Data-Based Decisions: What You Need to Know

Data-Based Decisions: What You Need to Know

Student Data-Based Decisions for Teachers, Educators & Schools

In schools today, making data-based decisions on students’ progress {both academic and behavior} is a NON-NEGOTIABLE if we want struggling students to show growth.  Fortunately, in many cases {not all}, having the data is not the issue.  Many times, teachers are actually ‘data drunk’.  They have so much data on a student, but no idea how to use it to drive instruction.  The idea of using data driven decision making at the student level is actually quite easy, once you’ve mastered the art of effective data PLCs (Professional Learning Communities).  So, what do you need to know?


In my coaching sessions with teachers around data, I always allow them to reflect by asking the following questions. After all, they are the ones who have all the data and know the children best.  These questions are designed for them to leave knowing how to problem solve using data, but without me.  “Give a man a fish and he’ll eat for a day.  Teach a man to fish and he’ll eat for a lifetime.”  Effective coaches build sustainability.


What data are you using to determine the effectiveness of your core?

I am a firm believer that without sound core instruction, total improvement cannot be made.  It’s the never ending exhaustion of the intervention treadmill.  This is true for both behavior and academics.  You cannot look at the growing the whole child until both are carefully considered.  There are several data points teachers use to determine the effectiveness of core.  For academics, we first look at our state assessment data.  We examine students’ proficiency and then we follow up with their growth using EVAAS and district adequate growth charts.  When you work in a school with an upside down triangle (meaning that the majority of your students are not proficient in a subject area), it’s likely that core instruction is not where it needs to be.  Once you see at least 80% of your students growing, you’ve found some effective strategies that work!  Because it’s best practice to use multiple data points to tell a story we use several other pieces of data as well.  We use universal screening data, district assessments, as well as common formative PLC assessments.  None of these data points will be effective unless they are analyzed authentically.  This is really difficult if the culture of your school is not a growth mindset.  That’s another topic we will dig into later!  When we are looking at our core behavior models we use ODR (office discipline referrals), attendance, and graduation rate as our core growth measures.  Any time you are measuring your core, you need to make sure you are using clean data.  This requires lots of training and consistency within your building and across the district.

What students are not performing adequately providing only core instruction?

Which students are struggling to maintain adequate performance without any supplemental support?  These are the students who need to be involved in an intervention system, whether it’s standard protocol or strategically targeted and designed.  Maybe the students have off grade level deficits that need to be addressed, or it could be there are instructional deficits that occurred because a child has moved or missed a critical component of core instruction.  Whatever the reason, these students need a little more than core instruction.  How much more, is yet to be determined.  Using intervention progress monitoring data is not difficult, but sometimes can be tricky.  To avoid the trickery, remember these things:

  1. your progress monitoring tool should be aligned to the intervention being provided
  2. depending on the intensity, intervention progress monitoring should occur more frequently than core
  3. intervention progress monitoring should reflect adequate growth, not proficiency

The reason we progress monitor interventions is to ensure the supplemental instruction for both academics and/or behavior is actually working for that student.  Far too often, I have seen children in a specific intervention not monitored frequently enough.  A child might receive 3 weeks of instruction and then be progress monitored, only for the teacher to realize that instruction wasn’t working.   It’s such a disservice to the child and wastes critical hours of instruction that we can use to turn the learning trajectories for these students.

What strategies work best for the students I’ve identified as needing supplemental instruction?

Once the easy part of using data to determine who is at need is completed, the hard part of determining what interventions work best for this subgroup and how to implement comes in to play.  The PLC component is critical here.  Since we are all stronger as a team it makes sense to make data driven decisions as a team.  Targeting and aligning interventions is an art.  There is no, “one size fits all hat” that provides for the needs of all students. We must look at every individual student with a team approach armed with knowledge of foundational reading and math strategies and with a strong understanding of behavior science.  See, in order to address the whole child, you need a data driven team.


 Because growing the whole child is an intricate art, it’s imperative that schools build effective data PLCs.  These PLC teams should be composed of several critical MTSS (Multi-tiered System of Support) lifelines, including, but not limited to: general education teachers, leadership, counselors, special education teachers, gifted and talented representatives, and English as a second language teachers.  Every category previously mentioned brings a different knowledge base to the table.  Using a laser focus approach on every individual child’s need, this team can best determine what will enhance growth.  We cannot continue to expect kids to grow until we collaboratively problem solve based on data.  It’s just not possible on a large scale.

 There are three ‘big rocks’ when creating a systematic data driven process.  First, a leveled data system to monitor both core instruction and intervention data must be available and used to fidelity.  Second, data driven decisions must be made as a team and not by one individual.  Last, data teams should be composed of a robust group with different skill sets that address both academics and behavior.


Because ALL Means ALL!

Because ALL Means ALL!

