PRACTICE FORUM
Local norms within a model of response to intervention: Implications for practice
By Kathrine M. Koehler-Hak, PhD, and Jill C. Snyder
In 2001, the U.S. Office of Special Education Programs (OSEP) sponsored a summit to discuss alternative determination models for students with learning disabilities. One of the models presented was Responsiveness to Intervention, or RTI. Much controversy has surrounded both the purpose and structure of RTI (Berkeley, Bender, Peaster & Saunders, 2009; Elliott, 2008), resulting in variations in the implementation across states, districts and even schools within districts. Moreover, professional organizations (i.e., Council for Exceptional Children [CEC], National Association of School psychologists [NASP], American Psychological Association [APA], Council of Administrators of Special Education [CASE], National Association of State Directors of Special Education ([NASDSE]) aimed at ensuring quality education hold fundamentally differing beliefs regarding RTI (Fuchs, Fuchs, & Stecker, 2010).
Fuchs & colleagues (2010) offer some insight into both the differences – and similarities – in approaches to RTI. The authors contend that there are two main belief systems with respect to RTI. First, many educators view RTI from the legal perspective of IDEA (2004) which advocates for an RTI model that facilitates accurate and timely identification of students with high incident disabilities (Marston, Muyskens, Lau, & Canter, 2003). From this perspective, RTI begins with the universal screening of children and the utilization of each individual child’s data in making instructional decisions. Second, other educators conceptualize RTI from the legal perspective of No Child Left Behind (NCLB), and it’s parent legislation, Elementary and Secondary Education Act (ESEA). Those adhering to the NCLB perspective of RTI contend that RTI “is nothing if not meaningful operationalization of the ‘right’ education, a promising bridge between federal policy and local practice” (Fuchs, et. al, 2010, p. 304). From this perspective, RTI is intrinsically linked to standards-based educational reform, early intervention and a merging of regular and special education (NASDE). RTI therefore would utilize aggregated universal screening data in making system-wide instructional decisions.
While both belief systems appear incompatible on the surface, there are a few essential commonalities to note. Both perspectives stress the early identification and prevention of learning and behavior problems, the use of universal screenings in core academics, the systematic increasing intensity of instruction and progress monitoring (Fuchs, et. al, 2004). The present authors contend that these commonalities link the intent of IDEA and NCLB legislation in ensuring quality education for all or our nation’s children. More specifically, data attained through universal screenings may be used for both individual (IDEA) and systemic (NCLB) educational decisions.
Universal screenings within RTI
Advocates of RTI, from either perspective, stress the need for universal screenings (i.e., assessing every child in basic skills at least 3 times a year) and an adherence to research supported educational practices and curriculum (Brown-Chidsey & Steege, 2005; Fletcher et al, 2007; Tilly, 2003). Therefore, data attained through universal screenings must provide information that is meaningful in determining educational needs and informing intervention for both individuals and groups of children. In essence, questions pertaining to IDEIA include “Is this child receiving sufficient benefit from the provided curriculum or intervention?” and “Is this child’s progress similar or different from his local peer group receiving the same instruction and curriculum?” Questions pertaining to NCLB are “Based on aggregated data, what are the strengths and needs of the system?” and “is the system working for most (e.g. at least 80%) of children?”
Individual student decisions
Data from universal screenings provides information on an individual student’s skills within a system. Analyzing individual data, within the context of local norms, assists educators in differentiating individual student problems from systemic-level problems. Local norms reflect the culture and community of a child in a given district and, therefore, are more effective in differentiating individual student problems from systemslevel problems (Stewart, L. & Kaminski, R., 2002, Stewart & Silberglitt, 2008). In addition, examining data for individual students can help address the growth of individual students as a result of evidence-based instruction and/or intervention programs. Individual students’ growth rates can be compared to the growth rates of his or her peers receiving the same instruction and curricula.
In reviewing classroom data, teachers are able to utilize universal screening data at individual and class levels to plan daily instruction. Each teacher may develop instructional groupings of students to best meet individual needs and tailor whole class instruction to address the specific needs of the class. For example, Figure 1 shows Mrs. White that only 8 students out of 21 have met or exceeded the third grade benchmark of 77 Words Correct Per Minute (WCPM) with 6 students scoring in the at risk range of < 52 WCPM. Given this information near the beginning of the academic year, Mrs. White is able to differentiate her instruction to better meet the needs of her class as a whole. Furthermore, when individual student names are protected, data are useful for reporting individual student progress as compared to a set criterion and other students within the peer group.
