As the academic year gets going, one objective at the forefront of many preschool program’s evaluation plan is fall academic screening. Typically, students are screened within a few weeks of starting class to establish a baseline status. This information, often collected with measures like IGDIs for early literacy and early numeracy skills, can be used to inform how instruction may most benefits the students in the classroom.
When screening occurs within a multi-tiered system of support (MTSS), the data can be used to formulate candidacy groups based on each individual student’s level of need. The IGDI measures use cut scores to evaluate each student’s level of performance, which in turn yields a color-coded tier level. Where Tier 1 instruction is green, Tier 2/3 is red, and scores that fall within one standard error of measurement (and thus require more information for clear classification) are orange.
Collecting screening data is an important part of understanding how to support student’s performance, but it is not sufficient. MTSS systems require a three part process of collecting data with high quality assessment tools, engaging in data-based decision making, and implementing interventions to meaningfully support preschool performance. This process is cyclical, such that the initial screening data informs decisions about interventions, which are in turn implemented, and continued data collection occurs through high quality progress monitoring tools, which in turn inform decisions about continuing or modifying the intervention selected.
This process is an important part of MTSS implementation but many educators get stuck trying to determine how to use the data to make an informed decision about intervention.
IGDIs provide the color-coded framework for MTSS, but also facilitate domain specific- intervention support. For example, if a child performs in the red on the Picture Naming and or Which One Doesn’t belong measures, it is important to choose a high quality language or vocabulary and comprehension intervention for Tier 2/3 level support such as:
In the phonological awareness domain, as measured by First Sounds and Rhyming, Tier 2/3 interventions are also available in the field:
- The MILLIE suite of evidence-based phonological awareness interventions include Reading Ready and PAths to Literacy
- Generic phonological awareness training
- Florida Center for Reading Research Phonological awareness materials
Or similarly, if a large percentage of students are struggling with oral language, phonological awareness and/or comprehension as indicated by low IGDI scores across the entire classroom, considering a high quality Tier 1 curricular change may be warranted.
With high quality screening data in hand and color-coded RTI criteria to aid in interpreting performance, we can make meaningful data-based decision about how best to support each student. For those students in the red, reviewing existing data, consulting other teachers who interact with the student and considering the student’s unique individual circumstances will help to inform what type and dosage of intervention will maximize learning. Once an intervention that is an ideal fit is selected, implementation must occur both with high levels of fidelity and at an intensity that will meet the student’s level of need.
To evaluate if the selected intervention is working we select companion progress monitoring measures, for example, the IGDI-PM tools (which are in development through a federally funded Institute of Education Sciences research grant) to frequently monitor the student so that additional data can be used to determine if the intervention is working or if further modifications are needed.
When this system is in place and is efficiently used by educators, we can move student’s through the tiers of MTSS to accelerate student learning and prevent students from falling behind. And once the model is deployed, the question of where to go from here becomes much less relevant, because the cycle MTSS process leads the way to more successful students and improved data-based decisions.