Pam Alfaro: Technically, one more minute for the afternoon, but good afternoon anyways. Good afternoon.

And so I see we have about 20 folks who are here already. Some, quite a few of you have been patiently waiting to come in, but we will get started right at 12 o’clock.

On the dot, because we know your day is busy, and you have other things to attend to, and so we want to make sure we make good use of your time this afternoon as appreciation for you joining us.

Okay, so we are right at noontime. Check, Priscilla, you’re there too. We’re 12 o’clock on the dot. So I want to say hello, and I want to welcome you to the second session of our technical assistance webinar series on maximizing targeted literacy instruction.

So, my name is Pam Alfaro, and I am a literacy project manager at UF Lastinger Center. My education background is in reading and special education. 

I have Priscilla Brown with me today. She is going to be co-facilitating this session with me, and I’m going to let her introduce herself. But I also have Kyra Purvis on, too, who is from the Lastinger Center as well, and Hope Miller, who is our comms person, who is also going to be helping us throughout this presentation. So I’m just gonna pass it to Priscilla real quick.

Priscilla Brown: Awesome! Hello, everybody! I’m really happy to be here with all of you this afternoon. My name is Priscilla Brown, and I am a literacy specialist here at the Lastinger Center, and I help to run the elementary and secondary micro-credential. So, if you have rubbed shoulders with that program, I’m so excited that you’re here, and excited that we get to support our amazing educators in this webinar this morning. Thank you for joining us.

Pam Alfaro: Yes, and so we’re really glad that you’ve joined us today because we’re going to be focusing on how to use data to create flexible, small group instructional groups. And so, this practice is especially important in a data-driven cycle, which we’re going to talk about a little bit as we go into the session.

And it really can help teachers strengthen the delivery of targeted literacy instruction to support that improved student literacy achievement. 

And so, the session itself is just really designed for classroom teachers, coaches, literacy specialists, school leaders and any other educator who’s interested in seeking some practical strategies to strengthen targeted literacy instruction in an effort to improve student learning outcomes. 

And so, in this session, we are going to learn about how to analyze data to make those informed grouping decisions. We’re going to talk a little bit about planning targeted instruction and applying some strategies to overcome some common implementation challenges that you might find as you’re working with your small groups. And so, let’s get started with that.

So again, this session is the second of a five-part series titled Maximizing Targeted Literacy Instruction.

And the webinar guides participants from understanding the research and using the data to create those instructional groups, and that’s covered in sessions one and two. You’re in one of those sessions today. 

And then session three really addresses developing some effective instructional practices, where we kind of move into the instruction part of it and classroom structures to really promote student engagement for successful targeted small group instruction.

The final session, which would be session five, really brings participants back to how to use that ongoing assessment and progress monitoring data, that we’re going to talk about today, to refine instruction and improve in an effort to improve those student literacy outcomes. 

Now, the first session has already happened. That was on delivering high-impact targeted literacy instruction. And that really focused on the why and when you use small group instruction for those targeted skills-based supports for students. Now, it is a pre-recorded session and can be accessed on our Lastinger Webinar website for this series, but later in the session, we will drop a link to the website for you to access that.

But today’s session now is really going to be focused on using data, again, for flexible instructional grouping for your targeted literacy instruction. And so we’re going to talk about some essential components of planning and facilitating effective small group instruction, including how you analyze those multiple sources of data, how you use that to identify your students’ needs and how you’re going to really look to make purposeful grouping decisions and plan for your targeted small group instruction.

Before we go into our learning objectives for this session, I’m just going to take a minute to share who we are at UF Lastinger Center. At the Lastinger Center, we’re an educational innovation hub, where we use research and data in an effort to improve teaching, learning and childcare from birth through grade 12. So we cover the whole range. And so, we really enjoy partnering with educators and leaders like you to provide professional learning, like we’re doing today, but we also provide that professional learning and early learning, literacy and Mathematics, and that is all in an effort to help students succeed.

Now, if you have access to the guided notes, I know Hope sent that out yesterday in an email, you’ll see on page one our learning objectives for this session. And if you don’t, Hope, do you mind dropping that link, in the chat? Okay, she’s already done it. I looked up and she’s got it. Go ahead, Hope. 

Okay, so by the end of this webinar, you’re going to identify some key sources of student data. Priscilla’s going to talk to us about that. How we’re gonna use that to inform our instructional decisions.

You’re going to understand how to analyze that data. We’re going to talk about that a little bit, too, to determine the needs and plan for your targeted instruction. We’re going to explore how to create those flexible groups. We’re going to do that today as well, based on current student data and the ongoing progress monitoring that you will be doing as you’re providing your instruction for students.

So, some of these strategies we identified may be familiar to you, and others may be new, but during today’s session, please make those connections in your notes. And lastly, what we’ll do is we’ll recognize some common small group challenges, because we know in our profession as teachers, there are challenges.

What we’ll do is try to provide you with some effective approaches to deal with those challenges and avoid some of the pitfalls related to small group instruction. So again, I want to note that you have the access to your guided notes to follow along, and if you notice at the bottom of the guided notes, there is a checklist that is something for you to consider as you implement targeted small group instruction within a classroom setting.

Also, I want to note that if you have questions during the session today, please drop them in the chat. We’ll address as many questions as we can at the very end of the session, but you can also submit questions through the survey. The survey will also happen at the end of the session.

And we’ll respond to every question from the chat and the survey in a follow-up Q&A document, so that you all have access to the questions and the answers, and that you can refer back to them. So we want to make sure that we address all your questions, now or in the future.

So, as we go throughout this presentation, if you can use your guided notes, and I know I’m saying guided notes again and again, on page one, because I really wanted us to, to take an opportunity to have everything in one place for you to see as we’re going along in the presentation. You’re going to reflect on your own experience with small group instruction.

And so, we’re going to take a moment now to reflect on your experience with flexible grouping for small group instruction, as it relates to the challenges and the successes. So, reflect on these questions.

What has been your biggest challenge?

It could be data analysis, being able to identify student groups, available resources for the groups, and then also you can, if you would, identify what has been your greatest success. That can also include data analysis. If you’ve been really successful with that, you’re seeing student growth, you’ve been really successful with creating your student groups.

Again, you have access to your guided notes, and I’m going to give you about 30 seconds to record your thinking in your notes on page one.

