New to the site? Try Quick User Guide

We use technology from the Learning Commons to track anonymous visitor behavior on our website to ensure you have a great experience. Learn more about our Privacy Policy and Opt In/Out.

Problems of Practice

Differentiation: How do I use data to adjust instruction for groups and individual students?

Key Takeaways

In order to differentiate instruction using data, educators should:

  • Adjust whole-group instruction to meet the broad needs of the entire class.
  • Strategically group learners to differentiate instruction based on shared needs.
  • Create individual learning pathways to support the unique needs of each student.

What is the problem?

To serve students equitably, teachers must support the holistic needs of all learners, especially targeting the needs of students who are furthest behind. Using data effectively helps educators better understand learners, build relationships, and personalize instruction to meet unique needs. However, translating data insights into instructional decisions can be challenging, particularly when students have a wide variety of growth areas to target. In order to harness the power of data and effectively personalize instruction, teachers can take a multi-level approach to differentiation.

Why is it important?

Given the number of potential sources to pull from, educators can often feel overwhelmed by a deluge of data. By developing clear processes and systems to identify trends and insights, teachers can adjust and improve instruction to meet learning goals, fostering stronger relationships and student ownership. Educators can personalize learning through the use of holistic data, designing instruction that meets the unique needs of students in a multitude of ways – not just through individualized pathways. By strategically using data to identify actions at the whole-group, small-group, and individual levels, educators can effectively and efficiently support all learners.

The research says...

  • Schools reporting strong usage of data to group students and share data with students show strong achievement gains.
  • Instruction that is explicitly and systematically differentiated (matched to student needs), as determined through data, has been shown to be effective in reading and math.

How: Solution

When determining instructional actions from data, it is important to use a variety of data to understand both common and unique student needs. By identifying trends across a class, educators can design whole-group, small-group, and individual learning experiences tailored to each student’s academic and social needs.

When exploring instructional strategies at the whole-group, small-group, and individual levels, think about:

  • Resources and time – How can I use tools, create time, and collaborate with instructional teammates to analyze and identify actions from data?
  • Holistic data – How can I use various types of data, including structured, unstructured, behavioral, and personal data, to understand my students holistically?
  • Types of student groups – How can I leverage mixed groups and leveled groups to best support student learning?


1

Adjust whole-group instruction and pacing

When driving instruction with data, It is important to first start with whole-group trends and needs. Targeted large-group instruction is necessary, especially when teaching grade-level content and curricula. As educators collect regular formative data on mastery, patterns may emerge that indicate the necessity of reteaching the concept to bring the class beyond only a small percentage of mastery. Based on whole-group mastery, teachers can consider:

  • Reteaching the lesson by offering new methods, visuals, and scaffolds for understanding the concept. By redesigning the lesson using formative data and specifically targeting misconceptions, educators can better ensure students grasp the content.
  • Remediating to address any prior skill gaps that students may have, ensuring they have the foundational knowledge to master the grade-level content of the original lesson.
  • Adjusting pacing to allow enough time for learning with concepts that may require extra time. Especially when using data from pre-tests, educators can anticipate standards that may be more challenging for the class.
  • Designing collaborative learning opportunities to allow students to learn from one another. Creating opportunities for peer learning can encourage discourse, facilitate the use of problem-solving skills, and promote the application of learning to support and reinforce mastery.

When exploring these strategies, think about these questions:

  • How can I support every learner through whole-group instruction?
  • What data insights might indicate a need for small-group instruction?
  • How can I conduct quick checks for understanding during the lesson to make sure students are comprehending the whole-group lesson?
2

Strategically group learners

Often, data indicate that all students in a class do not have uniform needs; rather, each student falls within three to five groups of mastery levels and needs. Educators can use various forms of data, particularly mastery data from diagnostic tests and formative assessments, to divide students into groups of three to six and establish more targeted instruction. With leveled, homogenous groups, students often engage in small-group instruction with a teacher, participate in student-led learning, or work through collaborative tasks. Alternatively, teachers may design mixed, heterogeneous groups, intentionally matching students with different academic and social strengths to facilitate both academic and social growth. Student groups should change regularly based on shifting mastery levels, creating flexible grouping based on need.

When exploring these strategies, think about the following questions:

  • What kind of learning activities are best suited to engage students in small groups?
  • Which student dynamics should I keep in mind when designing small groups?
  • When should I leverage leveled groups versus mixed groups?
3

Create individual pathways and identify interventions

Though students have many commonalities, they each have unique strengths and growth areas. As educators utilize data to develop a deeper understanding and create stories of each learner, the need for more personalized and targeted instructional design becomes clear. Teachers can design customized learning pathways, playlists with learning tasks, and personalized learning plans to address individual academic and personal needs. Whether through systematic choice boards or online learning platforms, educators can utilize data insights to set learning goals for students and create opportunities for individual mastery.

When exploring these strategies, consider these questions:

  • How can I build student investment and choice in their learning goals and pathways?
  • What accountability routines and practices can ensure students learn productively?
  • How will students individually demonstrate mastery?

Take it further

Once you have a grasp on identifying and implementing instructional actions from data, you can take data use and personalized instruction to the next level by:

  • Mapping your instructional model and how you use data to the Highlander Institute’s Personalized Learning Progression, identifying the required data and instructional designs to leverage various levels of personalization.
  • Building student ownership of their data to build intrinsic motivation and investment in their ongoing learning.
  • Sharing data with key stakeholders to support and share ownership of student success with the student’s family and other educators.


Additional Resources and Content: