Edited by:
Reviewed by:
*Correspondence:
This article was submitted to Developmental Psychology, a section of the journal Frontiers in Psychology
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Extensive evidence has suggested that early academic skills are a robust indicator of later academic achievement; however, there is mixed evidence of the effectiveness of intervention on academic skills in early years to improve later outcomes. As such, it is clear there are other contributing factors to the development of academic skills. The present study tests the role of executive function (EF) (a construct made up of skills complicit in the achievement of goal-directed tasks) in predicting 5th grade math and reading ability above and beyond math and reading ability prior to school entry, and net of other cognitive covariates including processing speed, vocabulary, and IQ. Using a longitudinal dataset of
Children’s success in schooling has long been a central focus of research, policy, and practice. In December 2015, the Every Student Succeeds Act was signed into law at a time that marked all-time high graduation rates and low dropout rates in the United States. The Every Student Succeeds Act, in concert with the Common Core State Standards, were meant to improve graduation rates and further minimize student dropout. Yet, still 6.5% of all students entering high school, and 11.6% of students who are born to families from the lowest income quartile drop out of high school. Further, these dropout rates are highest in the American South, and in rural areas across the country (
Extensive evidence has suggested that early academic skills are a robust indicator of later achievement (
For a child to succeed in modern society, they must be a successful reader. The ability to read is foundational for nearly all school-based learning and undergirds opportunities for academic and vocational success. Importantly, the development of reading has been characterized as a process in which the child must transition from “learning to read” to “reading to learn” (
As with reading, the late elementary grades appear to be an important transition time for the development of mathematics ability: Children who fail a math course in 6th grade have a 60% chance of dropping out of high school (
The importance of achievement in elementary school academics is not simply related to later academic attainment. Several studies have found that test scores prior to high school are positively associated with labor market outcomes, including income and employment, even when analyses control for educational attainment (
What, then, differentiates a successful elementary school reader and mathematician from an unsuccessful one? Extensive evidence from the last decade has suggested that early skills predict later skills: the strongest and most robust predictor of a child’s later academic skills is their earlier academic skills.
However, the development of academic skills does not occur in isolation: children are exposed to a multitude of academic settings that contribute to the promotion of math and reading skills. As such, intervention in the time between school entry and late elementary school could have an effect. Experimental studies have shown that curricular intervention in the early elementary years can result in improved domain-specific skills. However, the effects are limited. A meta-analysis of elementary school math intervention programs for typically performing students found that even the most successful intervention programs had a median effect size of +0.33 (
A separate, though highly related literature has suggested that there are other classroom skills that may contribute to the development of math and reading skills during elementary years (
A robust literature has indicated a relation between EF and reading skills throughout the academic lifespan. There is evidence that EF is related to early precursors to reading (
The association between EF and math has been similarly well documented. There has been extensive correlational evidence to suggest EF contributes significant variance to success in math across a wide range of age groups, from preschool and kindergarten (
Additionally, prior studies have suggested EF may interact with other early academic skills to moderate the association between early and later academic skills. Studies have shown through cross-lagged models that EF predicts change in math and reading skills over and above stability from PreK to kindergarten (
The objective of the present study is to investigate the unique role of EF measured in early childhood in predicting academic achievement in late elementary school, an important transition time in children’s academic career. In particular, we are interested in the predictors of academic skills for students from predominantly low-income and rural (non-urban) areas of the United States. These students are at elevated risk for failure to complete high school and dropping out of school. We analyze data collected on children’s EF and math and literacy skills, along with other cognitive functions such as IQ, speed of processing, and receptive vocabulary prior to kindergarten entry, then assess math and literacy skills again when children are in 5th grade.
