Maria Waltemeyer: Good afternoon everyone.
My name is Maria Waltemeyer and I'm with the National Student Attendance, Engagement, and Success Center.
Welcome to today's session number three titled, What We Learned About Data Systems In Early Warning Systems:
Designed for Districts and Schools. Please allow me to share some brief details about this session.
It will last for approximately 45 minutes including time for interaction and questions.
Our presenter is Paul Verstreate.
Paul has the masters in Teaching Curriculum
and in a master degree in Educational Technology from Michigan State University.
Paul is a facilitator for the School
and Students for Services Division at Talent Development Secondary Everyone Graduates Center at John's Hopkins
University. He has worked for JHU as an early warnings system facilitator,
supporting middle and high school implementation of the model in various cities across the country.
His work has concentrated on early warning systems and tier interventions.
Nationally, Paul has provided professional development on dashboard data analysis,
tier interventions and use of common planning time.
There will be multiple opportunities to enter questions via the chat function.
And discuss implications of the information presented.
Please feel free to log your questions and comments as Paul is presenting.
Maria Waltemeyer: If we do not have enough time to get through all the questions,
we will reply to all participants by email within five to seven days.
We're going to be placing everyone on mute, and this session is recorded.
Please know that you will receive a link of the recorded session.
We encourage you to ask questions and share your comments.
Before we transition to Paul's presentation, we would like to share with you the mission of our National Center.
The mission of the Center is to disseminate evidence based practices and build and facilitate communities of practice to help students attend every day, be engaged in school
and be successful academically so that they graduate high school prepared for college, career, and civic life.
So here's Paul.
Paul Verstreate: So sorry about that little technical issue. I thought I was talking on mute for a second.
Okay, so, thank you Maria, for that introduction. I did wanna add a little bit to my biography.
Having worked for JHU for the past six years now, I've spent a lot of that time working on data.
I'm the child of an art teacher and an accountant, so I love my data. But I love my data to be deep and meaningful.
And so just happy to have a conversation for the next 45 minutes about deep and meaningful data.
And how to make sure that our data is deep and meaningful for our students. And for their benefits.
And so this presentation is a lesson about data in five lessons.
So it's what we have learned about how to use data and data systems to better impact our students and their futures.
We have about five lessons we're gonna go through. Please, again, type your questions into the chat box as we go.
And so let's get started with those lessons.
The first lesson, as a data person, I love data, I may have started out in the classroom.
My career has turned a little bit more towards the spreadsheets direction over these past couple years.
But still in my mind is that classroom, looking out at my students
and being able to find ways of impacting them as greatly as I can.
And so when I talk about data and data systems, I'm talking about more than just the computer screen.
When we talk about data systems,
that's the first thing that comes to our minds is that computer screen that shows the data on it.
You're all right now looking at your computer screens.
So when we think of data systems, we think of that platform either web or individual desktop platforms that we use.
Anything from Excel to our district wide student information systems.
A lot of us, when we think about data systems, we think about that platform.
And so, I want to change that to when we think about data systems,
we think of about the processes that in which we use data to better our students' lives.
And so, how do we take that data as educators?
How do we integrate it into our daily activities to modify our teaching with it and support our students?
So just to start up with this process of product conversation, I'm going to be focusing primarily on the process side.
Except for as the product can be used to inform that process.
When it comes to district data systems, your IT department and your IT professionals are better situated.
To let you know which data systems and which platforms are out there that best fit within your network
and for your servers and things like that. So this conversation is gonna focus mostly on that process.
And when I say process,
I wanna make sure that I get across that I'm talking about a process that focuses on student impact.
A process in which we can help our teachers, our parents, our counselors, administrators, district personnel,
even the students themselves, to improve their likely outcomes. So that is the goal.
That is the goal of our data systems. It's to make sure that we improve their likely outcomes.
And that's that first lesson.
The first lesson is whatever data system we're talking about, we want to make sure that we emphasize the process.
The using of the data over the product itself.
If teachers are more comfortable using Excel spreadsheets over using a fancy web based data system.
