Development of Executive Function in Middle Childhood: a Large-Scale, In-School, Longitudinal Investigation

Abstract

This manuscript describes data collection, cleaning, and quality control procedures used in a large-scale, longitudinal, in-school study of executive function skills (EFs) and academic achievement in middle childhood, Project iLEAD (in-school Longitudinal Executive Function and Academic Achievement Database). Assessments were administered in real-world educational settings in a large sample of third through eighth grade students (ages 7 to 14; N = 1,280) over two years, with eight data collection timepoints in group settings. We assessed students with a novel, mobile EF assessment tool Adaptive Cognitive Evaluation (ACE). This battery included 11 tasks that were each designed to adapt to user performance in a trial-wise manner, allowing the same battery of tasks to be used multiple times within the same individual, and across a wide range of ages. Data quality analyses revealed that the adaptive algorithms were successful in equating challenge levels across ages 7 through 14 for 10 of 11 tasks. Data for each task were found to be approximately normally distributed and split-half reliability was acceptable across both accuracy and reaction time. ACE thus provides a reliable way to assess EFs in middle childhood using the same tasks while maintaining appropriate challenge level without facing ceiling or floor effects.

Linking inhibitory control to math achievement via comparison of conflicting decimal numbers

“…In the current study, we aimed to relate executive function measures to math achievement via a precursor skill. We employed tablet-based rapid assessments of EF capacities (Younger et al, 2021, April 20). Working memory and inhibition were assessed using computerized versions of classic tasks in the field: backward Corsi Block Tapping to measure visual spatial working memory and Color-Word Stroop to measure inhibitory control.…”

“…The Adaptive Cognitive Evaluation (ACE) program is a recently developed tablet application for the iPad with a variety of games aimed at measuring different aspects of executive function (Younger et al, 2021, April 20). Among the nine experimental tasks, we selected three games: Color-Word Stroop to measure inhibitory control, TaskSwitch to measure cognitive flexibility, and Backward Spatial Span to measure visual spatial working memory.…”

“…The ACE suite of games (Younger et al, 2021, April 20) provides participants with trial level feedback in all tasks. After each trial, a green, yellow, or red centralized fixation cross was displayed for 200ms (500ms in the case of Backward Spatial Span).…”

“…Of the EF measures, only inhibition was a significant independent predictor of Calculation performance, with a marginal effect for working memory and no effect for cognitive flexibility. In contrast to the well-established measures of working memory and inhibitory control (Corsi block tapping and color-word Stroop, respectively), our measure of cognitive flexibility was newly developed for the ACE platform (Younger et al, 2021, April 20), and did not display the expected pattern of switching costs. The lack of reliability in this measure may have contributed to the null effects found here, and point to the need to examine other measures of cognitive flexibility, such as the Wisconsin Card Sort Task (Grant & Berg, 1948) and the Alternative Uses Test (Guilford, 1967) to fully gauge the contribution of this construct to more advanced mathematical understanding.…”

Groupitizing reflects conceptual developments in math cognition and inequities in math achievement from childhood through adolescence.

“…We previously highlighted how groupitizing ability also accounts for unique variance in math achievement, and thus we explored the role of parental income in understanding groupitizing performance. To do so, we created a binary income factor for the model, flagging children from households whose net annual earnings fell below 70% of the state median income (see Younger et al, 2021). We then built a linear mixed effects model predicting CRPM with grade, grouping condition, array size, income, and their interactions as fixed predictors, and with participants and trials as random factors.…”