Pre‐service Teachers’ Interpretation of CBM Progress Monitoring Data

Abstract

Teachers must be proficient at using data to evaluate the effects of instructional strategies and interventions, and must be able to make, describe, justify, and validate their data-based instructional decisions to parents, students, and educational colleagues. An important related skill is the ability to accurately read and interpret progress-monitoring graphs. This study examined preservice special education teachers' graph reading and interpretation skills at two points in time. Participants used a think-aloud procedure to interpret a curriculum-based measurement (CBM) progress monitoring graph, and results were compared to those of CBM experts. Overall, preservice teachers tended to say fewer words than graph experts did. Furthermore, their descriptions of CBM graphs were less sequentially coherent, specific, and reflective. Little change was observed at time 2. Implications for improving teacher preparation in this skill area are discussed. is an Associate Professor of Special Education at Minnesota State University Mankato. Her research interests include understanding and improving teachers' use of data to make instructional decisions, and training and supporting special education teachers in evidence-based instructional practices.Stephanie M. Hammerschmidt-Snidarich, Ph.D., is a postdoctoral research associate at the Center for Applied Research and Educational Improvement (CAREI) at the University of Minnesota. She is also a literacy content specialist for Minnesota Reading Corps and Serve Minnesota, working to develop, research, and implement early literacy intervention programming that addresses the needs of students with reading difficulties. Her research interests include the intersection of student behavior, intervention fidelity, and academic outcomes for students with and without disabilities; data-based decision making; curriculumbased measurement in reading; and early literacy interventions, including computer-adaptive interventions.Christine A. Espin, Ph.D., is a Professor in the Department of Education and Child Studies (Special Education/Learning Disabilities) at Leiden University, The Netherlands. Her research interests include teachers' data-based decision making, curriculum-based measurement in reading, writing, and content-area learning for secondary school students with learning disabilities, and reading comprehension interventions for students with reading difficulties.Kathy Seifert, Ph.D., is a Senior Lecturer and Coordinator of the Learning Disabilities licensure program in the Department of Educational Psychology (Special Education) at the University of Minnesota. Her interests include preparing preservice teachers in the use of data-based decision making through Curriculum-based Measurement and in the use of evidence-based practice in determining appropriate Tier I and II research-based interventions for student with academic, behavioral, and functional disabilities.Kristen L. McMaster, Ph.D., is a Professor of Special Education in the Department of Educational Psychology, University ...

Improving Teachers’ Comprehension of Curriculum-Based Measurement Progress-Monitoring Graphs

“…Completeness scores in the current study were higher than those found in previous research. Wagner et al (2017) reported that preservice teachers mentioned only three of nine possible graph elements, and van den Bosch et al (2017) reported that inservice teachers mentioned six of nine graph elements. The higher completeness scores in the present study (seven of eight graph elements) may be due to the fact that teachers were asked to describe the graphs as if they were describing them to a parent rather than to tell all they were seeing and thinking about a graph, as was done in the previous studies.…”

“…At both pre- and posttest, graph comprehension was assessed via a CBM Graph-Description Task. In previous research, the CBM Graph-Description Task has been shown to differentiate graph comprehension of (a) preservice teachers from that of CBM experts ( Wagner et al, 2017 ), (b) inservice teachers from CBM experts ( van den Bosch et al, 2017 ), and (c) teachers with higher ratings versus lower ratings on understanding and interpreting CBM graphed data ( Espin et al, 2017 ). As an example, in the study by van den Bosch et al (2017) , scores on the CBM Graph-Description Task for inservice teachers versus CBM experts were, respectively, 97.5% versus 100% for accuracy, 5.7 versus 8.3 for completeness, 51.7% versus 85% for sequential coherence, 1.7 versus 4.8 for data-to-data comparisons, 1.7 versus 4.1 for data-to-goal comparisons, and 1 versus 5 for data-to-instructional links (see following section for a detailed description of these variables).…”

Tablet or Paper and Pen? Examining Mode Effects on German Elementary School Students’ Computation Skills with Curriculum-Based Measurements

“…Another suggestion is that teachers in Germany should be made aware about the idea of data-based decision-making. This topic should already be given a stronger focus in university education (Wagner et al, 2017), but also to teachers already working in school practice (Espin et al, 2017). In the literature there is a lack of evaluated concepts for further training and coaching of teachers for a safe handling of data in the sense of a data-based decision-making approach.…”

Raising the Power of Curriculum-Based Measurement Tools in Preservice Training

“…Assessment can allow teachers to determine where errors are occurring and implement effective practices to meet the learner’s needs. However, though teachers frequently assess their students’ learning, they often struggle to make data-based decisions that positively affect student achievement (Mandinach & Jimerson, 2016; Riccomini, 2005; Stecker & Fuchs, 2000; Wagner et al, 2017).…”