Focusing on that process over the product is vital to making sure that data is used on a more regular basis.
So that's our first lesson. The second lesson that I want to talk about is knowing your audience.
So when we talk about data systems, we're talking about data that can be used by multiple people, with multiple goals,
and multiple jobs.
And so when we are evaluating data systems when we are looking at how well the data systems are impacting our students.
What we want to talk about are those audiences.
And, so the audiences that we have when it comes to our Student Information Systems
and our Student Data Systems starts with the students themselves.
Then it also includes our parents, our guardians for those students.
The teachers are usually the most recognizable audience when it comes to student information.
They are the ones who are working most directly with the students' teaching and learning situations.
Then it moves up to the school itself where we were including administrators, counselors, social workers
and support personnel that are organized around the classroom, around the students.
And then finally we have the district.
The people that are generally the ones who decide which data systems the school districts will use.
And when I mean that they're deciding which data systems that school districts will use,
I mean those student information systems that they use.
Where that information is inputted into the system about the students.
The problem with that situation,
not that the administrators have that wonderful global perspective of every school in the district.
And that's an important perspective to have when thinking about which data systems, both the platform
and the process to implement for your school and your students.
But it's also important that we think about all of the audience in that chain down to the students,
and including the students.
Making sure that the data systems,
the platforms are usable at each of those audiences to make sure that we have our greatest impact.
It gets back to that old trite saying about the forest and the trees.
It's important to have both perspectives
when we're talking about all of our students versus a single student that we are are trying to support.
And increase their likelihood of graduation.
And so once we have our audiences in mind, it's important then to move on to that data for action.
Just like I said at the beginning the data systems that we have out there need to focus on ways in which we can improve
our students. I think I said that at least three or four times so far.
In that if we have a data system, both a platform and the process of people in which we are talking about data
and organize ourselves around data. In which that data is never actually used to work with the students.
Then we have missed the point entirely of a data based system.
And so this third lesson of making sure that we have data for action is one of the most vital ones.
In that all along this entire chain of audiences, each of them has a different perspective on how to use that data.
And our data system needs to take each audience into account.
Their goals and their needs for that data system, starting with the students themselves.
The students are the generators of data. They are the ones whose attendance that we put into the system.
They are the ones whose behavior referrals we put into the system.
They are the ones whose grades we put into the system, proficiency scores we put into the system.
They are the generators of that data.
And so we need to be able to make sure that our data system is able to capture that data,
In as easy a format as possible.
So that way we can then move on to our other users of the data, which we have our parents
and guardians as concerned monitors.
In many ways they are looking at their student's data, the report cards, that specifically.
They are looking at that to make sure when they look at that report card, they don't see any issues.
When you're looking at a report card you're looking for Fs or your looking for As.
You're looking for the number of absences versus the number of behavior grades.
A lot of our schools record a citizenship grade
or a motivational grade for how well our students are working in the classroom.
And parents and guardians are looking at the report card to make sure that
when they see the information on their students, they can track to see whether
or not their students are going to be successful.
That moves us again, to the most obvious the most recognizable user of data,
which is the person at the head of the classroom, the teacher.
The teacher, when using that data, beyond monitoring, which they do, beyond generating the data,
which in many ways they do. They are the ones entering the attendance, entering the course performance.
Beyond that, they're also looking for signs of teaching and learning to be able to see both sides of the equation.
To be able to see whether
or not the actions they are taking in the classroom are having an impact on their student's learning on their
proficiency. And so when they're looking at the data, when they are entering it in.
They wanna be able to determine whether
or not the best practices that they are engaging in are having the impact that they hoped for at the beginning.
Paul Verstreate: Moving on to the next step, when we're talking about the whole school.
We're taking a little bit further step away from the individual student,
and now we're talking about groups of students.
In many ways, the school is looking at different groups of students and grade levels, socioeconomic status.
They're looking at English language learners, and they're looking at the ethnicity, gender.
They're looking at all of these different groups.
And they're looking for gaps, to make sure that as they support their teachers and support their classrooms.