We pose two primary questions in the present study. First, we investigate predictors of academic skills in 5th grade. Our first hypothesis is that child EF measured prior to school entry will be uniquely associated with both later math and reading skills, even when controlling for cognitive functions and early math and reading with which EF is known to be associated. As such, we intend to estimate the amount of change in academic achievement attributable to EF above and beyond earlier academic knowledge and cognitive functioning. Second, we extend the analyses of
Participants were recruited as a part of a prospective, longitudinal study. The Family Life Project (FLP) recruited children and their families from two distinct geographical areas of the United States with high rates of poverty. Three counties in eastern North Carolina and three in central Pennsylvania were selected to be indicative of the Black South and Appalachia, respectively. Children were recruited to be representative of one of the six counties in which families resided at the time of the child’s birth. Low-income families were oversampled in both states, and African American families were oversampled in North Carolina. Full details of the sampling procedure have been described elsewhere (
A total 1,292 families were recruited to take part in data collection when the child was 2 months of age, at which point they were formally enrolled in the study.
Demographic data were drawn from regularly scheduled home visits conducted over the course of time when children were 2 months old to 3 years old. EF data were drawn from direct assessment conducted during a home visit when children were 5 years old. Academic skills were measured prior to kindergarten entry (PreK) and in 5th grade. Assessments took place in school settings when possible, or in home settings in cases that children were not enrolled in center- or school-based care at any of the time points. Children were also assessed in school settings during kindergarten, 1st, 2nd, and 5th grades. A subset of children was also assessed in school settings during 3rd grade. Additionally, children were assessed in the home seven times between when children were 2 months and 5 years of age. Only data from the PreK, age 5, and 5th grade data collection time points are included in the present study.
Executive function assessment comprised six tasks. All tasks were administered on an open spiral-bound notebook by a trained research assistant. These tasks are described in detail and evaluated elsewhere (
Children were shown a line drawing of an animal and a color inside an image of a house and asked to keep both the animal and the color in mind, and to recall one of them (e.g., animal name) when prompted. Task difficulty increased by adding items to successive trials: Children received one 1-house trial, two 2-house trials, two 3-house trials, and two 4-house trials. Responses were summarized as the number of items answered correctly within each item set.
This is a self-ordered pointing task in which children were presented with a series of 2, 3, 4, and 6 pictures and instructed to continue picking pictures until each picture had “received a turn.” Children are presented with successive pages in which the set of pictures within an item set is re-ordered. The ordering of pictures within each item set is randomly changed (including some trials not changing) so that spatial location is not informative. This task requires working memory because children have to remember which pictures in each item set they have already touched.
This task was modeled after the Day–Night Stroop task. Children were asked to make the sound opposite of that associated with pictures of dogs and cats (e.g., meow when shown a picture of a dog).
Children were given two response cards (“buttons”) and were instructed to touch the card consistent with the direction in which an arrow presented on the flipbook page was pointing. Training trials presented compatible images on the same side, and test trials presented arrows contralateral to the correct response (e.g., an arrow pointing right was presented on the left side).
This is a standard go no-go task in which children were instructed to push a button (which emitted a sound) whenever they saw an animal appear, except when the animal was a pig. The number of go-trials before a no-go trial varied, in a standard order, of 1-go, 3-go, 3-go, 5-go, 1-go, 1-go, and 3-go trials.
Children were shown two pictures that were similar on a single criterion (e.g., the same color; the same size), and were then shown a third picture, similar to one of the first two pictures along a second dimension of similarity (e.g., shape). Participants were asked to identify which of the first two pictures was the same as the new picture.
Item response theory (IRT) scoring was used for all tasks in the EF battery.
The Woodcock–Johnson III Tests of Achievement (
The Applied Problems (AP) subtest measures early math skills including counting, measurement, and verbal and non-verbal arithmetic and operations.
The Brief Reading Cluster (BFR) reflects the average of children’s scores on two Woodcock–Johnson subtests: Letter-Word Identification and Passage Comprehension. The Letter-Word Identification (LW) subtest measures basic literacy skills including letter recognition, letter sounds, and reading ability. The Passage Comprehension (PC) subtest also measures basic literacy skills including children’s ability to provide the missing word for a sentence so that it makes sense.
Individual- and family-level covariates were included in final models of analyses. These covariates included indicator variables for child sex (1 = male; 0 = female), as well as continuous variables for cumulative risk, processing speed, general intelligence, and receptive vocabulary.
