To begin with, I would like to thank my husband, who has been an amazing and supportive teammate through this and has always been willing to sacrifice his time and effort to help me see this opportunity through. I would also like to thank my children, who unknowingly started this process with me, but who have always been the best cheerleaders. This study examined the cognitive flexibility trajectories of children in kindergarten through fifth grade.
The findings suggested that the development of cognitive flexibility (CF) has multiple growth trajectories depending on students' initial CF levels (their .intercept) and their rate of change (slope). Both classes included individuals whose initial cognitive flexibility was well below expected levels.
Introduction
Executive functioning abilities assessed in early childhood (either directly or indirectly through teacher ratings) have been found to predict school readiness for both mathematics and reading (Espy et al., 2004; Specifically, these deficits increased their likelihood of being included in the risk growth trajectory for reading, math and science (Morgan et al., 2019) When reflecting on our own inner experience, most people report that it has a verbal quality (Baars et al., 2003).
Younger children (3–5 years) appear to benefit from prompting to use verbal labels, both in task switching (cognitive flexibility) and in other contexts, suggesting a lack of spontaneous use of inner speech at these ages smaller (Zelazo et al., 2003). There is also research that has been completed with adults that supports the role of inner speech in task switching or cognitive flexibility (Baddeley et al., 2001).
Literature Review
However, many now view executive functions as a unitary construct that includes relatively independent subfunctions (Baddeley, 1996; Miyake et al., 2000). Miyake et al.'s (2000) study proposed that the taxonomy of executive functioning consists of at least three basic functions: shifting (i.e., cognitive flexibility), updating (i.e., working memory), and inhibition. Many have found that executive functions remain unitary in nature at younger ages (i.e., the three components of working memory, inhibition, and cognitive flexibility are not separated) and that a two- or three-factor model does not fit.
However, relatively fewer studies have examined the development of executive functions and cognitive flexibility over extended periods of time or during other age ranges (e.g., 7–10 years of age) (Best et al., 2009). This is due to white matter (myelin) development and synaptic pruning (Klingberg, 2006; Sheridan et al., 2014).
Method
In the third sampling phase, approximately 23 kindergartens were selected from the list of all enrolled kindergartens in each of the sampled schools. Beginning with second-grade data collection, a computerized version of the DCCS developed for the National Institutes of Health's Neurological and Behavioral Assessment Toolbox (NIH Toolbox) was used. The change to the computerized version of the task was made so that the DCCS would remain age-appropriate until the end of data collection.
Testing conducted in the development of the NIH Toolbox DCCS indicates that 8-year-olds typically scored at ceiling on the pre-switch and post-switch trials. However, for ECLS-K: 2011, children were administered the version of the NIH Toolbox DCCS for ages 8 years and older, regardless of their age at assessment. In the second grade, approx. 90% of the EKLS:K 2011 children in the autumn partial test for second grade and approx. 40% of the children in the second grade spring subtests who had a score on the DCCS were not yet 8 years old when the DCCS was.
The decision to administer the same version of the DCCS from second grade onwards, regardless of age 8 or older, was made so that all study children received the same version of the DCCS task. A list of the scores of each wave where the same children were administered the DCCS at different times (fall of kindergarten through spring of fifth grade) can be found in Table 2. To contrast this, the mean performance reaction time of children within that wave were added to the scores of those children who did not include a reaction time.
The purpose of the scores from the complete assessment is to help determine if there are delays in certain areas of language development (Rainelli et al., 2017). The purpose of the "Simon Says" task asks children to follow simple, direct instructions given by the assessor in English. All children were administered the language tester as the first component of the direct cognitive assessment.
Results
Conventional growth modeling approaches give a single mean growth estimate, a single estimate of variance of the growth parameters, and assume a uniform. These are characteristics that differ significantly from those of the entire sample (Jung & Wickrama, 2008). Sampling weights were used for all analyzes completed due to the multi-stage sampling design of the ECLS-K:2011.
The time scores play three roles: they determine the shape (linear or non-linear) of the growth process. Most times, including the current study, the scorers' time scale is matched to the observed time scale (eg, fall in kindergarten to spring in fifth grade). The statistical fit can then be used to evaluate each of the models and establish the best way to model change over time.
