Newsletter
Volume 31 | 2008 | Number 3
Using RTI to Accomplish System Change
By Dr. Amanda VanDerHeyden, Education Research and Consulting, Inc.
Response to Intervention (RTI) is a science of data-based decision making to identify children in need of educational intervention and to deliver intervention to those students in the most efficient and least intrusive way. RTI is both logical and empirically supported as a vehicle for system reform and student learning. Direct performance data are used to identify where learning deficits are present, data are collected to identify interventions that are likely to be effective to resolve that particular learning problem, and then the intervention is implemented while progress monitoring data are collected to ensure that the intervention is having the desired effect. These data are used formatively to enhance intervention effects, and summatively to evaluate whether or not more intensive services should be provided. Children in need of more intensive intervention services can be made eligible to receive those services through special education resources, rendering nearly obsolete the need to determine disability prior to intervention service delivery in school systems. Hence, RTI is a system of prevention, where intervention services are provided at first notice of a learning problem and a child’s response to those interventions may serve as the basis for determining the need for additional services.
RTI is rooted in behavior analysis in education. Both in principle and in implementation, RTI is a process that adheres closely to the ideals and science of applied behavior analysis. Specifically, RTI maintains a keen focus on consequential validity (Messick, 1995) because the decisions are driven by child performance data collected in the settings in which the child must function to realize full adaptation or success (Fuchs & Fuchs, 1998). Procedurally, there is an emphasis on brief, direct measures of student performance (Deno, 1985; Starlin, 1979; White & Haring, 1981) collected at routine intervals to evaluate the effects of interventions and inform subsequent decisions about intervention need (Barnett, Daly, Jones, & Lentz, 2004). Brief experimental analyses of academic responding (Daly & Martens, 1994; Daly, Martens, Hamler, Dool, & Eckert, 1999; Daly, Martens, Kilmer, & Massie, 1996; Daly, Witt, Martens, & Dool, 1997) have been and will continue to be featured prominently in viable RTI models as school personnel struggle to identify effective interventions for the small number of students who fail to respond to standard interventions implemented with integrity. Direct instruction-type interventions are featured prominently in RTI models (Carnine, Silbert, Kame’enui, & Tarver, 2004). Finally, viable models include direct measures of intervention implementation or treatment integrity (Witt, Noell, LaFleur, & Mortenson, 1997) and include procedures for enhancing integrity. At the same time, RTI represents an extension of applied behavior analysis first forecast by Deno & Mirkin (1977) in developing decision rules that include both functional and norm-referenced criteria. This extension is critical because this extension is what has permitted RTI to become a system of educational decision making that is replicable and applicable as a model of system change.
RTI efforts are organized across tiers (generally three tiers of increasing intensity) with each tier representing more intensive/costly services and serving smaller numbers of children. Tier 1 is universal screening. Curriculum-based measurement (CBM) probes in reading, math, and writing are administered class-wide following standardized directions (Shinn, 1989). Screening requires approximately 1 hour and yields a fluency estimate of reading, math, and writing performance on a task that reflects expected grade-level performance at that time in the instructional program. Screening provides information about whether or not the core instructional program is working well for most students, which individual students or classes are at risk relative to their peers, and also provides a logical and efficient basis for evaluating the effects of supplemental programs (e.g., children receiving Title 1 services, children receiving special education services, children from certain demographic groups). Decision rules are applied to the data to identify which children are at risk relative to their classmates. Under the Screening to Enhance Educational Performance (STEEP) RTI model, the data are first examined to rule out a class-wide learning problem. If the class median score falls in the frustrational range (Deno & Mirkin, 1977), then a class-wide intervention is implemented prior to working with children individually from that class. Class-wide intervention follows a standard protocol and includes basic elements of direct instruction (modeling, guided practice, timed independent practice for a score with delayed error correction) using grade-level materials. When the class median reaches the mastery range on that task, any children remaining in the frustrational range are identified for further assessment. When class-wide problems are rare in a school, class-wide intervention is considered a Tier 2 intervention activity. When class-wide interventions are prevalent, then class-wide intervention is treated as a Tier 1 activity and efforts are made to examine the core curriculum for enhancement in addition to more intensive school-wide intervention efforts.
If the screening ruled out a class-wide learning problem, then a decision rule is applied to identify individual children in need of further assessment. STEEP applies a two-step rule that reflects both a local comparison (normative) and a functional criterion (external to the instructional program). Under STEEP, children who are in the bottom 16% of their classes and perform below the functional criterion (Deno & Mirkin, 1977) proceed to Tier 2 assessment which is the performance/skill deficit assessment (for full implementation details, see VanDerHeyden & Witt, 2008). This assessment is grounded in the brief experimental analysis research and is a brief assessment to test the effects of incentives on child performance. Standardized directions are followed to administer the assessment and if the student’s score improves with incentives to surpass the criterion, then no further assessment is conducted. If the student’s score does not surpass the criterion, then the child proceeds to Tier 3 assessment and intervention.
