A Teacher’s Toolkit for Assessment When Implementing Data-Based Individualization in Mathematics

Abstract

This article provides teachers with a toolkit for assessing students in the context of data-based individualization (DBI) in mathematics. Assessing students is a critical component of DBI because it provides teachers with information about what they may need to modify in their instructional programs. In this article, we provide teachers with step-by-step procedures for assessing students within the DBI process, including selecting an appropriate progress-monitoring tool, collecting baseline data, and analyzing graphed data to determine whether a student is making adequate progress. In addition, diagnostic assessment is discussed to help teachers target instructional priorities based on student needs. Providing a toolkit for assessment and supplementary resources should enable teachers to use DBI procedures more effectively and, ultimately, to boost students’ mathematics achievement.

Keywords

data-based individualization intensive intervention progress monitoring mathematics mathematics disabilities

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References

Dennis

M. S.

Calhoon

M. B.

Olson

C. L.

Williams

(2014). Using computation curriculum-based measurement probes for error pattern analysis. Intervention in School and Clinic, 49(5), 281–289. https://doi.org/hw6v

Deno

S. L.

(1985). Curriculum-based measurement: The emerging alternative. Exceptional Children, 52(3), 219–232. https://doi.org/10.1177/001440298505200303

Fuchs

L. S.

Fuchs

Hamlett

C. L.

Stecker

P. M.

(2021). Bringing data-based individualization to scale: A call for the next-generation technology of teacher supports. Journal of Learning Disabilities, 54(5), 319–333. https://doi.org/hw5p

The IRIS Center. (2019). Progress monitoring: Reading. https://iris.peabody.vanderbilt.edu/module/pmr/

Jung

P. G.

McMaster

K. L.

Kunkel

A. K.

Shin

Stecker

P. M.

(2018). Effects of data-based individualization for students with intensive learning needs: A meta-analysis. Learning Disabilities Research & Practice, 33(3), 144–155.

Lemons

C. J.

Al Otaiba

Conway

S. J.

Mellado

La Cruz

(2016). Improving professional development to enhance reading outcomes for students in special education. In Foorman

(Ed.), Challenges to implementing effective reading intervention in schools. New directions for child and adolescent development (Vol. 154, pp. 87–104). John Wiley.

Lemons

C. J.

Kearns

D. M.

Davidson

K. A.

(2014). Data-based individualization in reading: Intensifying interventions for students with significant reading disabilities. TEACHING Exceptional Children, 46(4), 20–29. https://doi.org/gh43th

National Center on Intensive Intervention. (2013). Data-based individualization: A framework for intensive intervention. U.S. Department of Education.

Powell

S. R.

Lembke

E. S.

Ketterlin-Geller

L. R.

Petscher

Hwang

Bos

S. E.

. . .Hopkins

(2021). Data-based individualization in mathematics to support middle school teachers and their students with mathematics learning difficulty. Studies in Educational Evaluation, 69, 100897.

10.

Powell

S. R.

Stecker

P. M.

(2014). Using data-based individualization to intensify mathematics intervention for students with disabilities. TEACHING Exceptional Children, 46(4), 31–37. https://doi.org/10.1177/0040059914523735

11.

Rojo

Nozari

Bryant

D. P.

(2022). Systematic progress monitoring of individualized education program goals in mathematics. Intervention in School and Clinic, 57(5), 322–328. https://doi.org/10.1177/10534512211032616

12.

Stecker

P. M.

Fuchs

L. S.

Fuchs

(2005). Using curriculum-based measurement to improve student achievement: Review of research. Psychology in the Schools, 42(8), 795–819. https://doi.org/10.1002/pits.20113

13.

Stecker

P. M.

Lembke

E. S.

Foegen

(2008). Using progress-monitoring data to improve instructional decision making. Preventing School Failure: Alternative Education for Children and Youth, 52(2), 48–57. https://doi.org/10.3200/PSFL.52.2.48-58

14.

Taylor

R. L.

(2009). Assessment of exceptional students: Educational and psychological procedures (8th ed.). Pearson.

15.

Wells

J. C.

Sheehey

P. H.

Sheehey

(2017). Using self-monitoring of performance with self-graphing to increase academic productivity in math. Beyond Behavior, 26(2), 57–65. https://doi.org/10.1177/1074295617711207