Okay, so as you’re wrapping up your thoughts through your guided notes about your successes and your challenges, we will begin this session with an anticipation guide. It’s going to be our first activity to activate your prior knowledge and get your thinking around today’s topic, and we really want to get you thinking about this topic of using data for creating your flexible groups. 

So, this is not a test, it’s really just a way to bring your thinking to the surface. So, we’re going to take a few minutes to read each statement and decide whether you agree or disagree. This is on page two of your guided notes, if you have those guided notes available to you. 

This is going to be based on your current understanding and experience with small groups. Now, know that we’re going to revisit these at the end of the session, after we engage with the content.

And so, I’m going to read them along with you, and you will determine whether you agree or disagree with each statement. 

So, statement one: Flexible grouping should be based on current student data, not fixed labels such as low or high group. Do you agree or disagree?

Statement two: Grouping improves student achievement, regardless of the instructional plan for each group.

Statement three: Students should remain in the same instructional groups for the entire year to maintain group consistency.

Statement four: Ongoing progress monitoring is necessary for flexible grouping and effective instruction.

And then statement five. Teachers should use multiple sources of data when planning for small group instruction.

Okay, as you lock in your answers for that, I am going to pass it on to Priscilla to move us into the next section. And I’m also going to mute myself.

Priscilla Brown: Alright, so as Pam already said, today’s focus is on flexible grouping strategies that you can implement during that small group instruction. So we’re going to begin by examining this John Hattie visible learning research, and what it tells us about the impact of flexible data-driven grouping on small group instruction.

The key here is that simply placing students into groups does not actually improve achievement.

What matters is why students are grouped, how they are taught and how often teachers are adjusting those groups based on that assessment data that you collect.

So, Hattie’s research shows us that the grouping itself is not an intervention. Effective instruction is based on student need. So, flexible grouping essentially creates the conditions for teachers to deliver explicit, evidence-based instruction that meets the student’s needs in real time.

When assessment data are used to continually assess, analyze, instruct, monitor and regroup, which we’re going to talk a lot about as we move through the presentation, teachers can maximize student learning and ensure that instruction remains responsive to students’ ongoing needs. And the key word there that I love is responsive, and so we want to make sure that in that data-driven cycle, we are responding consistently to students’ needs.

This is important because we want our instructional practices to be grounded in research and align with strategies that we know are going to help improve student achievement.

And it also helps us to develop a shared understanding of realistic expectations for our teaching practice and the student outcomes that we see in our classrooms.

Okay.

So we’re going to take a minute really quickly to establish the idea of what is data-driven instruction, right? And it exists to help us understand how to maximize that targeted instruction time that you have with your students. So, as we move through the session, we’re going to notice some important vocabulary.  So, these will be explained to help us talk about data and flexible grouping in a consistent way.

So, while we do this, think about each term and how it shows up school-wide, in your classroom, and then in your groups, okay? So you might already be using some of these words that you see on the slide, and some of these strategies that we shared with you today, we’re going to make that connection. So we’re going to be explicit about it so that you can make the connection with how you might be using it in your setting currently. 

So let’s start with this first term, data-driven instruction.

So, the next slide we’re going to see here is a graphic. And this graphic represents this continuous process. So, effective reading instruction, as you all very, very well know, is not a one-time decision. It’s an ongoing process of gathering information, responding to student needs and then adjusting that instruction based on evidence.

So, you’re gonna start by assessing first, right? And this includes gathering multiple sources of data, using appropriate assessments. And this can include screening, diagnostic and progress monitoring data to understand the specific skill gaps that need to be addressed. The goal of this step is to collect data, which can then be analyzed and used to inform the data-driven instructional decisions.

Next, teachers are going to analyze. They’re going to analyze the data to determine the students’ strengths and their areas of need. So, instead of grouping students by overall reading levels, teachers are going to look for specific patterns. 

For example, students may need support with phonemic awareness, decoding, fluency, vocabulary or comprehension. Analyzing the assessment data helps answer the question, what does the student need to learn next?

Based on the analysis, teachers can then determine the most appropriate targeted skills a student needs, and plan explicit and systematic instruction to support their growth.

This is where flexible Instructional groups are formed. Students are grouped by similar learning needs, not necessarily by fixed achievement labels or ability levels, okay? So remember what we talked about earlier, that Hattie’s research says that grouping alone does not necessarily improve achievement. The impact really comes from the targeted instruction that students receive and are taught within small groups.

And then lastly, teachers continuously monitor student progress in real time to determine whether students are actually responding to that instruction. If those students demonstrate growth, then they may move out of that group that they’re in and into a new instructional group.

Or if progress is slow, then teachers may need to adjust the instruction or provide additional support. Maybe that looks like increasing frequency, duration or level of support. And so this happens by analyzing data so that you can change the instruction accordingly, okay? Not necessarily so you can provide the same instruction, but so that you can change it to be responsive, like we said earlier.

Let’s consider how the data-driven instructional cycle that we just reviewed informs effective student grouping. So first, it’s important to distinguish data-driven grouping from achievement-level grouping, because the purpose of small group instruction is to provide targeted support, not to assign students by groups in those achievement levels.

So in a needs-based model, students are not grouped based on below, on or above grade level, because those labels don’t really provide enough instructional guidance. So instead, teachers are using assessments and other data points to identify the specific skills or concepts that students need to learn. 

So, for example, in third grade, you might have a student who needs additional instruction with multi-syllabic words. So it’s more specific. Students are then grouped according to those instructional needs that have been identified.

So when we consider data, we should ask ourselves these questions. Which reading skills are strong or weak, what should be taught next, and what instruction is most appropriate.

The focus, again, we’re going to repeat this over and over, it should always be on what students know and what they need to learn next, ensuring that that instruction they’re receiving is timely, targeted and responsive to individual learning needs, rather than just based on a fixed perception or achievement level.

Alright, so how are we going to do this? We’re going to talk really quick about types of assessment that provide this data. So different assessments serve different purposes, and using the right assessment at the right time is essential for effective data-driven instruction.

Different assessments answer different questions, helping educators identify who needs support, what skills need attention, and whether instruction is actually working. So, let’s explore some of these different types of assessments and how they’re used.

