Pamela M. Stecker

The “Blurring” of Special Education in a New Continuum of General Education Placements and Services:

For nearly 10 years, the response-to-intervention (RTI) policy initiative has engendered enthusiasm at federal, state, and local levels and among various stakeholders. Nevertheless, there are basic and important disagreements about its nature and purpose. The authors describe two groups with contrasting perspectives on RTI in an effort to examine its multiple meanings, to argue that neither group has a credible plan to educate children and youth with severe learning needs, and to encourage all interested parties to think productively about what they want to accomplish in the name of RTI.

Developing Legally Correct and Educationally Meaningful IEPs Using Curriculum-Based Measurement

The individualized education program (IEP) has been the cornerstone of special education since the Education for All Handicapped Childrens Act became law in 1975. Nevertheless, IEPs have been fraught with legal and educational problems. In this article we examine the process for developing IEPs and suggest that, by using curriculum-based measurement, school districts can ensure that they develop and implement IEPs that both meet the requirements of the law and provide meaningful educational programs for students with disabilities who have basic skills deficits. We begin by examining problems in IEP development, focusing on violations of the IEP process. Then, we discuss the three major components of the IEP. Finally, we present a case study of the use of curriculum-based measurement to develop legally correct and educationally meaningful IEPs.

Tertiary Intervention; Using Progress Monitoring with Intensive Services

(RTI) model, successive levels of instructional supports based on scientifically sound practices are provided to students who experience academic difficulties. Although professionals have described specific components of the RTI multi-tiered system in different ways, authors of the RTI series in this issue describe a three-tiered system of instructional service to students who struggle academically, including the identification and provision of special education to students with specific learning disabilities (SLD). What is common across discussions of RTI as a prevention–intervention model is that increasingly more intensive instructional support is provided during each successive tier to students who are designated as at risk or when these students demonstrate academic unresponsiveness in previous tiers. The third tier, or tertiary intervention, is the main focus of this article. In this model, Tier 3 includes the provision of special education services. Under the Individuals With Disabilities Education Improvement Act of 2004, states may no longer require the use of the discrepancy approach (i.e., between intellectual functioning and achievement) to identify individuals with SLD. The law permits states and districts to use data from student response to research-based interventions, such as those collected through an RTI approach, as an alternative route for identifying SLD. Although RTI practices have been in place in some locations for a number of years (see e.g., D. Fuchs, Mock, Morgan, & Young, 2003; Marston, Muyskens, Lau, & Canter, 2003; Tilly, 2006; Vaughn & Chard, 2006), no standard protocol has been mandated for directing the RTI process. Consequently, current models vary with respect to particular features of the model. What is common among approaches, however, is that progressmonitoring data are used for decisionmaking purposes. Data aid teachers in making judgments about the success of their instruction for individual students and to determine when additional support is needed, or conversely, when such intensive instruction no longer is needed because a student has responded well to intervention. For example, when progress-monitoring data illustrate good response to secondary prevention services (i.e., Tier 2), the student may be moved back to primary prevention (i.e., Tier 1). Progress monitoring continues, and, if the student experiences a serious lag in academic achievement, Tier 2 intervening support may be necessary again. In this way, progress-monitoring data support flexibility within the RTI model for moving a student back and forth through tiers and become central to RTI practices. The following section briefly reviews typical practices in Tiers 1 and 2 and describes how tertiary instruction (i.e., Tier 3) differs from previous tiers. In this model, intensive tertiary intervention includes the provision of special education. Next, the RTI process is described in the context of a classroom scenario with a hypothetical student who struggles significantly with reading. This case study illustrates how progress-monitoring data are used to move this student through the RTI tiers and subsequently to aid in identifying her as having an SLD. Within tertiary

One Elementary School's Implementation of Response to Intervention (RTI).

We provide a description of how a culturally and linguistically diverse elementary school in the Midwest implemented core features of a response-to-intervention (RTI) framework for improving school-wide reading instruction and decision making. A multi-year timeline illustrates how this school implemented additional elements of the RTI framework over time. This multi-tiered system relied on formative evaluation as a core component, including screening several times per year and progress monitoring for students receiving instructional interventions. The principal and staff made decisions collectively about implementation. We summarize student achievement results and discuss implications for the implementation of RTI models in other elementary schools.

A Comparison of Traditional Classroom Instruction and Anchored Instruction with University General Education Students.

The effects of a traditional instruction format and an anchored instruction format on the immediate and long-term acquisition of knowledge of 100 university general education majors was examined. Participants were administered multiple-choice and essay format pre-tests, post-tests, and follow-up tests. Results revealed somewhat different within group patterns as well as important between group patterns. Both groups performed better on the post-test and follow-up test than on the pre-test. No differences between the two groups on the post-test were recorded. The anchored instruction group outperformed the traditional instruction group on the multiple-choice follow-up test and the traditional instruction group outperformed the anchored instruction group on the essay follow-up test. Implications for future research are discussed.

Response to Intervention: Where It Came From and Where It's Going

Response to intervention (RTI) emerged from the 2004 reauthorization of the Individuals with Disabilities Education Act, but the roots of RTI are found embedded within the history of the field of learning disabilities (LD) as well as other sources of influence. In what follows, we provide a brief history of LD and highlight the connection between the controversies of LD and the emergence of RTI. We offer discussion on the evolution of RTI through current practice, along with implications and cautions regarding future practice so that school personnel might gain a better understanding of RTI.

