Intentional Differentiation Informed by Data

What if through intentional differentiation you could predict when students would have epiphanies? It would be like knowing the lottery numbers before they were announced.

Here’s the best way to increase your odds of setting up students for success: Review learner data as part of lesson prep.

Each approach should start with a review of the related data points so that intentional differentiation can be more efficient and predictable. In particular, look at data on students’ achievement, interests, and life experiences.

How Achievement Data Informs Learners’ Readiness

Learning objectives and assessments are foremost in lesson planning. We review these details along with the lesson steps to prepare and manage the resources needed. It’s important, however, to protect time for reviewing student achievement data about prior knowledge skills and concepts. These data points are predictors for which students may fall short of the objectives if specific gaps in skills or concepts are not targeted with scaffolds and adaptations.

The same data can predict which students have already exceeded the objectives’ expectations before the lesson is taught. Students who already mastered the skills or concepts become disengaged if the lesson content seems repetitive or like a regurgitation of what they already know. While some activities can be similar, these learners should not do all the same activities as others when they already know the content. During the lesson step in which skill gaps are addressed for others, advanced learners should participate in assignments especially constructed differently to meet their needs for growth. Such learning experiences offer enough nuance to help them build in-depth connections and context.

When informed about both groups of learners, teachers can evaluate and choose strategies and activities that best align with specific achievement needs. For example, a teacher might ask: Does a learner require time with me, such as during stations? Or collaborative learning, such as a tiered group activity? Or intensive individual practice? Achievement data factors into the designed activity, along with what the teacher knows about what encourages each student.

How Reviewing Student Interest Data Matters

Choice is often a strategy used to promote student voice based on their interests. There are two kinds of choices used. One is more efficient than the other, potentially leading to greater student engagement. The common approach is to create choices based on what teachers think that students would, generally speaking, find appealing. For example, write a persuasive article about a sport or meal of your choice. Or, choose one of the following assignment formats: video, podcast, or paper. Each of these ranges of choices is good in the sense that there are multiple options.

However, what if many or some of the students have limited or no interest in the choices? Collecting and reviewing data about actual student interests is the most efficient way to provide choices that align with what learners care about. Periodically conducting short surveys and listening to student conversations can provide data that informs improved designs of choice. Just before starting a new unit, conduct a short interest survey about the topics to be studied or possible formats for planned artifacts. This can take the form of an exit ticket or question on a digital survey through Kahoot or Google Forms. Conduct a five-minute focus group session with some students or a class to pitch your ideas for choices or formats. Get their real-time feedback. Embed real student interests by reviewing the data to shape the options for one or more lessons.

How Student Life Experience Data Supports Student Voice

While offering choices informed by student interest data is efficient, cocreating choices with students can lead to rich personalized learning opportunities by including student voice. For example, give students a voice to present their own option for a task. Preview units for key lesson activities and important assignments. Identify which ones should be adapted or modified by students for greatest learning impact. Before launching the activity or assignment, meet with some students to cocreate an adaptation that best suits their needs. It’s not necessary to meet with every student.

What’s fair in academics is not always equal. Review achievement data to identify students who would benefit most from addressing gaps in skills or concepts, as well as those who would gain most from a more in-depth learning experience. These students are the ones to target on a regular basis. All students can benefit from personalized learning. However, if time is an obstacle, focus only on the ones who need it the most.

Keeping the End in Mind

When cocreating with students, keep the end of the learning objective in mind. Communicate the required learning expectations so that students understand the framework they’ll work within and so that their ideas can best align with it. Tools include criteria checklists, rubrics, and learning contracts.

Intentional differentiation is an essential response to learner needs, whether it is students who struggle with mastering concepts and skills, or those who pace ahead of lesson expectations. All students deserve the opportunity to grow to close learning gaps and exceed expectations throughout the entire school year. When we commit the time to review the relevant data points about our students, the constructed and adapted lesson activities best align to their needs.

Not analyzing data about students’ current progress is like wearing a blindfold while crossing a busy intersection: You’re much more likely to get where you want to go when you can see where you are. Intentional differentiation informed by learner data helps students to move at the pace that best suits their learning needs at that given time—and makes achievement more predictable.