And make sure they're doing everything they can to help those who are directly supporting the students,
that there aren't these gaps cropping up between the different groups of students.
And using that data then to make sure that if there are gaps, to make sure that they can impact those gaps
and improve people out.
And then finally we move on to the administrator level, to the district level,
in which we're taking a further step away from the individual students.
At the district level, it's no longer whether or not this little boy
or this little girl is able to improve under their test scores or are able to show up.
In many ways, the district level is focusing on all of the little girls and little boys,
they are taking everything into account, from school to school, from classrooms within that school.
They're looking at this data in many ways for accountability, and making sure that not just teacher accountability.
I want to make sure I make that clear when it comes to accountability.
That I'm not just talking about individual teachers in their classrooms and what we think about
when we hear accountability in the news.
I'm talking about making sure that the district gets a return on their investments.
Making sure that if they bring in a program, that is meant to support students.
That they can then look at the data and make sure that it's having the impact that they want on that group of students.
And that's what I mean by accountability. It's the best efficient and targeted use of resources possible.
So that way the district is able to make sure that schools are reaching their goals.
So when it comes to data for action, the data system needs to lead to that action.
If the data system never gets there, again, it's important to note that we haven't reached our goal for data.
That if I can't show that a student of mine has gone from one level to the next
or if I can't show that I've been able to implement this many hours of support.
If it doesn't actually reach the point where I am interacting with my students, where I am changing my practices,
or I am working with some groups of students more than others to better target my support.
If the data does not get to that point then we have failed in our use of data systems.
Paul Verstreate: Which then gets to the next step which is what are those interventions that we're going to be doing?
So what are the things that we are going to do to make sure that we show that impact?
Because if that impact is our goal in the end we have to be able to show what our interventions were that brought us
there, otherwise we're lost in the woods, going back to that forest and trees metaphor.
We're lost not knowing where we are and where we hope to be.
So we need to make sure that the interventions that we are implementing for the students, that we track that.
That we make sure that we keep an eye on all of the work that we're doing.
So the interventions themselves, the support for students, the best practices that we use in the classroom,
the programs that we bring in. That we need to make sure that we track that data.
And that gets us to making sure that what we're doing is working, so that accountability piece.
And so I'm gonna spend a little bit of time on this matrix up here,
because this is one of the most important parts of a data system.
It's because this is the part in which we are able to determine if what we're doing is working,
how to improve on what we're doing, how to make sure if it's not working, how we can modify it.
And again, if it is working, how can we plus it up and make sure that it works for either more students
or is in some way able to have an even greater impact with the same student.
I wanna focus on this matrix for a little bit.
And so when we talk about the data so far, we are talking about both implementation data,
which is as I was saying the interventions, what we're doing to support our students as well as the impact data.
How well did what I do with those students increase their likelihood of having a better outcome?
And so I wanna start on the impact side first.
So on the impact side, what I'm talking about
and which is one of the question that was brought up during the registration,
is what data do we use to track our impact? And so what data points do we use?
And so I wanna bring this up as kind of a lesson zero
or a base line lesson for all of the lessons that I've just presented.
In that the data that we track needs to not only be accurate about the students.
When I talked about the students, the data that I found in the data system is valid for the student.
It's accurate, the attendance that the teacher's putting in is showing exactly when the student was there or not.
The demographics are accurate.
It has to be a true representation of who the students are, as well as be predictive of those student outcomes.
So data that can not only describe what's going on with the student now.
But also what their likely outcomes are for the future.
Is the best kind of impact data to track, because not only does it help to know what's going on now,
but it helps you know looking in your students' futures whether
or not they will be successful after they leave your classroom or your schools.
And so the best data for this that I have seen is engagement data. It's the early warning indicator data.
Which is attendance, behavior, and course performance.
In other words, it's showing up, knowing how to act, and doing the work. This data is the most basic of student success.
Because it's the most basic of any type of success.
If I show up, act right, and do the work, I'm going to be successful at what I'm doing.
Just like with students who show up, act right, and do the work in class they are going to be successful.
And so every day that they are able to show up, do the work, and act right creates a track record of success,
the habit of success that will follow them into the future.