The cumulative risk variable is a mean composite of
At the PreK visit, processing speed was measured using two subtests of Wechsler Preschool and Primary Scales of Intelligence (WPPSI;
At the age 3 home visit, children completed the block design and receptive vocabulary subtests of the Wechsler Preschool and Primary Scales of Intelligence (WPPSI;
At the PreK visit, receptive vocabulary was measured using the Peabody Picture Vocabulary Test-4th Edition (PPVT;
Our primary research question asks whether EF skills before kindergarten entry uniquely predict academic skills over and above earlier academic skills themselves. Simultaneous models were estimated in a path analysis to regress 5th grade math and reading scores onto EF, PreK math and pre-literacy skills, and other covariates measured prior to kindergarten entry. Next, we sought to investigate whether having high levels of EF prior to kindergarten would buffer against having lower academic skills. Two interaction terms between EF and PreK math and pre-literacy skills were added to the path model. Simple slopes of significant interaction terms were assessed. All models were estimated using Mplus (
All analyses are limited to children for whom a direct assessment of EF or academic skills was conducted. Thirteen children were excluded from analyses for having no available direct assessment data, leaving a total of 1,279 participants. For those participants who completed at least one wave of direct assessment, missing data was accounted for using Full Information Maximum Likelihood estimation. Full Information Maximum Likelihood estimation takes into account the covariance matrix for all available data on the independent variables to estimate parameters and standard errors. This approach provides more accurate estimates of regression coefficients than do listwise deletion or mean replacement (
Unweighted descriptive statistics and correlations for all variables in the analyses are presented in
Descriptive statistics.
Mean | Range | |||
---|---|---|---|---|
Age at time of 5th grade testing | 11.14 | 0.40 | 877 | 10.25–12.42 |
Applied Problems score PreK | 100.24 | 12.88 | 978 | 29–141 |
Applied Problems score 5th grade | 96.82 | 14.94 | 875 | 1–152 |
Letter-Word score PreK | 98.25 | 13.29 | 981 | 60–156 |
Brief Reading Cluster score 5th grade | 97.87 | 14.28 | 876 | 1–136 |
PPVT receptive vocab standard score | 93.90 | 15.87 | 964 | 43–138 |
WPPSI speed of processing | 96.04 | 12.61 | 850 | 65–133.5 |
WPPSI IQ | 93.57 | 16.51 | 1035 | 45–142 |
Cumulative risk | 0.00 | 0.69 | 1222 | -2.66–2.19 |
EF mean score | 0.29 | 0.48 | 1026 | -1.98–1.40 |
Bivariate correlations for all variables included in the sample are presented in
Correlations among variables.
Correlations |
||||||||||
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ||
---|---|---|---|---|---|---|---|---|---|---|
(1) | Male | – | ||||||||
(2) | Age at time of 5th grade testing | 0.062 | – | |||||||
(3) | Applied Problems PreK | -0.061 | -0.190*** | – | ||||||
(4) | Applied Problems 5th grade | 0.028 | -0.201*** | 0.630*** | – | |||||
(5) | Letter-Word PreK | -0.083** | -0.265*** | 0.563*** | 0.432*** | – | ||||
(6) | Brief Reading Cluster 5th grade | -0.105** | -0.326*** | 0.537*** | 0.701*** | 0.509*** | – | |||
(7) | Receptive vocab standard score | -0.045 | -0.136*** | 0.664*** | 0.528*** | 0.464*** | 0.513*** | – | ||
(8) | WPPSI speed of processing | -0.185*** | -0.223*** | 0.471*** | 0.398*** | 0.423*** | 0.353*** | 0.423*** | – | |
(9) | Cumulative risk | -0.032 | 0.104** | -0.435*** | -0.398*** | -0.358*** | -0.361*** | -0.489*** | -0.324*** | – |
(10) | EF mean score | -0.128*** | -0.137*** | 0.502*** | 0.506*** | 0.335*** | 0.435*** | 0.533*** | 0.381*** | -0.330∗∗∗ |
Results of the associations of predictor variables with 5th grade math ability are reported in Model 1 of
Models predicting Applied Problems scores.
