It's Tuesday afternoon, and you're using the ALN Worksort Protocol to sort through exit tickets from today's lesson on adding within 100. The quick, low-prep protocol encourages teachers to sort student work samples into piles by the strategy used. This makes the patterns jump out quickly. The first five look good. You are feeling good about today’s lesson. Then you hit the sixth, and something has clearly shifted. The seventh looks the same as the sixth. By the time you're through the stack, you've got a pile of ten exit tickets where students either added the ones correctly but missed the tens, or added the tens correctly and missed the ones. Ten out of twenty exit cards show the same pattern of errors.
So, what do you do tomorrow? You taught this. You modeled it. You were sure your students had it. So why does half the class look like they've never seen a two-digit addition problem before? It is important to know the exit tickets are not telling you that you or your students failed. The exit tickets are helping guide your next instructional move. If instruction didn't land the first time, saying it louder or slower doesn’t fix it. Instead, you decide to engage the AI Math Coach to support your decision making. ALN’s AI Math Coach can be an additional thought partner that supports your professional judgment. You can work through the patterns you noticed in the student work to get AI Math Coach to support sorting students into groups, designing a re-engagement task, or planning small group instruction. Think of it as one more tool in your instructional decision making toolbox.
The AI Math Coach encourages you to look at the student work and consider next instructional steps through the lens of the CRA (Concrete, Representational, Abstract). You wonder, is this a model gap (students can't picture the problem), a representational gap (students can't connect the visual model to the symbols), or a procedural gap (students can't execute the steps)? The AI Math Coach designs an instructional plan that will help students make connections between abstract, representational, and concrete models to allow for opportunities to make sense of the mathematics at hand. This gives your students the power to reconstruct the idea themselves, rather than just hearing it explained again. The goal isn't a better explanation. The goal is a different entry point into the task. You realize that your first entry point didn’t involve the hands-on opportunities that the AI Math Coach is leading you towards now.
With new instructional ideas, you go to your manipulatives collection and pull out base ten blocks, Unifix cubes, and a few Rekenreks for students to use as they step back and explore adding within 100. You put together a set of unknown-addend problems for students to tackle. You decide to pick the problem type where they have to figure out how many more got added, not just execute a memorized step. You set up four students at a concrete station with blocks, four students work on a task that requires them to represent their thinking on paper, and you let the rest work through a menu of choices focused on adding within 100.
For ten minutes, you observe students building and rebuilding the problem, "There were 37 students on the bus. Some more got on, and now there are 47. How many additional students got on?" Now you can finally see where their thinking broke down. Some students didn't yet understand the "10 more" relationship. Others understood it but couldn't translate it into symbols.
The math menu structure lets you pull a small group while the rest of the class works independently on menu choices. The goal is student-to-student discourse. You want students thinking strategically for productive discussion. You do not want one student handing the answer to the others in the group. Small group work shouldn't be you re-explaining in a quieter corner. Small group work should be students doing their thinking out loud, together.
This is where your sorted data directly drives who goes in which group and what they work on. Using the patterns you found in the exit tickets, you can build small groups based on what each student needs next. With the rest of the class working through the menu, no one is just sitting around waiting for you to finish with the small group. All students are engaged in meaningful, purposefully chosen work.
The initial negative feeling from unexpected exit ticket results is common. Even 20-year veterans get it. It doesn't mean you’ve done something wrong, it means you're paying attention.
The key to formative assessment is to make instructional decisions after collecting data, not to collect data for its own sake. If you can't name the instructional decision the data will inform, it is worth rethinking why you are collecting it.
Let's jump ahead to Friday afternoon, three days after that initial exit ticket changed your instructional path. You're circulating during math menu time, and you stop at a table where four of the students who struggled on Tuesday are working through a new problem, "There were 46 crayons in the box. A student added some more, and now there are 58." One of them reaches for the base ten blocks without you even suggesting it. They build 46, then add 4 ones to get to 50, and see that they need 8 more ones. They feel confident now that they have added 12.
You didn't re-teach the lesson. You didn't even mention Tuesday's exit tickets. You used your professional judgement, the assets the student work showed you, and with the support of the AI Math Coach, you paved the path towards understanding. Half the class not getting it isn't a red flag about your teaching. It is a checkpoint that acts as an opportunity to shift the focus of your next instruction. The real skill isn't avoiding this moment. It's knowing what to do next.
All Learners Network is committed to supporting pedagogy so that all students can access quality math instruction. We do this through our online platform, free resources, events, and embedded professional development. Learn more about how we work with schools and districts here.