And so when I'm talking about impact, that engagement data of the tenant's behavior
and course performance is the best way to be able to track whether or not what you've done works.
Because it will predict the student's futures.
It will predict whether or not they are going to make it to that graduation day and walk across that stage.
And so that's what I'm referring to when I talk about high impact. Sorry to take a brief detour there.
So what we want with impact is we wanna show that they weren't showing up a lot, and now they are showing up a lot.
Or they were having behavior problems, and they're no longer having behavior problems.
They were failing their classes, and they are now no longer failing their classes.
So that's the difference between high and low impact when I'm talking about engagement data.
So that's on the left hand side of our matrix. Up at the top, we're talking about implementation data.
Implementation is what I've done to make sure that the student shows up, acts right, and does their work.
So I'm talking about all of the programs we bring in, all of the best practices we do with our students,
all of the one on one conversations, the mentors, the social workers,
all of those things that we do to make sure our students succeed. That's the implementation data.
And where the low and high of each of those meets, it has a school and a students and a teacher,
trying to figure out different ways of moving forward.
So let me talk about what I mean by that by talking about the upper right hand corner.
High implementation and high impact. This is where, whenever we do anything, we wanna reach.
That means that with high implementation, I've done everything that I said I was going to do.
And with high impact, I had a goal that I set out to reach and I've reached it. And so when we call home with parents.
When we have one on one conversations with our students. When we are doing tutoring with our students.
The better we do that intervention, the higher the impact will be. That is that upper right hand corner box.
That's where we're trying to reach. That's number one goal for any intervention program.
Then we move into the upper left hand corner, where we have high impact but low implementation.
I like to think of this as the second best, because if our goal is high impact, to get kids to show up more,
act right more, and do the work more, then somehow we've reached it here. Meaning our kids are having better outcomes.
We're not exactly certain how we reached that, because the things that we said we were gonna do,
that we hoped to have this impact, we didn't do very well. But we still saw growth.
And so this is where we have to do a little bit of research
and find out what did happen to be able to create that impact.
And whatever that was, if it's possible to replicate it and continue using it.
Was there a part of the implementation that had this wonderful impact?
And therefore we can just carve away the excess and focus on that one thing that had that impact?
And so this is where when you have that mismatch you wanna try and find out why.
The third box, the lower left, where we have low impact and low implementation.
This is the beginning of our non-desirable outcomes.
Because, again, the focus is on having better outcomes for our students.
And so, with low impact, we don't have better outcomes for our student.
But the good thing here, if you can say that there's a good thing when you have low impact,
is that because we weren't able to do the things that we said we were going to do.
It's an argument for trying to overcome whatever barriers came up.
If we were gonna try
and make sure we had phone calls home with every single student the first time that they were absent.
But for some reason maybe because phone numbers were inaccurate in the system
or maybe because there wasn't time at the end of the day in which we were gonna have these phone calls.
Whatever it was, a barrier came up to where we couldn't do the intervention we said we were gonna do.
And therefore, we didn't have the impact we thought we were gonna have.
And so, if we find ourselves in the low-impact, low-implementation box,
what we wanna do is to able to make the argument for.
But if we're able to overcome those barriers and have high implementation, we will have high impact.
We will move from the lower left to the upper right. And now, we get to the worst box of all to be in.
Which is that lower right box. Which is we've done everything that we said we were going to do.
We met all of our goals for implementation, made all of our phone calls,
worked with all of the kids that we said we were gonna work on, do all of the best practices we said we were gonna do.
And we did not reach the outcomes we wanted to reach.
This is the most morally, morale deflating box to reach because it requires a rethinking of your assumptions.
And that is the silver lining to this box.
Is that not only do you reach this box because some assumption about what you were gonna do
and how it would affect the students was incorrect.
And if you could change that assumption and rework how you think about your impact,
you will be able to move into one of the upper boxes.
And so again, that last box is the box that whenever we do anything, none of us ever want to get into.
And so that's the matrix for overall implementation and impact.
This is how we are able to determine if what we did worked.