A piecewise model was assumed due to the nature of the DCCS scores and because waves 1–4 were assessed using a tabletop and included only accuracy, while waves 5–9 included a computer activity and included reaction time. Based on an assessment of the fit statistics, the linear, quadratic, and cubic models indicate poor fit to the data. A table showing the specific results of the 3-class, 4-class and 5-class LCGAs can be found in Table 5.
However, due to the computational burden of the BLRT, it may not be best to use it in the model exploration stages, but when a few plausible models have been identified. A six-class model was also estimated; However, issues with negative variance, a class containing less than 1% of the sample, and a poor LMR p-value indicated that this was not an appropriate choice. The initial levels and level of change for each of the five classes are described below.
R2 values indicate whether the outcome measures (wave 1 through wave 9) are well explained by the intercept and slope growth factors. The R2 values for class 1 for the observed outcome measures (wave 1 through wave 9) are varied but consistently higher than the other classes. For time points 5 through 9, the R2 values are above .60 (range .733 to .821), indicating that the variation in the observed measures is well explained by the intercept and slope growth factors (including the influence of SES since is included in the model).
For the time points included with slope 1, the R2 values range from 0.057 to 0.221, indicating that the variance of the observed outcome measures is not good. However, the Time 5 to Time 9 R2 values range from .460 to .566, indicating an increase in how well the outcome measures are explained by the intercept and slope growth factors. The R2 values for class 3 for the observed outcome measures (wave 1 through wave 9) are also different.
For the time points included with slope 1, R2 values range from 0.097 to 0.339, indicating that variation in observed outcome measures is not well explained by growth factors (intercept and slope) or variation in SES. However, Time 5 to Time 9 R2 values ranged from .555 to .680, indicating an increase in how well the outcome measures were explained by the intercept and slope growth factors. The R2 values for class 4 for the observed outcome measures (wave 1 through wave 9) are also different.
For time points included in slope 1, R2 values range from 0.066 to 0.251, indicating that variation in observed outcome measures is not well explained by growth factors (intercept and slope) or the SES covariate. However, the R2 values for Time 5 to Time 9 range from 0.467 to 0.578, indicating an increase in how well the outcome measures are explained by the growth factors for the time points included in slopes 2 and 3. However, the R2 values for Time 5 to time 9 range from 0.422 to 0.543, indicating an increase in how well the outcome measures are explained by the growth factors.
Speech and Language Deficits in Kindergarten will Predict Membership in the At-Risk Class of Cognitive Flexibility
These results indicate that higher SES is associated with better performance on the initial DCCS assessment and on the first slope (waves 1–4). These findings suggest that speech and language deficits in kindergarten increase the risk for membership in the at-risk and remedied cognitive flexibility trajectory classes. When considering intercept alone, as opposed to above where intercept and rate of change were considered, it was found that the probability of those who failed the speech screening being in the at-risk trajectory group was 79%.
When only the cutoff or starting CF score is considered, it is even more likely that a student struggling with speech and language delays will also struggle with CF.
Discussion
As a result, the study could not examine whether the observed predictive relationships between speech and language skills and children's cognitive flexibility trajectories were present during middle or high school. Deficits in EF and CF have been reported to increase primary school children's risk of academic problems (Clark et al., 2010; Mazzocco & Kover, 2007). Executive function and verbal self-regulation in childhood: Developmental links between partially internalized private speech and cognitive flexibility.
Occupational functioning impairment and ADHD in adults: Predictive utility of executive function (EF) assessments versus EF tests. Contributions of “hot” and “cool” executive function to children's academic achievement, learning-related behaviors, and engagement in kindergarten. A case for the development and use of "ecologically valid" measures of executive function in experimental and clinical neuropsychology.
A meta-analysis of the Dimensional Change Card Sort: Implications for developmental theories and the measurement of executive function in children. Decision-making and executive function in male adolescents with early-onset or adolescent conduct disorder and control subjects. Racial and socioeconomic gaps in executive function skills in early elementary school: Nationally representative evidence from the ECLS-K:2011.
The longitudinal relationship between early academic achievement and executive function: The mediating role of approaches to learning. Contributions of modern measurement theory to the measurement of executive function in early childhood: An empirical demonstration.
