Curriculum-based measurement (CBM) is an approach to measuring student academic growth and evaluating the effectiveness of instruction (Deno, Exceptional Children, 52, 219-232, 1985) that was developed, in part, based on characteristics of applied behavior analysis. Learning to administer and use CBM data is commonly part of teacher preparation programs, but less common in behavior analysis graduate programs (Schreck et al. Behavioral Interventions, 31, 355-376, 2016; Schreck & Mazur, Behavioral Interventions, 23, 201-212, 2008). This article describes a sequence of steps that educational teams can follow to use CBM within the multi-tiered system of support (MTSS) framework. These steps include (1) selecting a CBM publisher and gathering materials; (2) practicing administering and scoring CBM; (3) administering, scoring, and comparing student scores to grade-level benchmarks; (4) using CBM data to write ambitious and realistic IEP goals; and (5) using data-based individualization. Each step is described and includes a description of a case study that is based on our experiences working with pre-service teacher candidates, and special education and behavior analysis graduate students in K-12 and after-school instructional programs.

The purpose of this narrative synthesis of the curriculum-based measure (CBM) instructional utility literature is to deepen insight into the supports required to enrich teachers\' instructional decision-making within curriculum-based measure-data-based individualization (CBM-DBI) in ways that enhance the learning outcomes of students with intensive intervention needs, including students with learning disabilities. We begin by summarizing a recent meta-analysis of CBM-DBI studies focused on academic outcomes. We then reconsider studies from that meta-analysis to further explore the supports required to enrich teachers\' instructional decision-making within CBM-DBI and improve student learning. We next draw conclusions and propose a renewed program of instructional utility CBM-DBI research for capitalizing on technology\'s potential to enhance productive instructional decision-making for students who require intensive intervention, fulfill DBI\'s potential, and bring CBM-DBI to scale.

The purpose of this review was to synthesize research on the effect of professional development (PD) targeting data-based decision-making processes on teachers\' knowledge, skills, and self-efficacy related to curriculum-based measurement (CBM) and data-based decision-making (DBDM). To be eligible for this review, studies had to (a) be published in English, (b) include in-service or pre-service K-12 teachers as participants, (c) use an empirical group design, and (d) include sufficient data to calculate an effect size for teacher outcome variables. The mean effect of DBDM PD on teacher outcomes was g = 0.57 (p < .001). This effect was not moderated by study quality. These results must be viewed through the lens of significant heterogeneity in effects across included studies, which could not be explained by follow-up sensitivity analyses. In addition, the experimental studies included in this review occurred under ideal, researcher-supported conditions, which impacts the generalizability of the effects of DBDM PD in practice. Implications for research and practice are discussed.