Strategic Development for Middle School Students Struggling With Fractions Assessment and Intervention

Research has suggested that different strategies used when solving fraction problems are highly correlated with students’ problem-solving accuracy. This study (a) utilized latent profile modeling to classify students into three different strategic developmental levels in solving fraction comparison problems and (b) accordingly provided differentiated strategic training for students starting from two different strategic developmental levels. In Study 1 we assessed 49 middle school students’ performance on fraction comparison problems and categorized students into three clusters of strategic developmental clusters: a cross-multiplication cluster with the highest accuracy, a representation strategy cluster with medium accuracy, and a whole-number strategy cluster with the lowest accuracy. Based on the strategic developmental levels identified in Study 1, in Study 2 we selected three students from the whole-number strategy cluster and another three students from the representation strategy cluster and implemented a differentiated strategic training intervention within a multiple-baseline design. Results showed that both groups of students transitioned from less advanced to more advanced strategies and improved their problem-solving accuracy during the posttest, the maintenance test, and the generalization test.

Effects of Data-Based Individualization for Students with Intensive Learning Needs: A Meta-Analysis: EFFECTS OF DATA-BASED INDIVIDUALIZATION

We examined the mean effect of teachers’ use of data-based individualization (DBI) on the performance of students with intensive learning needs across academic areas and factors influencing the effects of DBI on student achievement. A total of 57 effect sizes from 14 studies were categorized into two comparisons: DBI Only (comparisons between DBI and a business-as-usual control) and DBI Plus (comparisons in which DBI implementers had access to additional information on student performance while they implemented DBI, compared to a control). The mean effect of DBI Only on student performance was g = 0.37; the mean effect of DBI Plus was g = 0.38. Differential effects of DBI were found depending on the nature of CBM tasks, frequency of CBM administration, and type and frequency of supports provided to teachers. Findings support the use of DBI to enhance student outcomes across academic areas.

Strategies Students With and Without Mathematics Disabilities Use When Estimating Fractions on Number Lines

We examined faulty strategies with possible underlying misconceptions, as well as execution mistakes, among middle schoolers with and without mathematics disabilities when estimating fractions on number lines. Fifty-one middle schoolers participated in this study, including 27 students with mathematics disabilities. Participants were asked to estimate 10 fractions on a 0-1 number line and 11 fractions on a 0-5 number line and explain their procedures. We identified two incorrect strategies (i.e., not-on-the-line and ruler-tick-mark counting strategy) and two execution mistakes (i.e., unequal segmentation based on denominator and inaccurate numerical transformation) on both number-line estimation tasks. We also identified one additional faulty strategy (i.e., treating 0-5 as the 0-1 number line) with the 0-5 number-line estimation tasks. Students with mathematics disabilities were significantly more likely to use the faulty strategies than students without mathematics disabilities. The faulty strategies, rather than execution mistakes, were consistent across two number-line tasks and predicted students’ performance in other fraction problem-solving tasks. Results illustrated specific problems that students with and without mathematics disabilities encountered when using number lines to illustrate fractions. We discussed possible negative influences of using rulers to teach fractions and how to help students to construct the concept of units.

Reflections on Teachers’ Data-Based Decision Making

Data-based decision making described in the articles in this special issue focus on how teachers, preservice teachers, and administrators make academic instructional decisions centered on student data representing an individual’s overall achievement in a particular academic area across a period of time. This process is grounded in the work of Deno and Mirkin (1977) who conceptualized and validated a system for modeling student academic growth over time that relied on simple and efficient assessment tools with sound technical characteristics, which became known as curriculum-based measurement (CBM; Deno, 1985). The purpose of this student information was to provide an objective database for making instructional decisions, especially for students who had significant learning difficulties. By tracking a student’s overall change (or lack thereof) across time, teachers could test systematically the effects of different instructional programs or techniques for individual students. When instruction was not working as anticipated, which happened often for students with disabilities, teachers could alter instructional components and use the same repeated measurement procedures to test the influence of the modified instruction. Making instructional changes for individual students based on their own patterns of progress can be seen as the essence of special education (see Deno, 1990). Although this process may appear straightforward, Fuchs (2017) described a distinction between the simple measurement tools and the more complex issue of how indicators of academic proficiency are considered when making instructional decisions. Some of these aspects of instructional decision making are addressed in this issue. Two studies, one in the United States (Espin, Wayman, Deno, McMaster, & de Rooij, 2017) and the other in the Netherlands (van den Bosch, Espin, Chung, & Saab, 2017), explored how teachers and experts in CBM interpreted student progress data. A third study in the United States compared experts’ and preservice teachers’ understanding of CBM-graphed data and examined change among preservice teachers across time (Wagner, Snidarich, Espin, Seifert, & McMaster, 2017). Differences between teachers and student teachers in Germany were examined in another study using a newly developed test of graph literacy that focused on student progress information (Zeuch, Forster, & Souvignier, 2017). An additional study in the Netherlands explored potential factors that may contribute to school differences in successful implementation of