Arithmetical and Cognitive Antecedents and Concomitants of Algebraic Skill
R305A110067 (IES) P.T. Cirino (PI) 08/15/11-08/14/15
The major goal of this project is to examine factors relevant for procedural skill and conceptual algebraic knowledge in middle and high school. The study aims to differentiate procedural and conceptual competency for algebraic knowledge experimentally, predictively, and concurrently. This study includes measures of working memory, language, and numerosity as predictors. Dr. Cirino is the PI, with Co-PIs Dr. Tammy Tolar at the University of Houston, and Dr. Lynn Fuchs at Vanderbilt University. Read more.
P50 HD052117-01 (NICHD, NIH) J.M. Fletcher (PI) 11/01/06-10/31/11
This is a competing continuation of a Center project with major goals to better understand reading comprehension, involving projects of measurement/classification, executive function, intervention, and neuroimaging. Dr. Cirino is the PI of Project 2, “Developing a Framework for Executive Functions in the Context of Reading Comprehension Skills and Difficulties”. Read a news story about this project.
Calculations, Word Problems, and Algebraic Cognition
R01 HD059179-01 (NICHD, NIH) L.S. Fuchs (PI) 12/01/08-11/30/13
The major goal of this project is to ascertain the uniqueness and overlap of cognitive profiles of 2nd grade students who struggle in computations, in word problems, or in both, within a response to intervention (RTI) context. Dr. Cirino serves as a co-investigator, focused on methodology and statistics. Read more about this project.
Conceptual Precursors of Mathematical and Reading Outcomes
R03 HD050422-01A2 (NICHD, NIH) P.T. Cirino (PI) 02/01/07-01/31/10
The major goal of this project is to ascertain the set of underlying cognitive precursors that best predict outcomes in mathematics and reading, and the degree of specificity of these precursors and outcomes. Dr. Cirino served as PI.
Spina Bifida: Cognitive and Neurobiological Variability
P01 HD035946-06A2 (NICHD, NIH) J.M. Fletcher (PI) 02/01/05-01/31/10
The major goal of this competing continuation program project is to identify genetic, CNS, and environmental sources of variability that explain the variations in neurobehavioral outcomes associated with spina bifida meningomyelocele (SB). Dr. Cirino served as a co-investigator within Core C (Database and Statistics).
Math Difficulties and Spina Bifida
Neurodevelopmental populations (of which spina bifida is a n example) have a variability in their outcomes cognitively, behaviorally, and academically (Mahone & Slomine, 2013) due to involvement of the central nervous system. Mathematics is one common area of difficulty in many neurodevelopmental populations (Murphy, 2009; Taylor et al., 2009). For example children with spina bifida often have clear math difficulties (English et at., 2009) but word reading skills are much better; other cognitive strengths and weaknesses are also apparent (Dennis & Barnes, 2010; Dennis et al., 2006). Understanding how individuals with spina bifida respond to math intervention is key not only because the intervention may be of direct benefit, but also because it may give insight into what makes math difficult for children more generally (Dennis, Berch, & Mazzocco, 2009).
There is a strong evidence base for interventions that target young students who struggle with learning to read (Lovett et al., 2013; NICHD/NRP, 2000; Vaughn et al., 2010; 2013). In contrast, less is known about math interventions for students who struggle in math, though recently, several are becoming available (Fuchs et al., 2010; 2014; Jitendra et al., 2011). However, there are almost no studies that evaluate math interventions efficacy in specific neurodevelopmental populations such as spina bifida (see Coughlin and Montague, 2011, for an exception), so it is unclear if academic interventions work the same way across groups.
The present project will test the effectiveness of a math intervention for students who have difficulty with math, with and without spina bifida. We expect that the program will work well for both groups who struggle with math. To the extent that the intervention program works the same for different populations who struggle with math, then researchers and clinicians may have more confidence in recommending these more generally. However, if the intervention program works differentially across groups, then we will benefit from understanding how it differs, and what characteristics predict those differences.