And this is done at every single level of that audience chain.
That if the student is asked okay, I want you to be able to go to this tutoring and it will improve your grade.
They can say, hey I went to that tutoring every day and it improved my grade or didn't.
We can talk to parents during our parent teacher conferences and say if you do this with your student,
then you will see the outcomes, the same as the teachers with schools in the district.
Once we start using this matrix to be able to guide how we work with our students, we are able to see whether
or not what we're doing actually works. Which then gets us essentially to what we're doing.
A couple of quick bullet points on how to tell and track what you're doing.
So that way, whatever box you reach, you'll be able to either make those modifications
or continue doing the things that you're doing to have greater impact.
So, to be able to track the things that you're doing,
the first thing you need to do is to know what your intervention is, a good description of it.
To know what your goal is. Where do I wanna see my students at the end of the intervention, whatever it is?
The target is what piece of data do I want to change?
So if I have poor attendance, then I would like to reach a goal of attendance where my attendance has improved.
The duration, how long you're going to have this intervention last, your intensity,
which is how much do I have to work to reach my goal? So, am I working every day?
Am I working once a week on this or am I doing this every period or is this a one a month,
what is my intensity of that level of work?
And that last one, the intensity will help you to determine whether
or not it's an intervention worth doing based on the limited resources that a teacher or a school has.
And then the very last one is a champion.
A person that can hold and is held by others accountable for whatever intervention is there.
At all the schools that I have worked at,
the interventions that have the best implementation are the ones in which one, maybe two, people are leading
or championing that intervention.
That they are doing the work to pull in other people to make sure that it gets done,
but they are the ones making sure that the resources are available.
That if we can have these one or two people that are championing an intervention,
it's more likely that you'll end up in that upper right-hand corner of our matrix.
And that is interventions as data, that is lesson 4. Which then gets us to our last lesson.
Time as a Precious Resource.
When we are talking about interventions, and when we are talking about analyzing data,
a lot of times people are thinking of creating spreadsheets, and creating graphs and tables,
and analyzing all that data to see what's going on.
Rarely do people think about the next step after that, the step that I've been talking about this entire time,
which is the action phase, which is doing something to help our students phase, which is those phone calls home,
the mentoring, the tutoring. And so I have this general guide for when I think about data versus action.
And again, I need to point out that I am a data nerd. But I love data and enjoy data.
My dad, the accountant, once said that whenever he would fill out a student, not a student, a person's tax returns,
and all the numbers added up, he would get this little smile on his face, and I know I got that from him.
And whenever I would put my data together and all the numbers would add up and my graph would come up,
and it was great. I mean, I even turned the idea of talking and using data into a graph.
But there's also that part of me that was in the classroom for several years,
that looked out over his students for several years.
And the time spent talking about data needs to vastly be overrun, and defeated, by the time using that data for action.
Which means that whatever data platform that a school or a district uses,
it needs to provide the users of that data with quick directions towards action.
Paul Verstreate: That every minute that I am working on graphs and tables,
needs to be followed up by 20 minutes of using that data for action.
For creating that impact with the students that I've been talking about.
So making sure that their outcomes are improved.
And so this is my reminder to not fall down that rabbit hole of making those pretty graphs
and thinking about the size of the text and the color of the bar graphs and all of that.
When it comes to data, if we are spending more time talking about it then using it,
again, we are on the wrong side of that equation. And so that gets me to one of my favorite quotes.
I'm a big fan of quotes that's the deep and meaningful side I get from my mom the art teacher.
And that whatever data you do have, it is important that whatever we do, what we can with it, wherever we are with it,
with whoever we are with it. That nothing is ever going to be perfect with our data.
That our data is going to bring up a ton of questions that we would likely need to go and find answers to.
But in the meantime, we still have those students that we're facing every day.
And so before we spend our time trying to answer those questions,
we need to make sure that those questions that come up.
Because, every time we look at a graph a question pops up about that graph.
We need to ask ourselves whether
or not answering that question is vital to me in proving that the outcomes from my students.
Is making sure that those students are going to walk across that stage on graduation day.
