Predicting Interim Assessment Outcomes Among Elementary-Aged English Learners Using Mathematics Computation, Oral Reading Fluency, and English Proficiency Levels

Abstract The current study examined the validity of curriculum-based measures (CBM) in mathematics computation (M-COMP) and oral reading fluency (R-CBM) in predicting spring mathematics and reading performance level and performance risk (>1 SD below the national mean) among students classified as English Learners (ELs). Additionally, the current study assessed the incremental predictive value of English language proficiency (ELP) beyond CBM performance. The results indicated that ELP explains a significant portion of variability above M-COMP and R-CBM and increases the accuracy of predicting at-risk performance status on spring measures of mathematics and reading. The findings highlight the challenges of assessing the predictive accuracy of M-COMP and R-CBM among students classified as ELs, as well as the extent to which comprehensive measures of ELP account for variance in both performance level and at-risk status beyond CBMs. The implications for school data-based decision-making for language-minoritized students and directions for future research are discussed. Impact Statement Equity in Response-to-Intervention (RTI) is predicated on accurate measurement of skills within universal screening. The current study’s findings suggest that CBMs alone explain less variance and are less predictive of academic performance than when combined with English language proficiency scores. The predictive accuracy of R-CBM and M-COMP varied between students classified as ELs and non-ELs but in only very limited circumstances were these measurable differences. These results indicated that although CBMs are an efficient system of screening among non-ELs, it is also necessary to consider students’ ELP levels when making decisions within RTI models.