Making Data-Driven Decisions in MTSS

Brian Lovell
Aug 25
3 min read

Updated: Sep 16

Practical Strategies for Every School Level

By Brian Lovell, MTSS Specialist

Girl sits at desk smiling with other classmates working around her.

In Multi-Tiered System of Supports (MTSS), data isn’t just a requirement - it’s a powerful tool to inform decisions, allocate resources, and most importantly, support students in meaningful ways.

Using data effectively means more than collecting it; it requires intentional analysis, collaboration, and action.

Whether you're working in an elementary, middle, or high school setting, there are key ways to bring data-driven decision-making to life in your MTSS work.

Foundational Principles of Data Use in MTSS

Before diving into level-specific strategies, it’s important to ground our practice in the core goals of data use within MTSS.

Identify students in need of additional support early
Determine the effectiveness of core instruction and interventions
Monitor student progress and adjust supports as needed
Promote equity by ensuring all students have access to high-quality instruction and support

With those goals in mind, let’s identify how we can accomplish this and support students at all levels in the educational environment. As a school, it is important to be analyzing data in these three areas for ALL students. Where is the data indicating a need for more support? Identify those areas and start digging in!

A teacher sits around a table with a group of several young children. All are actively engaged and holding up hands forming numbers with fingers.

Elementary School: Building Early Foundations

At the elementary level, data often focuses on early academic skill development, behavioral expectations, and supporting the foundations of emotional regulations.

Consider some of these data points:

Universal Screening: Conduct reading and math screening 3x/year; use this data to adjust Tier 1 instruction and form intervention groups.
Behavior Tracking: Use simple behavior tracking systems (e.g., daily point sheets or digital dashboards) to identify patterns and proactively address them.
Progress Monitoring: Implement frequent (e.g., weekly or biweekly) assessments for students receiving Tier 2 or Tier 3 interventions.
Classroom Walkthroughs: Analyze data from instructional walk-throughs or fidelity checks to support teacher implementation of evidence-based strategies.
Data Meetings: Hold monthly grade-level MTSS meetings to review both academic and behavioral data while refining groupings/interventions.

A group or teenagers sit around a table with an adult. They are smiling, listening to others, and engaged.

Middle School: Responding to Academic and Social Transitions

Middle school brings increasing academic complexity and significant developmental changes. The data being analyzed needs to reflect both and be embraced by a team who can make positive systemic changes for students.

Consider:

Early Warning Indicators: Track data on grades, attendance, and behavior to flag students showing early signs of risk.

Tier 2 Behavioral Supports: Monitor referral data to identify students needing structured interventions such as Check-In/Check-Out or social skills groups.
Subject-Specific Supports: Use common assessment data (e.g., in math or writing) to provide short-term skill-building interventions or re-teaching cycles.
Student Voice: Incorporate student self-assessments or reflection surveys into your data review process, especially around engagement and belonging.
Weekly Team Reviews: Use grade-level or content-area teams to review student data collaboratively and identify supports.

High School: Targeting Graduation Readiness

In high school, data must focus on long-term outcomes: course completion, credit accrual, college and career readiness, and engagement. What data is your team collecting across the entire student population to support these needs?

Some data to consider:

Credit Tracking: Regularly review transcripts and course pass rates to identify students falling behind on graduation requirements.
Chronic Absenteeism: Use attendance data in combination with other indicators to identify and support students disengaging from school.
Academic Recovery: Offer flexible, data-informed supports like tutoring, credit recovery, and summer learning for students who need to catch up.
Behavior/Discipline Trends: Analyze data by subgroup and infraction type to ensure equitable discipline practices and targeted Tier 2 supports.
Postsecondary Planning: Use interest surveys, career pathway data, and counselor checklists to support postsecondary goal setting and planning.

Final Thoughts: From Numbers to Action

Effective MTSS implementation isn’t about drowning in data - it’s about using the right data, in the right way, at the right time.

This means:

Establishing clear protocols for collecting and reviewing data
Involving teams in collaborative data conversations
Matching data insights to evidence-based responses
Regularly revisiting decisions to ensure students are progressing

Data is a tool, not a solution. Let’s say that again to give it time to sink in - data is a tool, not a solution. However, if no data is being reviewed, then proactive solutions cannot occur. In the hands of reflective educators, it becomes a catalyst for real change.

Let’s continue using data not just to track our students, but to truly support them.

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