A Guide to Data-Driven Decision Making

In today’s educational landscape, the amount of data available to teachers is vast and unprecedented. While this wealth of information can be a valuable tool for improving student outcomes, it can also be overwhelming. Teachers may feel exhausted by the constant demand to analyze data and incorporate it into their instructional practices. However, the ability to effectively analyze data and collaboratively engage in data-driven decision-making is crucial to the success of Multi-Tiered System of Supports (MTSS) processes in high schools.

MTSS is a collaborative, evidence-based approach to differentiating and personalizing instruction and intervention for all students, across academics and behavior. It aims to ensure that every student can achieve academic and life success. Here are four key considerations for using MTSS data in a high school setting:

1. Using the Right Data: It’s important to use the most relevant data when evaluating student growth and determining the level of intervention needed. This begins with analyzing the impact of teaching and learning for each student, which is part of Tier 1 instruction that all students receive. Universal screening data, along with other sources, help identify students who may need additional support at Tier 2 (strategic) or Tier 3 (intensive) levels. Different data sources should be used frequently to evaluate the impact of instruction and intervention on student growth.
2. Analyzing MTSS Data Correctly: Analyzing data correctly involves determining a student’s Rate of Improvement (ROI) over time to make informed decisions about their progress. This requires collecting multiple data points to create a trend line that shows growth. Understanding whether a student is making sufficient, uncertain, or insufficient growth towards a goal is essential for guiding instructional decisions.
3. Utilizing Data-Based Decision Making: Collaborative, data-based decision-making is a fundamental aspect of MTSS processes. This collaboration occurs at various levels, including school leadership meetings, grade/content team community meetings, and individual student support meetings. These meetings allow educators to discuss trends, review student progress, and determine instructional next steps based on data.
4. Fidelity in Practice: Fidelity refers to how closely prescribed procedures are followed when implementing curriculum, assessments, interventions, and other elements of MTSS. Evaluating fidelity helps ensure that data accurately reflects the effectiveness of these practices. Questions about adherence to the plan, the quality of delivery, student engagement, and other factors guide discussions about fidelity and data accuracy.

In conclusion, using data effectively in MTSS processes requires a strategic approach that focuses on using the right data, analyzing it correctly, collaborating with colleagues, and ensuring fidelity in practice. By following these guidelines, high schools can create a culture of instruction that supports all students’ needs and promotes academic and life success.