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This video by the Data Quality Campaign explains the importance of collecting and using quality data to transform our Nation's education system and improve the achievement of every student.

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Collecting Data & Tracking Data (Chapter 1 & 2) from Teach Like a Champ 2.0

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Collecting Data & Tracking Data (Chapter 1 & 2) from Teach Like a Champ 2.0

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Teach Like a Champion Technique 18 - Check for Understanding Part 1

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Teach Like a Champion Technique 18 - Check for Understanding Part 2

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Teach Like a Champion Technique 22 - Cold Call

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Teach Like a Champion Technique 49 - Normalize Error

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Affirmative Checking - Check for Understanding and Ratio - English

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Using Data for Meaningful Classroom Change

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Using Data-driven Decision Making to Support English Learner Students

REL Pacific
August 2, 2021

This blog is based on Part 1 of our recent two-part webinar series on Effective Data Use to Support English Learner Students, presented by our colleagues Eric Crane and Elizabeth Burr from WestEd.

Data-driven decision making, a systematic process for collecting and analyzing data regularly as part of the education decision-making process, is a critical strategy for addressing the varying needs of different groups of students, including English learners. Put simply, data-driven decision making re-quires two essential components, each of which can be complex and multifaceted: technology tools and human capacity. Technology tools, such as data warehouses, student information sys-tems, instructional management systems, and assessment systems, provide for the collection, stor-age, analysis, and reporting of data. Human capacity, or data literacy, allows individuals and systems to inform and change practice or redistribute resources based on data. In other words, if technology is the "what?," of data-driven decision making, human capacity is the "what's next?"

Creating a Culture of Data Use

Creating a culture of data use can help facilitate consistent data-driven decision making by setting a shared expectation for attitudes, values, goals, norms of behavior, and practices, accompanied by an explicit vision for data use by leadership for the importance and power that data can bring to the decision-making process. Mandinach (2012) identified five essential characteristics of a culture of data use in education settings:

Data are part of an ongoing cycle of instructional improvement.

Students learn to examine their own data and to set their own learning goals.

District or school leaders and staff, along with the community, set an explicit vision for how data should be used in the district or school.

Supports and resources are provided to establish and sustain a data culture within schools, including setting aside time for teachers to review and have discussions based on data.

The state or jurisdiction is responsible for developing and implementing a systemwide data system.

Embedding expectations for data use within the system can have multiple benefits, fostering collaboration within a school community, enabling educators to more easily spot data patterns (or shifts in patterns) and adjust their instruction accordingly, and flagging potential areas for professional development.
Not All English Learners are Alike

When we're considering how to use data-driven decision making to inform practice for English learner students, it's important to keep in mind that there are different kinds of English learner students. When comparing students' performance and progress to those of other students, it helps to have the closest comparison that the data allow, which requires comparing within type. For example, English learner students may include newcomers, or students with limited or interrupted formal education, long-term English learners, or English learner students with disabilities. Although some datasets may not allow for these within-type comparisons, being able to make precise com-parisons can make it easier to make inferences about an individual student's performance or progress and from there, to best decide what supports that student may need.

Key Data for Monitoring the Progress of English Learner Students

For data to be effective and useful, they need to be clear and unambiguous, consistent (the same data are collected over time so change can be measured), feasible (connected to potential actions), and meaningful.

So what are some key data for monitoring the progress of English learner students?

Content assessment results: English language arts, mathematics, other content areas.

English language proficiency assessment results.

Teacher reports of student's language learning progress: Teachers are often the best source of data for how students' English language skills are evolving. And in fact, if we can get observations in more than one setting, then we can have a more complete picture of the student's level of language mastery. And again, teachers can often identify the difference between a student's conversational English and academic English, so how a student is talking on the playground, for example, versus with the more academic terms in the classroom.

Family input: Surveys, interviews, informal conversations with parents or other caregivers.

Students' cumulative files: Report cards, attendance, behavior history, participation in programs or academic interventions, primary language, English language proficiency.

Data on other factors: health, environment, past education, trauma.

Questions to Consider

The following questions can help guide your conversations as you think about how to best use data to support English learner students within your own context:

In your state or jurisdiction, are data available that allow you to compare the performance of English learner students to other student groups?

Do the data allow for finer comparisons to true peers?

What do the data say about the time it takes English learner students in your context to become proficient in English?

For students who continue to receive English learner services between five and seven years, what did the data say about their performance?