All right, now hang with me. So, first one, we have our screening assessments. And these are standardized, and they’re used to quickly identify students who might be at risk and in need of additional support or further evaluation through additional assessments.

Really important, too, screeners also provide a broader picture of the health and effectiveness of Tier 1 instruction, and they do this by showing how well core instruction is meeting the needs of students overall.

Every student is screened regardless of their achievement level. And so in Florida, we do this through the STAR Early Literacy, the STAR Reading and FAST ELA, specifically PM3.

Those are the ones used for screeners. Other common screening measures include DIBEL’s 8th Edition and EZCBM.

Next, we have diagnostics, which are used to determine those specific skill strengths and areas of need to help guide that targeted instruction. It’s used when screening results tell us that a student needs a more in-depth assessment.

Some are standardized, some are not, and they may or may not be timed. Some common diagnostic assessments are, you may be familiar with Core Phonics Survey and UFLI Foundation’s Placement Test.

And while the scores on the diagnostics are helpful to determine skill gaps, the reason for the score is more important. So, for example, the student reads the digraph /sh/ as /s/, which then leads them to read ship as sip, sharp as sarp and show as so.

You want to choose a diagnostic based on concerns that were analyzed in those screener results.

The next type of assessment is progress monitoring, okay? Now, this one’s awesome because it’s used to track student growth and evaluate the effectiveness of instruction and intervention. They’re formative assessments that are sensitive to change over a short period of time, and include multiple forms for assessing the same skill at the same level of difficulty.

The FAST Progress Monitoring PM2, right, that mid-year assessment, is used to track student progress toward those grade-level standards. Some common progress monitoring assessments that are used to track student growth and evaluate the effectiveness of that instruction that you’re providing can also be the Dibble’s 8th Edition and EZCBM.

So, by assessing and tracking skills over time, teachers can determine if that targeted instruction being taught to the student is actually helping that student to make the appropriate gains for that skill that has been identified as needing support.

And then lastly, you’ll see here we have outcomes. So, outcome assessments are used to evaluate overall achievement and program effectiveness.

These are typically given at the end of a unit, the end of a course or the end of a year. For example, the FAST end-of-year achievement data, that PM3, evaluates student proficiency on grade-level standards, and it also serves as the state’s summative accountability measure. 

So data from these assessments are used to summarize the learning of each student, and also evaluate the effectiveness of the instructional system, which is really important.

The most common outcome assessments are reading achievement tests.

Okay, so again, we’re gonna summarize by saying screening assessments identify who may need support, while diagnostic assessments help determine what specific skills need to be addressed through instruction.

And then progress monitoring helps to determine student progress and effectiveness of instruction, while outcome data helps evaluate overall student achievement and program effectiveness.

Alright, so let’s focus a little bit more attention on these two types of progress monitoring. 

These are used to track student growth, like I’ve said before, and evaluate the effectiveness of that instruction and those interventions that you are providing in those small groups. The data from progress monitoring measures are especially helpful for flexible grouping because they’re sensitive to change over a short period of time.

And they include multiple forms of assessing the same skill at that same level of difficulty, which is incredibly important when you’re actually trying to determine if that instruction is working. So progress monitoring assessment is most directly used to inform instructional adjustments and regrouping.

So, the graph that you see on your screen compares curriculum-based measurement, which is shortened CBM, and mastery monitoring. Both help determine whether students are benefiting from instruction, but they answer different questions.

CBM provides a big picture view of growth, while mastery monitoring focuses on specific skills.

Together, the data from both tools inform instructional decisions and flexible grouping. So when do you use each of these?

Alright, so for CBM, curriculum-based measurement, a brief, standardized assessment used to monitor student progress over time, okay, and we’ve said this can include DIBEL’s 8th edition, Acadian, StarCMB, and Amesweb. These are best for tracking overall reading growth. They can be used regularly, weekly or bi-weekly to determine if that targeted instruction is effective.

And then it also helps teachers decide whether to continue, intensify or modify instruction.

The question that I want you to ask yourself is, is the student making enough growth over time to reach the expected outcome?

And then the next one is mastery monitoring. So this could look something like a test, a quiz or regular check given by the teacher to measure a student’s understanding and proficiency in a targeted skill.

So, for example, this could be exit tickets, chapter tests, comprehension quizzes or weekly skills assessments. These mastery monitoring assessments are used to best determine whether the student has mastered a specific instructional objective. It can be used after focus instruction on a targeted skill.

And then it also helps teachers know when a student is ready to move on to the next skill, right? So did the student actually learn the skill I just taught? That’s a great question to ask after those mastery monitoring assessments.

So, now we’re going to talk about using this data that we’ve spoken about to form flexible grouping.

Gathering classroom data is the first step. But the impact really comes as an educator, as you analyze the data and use that data to make informed instructional decisions.

So one of the most effective ways to respond to student needs is through flexible grouping. So let’s look at how teachers can use this data and form flexible groups during their small group time. 

So flexible grouping is most effective when it is driven by data and responsive to students’ changing needs.

The goal is not necessarily to create permanent groups, although that would be a lot easier to do. The goal is really to use assessment information and be responsive to student needs so that we can provide instruction that is targeted, timely and meaningful.

And so, I want you to keep these points in mind. You want to analyze multiple sources of assessment data to identify the student’s current strengths and their instructional needs.

And then create and adjust flexible groups based on ongoing student progress monitoring data to ensure that that instruction remains responsive to changing needs.

And then lastly, to deliver targeted, evidence-based instruction aligned to specific learning goals for each group, and then monitor students’ progress to evaluate the effectiveness of that instruction.

So, the bullet points I just shared, they really emphasize what we previously saw, that continuous cycle of data-driven instruction, which includes assess, group, instruct, monitor and then regroup, which is the foundation of data-driven, flexible grouping.

And then I want you to think about this progression from the system to the classroom to your groups.  So you have all of these assessment systems, so, what data is available, right? What data has been performed at a school-wide level? What can I do in my classroom? 

And then those sources of classroom data, right? What evidence do teachers gather with all of the students’ work, any assessments that you administer in the classroom and then using that data for flexible grouping. So, how do teachers use that evidence to then improve instruction?

Awesome. So, we’re gonna do a deep dive on assessments, alright? Not all assessments serve the same purpose, and understanding when and why each is used helps teachers to make informed decisions about their instruction. 

