform is an effective tool for simplifying the process of collecting and analyzing individual student reading data. With Big Universe’s balanced literacy tools, teachers can use real-time report data to see what and how much students are reading, as well as their performance on embedded assessments.

Reporting in the teacher account is a great tool to assess reading comprehension and provides an up-to-date snapshot of student progress. Reports are available to monitor reading logs, track usage, view quiz results, and more. Additionally, teachers can manage their rosters and receive emailed reports with weekly reading trends. At a glance, teachers will know who needs to be challenged, who needs support and encouragement to read more, and which literacy skills may need additional review.

Schools all over the country have adopted data-driven instruction as a new method of teaching. Discover how Big Universe can help teachers in your school or district tailor their students’ journey to literacy.

Request a demo today or visit our website to learn more.


1Sparapani, E. and Callejo Perez, D. Journal of Education & Social Policy, Vol. 2, No. 5; November 2015

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