Contextual Background Overview
Math educators have a complex relationship with the concept of ‘interventions’ in mathematics. In this blog, we aim to explore the concept of interventions and examine what research tells us about them. We will follow up with a subsequent blog addressing the challenges and the promise of ‘interventions’ in mathematics.
Improvement of student performance in mathematics has been a perennial goal of reform efforts throughout the country. Often this takes the form of high state standards, adopted to provide a clear and consistent framework for math education. Such initiatives are often followed by the adoption of high-quality instructional materials, which represent a significant step in the right direction. Of course, any initiative to enhance students’ performance would be incomplete without including professional learning opportunities for math educators, which districts across the nation are actively pursuing. Most communities are going a step further to incorporate technology into math instruction, using tools like interactive software and online resources to engage students and provide personalized learning experiences (NETP, 2017).
Efforts to improve students’ performance in mathematics range from accelerated tracks (e.g., Algebra 1 in 8th grade and AP courses in high school) to various recovery programs focused on addressing gaps in foundational math skills, and everything in between. However, the sobering truth remains that, despite all these efforts, many students still do not make adequate progress; some need more support. Many would argue that they need ‘interventions.’
The word ‘intervention’ prompts us to think of afterschool or Saturday math activities, designed to support students who need more time and practice. The most common types of academic support during afterschool hours are various configurations of homework assistance and tutoring programs. However, many such programs have been tried and fallen short. The fact remains that, according to results from the National Assessment of Educational Progress (NAEP) long-term trend (LTT) 2022–23 assessments, the average scores for 13-year-olds declined by 9 points in mathematics compared with 2019–20 and by 14 points compared with 2011–12 (Irwin et al., 2024).
To be sure, the concept of providing students with additional support is a sound one, and extending school time to provide such support seems appropriate. Then why do so many of these efforts fall short? Several factors contribute to this issue, notably capacity and participation. Fashola 2002, cited ‘capacity’ as a significant contributor; afterschool programs rely on minimally-trained volunteers and often do little to boost student performance—such circumstances are still prevalent today. Additionally, plagued by attrition and low student participation, many afterschool programs fail to produce the desired positive outcomes in student performance (Grossman et al., 2002; Somers et al., 2015). In my experience, the worst offender is the lack of alignment between the regular math curriculum and afterschool activities.
A Model that Works
The ironic part is that researchers from numerous institutions have demonstrated what works. There is overwhelming evidence that a Multi-Tiered System of Support (MTSS), also known as Response to Intervention (RTI), is effective, even capable of convincing the most skeptical educators. This framework has undergone extensive research and consistently demonstrates an effect size of 1.07, outperforming nearly all other approaches to intervention (Hattie et al., 2016, 214). While most of the evidence pertains specifically to RTI, the model of MTSS is more inclusive, as the latter encompasses the former. In other words, RTI focuses solely on academic support, whereas MTSS is a comprehensive framework that includes academic, behavioral, and social-emotional support (i.e., MTSS = RTI + emotional support).
What is MTSS?
As the name suggests, a Multi-Tiered System of Support (MTSS) is a model that emphasizes providing high-quality instruction and interventions tailored to students’ needs. This model incorporates data to inform decisions regarding changes in instruction or goals; therefore its use of data is a fundamental component of the model for educational decisions. The overarching concept is to identify and address students’ needs early, ensuring that all students receive the necessary support to succeed. Early detection of difficulties is followed by more in-depth inquiries, leading to tiered supports; thus the use and interpretation of data are key drivers of MTSS (Preston et al., 2016).
Without Data, There is No MTSS
The model uses three assessments to get data on the learner: screeners, diagnostics and progress monitors. Together, these 3 assessments are the key drivers of MTSS and without them, there is no MTSS. For this reason, it is important to provide some brief descriptions:
A screener assessment is a brief evaluation tool used to identify students who may be at risk for academic difficulties or who require further assessment in specific areas of mathematics. Screeners should be administered to all students at the beginning of the school year and at designated checkpoints throughout the year. The primary purpose of a screener is to efficiently gather data that informs educators about students who are “at risk.” These assessments are norm-referenced and evaluate essential skills or knowledge areas. Additionally, they are designed for quick administration, allowing educators to assess a large number of students in a short amount of time (Outhwaite et al., 2024, 2)
A diagnostic assessment is a more in-depth evaluation tool used to identify a student’s specific needs in a particular math skill. Unlike screening assessments, which provide a general overview of a student’s performance, diagnostic assessments delve deeper into understanding the underlying knowledge, skills, and issues that may affect a student’s academic progress.