Model 1 |
Model 2 |
|||||||
---|---|---|---|---|---|---|---|---|
Beta | Significance | Beta | Significance | |||||
AP PreK | 0.367 | 0.041 | <0.001 | ∗∗∗ | 0.439 | 0.052 | <0.001 | ∗∗∗ |
LW PreK | 0.059 | 0.035 | 0.097 | 0.030 | 0.041 | 0.471 | ||
Male | 0.146 | 0.027 | <0.001 | ∗∗∗ | 0.143 | 0.027 | <0.001 | ∗∗∗ |
Receptive vocab | 0.045 | 0.044 | 0.303 | 0.038 | 0.043 | 0.378 | ||
Processing speed | 0.081 | 0.037 | 0.028 | ∗ | 0.077 | 0.036 | 0.034 | ∗ |
IQ | 0.116 | 0.041 | 0.004 | ∗∗ | 0.117 | 0.040 | 0.004 | ∗∗ |
Cumulative risk | -0.024 | 0.032 | 0.460 | -0.033 | 0.031 | 0.295 | ||
EF mean score | 0.209 | 0.038 | <0.001 | ∗∗∗ | 0.593 | 0.246 | 0.016 | ∗ |
Age | -0.042 | 0.026 | 0.106 | -0.043 | 0.027 | 0.156 | ||
EF∗AP PreK | -0.805 | 0.284 | 0.005 | ∗∗ | ||||
EF∗LW PreK | 0.390 | 0.258 | 0.130 |
Results of the simultaneous regression of 5th grade reading on predictor variables are reported in Model 1 of
Models predicting Brief Reading Cluster scores.
Model 1 |
Model 2 |
|||||||
---|---|---|---|---|---|---|---|---|
Beta | Significance | Beta | Significance | |||||
AP PreK | 0.157 | 0.05 | 0.002 | ∗∗ | 0.238 | 0.065 | <0.001 | ∗∗∗ |
LW PreK | 0.236 | 0.043 | <0.001 | ∗∗∗ | 0.219 | 0.054 | <0.001 | ∗∗∗ |
Male | -0.007 | 0.029 | 0.803 | -0.011 | 0.029 | 0.706 | ||
Receptive vocab | 0.128 | 0.05 | 0.011 | ∗ | 0.117 | 0.049 | 0.017 | ∗ |
Processing speed | -0.005 | 0.036 | 0.893 | -0.007 | 0.035 | 0.947 | ||
IQ | 0.081 | 0.047 | 0.087 | 0.086 | 0.047 | 0.065 | ||
Risk | -0.034 | 0.041 | 0.404 | -0.045 | 0.039 | 0.255 | ||
EF | 0.149 | 0.039 | <0.001 | ∗∗∗ | 0.740 | 0.277 | 0.008 | ∗∗ |
Age | -0.173 | 0.03 | <0.001 | ∗∗∗ | -0.169 | 0.030 | <0.001 | ∗∗∗ |
EF∗AP PreK | -0.837 | 0.353 | 0.018 | ∗ | ||||
EF∗LW PreK | 0.202 | 0.310 | 0.255 |
To test whether high levels of early EF would buffer against low levels of early academic skills, we added interaction terms of both EF with PreK math and EF with PreK pre-literacy scores to path model above. Results are reported in Model 2 of
Inclusion of the interaction terms in the model slightly improved the amount of variance being explained in both 5th grade math (
Analysis of simple slopes revealed that for children who at the sample mean for EF, there was a moderate association of PreK math with 5th grade math (β = 0.426,
Interaction of executive function (EF) and PreK math predicting 5th grade math.
Interaction of EF and PreK math predicting 5th grade reading.
The goal of this study was to investigate the role of EF in predicting academic achievement in late elementary school in a diverse sample of children from low-income families. In particular, we were interested in whether there was an association between EF and 5th grade math and reading achievement over and above the predictive value of earlier math and reading scores and other cognitive abilities. We also sought to investigate whether the predictive value of PreK math and reading abilities varied as a function of child EF.
