How to successfully utilize MTSS problem-solving models in education 

Utilizing MTSS problem-solving models in education is the best way to help students who are struggling in school. So, what does the problem-solving process look like?

In this blog, we’ll explain how to approach MTSS problem-solving models in education with Renaissance’s five-step problem-solving method.

How to approach MTSS problem-solving models in education

When a school implements an MTSS model, how can educators figure out what type of support each student needs and whether a student should receive intensive, one-on-one interventions? One approach that schools can take is to have a problem-solving team.

Such teams can help teachers plan and implement tiered interventions and collect data to identify which students need more intensive academic or behavioral support. Usually, a referral is made to the problem-solving team when a student is not improving despite receiving Tier 1 (e.g., core) plus Tier 2 (e.g., small group) intervention.

What does a problem-solving team look like?

The members of a problem-solving team (PST) act as consultants to teachers and other staff who have worked with a student. Because many of the referrals could reflect various academic and behavioral problems, it is important to make sure that the team is made up of people from different educational positions who can offer different perspectives about students’ needs.

Ideally, a PST should include at least one classroom teacher, a special educator, a school psychologist, a counselor or social worker, and the building principal. All PST members should be encouraged to think about students’ school difficulties in relation to the expectations of students in the same grade level. The primary function of the PST is to reduce any differences between those expectations and the student’s current school performance.

Responsibilities of the PST

The PST is the engine that drives the MTSS system. By reviewing school-wide data, the PST can proactively address system needs and support individual student growth.

The team members should meet regularly throughout each month with a structured agenda that varies to:

• Review universal screening data
• Provide expertise related to MTSS professional development
• Review school-wide data and make data-based decisions
• Collaborate with general education teachers to support grade levels/departments serving students during interventions
• Provide expertise regarding school site enrichment/intervention schedule, course offerings, and curriculum
• Communicate MTSS-relevant issues to the site administrator
• Hold problem-solving meetings with parents for individual students
• Collaborate and consult with teachers, counselors, administrators, and parents about the MTSS problem-solving model
• Review data and refer students for comprehensive special education evaluations when warranted

Communication and collaboration is critical to the functioning of an effective PST.

What is an MTSS problem-solving model?

The MTSS problem-solving model is a data-driven decision-making process that helps educators utilize and analyze interventions based on students’ needs on a continual basis.

Traditionally, the MTSS problem-solving model only involves four steps:

1. Identifying the student’s strengths and needs, based on data.
2. Analyzing data and formulating appropriate interventions.
3. Implementing these interventions.
4. Reflecting on and evaluating intervention outcomes.

Let’s consider how this compares to Renaissance’s five-step MTSS problem-solving model.

Renaissance’s five-step MTSS problem-solving model

While similar to the traditional four-step MTSS problem-solving model, the Renaissance problem-solving method adds an extra step to fully flesh out the process. Renaissance endorses a five-step problem-solving method that includes:

1. Problem Identification
2. Problem Analysis
3. Plan Development
4. Plan Implementation
5. Plan Evaluation

When used at any time of the school year, MTSS problem-solving models in education help school teams engage in data-based decision-making for the benefit of all students.

Let’s consider each of the components in detail.

#1: Problem identification

Every educator knows that a student can develop school-related problems at any time of the school year. Problem identification is the point in time when a possible problem first shows up on the “radar” among school staff.

For example, in the spring, especially before the final screening period, problems that may arise include issues like:

• Students who have not (yet) responded to intervention
• Students who were doing well earlier in the year but are now struggling with more challenging material
• Efforts to have 80% or more of students meet the winter learning benchmarks were not achieved

In each of these cases, the next step is to analyze the problem based on available evidence.

#2: Problem analysis

Problem analysis involves using collected data to identify the size and effects of the problem. Some problems might be small enough that they do not justify additional resources to address.

For example, if a student’s progress data indicated somewhat lower scores after a school break, but quickly returned to higher levels, giving attention to this probably does not make sense because the student’s performance improved once school resumed.

A bigger problem could be if many students in a class or grade did not meet a benchmark screening goal. It is expected that all students will make growth throughout the school year.

To recognize this growth, benchmark goals are adjusted upwards for each screening period during the school year. If there are students who met the benchmark in the fall but not the winter, more information to analyze the source of the problem is needed.

Examples include:

• What percentages of students did and did not meet the goal?
• Were they all from the same class or different classes?
• Did they have similar scores in the fall?
• How close to the goal are they now?
• Exactly what instruction has been provided to these students?

To address the above questions and complete a thorough analysis, additional data about student performance and instructional practices might be needed. The most recent (e.g., winter) screening scores answer the first four questions. An interview with teachers and/or classroom observations could answer the final question.