And we consider not only what each assessment measures, but also when it should be used, why it is administered and how the results can inform instructional decisions.

So we’ve talked about the importance of using data to drive instruction, but now let’s look more closely at where this data comes from.  One of the biggest misconceptions is that all assessments serve the same purpose.

But Stollar and Winn, their work explains how every assessment is designed to answer a different instructional question.

The key is knowing when to use an assessment, why you’re administering it, and how the results should influence instruction.

So first, we begin with screening assessments. Screening is administered to all students at designated points during the year, typically the beginning of the year and during those benchmark windows. The purpose is not necessarily to diagnose a problem, but to identify students who may be at risk of reading difficulties.

Think of screening as an early warning system. It answers the question, who may need additional support? The results help us determine which students are likely to succeed with that core instruction alone, or which students may need further assessment or supplemental intervention.

Screeners can include computer adaptive measures, so this helps identify students with overall reading and comprehension difficulties.

And then those curriculum-based measures, right, which identify difficulties within a specific grade-level skill, that can also be predictive of that overall reading proficiency.

And then, very important, too, screeners function as a temperature check for Tier 1 instruction. Rather than simply a way to identify individual students for additional support, it can also help us see if our core instruction is reaching most of our students and is effective in their growth.

So, once a screening identifies students who may be at risk, then we move to this second one, diagnostic assessment. And diagnostics answer a very different question.

Why is the student struggling? And they help give us deeper insight. So instead of just telling us a problem exists, those diagnostic assessments help pinpoint the specific literacy skills that need attention.

So, for example, a student may have difficulty with reading comprehension, and that diagnostic assessment may reveal that the underlying issue is vocabulary, or decoding multisyllabic words.

So now this information allows teachers to provide explicit, targeted instruction, rather than simply relying on assumption.

So, after instruction or intervention begins, now we need to know:

Is the instruction we are providing working, right? That is the purpose of progress monitoring. These types of assessments are administered regularly, weekly, bi-weekly or monthly, depending on the level of support. And now progress monitoring answers a very important question. Is the student responding to instruction?

If students are making expected progress, then we continue that plan, right? The instructional plan that we created. But if they’re not, the data tells us that it might be time to adjust

either intensity, our instructional approach or the intervention that we’re using. Now, this is where the data becomes a tool for making those adjustments with grouping and/or instruction.

And then finally, we have outcome assessments. And these occur after instruction has been completed, such as as the end of a unit, semester or school year. Their purpose is to determine whether students have met those expected learning goals and standards. 

So, outcome assessments answer the question, did students learn what we intended them to learn? These results help teachers then evaluate the effectiveness of instruction, identify patterns across classrooms or grade levels and also inform future instructional planning. So those outcome assessments function more at that higher system school-wide level.

And so, now we’ve discussed some more formal assessments, such as screening, diagnostic, progress monitoring and outcome assessments.

However, and all of you know this, some of the most valuable instructional decisions are informed by classroom evidence that teachers are gathering naturally during instruction, and this can include observations, work samples and anecdotal records that help us understand not only what students know, but also how they are learning.

And when you combine observation work samples and anecdotal records with assessment data, those more formal ones, these sources provide a more complete picture of a student’s progress and their instructional needs.

So, before we can use assessment data effectively, we have to understand what each assessment is designed to tell us. A screening assessment, diagnostic assessment, progress monitoring measure and classroom assessment each answer different questions.

When we match the assessment to its purpose, we’re better able to interpret the data, and then make instructional decisions that support the student’s growth.

Having an effective assessment system is not necessarily about just collecting more data and administering assessments just for fun. It’s about collecting meaningful data that serves a purpose. So, assessments should always help us answer instructional questions, and then guide our decisions about student needs.

Collecting data gives us information about student performance, while using data helps us determine what actions to take. By analyzing multiple sources of evidence, teachers can identify instructional needs, create those flexible groups and plan targeted instruction that responds to student learning needs. The goal is not more data, the goal is meaningful data that leads to informed instructional action.

So now that we understand when, why, and how assessments are used, let’s pivot a little bit and apply that knowledge by examining the different types of assessments and the role that each of them play in guiding those instructional decisions.

So, the goal of matching assessments to their purpose is not just to determine the assessment type, but to understand the instructional question that each assessment helps answer, and how the information guides next steps.

So at the screening level, right, we see here assessments such as FAST, the PM3, Progress Monitoring Window 3, helps us identify students who may need additional support and establish a starting point for instruction.

And then diagnostic assessments, such as the core phonics survey, provides more specific information about students’ skills, strengths and gaps, so teachers can deliver that targeted instruction.

Now, it’s important to note that if you already have an intervention program that is known to accurately target those student needs, and maybe that program has a placement test, that it’s better to use that placement test than to do the core phonics survey, for example, and to stick with the placement test and then the progress that that intervention program is offering.

And then the progress monitoring assessments, such as the CBMs, help us to determine whether students are making expected growth, and whether those instructional adjustments are actually needed.

And then lastly, we have our outcome assessments, such as the FAST PM3 and End of Unit Assessment. So these help us evaluate whether the student has met the learning goals and standards for that year.

And like I said earlier, the more informal assessments, we also want to recognize that is valuable classroom data, and they come from sources beyond those formal assessments. They’re student observations, anecdotal records, work samples that provide ongoing evidence of student learning, behaviors and skill application.

These sources help teachers better understand student needs, and then help to make day-to-day instructional decisions. So when we take into consideration these assessment types and other formal and informal assessments, the data sources provide a more complete picture of student needs, and help teachers create flexible and responsive instructional groups.

And again, it is very important to remember that no single assessment is going to provide a complete picture of a student. Effective data-driven instruction comes from using multiple sources of data and evidence, understanding the purpose of each and then using those results to make informed decisions about instruction and intervention for each of your students.

So, we’re gonna engage in an experimental activity, focusing on determining learning needs and planning instruction for multiple data sources, so you’ll have a little bit of practice here.

So, effective small group instruction begins with understanding exactly what students are able to do and what they need to learn, okay? So, think about how you use multiple data sources to identify learning needs, and then design that targeted instruction. So, what could this look like when analyzing student data?

