We evaluated the effects of one year of schoolwide cluster grouping on the academic achievement growth of gifted and non-identified elementary students using a piecewise multilevel growth model. Scores from 186 non-identified and 68 gifted students' Measures of Academic Progress Reading and Math scores were examined over three school years. In 2008-2009 within-class ability grouping was used. In 2009-2010 schoolwide cluster grouping was implemented. In 2010-2011 students once again were grouped only within classrooms by ability and students identified as gifted were spread across all classrooms at each grade level. Results suggest that schoolwide cluster grouping influenced student performance in the year following its implementation, but only for mathematics and not the area of reading.

We evaluated the effects of one year of schoolwide cluster grouping on the academic achievement growth of gifted and non-identified elementary students using a piecewise multilevel growth model. Scores from 186 non-identified and 68 gifted students' Measures of Academic Progress Reading and Math scores were examined over three school years. In 2008-2009 within-class ability grouping was used. In 2009-2010 schoolwide cluster grouping was implemented. In 2010-2011 students once again were grouped only within classrooms by ability and students identified as gifted were spread across all classrooms at each grade level. Results suggest that schoolwide cluster grouping influenced student performance in the year following its implementation, but only for mathematics and not the area of reading.

This quantitative study examined seventh-grade reading scores to determine the extent to which certain demographic variables (race/ethnicity, gender, socioeconomic status) explain and MAP reading scores predict reading scores on the State of Texas Assessment of Academic Readiness (STAAR) in a selected northeast Texas public school. Standardized assessments only compare the relative performance of an individual student to other groups of students using scaled scores, which can vary from year to year and from state to state. With the advent of computer adaptive testing, this study provides information on the predictive validity of benchmark assessments. Specifically, this study looked for predictive evidence that indicates how accurately test data can predict criterion scores. Findings revealed, through a multiple regression analysis, that the fall MAP Rasch Unit (RIT) scores predicted the STAAR scale scores. Using SPSS version 22, the data were entered and analyzed in a multiple regression model to determine the presence of a statistical trend or lack thereof. Demographic data and MAP scores were entered into the regression model to examine the predictive validity of the MAP assessment in determining student performance on the STAAR seventh-grade state-mandated reading assessment. The statistical analysis revealed that MAP RIT scores explain a significant variance related to seventh-grade STAAR reading scale scores. There is a vital need for tools that improve a student's academic development and MAP assessments have been found to predict performance on state-mandated assessments.

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