In our analysis of main effects only, we found that, while early math and reading were both important predictors of later math and reading, PreK EF was associated with more than 1/5th of a standard deviation in math (three points on the standardized measure of 5th grade math used in the present sample), and nearly 1/7th of a standard deviation in reading (over two points on the reading measure). This association was net of other cognitive covariates, including IQ (1/10th of a standard deviation in math), processing speed (1/12th of a standard deviation in math), and receptive vocabulary (1/8th of a standard deviation in reading), and the predictive value of EF was greater than that of other cognitive covariates.
In testing the interaction between EF and early academic ability, we found a significant interaction between EF and early math (but not EF and pre-reading skills) indicating that higher EF ability can compensate to some extent for limited academic knowledge prior to school entry. Children with initially low math ability but with higher EF may still reach the levels of achievement in math and reading typically associated with more proficient domain-specific prerequisite skills. This suggests EF may serve as an important skill set that helps students “catch up” with their higher-achieving peers in academic settings, even if they start out behind.
Notably, prior investigations (e.g.,
Altogether, our results suggest one major theme: early EF is important in the development of later academic skills. Not only is EF a unique predictor of 5th grade math and reading ability, but our analysis suggests that high levels of early EF can help to compensate for low levels of academic ability in PreK. This interaction between math and EF in PreK is of particular interest and merits additional investigation. This finding in the present study extends prior analyses from this dataset demonstrating that EF moderates the magnitude of the association between PreK and kindergarten math (
The relation between PreK math and 5th grade reading, as well as the relation between the interaction of PreK math and EF and 5th grade reading merits additional discussion. As was suggested by
Ultimately, the present investigation contributes to the growing literature about the role of EF in education. Other studies have found EF is a strong and stable predictor of later academic skills (
The role of EF in the development of math skills is well established. This study is consistent with the findings of a number of prior analyses, which suggest early EF is related to math ability throughout the academic lifespan (
Of additional interest, our results reveal an association of child gender with scores on math, but not reading. Extensive research has suggested a correlation between cultural beliefs of gender stereotypes in academic performance and the realized gender-based gap in performance on math and science on an international level (
There are several limitations that must be addressed in the context of this investigation. First, it is important to note that while this study is longitudinal in nature, causality cannot be inferred. Second, there is a large literature that has described the importance of teacher, school, and classroom characteristics in the development of early academic skills (including math, reading, and EF) and growth in those academic skills throughout schooling. In the present study, we lack measurement of instructional quality and school and classroom context. These are important omitted variables that may account for additional variance in outcome measures. Additionally, the present sample is limited to only two regions of the United States, and results may not generalize to others or to regions outside the United States. The current findings may only apply to children from rural areas of the United States, or to children born to low-income families. Finally, it is important to note that assessment of academic skills was limited to research assistant administered standardized assessments. While performance on these assessments is generally correlated with performance on formative and summative assessments in school contexts, it is likely these assessments capture only some aspects of math and reading achievement. Finally, it is important to note that the measurement of both EF and math and reading is complex, and though we use well-established and comprehensive measures, there remains aspects of those constructs that go unmeasured. For example, one of our assessments of EF assesses aspects of short term memory in addition to working memory, and working memory cannot be isolated. Similarly, the assessment of math ability privileges certain aspects of mathematics knowledge (e.g., counting, cardinality, and operations) over others (e.g., geometry).
Despite these limitations, results from the present investigation make a strong case for the importance of early skills. Beyond math and reading, there should be a focus in early childhood education on the development of EF, as EF fosters the development of high level math and reading in late elementary school, and may even serve as a mechanism by which children can catch up to their high achieving peers.
This study was carried out in accordance with the recommendations of the Institutional Review Board at Pennsylvania State University and the Office of Human Research Ethics at the University of North Carolina with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the Institutional Review Board at Pennsylvania State University and the Office of Human Research Ethics at the University of North Carolina.
AR conceptualized the study, carried out the initial analyses, drafted the initial manuscript, and approved the final manuscript as submitted. CB and MW reviewed and revised the manuscript, and approved the final manuscript as submitted.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.