So the activity we’re about to engage in is designed as an experimental learning opportunity, right, to help us illustrate this process of analyzing data to determine student learning. The examples and student profiles that have been simplified to engage in this activity. 

However, we know that in real time, in an authentic classroom, it is important to recognize that analyzing this data is complex and can also be nuanced. Keep that in mind as we go through this activity. 

So, you’re going to, in your guided notes, turn to page six.

Now, this is a sample grade two scenario, all right? That first part of the data-driven cycle, the gather, assess, we have done, right? So, we have listed all the data sources and the evidence and results.

So, next, we’re going to analyze the multiple sources of data, and identify the highest priority instructional need, and then plan targeted evidence-based instruction. 

And then fourth, we’re going to monitor that student progress and adjust the instruction.

So, here’s the data. What story is this data telling us?

So, the second grade FAST PM2 benchmark identifies that the student is at risk. They scored a level one, which is well below grade level expectations.

The domain scores in this assessment would be foundation skills, reading and vocabulary. But remember, we don’t just use achievement levels. We want to use it to help us identify and flag, but then we’re going to go a little deeper. 

So then you’ll see here the DIBELS, which was the Oral Reading Fluency CBM. It indicated some risk with reading fluency, right? So they’re in that 59 to 77 range, which shows a sum risk.

And then we have the core phonics Survey, which then is pinpointing the decoding deficit. So, students might read pad instead of paid, right? Or lep instead of leap, so, we are identifying their decoding deficits, specifically.

And then we’re gonna use work samples, right, to show how the deficit actually appears during classroom tasks.

And observations that reveal reading behaviors, right? Such as guessing, or maybe, you know, becoming overwhelmed and not really understanding what they’re reading, and so those are your observation evidence.

So I’m gonna stop right here, and I’m going to give you about a minute to review the data and make notes, and you can see that, like I said, on page six.

So go ahead and take about a minute to take notes and answer some of these questions. So, what patterns do you notice? What is the student’s greatest instructional need that you see? And what would you teach during targeted small group instruction?

Okay, about 30 more seconds.

So, from looking at this evidence, we can see that this student is struggling with oral reading fluency. They have low reading comprehension and they’re having difficulty decoding vowel teams.

So, we’re going to provide explicit instruction in decoding vowel teams first, with opportunities to apply that skill in connected text.

So once students start to demonstrate mastery with vowel teams, instruction in multisyllabic words, especially with those vowel teams, with opportunities to apply that skill in connected text, would come next.

So, effective, targeted instruction begins by triangulating multiple sources of data to identify the highest leverage instructional need. Rather than trying to address every area at once, we prioritize the skill that will support the greatest progress.

Multiple data sources, they help us to identify the most needed skills with confidence. So again, it’s this process where we gather information from multiple sources of data. We are looking for patterns across the data. Identifying the student’s greatest or most immediate learning need, that highest leverage instructional need. The skill that, if taught correctly, is most likely to improve a student’s overall reading success and accelerate future learning.

And then we’re planning instruction that directly addresses that need.

So the highest leverage needed for this case study, this student here, is decoding vowel teams, because instruction and mastery of this skill will improve decoding accuracy that will then in turn support fluency, which then in turn supports comprehension.

This student may also have broader needs also in word-reading automaticity, fluency and comprehension. So, instruction should target the student’s specific decoding needs through explicit, systematic instruction with practice, and practice with vowel teams. And then, of course, using those Tier 1 materials when appropriate.

Awesome. I hope that getting to practice that in real time is helpful, collecting the data, or looking at the data, and applying the skill of understanding that highest leverage need. So now I’m going to pass it to Pam, who is going to take us a little deeper.

Pam Alfaro: Okay, I unmuted, so now I have to click back over here. Excuse me. I’ve been quiet for a little bit, so now I have to get my voice back. So, let’s look at using that data to create flexible groupings.

So, research by Castle et al. from 2005 really found stronger learning outcomes with flexible grouping. So, when we group students flexibly, we have stronger learning outcomes. 

Grouping students by their current instructional need really helps students. You’re adjusting those groups as those students’ needs change, because we know students’ needs are not going to stay the same. Not from the beginning of the year to the end of the year, not from the beginning of the year to the middle of the year. And that is really going to allow us to have more responsive instruction for students.

So, I’m going to talk just a little bit about this study. This study examined flexible grouping in a high-need school over five years. They tracked non-transient, below-goal elementary students across multiple literacy assessments.

And so teachers in this study really attributed the positive impact of flexible grouping to more focused instruction aligned to specific learning needs, because the teachers focused this instruction on what the students needed to learn.

They had greater student attention to the instructional tasks, because it’s really focused on what the students need. It also increased student confidence and engagement, because we’re really honing in and focusing on the needs of those specific students and the groups that we place them into.

So overall, the findings supported flexible grouping as a responsive approach. We always talk about, like, how do we differentiate for students?

It’s really about how we respond to the students’ needs, and that can really improve overall learning for students. When we’re talking about below goal students, we can be talking about students who just need a little bit more support. They may not necessarily be below goal. They could be on track, but just need support, so they don’t become below goal students. 

And we want to do that without the potential drawbacks of ability grouping, because we know that we’re not maximizing that instruction just based on what we know about students’ ability in the grouping. And so, flexible grouping is responsive to students’ needs through the use of assessment data. 

And I know Priscilla pulled us all the way through talking about assessment data, but really, we want to turn to our assessment data to maximize that targeted small group instruction that we provide for students.

And so, we were on page six, I think we’re still on page six. So, in the previous activity with Priscilla, we identified the highest priority instructional need for one student.

So now we’re going to look at a data set for four students, and I’m going to give you time to look at the data set as well, in a classroom. So, effective small group instruction really begins with understanding exactly what students need to learn.

Think about how you’re going to use multiple sources of data to identify the learning needs, and you’re going to look at it for one student, but then that one student within all of your students, and then that’s going to help you be able to group the students. So what could your small groups look like once you have analyzed this student’s data?

So here we have–the middle of the year, the data for four second grade students. We have collected multiple sources of data to analyze for creating our flexible grouping. We’re getting ready to analyze this data.

The next step is deciding which students have similar instructional needs and can benefit from that targeted small group instruction that’s going to address those needs.