How large a group within the English learner student group is that group of long-term English learners, and how do they perform on other measures?

If the data don't allow some of these questions to be answered, what would it take to get there?

For more on this topic, view the full webinar, and stay tuned for our next blog on considerations for English language learners within an assessment system! ____________ Footnotes:

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educational improvement; excellence in education; educational research; statistics; statistical data; statistical surveys; regional educational laboratory program; rel; rigorous studies; fast response projects; no child left behind; american samoa; the commonwealth of the northern mariana islands; the federated states of micronesia (chuuk

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and yap); guam; hawaii; the republic of the marshall islands; and the republic of palau

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Using Data-driven Decision Making to Support English Learner Students

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REL Pacific is dedicated to promoting IES' goals to ensure the rigor and relevance of research studies and projects along with the widespread dissemination of research findings.

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Data Driven Instruction in Inclusive Education

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Data Driven Instruction in Inclusive Education

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There was a time during my teaching career when second- and third-graders wandered aimlessly through books they chose, wrote in notebooks about self-selected topics, and marched through math exercises quickly. The teachers documented their students' progress in anecdotal notes about their learning behaviors, not their learning progress. They listened to students read and recorded if it sounded "choppy" or "smooth." Notes were dutifully written in the margins of students' writing assignments, complimenting them with no mention of writing principles. Pre-assessments were given at the beginning of every math unit, and tests were given at the end. Scores were entered at the end of the unit with no other data because no other information was gathered.

It was a fluid, organic classroom with little information for teachers to learn from, and it spurred my curiosity about data-driven instruction. How could we do better as teachers to understand our students and guide them properly?

In the 1990s, I worked hard to put into practice what researchers were saying about effective instruction as I implemented reading and writing workshops. In a multi-age, multi-ability classroom of second- and third-graders, I studied and implemented best practices for mathematics using pre-assessments to determine individual and group instruction, along with designing tiered assignments for flexible grouping.

Developing new instructional practices was the focus as practices of the last century were being pushed to the wayside, but data collection, analysis, and implications were not truly on my radar until the early 2000s with the introduction of No Child Left Behind.

Since that time, data-driven instruction has increasingly taken center stage and rightfully so. But what is data-driven instruction and why is it so important?

The Importance of Data-driven Instruction

Our sole purpose as educators is to ensure that every student learns. Gathering and using information about what has been learned and what needs to be learned is pivotal to that. Without data, students in schools would be like patients going to doctors' offices and being prescribed treatment according to their age instead of their symptoms. Too often still, next steps for instruction are determined by set lesson plans, curriculum guides, and textbooks instead of student readiness and need. We need to look at the students as individual learners to provide the best instruction. That's where data-driven instruction comes in.

Simply put, data-driven instruction is using information gathered from learning results to determine what comes next in instruction. Within a classroom, data comes in two forms: formative and summative. Formative is data is the information gathered as teaching and learning are occurring. Summative data is the information gathered at the end of a learning period, typically a unit of study or a pre-determined period of time. Effective teachers collect both forms of data and analyze it to look for patterns of success and needs.

Oftentimes what our gut tells us as teachers isn't what the numbers show. Once, I met with a kindergarten teacher who was in her first year of teaching to discuss the reading needs of her students. She brought basic reading data on letter recognition, letter sounds, vowel sounds, CVC patterns, and known sight words to the meeting. After looking at the percentages of what the students knew and didn't know, it became apparent that the students knew all of their letters and sounds but were struggling with CVC patterns. Specifically, they were having difficulty putting the sounds together to make words. The teacher, who was in her first year of teaching, wanted to focus her instruction on the student memorizing the sight word list. Her reasoning was that she thought sight word recognition was important. The data indicated otherwise.

After some discussion of what the data showed and what research said about early basic reading skills, the teacher left with plans to design activities based on her students' needs. Had it not been for the data, this group of students would have had instruction focused on what the teacher thought was needed instead of what they actually needed.

Using Data to Design Your Lessons

Data collection and analysis can be overwhelming and time consuming, but it is critical for meeting the learning needs of students. If data-driven instruction is new to you, start small. Here are some steps to ease yourself into data-driven instruction:

Begin with one subject

Decide what skills and knowledge you want students to know and understand

Determine what data you will collect and how you will collect it

Narrow your focus on the key concepts/skills you want the students to gain

When considering how to gather the data you want, keep in mind when you will collect it. Will you get the data by asking three questions at the end of the lesson or at the end of four days of instruction, etc.? This is the collection process for your formative data.