Within RTI framework, educators must rule out lack of effective instruction when identifying a child with a learning disability. Local normative data from Figure 2, for example, provides data on oral reading fluency for the fifth-grade. It may not support the assumption that adequate access to instruction in the area of reading fluency has been provided, leading educators to question whether individual student performance is reflective of an overall systems-level problem rather than a true learning disability.
In either scenario – that of Figure 1 or that of Figure 2 — the child would be in need of intervention. However, the way in which the intervention would be delivered might be different. Systemic problems with curriculum and instruction should first be addressed at the whole school, grade or classroom level through the implementation of supplemental curricula and/or instructional strategies.
Figure 1
Fall Oral Reading Fluency for Mrs. White's third grade class
Note: Students are expected to be reading > 77 Words Correct Per Minute (WCPM): students reading < 52 WCPM are at-risk
System level decisions
When aggregated across grade, school, or district, universal screening data provides a reference point for schools to evaluate their system in comparison to other schools and districts (Deno, 2003). This practice allows districts to utilize a normative comparison group at the local level (i.e., local norms) as one piece of information when monitoring district performance and accountability goals. Looking at the aggregated data, or local norms, allows educators to determine whether their system is truly effective for most (e.g. at least 80%) students. Examining outcomes for various groups can help address the strengths and needs of the system as a whole. Specifically, local norms allow educators to: (a) identify system wide performance and goals, and (b) monitor the growth and performance of various groups of students.
Identify system-wide performance and goals
Given the current emphasis on accountability, schools must document and be accountable for systems level outcomes. When aggregated at the classroom, grade, school, or district level, local norms provide a means of formatively evaluating systems level progress and documenting the effectiveness of system wide instructional programs. For example, Figure 2 provides systems data for fifth-grade oral reading fluency (ORF) scores in the fall, winter and spring. The “box” on each graph represents the 25th percentile to the 75th percentile, or middle 50% of students. The line extending upward from the box represents the upper 25% of students and the line extending below the box represents the lower 25% of students in the fifth grade of the given district. The end of year goal for fifth graders in ORF is 124 words correct per minute (WCPM) (Hosp, Hosp & Howell, 2007).
At a systems level, data may be analyzed to address the question of system-level progress and goals. Comparing the present cohort of fifth graders to the criterion of 124 WCPM on oral fluency measures, it is apparent that slightly less than 75% of students attained this goal. In fact, the median score for fifth grade ORF in the spring is 120.5 WCPM, or 4.5 WCPM less than the criterion score. Furthermore, when comparing progress of fifth graders across the year, a pattern emerges. The middle 50% (or the area represented in the box) improves only 5 to 10 WCPM over the course of the year. Additionally, both the highest performing students (>Q3) and lowest performing students (<Q1) score lower in the spring than in the fall, indicating a slight negative rate of growth over the course of the academic year. Administrators viewing this graph could conclude that the fifth grade curriculum does not meet the needs in reading fluency for most children.
Certainly, there may be many reasons for the lack of progress demonstrated on the fifth-grade ORF. Some examples might include a lack of fidelity of program implementation, lack of match of curricular focus and student needs, lack of student motivation, and lack of high quality instruction (Coyne, Kammenui, & Carnine, 2010). Careful functional analysis of the curriculum, instruction and environment, along with the analysis of local norm data provides a basis for setting system-wide improvement goals and intervention recommendations (Koehler-Hak, 2008).
The data presented in Figure 2 are applicable to the documentation of AYP for NCLB. While summative evaluation data (e.g. high-stakes testing) provide comparison of school performance from year-to-year, local norms derived from CBM provide a comparison of progress throughout the year. The frequent collection of performance data allows educators to utilize the data in a preventive rather than reactive manner (Shapiro, 2010). Additionally, data from Figure 2 is applicable to determining special education eligibility.
Likewise, continual collection of CBM data over the years allows schools to set appropriate goals and monitor the effects of instructional decisions (e.g. changing curricula, implementing supplemental or intervention programs, etc.). For example, after reviewing Figure 2, the school may decide to implement supplemental intervention focused on reading fluency for their 5th graders. This system-level intervention could be by comparing one year of data (as depicted in Figure 2) with the same group of students the following year and the same grade the following year. The long-term systems goal would be for the majority of students (represented by the “box”) to score at or above the end –of-year benchmark of 124 WCPM. Therefore, similar to tracking progress for individual children using benchmark data, the system would be tracked.