So, we’re placing students into instructional groupings based on need, rather than overall performance level, and Priscilla spoke to that. So, you’re going to think about these questions, and they are in your guided notes, but we’ll also put them in the chat. And Priscilla, can you go ahead and put these questions in the chat for, for the participants? 

So, which students could benefit from similar instruction? What skill could be the focus for each group moving forward?  And then, how does the data that you see here influence your decision?

And like Priscilla said, we know this has been simplified, but we really want you to think about that data. When we’re creating groups, we’re really creating them based on specific need, and I want to make this explicit. In decoding, fluency, vocabulary and comprehension, those components of literacy, rather than performance level. 

A student reading on level G does not tell me anything about their need. I need to know what is their need in decoding? What is their need for fluency? What is kind of keeping them from understanding what they are reading? 

And so, as you take a minute to look at this data, you’re looking again at second grade, FAST PM2, that domain score and those foundational skills, reading and vocabulary, we’re looking at the DIBELS oral reading fluency, the core phonics survey that has been provided there, teacher observation notes, which are extremely important, to decide which students should be grouped together. 

And I’m just gonna let you know there’s gonna be two groups.

And then what should be the instructional focus for those groups. So I’m going to give you about two minutes for you to review the data and answer those questions. So it’s going to be quiet for a little bit. I’m going to give you two minutes to look it over and take notes.

Okay, I’m gonna give you about another minute. 

And we have about 30 more seconds.

Okay, so go ahead and lock in your notes, and we’re going to talk about this a little bit. Okay, so group one, and I’m just calling it group one, but it contains students A and C.

So, for students A and C, we are looking at the instructional focus of decoding vowel teams. Again, the highest leverage, the skill if taught effectively, is most likely to improve student overall reading success and accelerate their future learning.

There can be some opportunities to interleave R-controlled vowels as they are mastering those vowel teams, because student C has that inconsistency in decoding the R-controlled vowels, too, but we’re definitely going to focus on decoding the vowel teams.

Now, students can get additional support in word reading accuracy, and that’s where we make sure that they have learned how to decode those patterns to read words accurately. We need that for students to be able to do the decoding.

And then, how are they going to practice that skill in connected text? And connected text just simply means words in a sentence, the words in a paragraph, the words on a page. So we’re definitely looking at how they’re going to practice that, because the highest leverage skill that’s going to provide that greatest impact for success for this group is, as we said, applying vowel team patterns to unfamiliar words, and then transferring that skill to connected text. Sometimes, as teachers, we miss that. 

We’re not practicing decoding in isolation. We practice it to make sure that the students master it, but when we give those opportunities for them to then practice it in connected text as they are reading.

And so, with group two, we’ll look, and as Priscilla said, we’re not just looking at that fast PM and stopping. Okay, A, B, C, D. No, we’re gonna say student B and D have very similar instructional needs.

The students there are going to have that flexible syllabication, and that’s the highest leverage, the skill, if taught effectively, finding those prefixes and suffixes, using the number of vowels to determine the number of syllables, then using that set for variability to fix any mispronunciations after they are blending those multisyllabic words together.

If they can do that, that is most likely to improve a student’s overall reading success and accelerate their future learning. 

There’s also opportunities for morphology, right, because we’re talking about prefixes, suffixes, root words. That is also going to support decoding. It is also going to support vocabulary.

 And then, again, pulling those multisyllabic words that students have been practicing for accurate reading, making sure that they can do that in connected text. We want to make sure that we’re giving students opportunities to practice those skills in connected text. 

I really want to be clear about this. When identifying the highest leverage need, we are looking beyond the isolated skill gaps. I know sometimes we get trapped with that. This student needs this particular skill, we’re going to kind of, like, skill and drill and skill and drill. 

No, we really want to look at the underlying skill with the greatest impact on learning. So, for these students, the need is not just simply being able to decode vowel teams or read longer words. I mean, we say that sometimes, just in general. “They gotta be able to read these longer words.”

Your instruction is targeted at helping them to develop the word-reading analysis strategy. That is your sweet spot. When you’re helping students learn how to decode and read multisyllabic words, you really are helping them with approaching a word and having strategies to read those words. 

It’s greater than just the deficit itself, which is gonna then support that overall literacy growth for students.

And so, as we conclude this section on data, we’re still on page six around why flexible grouping works. So, we want to remember that the power of flexible grouping comes from its responsiveness.

We talk a lot about differentiation, but it’s really about how you are responding to students’ needs. Students are not grouped based on fixed labels or overall achievement levels. They are grouped based on specific instructional needs.

And that could be any student across any achievement level, across any label. If they have the need, they can be grouped with a student who may not be at the same achievement level.

This really is going to allow teachers to focus, and provide that focused instruction. They’re going to be able to adjust the support for students as they grow, and they’re going to ensure that you’re using your instructional time effectively. So, those flexible groups are going to be designed so that they do change, you will expect change, because students’ needs will change. 

Students won’t be in the same place at the beginning of the year. We don’t want that, right? We want students to change, we want them to grow. That flexible grouping when you’re using your data, really allows you to provide that targeted, responsive instruction based on students’ needs. And in that, you can support students in increasing their engagement, and knowing that you’re going to adjust your instruction based on the students’ growth, and however their needs are changing.

Now that we have reviewed how to use those multiple sources of data to identify students’ needs, Priscilla did a beautiful job taking us through that, and how we can use the data to create those flexible groupings based on the similar needs.

The next session, if you are going to join us, is going to really be about planning explicit and systematic instruction, and you guys know explicit, I do, we do, you do. Systematic instruction is that easy to difficult, low to high utility instruction, but we’re looking at the data. 

We’re going to start with data, right? And so, as needs change, groups and instruction should change, too. And this is going to be on page seven of your guided notes. So, while flexible grouping is grounded in evidence-based practices, implementation can present a challenge. 

And we’re here, we’ve been in classroom settings, I’ve been in a classroom, I’ve been a  classroom teacher, I’ve been an interventionist, so we understand that there are challenges, and so let’s explore some common challenges educators face, and some strategies that we hope can help, to address those. 

So the first one is around overload of assessment data, which may not provide an accurate picture of student current performance or need. You’ve collected all this data.

What do you do with it? Challenge two. Students’ movement between groups is difficult to determine. Sometimes we’re really busy, and it’s hard to kind of get that and pinpoint that.

