REL Central Ask A REL Response

“This article provides a description of how a beginning special education teacher in an inclusion classroom used pre-assessment, self-assessment, and on-going assessment to implement the principles of differentiated instruction to become more responsive to her students’ needs in a systematic way. This article describes a case study of one beginning teacher’s use of differentiated instruction. First, a discussion of the usefulness of differentiated instruction in increasing the likelihood of success for children with disabilities is provided. Next, qualitative data supported the implementation of the differentiated instruction process to help the teacher realize how she could positively impact students’ learning using Tomlinson’s (2000) categories of content, product, process, and learning environments. Finally, recommendations are provided for how to engage teachers to implement differentiated instruction as a data-based iterative process of using evidence-based practices to meet the needs of all children in an inclusion classroom.”

“As educators face increasing pressure from federal, state, and local accountability policies to improve student achievement, the use of data has become more central to how many educators evaluate their practices and monitor students’ academic progress. Despite this trend, questions about how educators should use data to make instructional decisions remain mostly unanswered. In response, this guide provides a framework for using student achievement data to support instructional decision making. These decisions include, but are not limited to, how to adapt lessons or assignments in response to students’ needs, alter classroom goals or objectives, or modify student-grouping arrangements. The guide also provides recommendations for creating the organizational and technological conditions that foster effective data use. Each recommendation describes action steps for implementation, as well as suggestions for addressing obstacles that may impede progress. In adopting this framework, educators will be best served by implementing the recommendations in this guide together rather than individually.”

“We examined effects of research-based early writing intervention delivered within a data-based instruction (DBI) framework for children with intensive needs. We randomly assigned 46 students with and without disabilities in Grades 1 to 3 within classrooms to either treatment or control. Treatment students received research-based early writing intervention within a DBI framework for 30 min, 3 times per week, for 12 weeks. Control students received business-as-usual writing instruction. We measured writing performance using curriculum-based measures (CBM) and Woodcock Johnson III Tests of Achievement (WJ III). We found significant treatment effects on CBM outcomes (Hedges g = 0.74 to 1.36). We also found a significant interaction between special education status and condition on the WJ III favoring treatment students with disabilities (Hedges g = 0.45 to 0.70). Findings provide preliminary support for using a combination of research-based intervention and DBI with students with intensive writing needs.”

“Few teachers receive adequate preparation to provide effective individualized instruction for children with intensive early writing needs. In this article, the authors describe an attempt to close this learning gap, by developing Data-Based Instruction-Tools, Learning, and Collaborative Support (DBI-TLC), a comprehensive professional development (PD) system that provides tools, learning opportunities, and ongoing collaborative supports for teachers to implement DBI in early writing. They describe the theoretical framework that has guided this work, the teacher population with whom they worked, their approach to assessing important teacher outcomes, and their development process. They highlight key findings that align with their theory of change, and discuss implications for further research and teacher preparation.”

“It is a few weeks before the end of the school year. Mrs. Arnold, a special education teacher at Cornelius Elementary, is analyzing the most recent progress-monitoring data for one of her third grade students, Rashan. As she looks at the data she has just plotted on a graph, Mrs. Arnold pauses to reflect. Despite her efforts to increase Rashan’s reading skills, the improvements he has made across the school year are inadequate. Mrs. Arnold remembers a presentation that she attended at the annual CEC conference earlier in the year and accesses the website she had written in her notes–www.intensiveintervention.org. She begins to explore the website and wonders whether the recommended approach, data-based individualization (DBI), could help her increase Rashan’s reading performance.”

“The Office of Special Education Programs (OSEP) in the U.S. Department of Education funded the National Center on Intensive Intervention (NCII, or the Center) in 2011. NCII’s mission during the first five years of funding was to build the capacity of local education agencies (LEAs) to support schools, practitioners, and other stakeholders in the implementation of intensive intervention in reading, mathematics, and behavior for students with severe and persistent learning and/or behavioral needs. The purpose of this document is to provide an overview of the Center’s accomplishments during the initial funding cycle and to highlight a set of lessons learned from the 26 schools that implemented intensive intervention while receiving technical support from the Center. First, the authors provide a description of NCII’s approach to intensive intervention and summarize the Center’s initial technical assistance efforts. Next, they describe their methodology and outline the lessons learned from the implementation sites. Then, they offer guidance for practitioners who are interested in implementing intensive intervention. Finally, the authors close with an overview of NCII’s plans for building upon this work over the next five years.”

“Within the Response-to-Intervention framework, students who fail to profit from high-quality general education instruction, accommodations, and supplemental instruction progress to a more intensive intervention program, sometimes referred to as ‘Tier III.’ This article describes a problem-solving approach to designing such intensive, data-based, and scientifically supported interventions for students with pervasive reading problems who have failed to respond to less rigorous services. The application of well-established (i.e., progress monitoring) and emerging methods (i.e., brief experimental analysis) for optimizing interventions are described. Two case studies are provided to illustrate how these techniques may be combined to implement Tier III interventions.”

“This study explored the data-based decision making of 12 teachers in grades 6–8 who were asked about their perceptions and use of three required interim measures of reading performance: oral reading fluency (ORF), retell, and a benchmark comprised of released state test items. Focus group participants reported they did not believe the benchmark or ORF tests accurately reflected students’ comprehension abilities. Teachers held more favorable opinions of retell but admitted improvising their use of the measure rather than following mandated implementation procedures. Participants reported that only summative state assessment scores were used to plan appropriate instruction and only for large groups. Results suggest the need for improved support for data-based decision making and the development of technically adequate interim measures with relevance to the teachers expected to use them.”

“This multiple baseline across participants study examined the efficacy of a data-based individualization word study intervention for students with autism spectrum disorder (N = 5) and low word reading skills. An experienced interventionist provided 1:1 word reading instruction in 30-minute sessions five times per week for an average of 10 sessions per participant. Intervention effects for directly taught words and words with similar spelling patterns were estimated using visual analysis and calculation of mean differences across baseline and intervention phases. Results indicate immediate and consistent improvements in word reading outcomes across all participants.”

“Much is being said in education about the value of adopting data-based or analytics approaches to instructional improvement. One important group of stakeholders in this effort is the faculty. ‘In many cases, the key constituency group is faculty, whose powerful voice and genuine participation often determine the success or failure of educational innovations, especially those that involve pedagogical and academic change’ (Furco & Moely, 2012, pg. 129). This paper reports the results of an exploration of factors that influence faculty to consider or reject using analysis of student data to improve instruction based on social cognitive theory. Self-efficacy, value of the outcome, and feasibility of using a student data-based reflection process were found to be related to the actual use of components of the reflection process by faculty.”