You may have heard the old saying, “Many make decisions by guessing or using their gut. They will be either very lucky or very wrong.” Because of these wise words, educators have worked diligently over the last decade to learn how to obtain, gather, compile, view, analyze and use data to avoid leaving student success to being “very lucky” or “very wrong.”
Fast forward to today, nobody could have predicted the vast amount of data available daily to teachers or foresee schools that try to use data to fix “every” issue. Unfortunately, this abundance of data can be overwhelming, and teachers can often feel exhausted when it comes to data use and practice in schools. That said, the ability to analyze data and collaboratively engage in data-driven decision-making is critical to MTSS (Multi-Tiered System of Supports) processes, as they promote a culture of instruction catered to supporting students’ needs. This process takes time and intentionality, but promoting these processes will significantly impact student success.
As discussed above, it can be overwhelming to consider all the student data available for teachers. So part of this process is knowing what data is most beneficial to use when evaluating student growth and at what level of intervention (Little, 2016).
MTSS is a system designed to support ALL students, but every piece of student data isn't necessary to provide effective support for all students. Teachers can use different parts of data to evaluate student growth depending on each student's instructional needs.
Starting at the broadest level, teachers should analyze the impact of teaching and learning for each student. A differentiated approach to core instruction (also known as Tier 1) is required within the MTSS framework, and ALL students receive Tier 1 instruction. Even if some students need additional support, they will still receive the day-to-day instruction representing the core curriculum.
Universal screening data (among other sources) provide much information that helps identify if students require additional support with academic, social-emotional learning (SEL) needs or behavior. As students are identified as needing support beyond core instruction at either the Tier 2 (strategic) or Tier 3 (intensive) levels, additional data sources should be used frequently to evaluate the impact of instruction and intervention on student growth.
The tables below outline what data can be used for this process within an MTSS framework, which students should be monitored at a given data level, and how often this data should be collected.
Using the data from the charts above allows educators to determine a student's success with given support and determine the student's Rate of Improvement (ROI) to facilitate better decision-making about the next instructional steps. This ROI analysis shows whether a student is making sufficient growth towards a specific goal and identify if a student's growth is either uncertain or insufficient towards the given goal.
The image below demonstrates what student data could look like if a student has made sufficient growth, uncertain growth, or insufficient growth towards a goal. To establish the ROI, it is essential to remember that multiple data points must be collected over a set period (as suggested in the charts above) to create a trend line that shows student growth.
Collaborative, data-based decision-making, and reflections are tenets of MTSS processes and are based on data collected. This collaboration takes place in three ways:
The School Leadership Meeting: This meets three times a year and discusses campus-wide trends found;
The Grade/Content Team Community Meeting: Monthly meetings are held during a dedicated grade-level team meeting to discuss student’s areas of strength and need, review student’s ROI, and determine instructional next steps for students receiving learning supports or targeted interventions at Tier 2 or Tier 3 levels; and
The Individual Student Support Meeting: During this meeting, individualized deep-dive problem-solving for students not making sufficient progress when supported by the Grade/Content Team Community Meeting.
A critical aspect of understanding and analyzing the data is evaluating whether the data is accurately portraying whether or not the curriculum, learning supports, interventions, assessments, and observations are carried out with fidelity. Fidelity, in this context, refers to “How closely prescribed procedures are followed and, in the context of schools, the degree to which educators implement programs, assessments, and implementation plans the way they were intended” (National Center for Intensive Intervention, N.D.).
The National Center for Intensive Intervention suggests many different questions regarding the elements of fidelity that can be considered when reflecting upon whether or not the student’s ROI is accurate:
These elements of fidelity for reflection should guide discussions during collaborative meetings and can also help to determine if the collected data about a given student is providing the most accurate picture of their performance and skill level (depending on answers to the questions above). This process of collaboratively using specific data with fidelity to make instructional decisions can be considered student-focused decision-making and is embedded within MTSS. Student-focused decision-making allows teachers to rely not on "luck" or their "gut" but on reliable evidence collected to take the next instructional steps.
Citations
Collaborative for Academic, Social, and Emotional Learning. (2022, March 11). Fundamentals of SEL. CASEL. Retrieved June 2, 2022, from https://casel.org/fundamentals-of-sel/
Gummer, E.S. & Mandinach, E.B. (2015). Building a conceptual framework for data literacy. Teachers College Record, 117 (4), 1-12.
Little, M. (2016, September). Why is data and data-based decision making such a critical part of instruction and intervention? National Center on Intensive Intervention. other, National Center on Intensive Intervention at the American Institute for Research. Retrieved June 1, 2022, from https://intensiveintervention.org/resource/why-data-and-data-based-decision-making-such-critical-part-instruction-and-intervention.
National Center on Intensive Intervention at the American Institute for Research. (n.d.). Considerations for effective implementation: 5 Elements of Fidelity. Considerations for Effective Implementation: 5 Elements of Fidelity | NCII. Retrieved June 2, 2022, from https://intensiveintervention.org/resource/five-elements-fidelity
National Center for Intensive Intervention. (n.d.). Fidelity Tools. Fidelity Tools | NCII. Retrieved June 15, 2022, from https://intensiveintervention.org/implementation-intervention/fidelity#:~:text=Fidelity%20refers%20to%20how%20closely,the%20way%20they%20were%20intended.