Longitudinal Relations Among Inattention, Working Memory, and Academic Achievement: Testing Mediation and the Moderating Role of Gender

Introduction

• Study published on May 19, 2015, examining longitudinal relations among inattention, working memory, and academic achievement, testing mediation and the moderating role of gender.
• Authors: Sarah A. Gray, Maria Rogers, Rhonda Martinussen, and Rosemary Tannock.
• The study aimed to determine whether working memory (WM) mediates the pathway between inattentive behavior and subsequent academic outcomes.
• Participants: 204 students from grades 1–4 (49.5% female).
• Assessments of WM and achievement at baseline and one year later.
• WM measures: visual-spatial storage task, auditory-verbal storage and manipulation tasks.
• Teachers completed the SWAN behavior rating scale.
• Mediation analysis with PROCESS (Hayes, 2013) was used to determine mediation pathways.
• Key Finding: Teacher-rated inattention indirectly influenced math addition fluency, subtraction fluency, and calculation scores through its effect on visual-spatial WM, only for boys.
• There was a direct relationship between inattention and math outcomes one year later for girls and boys.
• Children with better attention had higher WM scores, and children with higher WM scores had stronger scores on math outcomes.
• Bias-corrected bootstrap confidence intervals for the indirect effects were entirely below zero for boys, for the three math outcomes.
• WM did not mediate the direct relationship between inattention and reading scores.
• The findings identify inattention and WM as longitudinal predictors for math addition and subtraction fluency and math calculation outcomes one year later, with visual-spatial WM as a significant mediator for boys.

Background

• A strong body of literature has provided evidence of a link between inattentive behavior in the classroom and academic underachievement
• Attention is measured using behavioral rating scales (e.g., SWAN rating scale, Conners-3).
• Inattention refers to off-task behavior, including disorganization.
• Inattention, rather than hyperactivity, is consistently found to be a risk factor for poor academic achievement.
• Teacher-ratings of behavioral inattention are more strongly linked to academic outcomes than are parent-ratings and are more sensitive to the demands of the classroom environment.
• Teacher-rated inattention is an independent predictor of performance in multiple achievement domains:
  • Arithmetic fluency
  • Arithmetic word problems
  • Algorithmic computation
• Arithmetic fluency involves solving simple math facts quickly and accurately.
• As children become efficient counters, associations between pairs of numbers become consolidated in long-term memory, therefore relying more on retrieval memory and putting less burden on working memory (WM) for answering math fact questions fluently.
• Arithmetic fluency is genetically distinct from other non-timed measures of math calculation, problem-solving, and number concepts.
• Arithmetic fluency plays a role in the development of algorithmic computation, which includes carrying and borrowing and necessitates the ability to follow procedural steps and rely on math fact retrieval.
• Reading fluency is a consistent predictor of later reading comprehension skills.
• Reading fluency in the early grades is predictive of high-stakes achievement test scores in elementary and middle school and continues to predict reading comprehension scores into adulthood.
• Inattentive behavior is a predictor of poor reading fluency outcomes in typically developing school children.
• Children with Attention-Deficit/Hyperactivity Disorder (ADHD) have lower reading fluency outcomes than their peers.
• The mechanisms of association between inattention and math and reading outcomes are not yet delineated.
• Attention encompasses both behavioral and cognitive components, which do not readily map onto each other.
• Cognitive attention refers to a complex set of processes that operate through a series of neural networks: alerting, orienting, and executive control.
• Working memory (WM) has been implicated in math and reading achievement and is strongly related to inattention and thus presents as a possible mediating variable within this relationship.
• Baddeley’s multicomponent model of WM views WM as a limited-capacity system that temporarily holds and manipulates information, including separate storage modules for auditory-verbal (phonological loop) and visual-spatial information (visual-spatial sketchpad) and a central executive component.
• Both short-term storage modules and modules that process or manipulate information are considered to be part of WM.
• Differences have been found between tasks that require short-term storage or manipulation, with the latter showing relationships to fluid intelligence and cognitive aptitudes.
• Children with poor WM ability demonstrate impaired academic performance, including impaired performance on tests of overall reading and math and reading fluency.
• These same children are rated by teachers as having more problems with inattention and distractibility.
• WM is a long-term predictor of literacy and numeracy outcomes.
• WM (a composite of both auditory-verbal and visual-spatial WM) was an important predictor of math achievement for students with high levels of ADHD symptoms.
• Visual-spatial storage, when measured in pre-school children, was also found to predict first grade math outcomes.
• Working memory deficits often co-occur with attention difficulties, both in those individuals with disorders of attention and across the spectrum of typical behavior.
• Inattention, WM, and academic fluency share a significant amount of variance.
• There is currently no robust evidence regarding the direction of the relationships within this triad of impairment, and causal pathways are unknown.
• Trajectories of ADHD behavior could be established based on cognitive features at 15 and 24 months, and those with more severe ADHD symptoms in grade 3 did show some behavioral differences prior to starting school.
• Early signs of both behavioral and cognitive difficulties were associated with a stable trajectory of poor academic achievement into grade 3.
• A measure of executive function (EF) (including visual-spatial WM) did not contribute unique variance to teacher-rated inattention scores in preschool, but visual-spatial span did contribute unique variance to these scores in primary school.
• Other studies have investigated possible mediators that provide some account of the consistent relationship between inattention and academic achievement.
• WM was found to be a mediator of the relationship between inattention and reading and math composite scores in high school students with clinical and sub-clinical levels of ADHD symptoms.
• An EF score mediated the relationship between inattention and pre-academic skills in kindergarten-aged children.
• This study sought to extend these studies to a community sample of elementary school children and to further delineate the nature of the relationship between classroom inattention, WM domains, and academic achievement through using a longitudinal mediation design.
• Differential influences of visual-spatial and auditory-verbal WM are of interest.
• Sex differences are often evident in overall levels of inattentive behavior.
• Sex was included as a moderator of the direct and indirect effects, given limited evidence that sex differences in attention disorders may be due to underlying genetic and cognitive differences between the sexes.
• Hypotheses:
  • There will be a direct relationship between classroom inattention at one point in time and both math and reading outcomes one year later.
  • Inattention will indirectly influence math outcomes through visual-spatial and auditory-verbal WM and indirectly influence reading outcomes through auditory-verbal WM.