These assessments are often administered to students flagged by the screener to identify potential ‘false positives’ in the screening data and to determine whether interventions are necessary. Diagnostic assessments can take various forms, including performance tasks and informal observations. The results of these assessments are used to inform instruction, guide individualized learning plans, and facilitate targeted interventions to support student learning. (Gordon & Rajagopalan, 2016)
Progress monitoring is a systematic tool for assessing student performance and growth over time. It involves the regular collection and analysis of data to evaluate how effectively students are responding to instruction, such as academic interventions, and to determine if they are meeting specific learning goals. Academic interventions utilize progress-monitoring tools to measure students’ responses to the intervention. (National Center on Intensive Intervention, 2019)
Next Blog
In our next two blogs we will explore the different Tiers of Support and take a deep dive into the relationship between core instruction and intervention.
References
Fashola, O. S. (2002). Building Effective Afterschool Programs. SAGE Publications.
Fuchs, L. S., Bucka, N., Clarke, B., Dougherty, B., Jordan, N. C., Karp, K. S., & Woodward, J. (2021, March). Assisting Students Struggling with Mathematics: Intervention in the Elementary Grades (WWC 2021006). Washington, DC: National Center for Education Evaluation and Regional Assistance (NCEE), Institute of Education Sciences, U.S. Department of Education. http://whatworks.ed.gov
Gersten, R., Beckmann, S., Clarke, B., Foegen, A., Marsh, L., Star, J., & Witzel, B. (2009). Assisting Students Struggling with Mathematics: Response to Intervention (RtI) for Elementary and Middle Schools (NCEE 2009-4060). National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education. http://ies.ed.gov/ncee/wwc/publications/practiceguides/
Gordon, E. W., & Rajagopalan, K. (2016). The Testing and Learning Revolution: The Future of Assessment in Education. Palgrave Macmillan.
Grossman, J. B., Price, M. L., Fellerath, V., Jucovy, L. Z., Kotloff, L. J., & Raley, R. (2002). Multiple Choices After School: Findings from the Extended-Service Schools Initiative [Executive Summary]. Public/ Private Ventures.
Hamilton, L., Halverson, R., Jackson, S. S., Mandinach, E., Supovitz, J. A., & Wayman, J. C. (2009). Using student achievement data to support instructional decision making (NCEE 2009-4067). Washington, DC: National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education. https://ies.ed.gov/ncee/wwc/publications/practiceguides
Hattie, J., Fisher, D., Frey, N., Gojak, L. M., Moore, S. D., & Mellman, W. (2016). Visible Learning for Mathematics, Grades K-12: What Works Best to Optimize Student Learning. SAGE Publications.
Irwin, V., Wang, K., Jung, J., Kessler, E., Tezil, T., Alhassani, S., & Filbey, A. (2024, May 30). Condition of Education 2024. National Center for Education Statistics. Retrieved November 3, 2024, from https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2024144
National Center on Intensive Intervention (NCII). (2019). 2019 Call for Submissions of Academic Progress Monitoring Tools. https://intensiveintervention.org/sites/default/files/NCII_AcadProgMonitoring_CallForSubmissions_Aug2019.pdf
National Education Technology Plan. (2017). Future Ready Learning: Reimagining the Role of Technology in Education. https://tech.ed.gov/files/2017/01/NETP17.pdf
Outhwaite, L. A., Aunio, P., & Ka Yu Leung, J. (2024, September 5). Measuring Mathematical Skills in Early Childhood: a Systematic Review of the Psychometric Properties of Early Maths Assessments and Screeners. Educational Psychology Review, 36(110), 2-71. https://link.springer.com/article/10.1007/s10648-024-09950-6
Preston, A. I., Wood, C. L., & Stecker, P. M. (2016, Aug 25). Response to Intervention: Where It Came From and Where It’s Going. Preventing School Failure: Alternative Education for Children and Youth, 60(3), 1-10. https://doi.org/10.1080/1045988X.2015.1065399
Somers, M.-A., Welbeck, R., Grossman, J. B., & Gooden, S. (2015, March). An Analysis of the Effects of an Academic Summer Program for Middle School Students. MDRC publications.
STEM; NYC DOE. (2022, June). Multi-Multi-Tiered System of Supports for Mathematics (MTSS-M).