The current research study examined one district's journey through Response to Intervention (RtI) implementation. RtI is a program, now written into federal legislation (IDEIA, 2004; NCLB, 2002), that allows school districts to intervene with additional research-based education resources for struggling students. Districts are able to do this as a means of intervention prior to referral to special education in addition to identifying a student as eligible for special education services. The literature guiding districts through the RtI implementation process describes the components of RtI (Fuchs & Fuchs, 2006; Fuchs & Fuchs, 2007) but lacks empirical research to establish the effectiveness of RtI in terms of student achievement. This dissertation seeks to add to the current literature by examining student outcomes before and after implementation of an RtI framework, analyzing the use of assessments to determine student placement into intervention services, and noting the perceptions of teachers during implementation. The results of this study will assist school districts by presenting student data trends that occurred before and after implementation, which may affect district practices and policy. In addition, implications for future research are discussed. / Educational Psychology

This researcher aimed to determine how data collected from computer-based assessment programs, specifically EasyCBM and AIMSweb, was used in data-driven instruction and used to identify risk levels in math and reading areas proficiency. Data from intervention programs were collected from six participating high schools. The data collection included math and reading universal screening scores and levels of risk indicators from Tier 2 and Tier 3 levels of their response to intervention (RTI) programs. Section A included math data within a baseline score and a risk indicator level. Section B had reading scores with a baseline score and a risk indicator level. A descriptive quantitative study was conducted to determine if significant differences in EasyCBM and AIMSweb exist in student universal screener scores over an academic calendar year. Independent variables included: math and reading universal screener scores, tier level identifiers, and level of risk indicators. Factors that influenced the rates of effectiveness included: interventionist utilization of data, student entry tier levels, and time spent in intervention from the fall to winter benchmarking period.

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