Mathematics Screening and Progress Monitoring at First Grade: Implications for Responsiveness to Intervention

The predictive utility of screening measures for forecasting math disability (MD) at the end of 2nd grade and the predictive and discriminant validity of math progress-monitoring tools were assessed. Participants were 225 students who entered the study in 1st grade and completed data collection at the end of 2nd grade. Screening measures were Number Identification/Counting, Fact Retrieval, Curriculum-Based Measurement (CBM) Computation, and CBM Concepts/Applications. For Number Identification/Counting and CBM Computation, 27 weekly assessments were also collected. MD was defined as below the 10th percentile at the end of 2nd grade on calculations and word problems. Logistic regression showed that the 4-variable screening model produced good and similar fits in accounting for MD—calculation and MD—word problems. Classification accuracy was driven primarily by CBM Concepts/Applications and CBM Computation; CBM Concepts/Applications was the better of these predictors. CBM Computation, but not Number Identification/Counting, demonstrated validity for progress monitoring.

[1]  Robbie Case,et al.  The Role of Central Conceptual Structures in the Development of Children's Thought , 1995 .

[2]  Russell Gersten,et al.  Early Identification and Interventions for Students With Mathematics Difficulties , 2005, Journal of learning disabilities.

[3]  M. Mazzocco,et al.  The Utility of Kindergarten Teacher Ratings for Predicting Low Academic Achievement in First Grade , 2001, Journal of learning disabilities.

[4]  James G. Greeno,et al.  Developmental analysis of understanding language about quantities and of solving problems. , 1988 .

[5]  Marvin L. Simner,et al.  Printing Errors in Kindergarten and the Prediction of Academic Performance , 1982, Journal of learning disabilities.

[6]  Mary S. Riley,et al.  Development of Children's Problem-Solving Ability in Arithmetic. , 1984 .

[7]  J A Swets,et al.  The science of choosing the right decision threshold in high-stakes diagnostics. , 1992, The American psychologist.

[8]  G. S. Wilkinson,et al.  Wide Range Achievement Test 4 , 2016 .

[9]  E. Daly,et al.  Measures of Early Academic Skills: Reliability and Validity with a First Grade Sample. , 1997 .

[10]  M. Barnes,et al.  Assessment of Reading and Learning Disabilities A Research-Based Intervention-Oriented Approach , 2002 .

[11]  J. Monahan,et al.  A Classification Tree Approach to the Development of Actuarial Violence Risk Assessment Tools , 2000, Law and human behavior.

[12]  David J. Chard,et al.  Using Measures of Number Sense to Screen for Difficulties in Mathematics: Preliminary Findings , 2005 .

[13]  Lynn S. Fuchs,et al.  The Prevention, Identification, and Cognitive Determinants of Math Difficulty. , 2005 .

[14]  Rollanda E. O'Connor,et al.  EARLY IDENTIFICATION AND INTERVENTION FOR YOUNG CHILDREN WITH READING / LEARNING DISABILITIES , 2002 .

[15]  Mark R. Shinn,et al.  A Preliminary Investigation Into the Identification and Development of Early Mathematics Curriculum-Based Measurement , 2004 .

[16]  John A. Swets The science of choosing the right decision threshold in high-stakes diagnostics. , 1992 .

[17]  G J Hitch,et al.  The prevalence of specific arithmetic difficulties and specific reading difficulties in 9- to 10-year-old boys and girls. , 1994, Journal of child psychology and psychiatry, and allied disciplines.

[18]  P. Joy,et al.  NEUROPSYCHOLOGICAL FUNCTION AND MRI ABNORMALITIES IN NEUROFIBROMATOSIS TYPE 1 , 1995, Developmental medicine and child neurology.

[19]  Lynn S. Fuchs,et al.  Redefining Learning Disabilities as Inadequate Response to Instruction: The Promise and Potential Problems , 2003 .

[20]  Y Okamoto,et al.  Exploring the microstructure of children's central conceptual structures in the domain of number. , 2008, Monographs of the Society for Research in Child Development.

[21]  Joseph C. Witt,et al.  The Reliability and Validity of Curriculum-based Measurement Readiness Probes for Kindergarten Students , 2001 .

[22]  William J. Long,et al.  Using Classification Tree and Logistic Regression Methods to Diagnose Myocardial Infarction , 1998, MedInfo.

[23]  G. Casella,et al.  Statistical Inference , 2003, Encyclopedia of Social Network Analysis and Mining.

[24]  O Manor,et al.  DEVELOPMENTAL DYSCALCULIA: PREVALENCE AND DEMOGRAPHIC FEATURES , 1996 .

[25]  Nancy C. Jordan,et al.  Mathematical Thinking in Second-Grade Children with Different Forms of LD , 2000, Journal of learning disabilities.

[26]  Louis Danielson,et al.  Identification of learning disabilities : research to practice , 2002 .

[27]  James M. Moser,et al.  The Acquisition of Addition and Subtraction Concepts in Grades One through Three. , 1984 .

[28]  Lynn S. Fuchs,et al.  The cognitive correlates of third-grade skill in arithmetic, algorithmic computation, and arithmetic word problems , 2006 .