And when you make changes, it can be disrupted to your classroom workflow. You got your classroom in a good flow.

Now you gotta make some changes, and that is hard to do, and I totally understand that. So, while these can be major challenges for implementation, there are some strategies that we’re gonna provide you to address them. And so, in the next couple of slides, we’re going to make some considerations for some supportive activities to meet these challenges.

And so, again, we’re on page seven of your guided notes.

So, to address the challenges of that data overload, or even outdated data, teachers should intentionally collect and review data. It is teacher’s responsibility to collect and review the data that you need to inform your instruction and the instructional decisions you make. So you need to have routines in place to make those data reviews a regular part of your planning and data collection as a regular part of your instruction. 

Once you can do that, your goal is around collecting relevant data, and as Priscilla said, not more data. We’re not collecting data just for fun. This is not fun. But we do need to collect that relevant data.

Teachers can really accomplish this by establishing some consistent decision rules. They’re going to be connecting data points to identify students’ needs. So then you know what kind of data you need when you’re doing this. And so you’re going to look to use a systematic approach. And this was something we did when I was at my last school. 

We used a systematic approach about maintaining data folders, digital dashboards, where student data can be captured, accessed, and reviewed. We really need to have a place to be able to go in and input data and see the data.

Having collaborative data discussions. This was really important when we did our PLCs, with our colleagues to verify our interpretations. Sometimes we’re looking at data and we’re like, “I might need to phone a friend,” right? I need to be able to identify, to verify these interpretations, identify the patterns, somebody can help me make decisions and plan for instruction, so in our grade-level PLCs, we did a lot of this when we collected the data. We sat down and took a look at it.

And so, analyzing those multiple sources of data. And when you’re doing that, and you start recognizing those performance variations across data, I remember teachers always saying, well, one, this student did this well on this, and then in the classroom, they didn’t do so great, or they did great in the classroom, but then on their assessments, they bombed them.

Like, we need to be looking at that to really see where the students are. So, some data reviewing protocols, if you have some of those in place, that would be great. We had data reviewing protocols so that we were consistent about analyzing our data across the board, so it didn’t matter who was looking at the data. We had some protocols to say, if this, then this, and so then we kind of could move with a baseline, and then when we met, we could talk more about it.

I’m going to give an example. Say I am preparing to group students for targeted small group instruction, and I have all this data, screening, all the things that Priscilla talked about, work samples, observations. I have data from the previous instructional cycle, because we have lots of data.

That is a lot of information to consider. It can be overwhelming. So, the challenge is that these data points may not necessarily tell me the same thing, or tell me what students need right now, and so I just want to defer to the most prominent data that I have, maybe the benchmark data. So I want you to stop.

Rather than do that, looking at the most prominent data, or just the screening data, we need to pause, and I’m gonna pause, and I’m gonna consider the data that I’ve collected. Having more data doesn’t necessarily mean I have a clearer picture of the student’s current instructional needs.

What I want to do is make sure that the data that I’ve organized, I’m organizing it by recency, the type, which Priscilla went through, instructional relevance: is this relevant? Is this data even relevant to what I’m trying to, you know, figure out about the students? 

Analyze patterns across those multiple sources of data, and when you start analyzing patterns, and you realize, I might need to collect some other evidence before I can make some decisions about this student, you’re going to prioritize that evidence, and you’re going to identify discrepancies, because I used to analyze data a lot, there are going to be discrepancies, and that presents a challenge in itself. 

And so, when you’re doing that, you’re going to identify the student’s most immediate instructional need, based on the most relevant data that you have. Do we always get it right? Not always. Sometimes it’s a process. It really takes time.

And so, reviewing that information, you definitely want to make sure that you’re on point about making those decisions about students’ placement. Sometimes the decisions we make, we might need to make adjustments. Every decision is not perfect, right? And so, when data is overwhelming, we’re going to focus on the evidence that is going to support us in making our instructional decision.

We’re going to really hone in on that. And not every data point is equal. Not every data point is going to need to be used. So more data, incomplete data, outdated data, doesn’t mean you can’t move forward. It just means you need to be more intentional about your data gaps and filling those in. So before you make any high-impact instructional decision, those students are spending some precious time just with you as the teacher, and that is a high-impact decision. 

When you’re doing this. And so ask yourself. Do I have enough evidence to identify the student’s most immediate instructional need?

If not, you need to figure out what it is that you’ll need and collect that data to make those decisions rather than making assumptions. I know, as a teacher, you know your children. I knew my students, and sometimes you just kind of defer to assumptions, but you really want to be careful with that. You want to make sure that you’re using that data.

So, if we’re grouping students based on achievement level, this is my high group, this is my low group, this is my in-between group, without identifying their need.

You could possibly be losing valuable instructional time with the students. And so the goal is to purposely gather your data, analyze your data, you don’t have to wait for those data cycles, you can gather your own data through your progress monitoring, which is that real-time data, to make those decisions.

So, to address the challenge for two, students should have consistent data routines. You’re going to move students between groups based on growth and progress, and you’re going to normalize the idea that change is a way of addressing their needs. This is going to be on page eight.

You’re going to keep students on track and focus on whatever the expectations are for participation and collaboration in those routines.

You’re going to communicate to students that they should see movement between groups. That’s a regular part of our classroom, guys. We’re going to have movement. Using your multiple sources of data to help make those instructional decisions, and then maintaining those predictable routines so students know what to expect and can focus on learning, rather than adjusting to any new routine and a new group. 

So, for example, I have done my assessment data and I have three small groups. I’ve reviewed, and I’ve done that. I’ve done that instruction with these groups, and I’m now looking at their progress monitoring data.

I noticed that some students in the group have started to master the targeted skill, and are ready to move on, while the other students are still needing support with the targeted skill.

I’m concerned about disrupting the routine when I move students who have mastered out of groups or to another group, and then what am I going to do with students who continue to need support? So, when I tell students they’re moving, they want to know why they’re moving, “I like this group, I want to be with my friend,” some students are going to be thinking that they’re not meeting expectations. A lot of things could happen based on, you know, what we’re sharing with students about their growth.

And so, what I’m going to do to address this challenge, I’m going to review data with the students. So review your data with the students, let them know it’s time for you to move on, or you’re going to need a little bit more support. Students are good with that. They actually like to look at their data. Explain that grouping changes because the learning needs change. And they’ll continue to change, because we want that to happen. We want students to grow.