Teachers and Educators – How to reach ALL of the students in a classroom

5 Key Components to Ensure Success:

People become teachers because they have a passion for helping children learn and be successful.  It is so disheartening to be a teacher and feel as though you have failed a child.  Never again will failing a child be an option.  Below are 5 key components of initial implementation that will help ensure that you are set up for success to reach all children in your class/grade/school/district.

1. Systematic process for implementing RtI/MTSS district or school-wide

Systematic is the key here!  If there is no consistency in screenings and/or assessments used, there is no valid way to measure effectiveness of your implementation

Systematic universal screener(s)

  • Systematic universal assessment(s)
  • Systematic method for meeting and analyzing data in PLCs
  • Systematic process for developing Tier 1: Differentiated Core Instruction Plans
  • Systematic plan for reviewing the grade level data to revise/update the plan created as needed.

2. Universal Screening & Universal Assessment Data

It is imperative to have universal data to analyze.  Because you can’t always rely on just one data point, it is most effective to have a triangulation of data (at least 3 data points).

  • Universal Screener(s) (skill specific screener(s) – fluency, comprehension, vocabulary, etc.)
  • Universal Assessment (grade level content/standards-based assessment(s) – baseline assessment, benchmark assessment, prior year End of Grade/Course assessment, etc.)

PLCs strategically meet to analyze data, problem-solve as a PLC, make data-based decisions, and create an effective Tier 1: Differentiated Core Instruction Plan based on that data analyzed.

3. Identify students who are at-risk

Identify all students who did not meet the target/cut scores on the screener(s)/assessment(s) identified by the grade/school/district.

  • Students who have at least 2 out of 3 risk indicators based on the target or cut scores from the universal screener(s) and/or universal assessment(s) should automatically be flagged by the team as being Tier 1 at-risk.
  • Students who have 3 out of 3 risk indicators – are your most at-risk students and should begin intervention immediately – unless your data states that it is a core instruction issue.

Early Warning Systems: If you are lucky enough to have access to an early warning system – such as the one offered in RtI: Stored! – you will be able to also analyze the following data:

  • Absentee Data
  • Classroom Grades
  • Office Discipline Referral Data (ODRs)

4. Digging Deeper into Diagnostic Data:

If a student’s universal screening data indicates that the student is below or well below grade level, then it is time to dig deeper to identify the student’s foundation specific skill deficit area(s)

5. Creating groups for intervention/enrichment blocks:

Using all of the data, the PLC team can begin creating intervention groups and enrichment groups.

  • Teachers should be assigned to instruct the groups based on their strengths.  This too should be based on data!

These 5 steps will allow you to truly see each and every student.  Once you truly see them and identify their possible risk indicators, as well as gap areas, based on their data, then you have the ability to actually reach all of them.  Because it is not an option to fail any child – and – Because all means all!

Are You Data Rich or Data Drunk?

Are You Data Rich or Data Drunk?

Have you ever sat down at the table to begin problem-solving with a district, school or PLC, and within 5 minutes of the conversation you realize that the amazing amount of data being analyzed is not actually giving you the information you need to move forward, however, the team is so data drunk that they don’t even realize it?

This has happened to me more times than I can count lately. Collecting data for children should never be about the quantity of data, but instead the quality of information the data is giving you. In the same breath, if you are screening and assessing children but do nothing with that data, it is not only a waste of your time, but a waste of theirs too.

The data points that you want to analyze at the beginning of the year are a universal screener, a diagnostic screening, and some type of content specific assessment data (prior year End of Grade assessment; Beginning of Year (BOY) baseline assessment; some Common Formative Assessment (CFA) that is universal to that grade; etc.).

There is no need to have more than one universal screener that assesses the same skill(s). Teachers do not want to take time away from teaching to double assess students, just as much as students don’t want to take multiple assessments on the same skills to show that they either get it, or that they don’t.

There is a plethora of research on the importance of universal screenings, and I am 100% behind them, however you need to make sure that your screener is indeed a screener – a brief assessment, typically skill based, that is given to all students in the same class, grade, school building or school district to identify or predict students who may be at-risk for poor learning outcomes. Once you have your screening data, then you can determine which students need further assessment through a diagnostic screening tool to determine root cause and begin developing an intervention plan.

Knowing what data you need should inevitably drive the data you are collecting. Then you will be able to answer the following questions: Is the data you are collecting giving you the information you need to move forward and effectively problem-solve on the behalf of children? Are you analyzing at least 3 data points – a triangulation of data – or making decisions based on just 1 data point? Were you able to identify specific skill deficits through the diagnostic screening tool to help in determining root cause of the student’s struggles? Do you have the information you need to create an effective intervention plan for the student(s)? These are important questions the team needs to ask in order to have the most effective discussions and effectively problem-solve on behalf of the students’ academic and behavior success.

Remember, you want to be data rich, not data drunk!

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