At Tier 3, a brief individual assessment session is conducted to identify instructional level materials for intervention, to select rewards, and to conduct a brief trial of the intervention (to ensure intervention effects). All intervention materials are then provided to the classroom teacher or peer tutor and training is conducted to ensure that the teacher or peer tutor can independently follow the intervention protocol to correctly complete all the steps of the intervention. Once training is complete, the intervention begins. At this point, the RTI consultant works with the child once per week to administer a generalization probe, an integrity check, and to troubleshoot intervention effects. If performance on the generalization probe does not surpass the criterion following 15 consecutive days of intervention implemented with integrity, then this information is shared with the school special education referral team with a recommendation to consider a full psycho-educational evaluation for the student. Data collected at routine intervals (e.g., screening data collected three times per year) provide an opportunity to follow-up on children who have received intervention with a successful response and continued in the general education curriculum without assistance.
Research examining the components of RTI models (e.g., screening, intervention) are prevalent (Vaughn, Linan-Thompson, & Hickman, 2003; Vellutino et al., 1996). Research has also occurred examining the overall model (screening, intervention, outcome decision-making) with researchers implementing most of the procedures (Case, Speece, & Molloy, 2003; Speece, Case, & Molloy, 2003). Research findings evaluating RTI implementations in districts have been somewhat rare. Dr. VanDerHeyden will describe findings from her own research ranging from experimenter-implemented procedures to a district-wide implementation trial where RTI procedures were implemented by school personnel. Data will be shared concerning the diagnostic or decision-making accuracy of RTI-based risk judgments relative to other sources of identification like teacher referral (VanDerHeyden, Witt, & Naquin, 2003); the effect of RTI procedures on proportionate identification of children for special education services (VanDerHeyden & Witt, 2005; VanDerHeyden, Witt, & Gilbertson, 2007); the percentage of children identified and at Tiers 1, 2, and 3 (VanDerHeyden et al., 2003; VanDerHeyden et al., 2007); the effect of RTI data on special education referral decisions (both number and accuracy) at the school level (VanDerHeyden et al., 2007); the relative cost of RTI procedures and traditional referral, evaluation, and placement costs (VanDerHeyden et al., 2007); and effect of RTI procedures on student learning outcomes (VanDerHeyden & Burns, 2005; VanDerHeyden, Witt, & Gilbertson, in preparation).
Dr. VanDerHeyden will discuss how to plan and implement RTI within schools in ways that promote the capacity of schools to use data to improve every day instruction and schooling decisions. District-wide implementation in Vail Unified School District will be described as a case example for participants. Data will be shared concerning effective ways to enhance learning within a system using RTI. Current data on educational decision-making as well as ideas for the future will also be highlighted.
References
- Barnett, D. W., Daly, E. J. III, Jones, K. M., & Lentz, F. E. (2004). Response to intervention: Empirically based special service decisions from single-case designs of increasing and decreasing intensity. The Journal of Special Education, 28, 66-79
- Carnine, D. W., Silbert, J., Kame’enui, E. J., & Tarver, S. G. (2004). Direct instruction reading (4th ed.). Upper Saddle River, NJ: Merrill Prentice Hall.
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- Witt, J. C., Noell, G. H., LaFleur, L. H., & Mortenson, B. P. (1997). Teacher use of interventions in general education settings: Measurement and analysis of the independent variable. Journal of Applied Behavior Analysis, 30, 693-696.
- VanDerHeyden, A. M. & Witt, J. C. (2005). Quantifying context in assessment: Capturing the effect of base rates on teacher referral and problem-solving model of identification. School Psychology Review, 34, 161-183.
- VanDerHeyden, A. M., Witt, J. C., & Naquin, G. (2003). The development and validation of a process for screening and referrals to special education. School Psychology Review, 32, 204-227.
- VanDerHeyden, A. M., & Witt, J. C. (2008). Best practices in can’t do/won’t do assessment. In A. Thomas & J. Grimes (Eds.). Best Practices in School Psychology, 5th Edition, Volume 2 (pp. 131-140). Bethesda, MD: National Association of School Psychologists.
- VanDerHeyden, A. M., Witt, J. C., & Gilbertson, D. A (2007). Multi-Year Evaluation of the Effects of a Response to Intervention (RTI) Model on Identification of Children for Special Education. Journal of School Psychology, 45, 225-256.