The goal is to develop a hypothesis about exactly why an unexpectedly large number of students did not meet the winter benchmark.

#3: Plan development

With a hypothesis in hand, the school team then turns to consider possible plans that can address the problem. The planning process needs to cover what steps will be taken to improve the scores of the underachieving students.

One approach could be to add those students to existing intervention groups so that they can participate in additional instruction. This might work if the number of students is small, but if many students need intervention, there might not be enough groups or interventionists to teach them.

Another approach would be to revise the Tier 1 core instruction to include the specific skills that these students lack. This approach has the benefit of meeting the needs of more students at one time, but it will work only if the students have relatively similar learning needs.

The team might need to explore both of these options and then compare the costs and benefits of each to make a decision.

In addition to developing a short-term solution to the students’ learning gaps, the team should think about and work on plans to develop a way to prevent the same thing from happening next year. Collecting and using continuous data to set longer-term goals is an important part of system-level data-based decision-making.

If the team checks and reviews the effects of the selected plan to support the struggling students throughout the rest of the school year, the information gained can help in a decision about whether additional new plans are needed.

Specifically, in the course of observing the effects of either small-group interventions or whole-class instructional changes, the team will likely have more information about whether the original Tier 1 core instruction that appeared to work in the fall needs to be changed in the future.

Such changes could include additional training for teachers, a new pacing guide that narrows the focus of what to teach, or an entirely new set of materials and methods.

Using group-level data from the current school year is an essential and effective way to plan for the future needs of all students.

#4: Plan implementation

Once a specific plan for addressing the student’s needs is developed, the next step is to implement it. For any plan, additional resources will likely be needed.

For example, if the students are added to existing intervention groups, who will gather and prepare the necessary materials? If the Tier 1 core instruction is changed, how will teachers learn about the changes and become ready to teach the revised lessons?

To support those charged with plan implementation, it’s very helpful for a member of the school’s problem-solving team to conduct regular “check-ins” with those implementing the plan. These checks can be weekly and will help the staff know that they are supported in their efforts to meet individual students’ needs.

Another component of implementation is to verify intervention or teaching accuracy. This is also called teaching integrity, or fidelity. It is important because it provides data about whether the planned change was done correctly. Checking on teaching integrity can involve interviewing the teachers or observing lessons.

The method used should match the…

• Type;
• Location; and
• Nature

…of the instruction.

Having teaching integrity is important because unless the data collected as part of the instructional change can be trusted, there is no point in evaluating the plan.

#5: Plan evaluation

The final step of the problem-solving model is to assess the data collected during and after the changes to see whether they worked.

In the case of instructional changes made between the winter and spring benchmark periods, one way to evaluate the plan would be to see if the students’ spring screening scores reflect a significant improvement over the winter scores.

The downside of relying entirely on the spring screening scores is that they might not be collected for weeks or months. Instead, it could be better to gather additional data on student performance before the spring screening.

If the team opted to have the students join existing intervention groups, the newly added students should complete regular progress measures alongside all the other students in the groups.

Given the time of the year and the urgency of the learning needs, weekly progress monitoring would be recommended. If the team opted to change the Tier 1 core instruction, alternate assessments might be better, depending on whether there are brief and easy-to-administer options that all students could complete.

Renaissance offers two solutions—FastBridge and Star Assessments— that can be used for regular progress monitoring and whole-class interim assessments.

FastBridge assessments for reading that would work for both small and large groups include AUTOreading and COMPefficiency. For math, FastBridge offers CBMmath-Automaticity and CBMmath-Concepts and Applications (CAP).

These are all computer-administered and scored assessments that provide immediate feedback on student performance. For younger students, selected measures from FastBridge earlyReading and earlyMath can also be used for progress monitoring as they are used at least monthly.

Similarly, the Star suite includes both computer-adaptive assessments (CATs) and curriculum-based measures (CBMs) for reading and math. Star CATs and CBMs support progress monitoring and whole-class interim assessment in both English and Spanish, from preschool through grade 12.

Utilizing MTSS problem-solving models with Renaissance

Both FastBridge and Star Assessments are aligned with a problem-solving approach to assisting students.

The five problem-solving steps can be used continuously throughout the school year to identify, define, and address individual and group learning needs. Educators do not need to wait until the new school year before addressing such learning needs.

Instead, they can continue to use the problem-solving steps with new issues as they arise. Using the problem-solving approach throughout each school year has benefits for both individual students and groups:

• At the individual student level, the benefit is improvement in skills and a more satisfactory school experience.
• At the group (e.g., grade or school) level, the benefit is planning for improved system-level practices and will make both students’ and teachers’ school experiences better in the future.

To learn more about FastBridge or Star Assessments and how they support the MTSS problem-solving model, connect with an expert today.