We do want to avoid names, grouping names, like blue group, red group, those labels can be misinterpreted as achievement levels when we do that. So instead, you can refer to the group based on the instructional task or the skill that you are focused on. So reinforcing that groups are flexible and based on current learning needs. 

[I keep looking this way because I see your faces on this side, and the screen is here, so if I keep looking over, it’s because I’m looking at some faces there.]

So, an example for reinforcing that is an example. So, this is an example. Students working on vowel teams, “Please bring your reading folders to the small group table.”

The students know they’re the group working on vowel teams, and if you have multiple students working on different vowel teams, you can make it more. But that just really lets students know, oh, I’m working on vowel teams, here I go, and they’re already set.

So you want to keep those routines consistent, so students are focused on the learning task, and we know students get concerned about who’s in their group and where they are moving to. So we want to make sure that we are focused on skills that students need, and not the groupings themselves. So, when we’re giving these consistent routines, it really helps us to make purposeful grouping decisions about students.

And so, with that, we’re getting close to the end. So, we are going to talk about some practical ways to get started. So, what you can do is begin by focusing on what is within your sphere of control, and I know people say that. Now, some challenges will require some broader system-level support.

Now, teachers can really focus on the practice, because we have a lot of that power within the classroom to focus on practices, routines, and instructional decisions.

We influence directly. So small, intentional actions can lead to some big, improved teaching instruction and increased student engagement and learning outcomes. So I’m just going to give you a quick cross-reference example for starting small and making those incremental changes, right? 

So in the medical field, when it is determined through patient data, we talk about data, that there is a need to prescribe a medication for treatment. There’s something we have to do.

Healthcare professionals then identify the need. They prescribe the medication to address the patient’s specific need, and then they monitor. So we’ll be monitoring the progress to the response to that medication.

As you’re monitoring the progress, what are you doing? You’re gathering more data on the patient’s response. They are gathering on medication and their overall treatment plan. So, this is really helping to guide their decisions about making adjustments, needed adjustments and next steps to determine and ensure that medication is safe and effective.

And they’re looking out for the overall patient well-being over time, which is what we’re doing for students. We are really looking out for their overall well-being over time by monitoring them, by checking on them, by making sure that what we give them is exactly what they need, and when we give them what we think that’s going to work for them, that we’re monitoring that, and we’re checking on them regularly. 

You don’t have to make big sweeping changes. Just small, incremental changes will do the trick.

And so, tips for a successful implementation. So, this is on page nine of your guided notes. Priscilla talked about this cycle earlier, using your data, identifying the need, determining the focus, creating your groups.

I know we’ve been talking about this, collecting and reviewing your data, adjusting your instruction and your groups. So, we really want to keep that cycle going so that we’re being very responsive, purposeful, and we’re focused on helping students make that meaningful progress. That is our focus.

Number one, we want students to make progress. When our students are growing, we are happy, we are over the moon about that, and so we really want to push and move students in that direction. 

And so, we’re going to use those multiple sources of data that Priscilla talked about earlier, screening, diagnostic, to identify some patterns. We’re going to start with one targeted skill, and I know this can be difficult.

Avoid trying to teach multiple skills in one small group lesson. Now, your one small group lesson may take two or three times to meet with those students before things start to move forward. But you want to stay focused on one targeted skill at a time.

You’re going to create those groups based on the identified need, and then you’re going to, like we said with the medical field, monitor students’ responses continuously. We’re questioning, we’re doing our observations, we’re progress monitoring, we’re doing those quick, formative checks to determine when the students are making growth, who is still needing some support, and how we regroup. 

And so we’re going to keep these groups flexible, based on data, rather than those fixed achievement levels. And I know, I mean, I’ve done that too. This is my high group, this is my low group, but we really want to make sure that we’re looking at our assessment data to make those decisions about students.

And so, here we go as we wrap up. We are coming back to your statements to decide about agreeing or disagreeing. This is on page ten. You’re going to check your notes, and we are just gonna go ahead and see what we’re gonna do with this. 

So, statement one. Agree. Flexible grouping based on data allows for instruction to be responsive to students’ changing needs. If we do fixed groups, we can really limit students’ growth. And not only that, we can be creating low expectations for groups that are considered low or middle. We really want to make sure we’re using our data.

And so, statement two, disagree. Grouping alone, the Hatties Research 2009, right, Grouping alone will not improve achievement. Without that intentional planning, clear goals, targeted instruction, students may not meet their needs. We might not be meeting their needs or supporting their success, and that would not be good, right? 

And so, statement three. Disagree. Students really need change. Their needs change over time, so we’re going to make those changes as their needs change. So, keeping students in fixed groups can really limit instruction matched to the students’ needs, and it can reduce engagement. That could be a side effect of that.

And so, statement four. Agree, progress monitoring, and Priscilla did that beautifully today, helps us to make those informed instructional decisions as it supports the identification of those learning gaps, and we can make those adjustments in our groups and make sure that our students are getting exactly what they need. 

And then five, those multiple sources of data. Again, Priscilla walked us through that, showing a more complete understanding of the students, what their needs are, what they have learned, and that’s going to lead us to more effective small group instruction that’s better aligned to the students’ needs. And that’s what we want. 

So which statements did you change, you’re thinking about? And just think about that, and why.

So, you can join us for the next session, which will be coming up, and we’ll be advertising this, where we’re going to discuss high-impact teaching practices. So, we talked about the data, now we’re going to talk about teaching practices.

Really going to be focused on incorporating evidence-based instructional strategies and evidence-based routines for providing targeted small group instruction. And again, we’ll address some common challenges for implementing these components during small group instruction in a class. 

So if you can make it, you see we’re moving right along to the next one, that’d be great. We’d love to have you.

And so now we’re going to take the opportunity for you to scan this QR code on the slide, to take the survey. Your feedback is extremely valuable to us, and it helps us to evaluate the effectiveness of our sessions. We really want to improve on future presentations. I’m developing them, and I’m working on them and available resources that can better support your needs. 

I’m going to give you about two to three minutes to go ahead and scan that code. I checked it this morning, so it should be working. Make sure you check the box for today’s date, 9/22, because this is the one that you are in today.