And so yes, when you look at a graph and a question comes up, try to find the answer to that question.
But make sure that that answer doesn't get in the way of doing the work to help the kids.
And that's lesson five about precious resource. Because when a student is showing that they are in trouble.
When they are not showing up. When they are misbehaving.
That's when we are talking about, making sure that we can impact our students.
And that they are warning us that they're having trouble and we need to support them.
And so that is the last lesson, is making sure that time is precious. And so those are my five lessons for data.
So making sure that, when we look at data, we make sure that the process of using that data takes precedence.
That is making sure that we know who the data is for.
And make sure that that data platform honors all of those audiences.
That that data, however it is presented, is used for action.
That we also have a place within our data systems for the work that we're doing to improve our students.
Not just be impacted and their attendance, behavior, course performance, proficiency.
But also place for the work that we are doing to help our students.
And finally making sure that the data platforms that we do end up using quickly lead to action.
That I am able to stay on the right side of the time, talking about data versus time using data equations.
All right, and that's the recap which then gets us to the questions that were brought up at the registration time.
I'm gonna go over a couple of those while we have time.
And as I'm answering these questions, please type in any questions that you have
that may have come up during this presentation, or may come up while I'm talking right now into the chat box.
So the first question that's, the first group of questions that I want to thank my facilitator for this, Maria
for grouping these questions into different areas. It helps to be able to answer them.
My lesson zero, is that data systems customization lesson in the upper right hand corner.
What key metrics do you need for an accurate EWS and how do you get people to think from ADA to chronic absenteeism?
That goes right to the engagement data. The tracking attendance, behavior, and course performance.
As the primary way of tracking the data, because if you can get a kid to show up today and do their work today,
and act right today. And then again tomorrow, and the next day and the next day and the next day.
That engagement becomes a part of their every day habits, they're part of who they are.
And so, it better predicts how well they'll do in the future.
So, when it comes time to get a career, or go onto post secondary activities.
That the more they show up, the more they act right and the more they do the work predicts that future.
And so that's the first green upper right hand corner set of questions.
And so the other wonderful set of questions is, I'm gonna start with the timeline
for how early do we wanna track this data? At what grade levels and getting in that real time data?
So when it comes to our individual students,
the engagement data has been tested across grade levels, across geographic regions and across schools within cities
or within urban versus rural versus suburban, and some form of attendance, behavior,
and of course performance is still predictive of future outcomes.
The level may be a little bit different,
but by tracking these three at any grade level at any school across the country.
You will get a better picture of that student's success track record.
And therefore be able to impact their future outcomes. So, and when it comes to getting real time data,
one of my favorite stories about this is this one school who were able to export gradebook grades.
And they were very happy to be able to export that.
Because that would mean that every single day they would know how that student is doing in that class.
But the problem is that the gradebook grades were not accurate all the time.
Because the school worked on a progress report, quarterly report, semester report system.
The grades in the grade book were only accurate leading up to progress reports.
Leading up to the quarterly reports or leading up to the semester reports.
And so, whether
or not they were able to get it real time actually hurt their ability to be able to identify the right student.
So, when it comes to getting real-time data, I would add one word to that. Which is, getting real-time accurate data.
Attendance is one that generally you can get every day, and it is accurate.
And the same thing for behavior as long as you're putting referrals in regularly.
Just so make sure that that is accurate. And
yeah, I think that based on the time I think we are coming up against the last bit of time.
So if there are any questions that I wasn't able to get to here, please, I'd be happy to answer them, if you want.
Put your name in the chat, and we'll reach out to you afterwards.
And make sure that any of your questions are answered after this presentation is done.
And so at this point, I do wanna turn it back over to Maria, for the last couple of parts of our presentation today.
Paul Verstreate: Maria?
Maria Waltemeyer: Thank you Paul. So what you see right now we're currently displaying is a list of our upcoming events.
And it's really a way for us to invite you to participate in our upcoming webinars.
If the resources are helpful to you, to sign up for the newsletter that we have.
And just finally just to thank you so much for attending today.
And we hope you found this session helpful and informative.
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