Monitoring the response of groups of children to curriculum and interventions
District goals may be established and evaluated through the ongoing gathering of local norms in the fall, winter, and spring. In the scenario provided in Figure 2, patterns of student strengths and challenges may indicate the strengths and limitations of instruction or curricula. Districts may use these data to plan and evaluate the use of supplemental instruction or curriculum. For example, supplemental intervention focused on reading fluency could be evaluated using local norms Schools would then compare one year of data (as depicted in Figure 2) with the same group of students the following year and the same grade the following year. The long-term systems goal would be for the majority of students (represented by the “box”) to score at or above the end-of-year benchmark of 124 WCPM. Therefore, similar to tracking progress for individual children using benchmark data, the system would be tracked. A system goal would be to have the entire “box” and above (representing 75% of students) falling at or above 124 WCPM at the spring universal.
Figure 2
Fifth-grade aggregated Oral Reading Fluency data
Note: ORF = Oral Reading Fluency. Q1 = 25th percentile. Q3 = 75th percentile. Min = minimum score. Max = maximum score. End of year benchmark score = 124 Words Correct Per Minute (WCPM).
Conclusion
Local norms are an essential component of school wide data for systems implementing an RTI method of service delivery. Schools involved in the process of collecting and utilizing local norms may find the data useful in informing a range of educational decisions including: (a) identifying system wide goals, (b) monitoring the performance and growth of specific groups of students, and (c) monitoring the performance and growth of individual students. With the development and use of local norms, RTI functions to track the effectiveness of the educational system in meeting the needs of all children.
References
Berkley, S., Bender, W. N., Peaster, L. G., & Saunders, L. (2009). Implementation of response to intervention: A snapshot of progress. Journal of Learning Disabilities, 19, 579-586.
Brown-Chidsey, R. & Steege, M. W. (2005). Response to intervention: Principles and strategies for effective practice. New York: Guilford.
Coyne, M.D., Kameenui, E.J., & Carnine, D.W., (2010). Effective teaching Strategies That Accommodate Diverse Learners (4th ed.). Upper saddle River, NJ: Prentice Hall.
Deno, S. L. (2003). Developments in curriculum-based measurement. Remedial and Special Education, 37, 184-192
Elliott, J. (2008). Response to intervention: what & why?. The Free Library. Retrieved March 19, 2011 from http://www.thefreelibrary.com/Response to intervention: what & why? Neither a fad nor a program,...-a0195680151.
Fletcher, J. M., Reid Lyon, G., Fuchs, L.S., & Barnes, M.A. (2007). Learning disabilities: From identification to intervention. New York: The Guilford Press.
Fuchs, L.S., Fuchs, D. & Stecker, P., (2010). The “blurring” of special education in a new continuum of general education placements and services. Exceptional Children. 76(3), 301-323.
Gresham, F. M. (2005). Response to intervention: An alternative means of identifying students as emotional disturbed. Education and Treatment of Children, 28, 328-344.
Hosp, M. K., Hosp, J. L., & Howell, K. W. (2007). The ABCs of CBM: A practical guide to curriculum-based measurement. New York: Guilford Press.
Individuals With Disabilities Act. 20 U.S.C. § 1400 et seq.(2008).
Koehler-Hak , K. M. (2008). Functional assessment of academics: A paradigm shift necessary for improved student outcomes. The School Psychologist. 62(2). 50-55.
Koehler-Hak, K. & Snyder, J., (in review). Measuring education achievement within the context of local demographics: General Outcomes Measurement as a basis for the development of local norms.
Marston, D., Muyskens, P., Lau, M., & Canter, A. (2003). Problem-solving model for decision making with highincidence disabilities. Learning Disabilities Research & Practice, 18, 187-200.
No Child Left Behind Act of 2001, PL 197-110, 20 U.S.C. § 6301-6578 et seq. Shapiro, E.S., (2004). Academic Skills Problems: Direct Assessment and intervention. New York, NY: Guilford Press.
Stewart, L., & Kaminski, R. (2002). Best practices in developing local norms for academic problem solving. Best Practices in School Psychology IV (Vol. 1, Vol. 2) (pp. 737-752). Washington, DC US: National Association of School Psychologists.
Stewart, L., & Silberglitt, B. (2008). Best practices in developing academic local norms. Best Practices in School Psychology V (Vol. 2) (pp. 225-242). Washington, DC US: National Association of School Psychologists.
Tilly, W. D. (2003, December). How Many Tiers Are Needed for Successful Prevention and Early Intervention?: Heartland Area Education Agency’s Evolution From Four to Three Tiers. Paper presented at the National Research Center on Learning Disabilities Responsiveness-to Intervention Symposium, Kansas City, MO.