After gathering data and analyzing the results, you will know if you must reteach some or all of the concepts or if you will be able to move on. The results will also tell you which students need remediation and which ones need acceleration. The data should inform your instruction and guide your lessons. Adjusting your instruction along the way based on data will improve students' learning and your end results, or summative data, will be better.

Be informed about the results of your teaching and let data be your guide to better instruction.

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Using Data-Driven Instruction in Your Classroom

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Data-driven instruction has increasingly taken center stage and rightfully so. But what is data-driven instruction and why is it so important?

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Special Education Data Can Improve Learning For All Students

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Special Education Data Can Improve Learning For All Students

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I've spent my whole career helping people be more effective at their jobs by giving them better information and better tools. The idea that teachers would use data to make sure every student gets the support they need seems intuitive. But it doesn't happen in most schools, in part because they lack the right technology.

I had a conversation the other day with Joe Gallagher, student supports coordinator at Milwaukee Excellence Charter School, who described how data and technology inform the special education strategies they use to support the neediest students. That information, however, benefits everyone in the school: students, teachers, parents, and administrators.

Gallagher reflected on some successes and insights.

Inclusion Techniques Benefit Everyone

Studies show that pulling students out of class creates a stigma and costs them valuable classroom time, making it harder for students to catch up. This can impede a student's efforts to get a diploma, even in cases where more moderate accommodation and intervention strategies could have been sufficient. The studies also show that other students in the class benefit from inclusion as well.

Gallagher pointed to another benefit I've heard discussed less often: the pluses for teachers. He described a common scenario of sitting with a teacher to review mandated supports for students with IEPs. They'll agree that the best approach is to give students multiple options for how to display mastery of material. The teacher will then realize that the approach will benefit many other students as well.

"So often the accommodations that students receive under a tiered support structure or IEP results in differentiation that is actually the best practice for increasing the likelihood that every kid is going to be successful," he said.

That's not to say that differentiation is easy, especially if teachers don't have the right tools to help them know where to start.

Data-Driven Strategies Help All Students

Gallagher also described past struggles getting parents and teachers aligned around student behavior.

"If the teacher says, 'Sarah's been talking out of turn in class,' the first question everyone is going to ask is, 'How many times did Sarah talk out of turn in class?' Was it five times or fifty times?" he said.

Before using the Schoolrunner app, Gallagher said that type of data was hard to come by. If he wanted it, he'd have to spend time manually noting it, instead of walking around the classroom helping kids. Data collection tools for teachers help take the either/or out of the equation.

When thinking about using data in a special education context, Gallagher said most teachers believe they don't need hard data because they already know the individual students so well.

But then he flipped this idea: "We're starting to build the habits of using the data so that we'll be ready when we grow."

The genius part of this strategy is that analyzing data in the abstract can be a challenge for anyone. However, practicing data-driven strategies when you already have an intuition of what the answer should be, is actually the best way to build confidence in your skills before moving on to larger data sets with more unknowns.

Data Can Motivate Students

Behavior data at the school level often means having a record of the number of referrals and suspensions for each student and not much more.What a limited conception of how a student is engaging in the cultural values of the school!

The more that behavior is a vague concept to students, the harder it is to address their challenges. When trying to work with a student on their behavior, Gallagher said he asks, "What do you think your most common infraction is?"

He can then report the answer, using data. And he can also use more positive data as a counter-balance. He can note that the student has also been recognized for integrity numerous times. The ability to track clear goals for personal organization or anything else can be a big motivator for students.

"I had kids who had an IEP goal to complete their homework two days a week. John now does his homework on average 4.5 times per week," he said.

Gallagher also talked about recognizing signs of other problems in this data. For example, if he looks at his dashboard and sees that the ratio of negative to positive behavior reinforcement for a teacher is 2:1, what does that imply?

"If you're a first-year teacher struggling with classroom management, you end up spending your time doling out consequences. Instead, let's work together to address the underlying issue, for example, by ensuring your instructions are clear and consistent," he said.

Without access to the data, it's impossible to systematically spot potential problems like this.

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Special Education Data Can Improve Learning For All Students - Market Brief

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What happens when educators take strategies designed to accommodate special needs and see the benefits for all students? I had a fascinating conversation...