Methods

• Participants: 204 elementary school-aged children (49.5% female) in grades 1–4 (ages 5–9, M = 7.67, SD = 0.91).
• Students were recruited from a large suburban and rural school district in Southern Ontario, Canada.
• The 7 participating schools (20% of the 33 schools in the district) were stratified across socio-economic groups.
• Stratifying for sex, this subsample of 204 was created by taking 2–3 students in each class from the highest, middle and lowest ranking levels of attention, based on teacher ratings of inattentive behavior in the classroom, which were rank ordered.
• The majority of participants were Caucasian (80.6%) with English as their primary language (83.3%).
• Eligibility: Students in mainstream English or French classrooms without major sensory or motor impairments.
• Teacher reports indicated that 11.8% of the sample had an Individual Education Plan (IEP) with 5.5% identified with ADHD, 3.8% a learning disability, 4.9% a language impairment, 1.6% a behavior difficulty, 0.5% a developmental disability.
• Parent education level: 2.7% less than high school, 5.5% high school graduate, 57.7% college or university graduate, 11% postgraduate degree.
• No sex differences were found on any demographic variables, with one exception: females were more likely to have a parent (92.3% of parents who filled out questionnaires were mothers, and 7.7% were fathers) with less than high school education.
• Four waves of data collection across two years:
  • Year 1 term A (Year 1A): November of study Year 1.
  • Year 1 term B (Year 1B): April of Year 1.
  • Year 2 term A (Year 2A): November of study Year 2.
  • Year 2 term B (Year 2B): April of Year 2.
• Teachers and parents completed questionnaire packages in November of Years 1 and 2 (Year 1A, Year 2A).
• Children participated in academic testing sessions in November of Years 1 and 2 (Year 1A, Year 2A).
• A subset of students from each class, from the lowest, middle and highest bracket of the continuum of attention were selected to participate in further tests of cognitive (including working memory) and academic functioning in April of study Years 1 and 2 (Year 1B, Year 2B).

Measures

• Classroom attention was measured using the Strengths and Weaknesses of Attention-Deficit/Hyperactivity Disorder Symptoms and Normal Behaviour Scale (SWAN).

  • 7-point scale (3 = Far below average, 2 = Below average, 1 = Slightly below average, 0 = Average, −1 = Slightly above average, −2 = Above average, −3 = Far above average).
  • The scale is divided into ‘inattention’ and ‘hyperactivity’ subscales; only the inattention subscale was used in this study.
  • The inattention subscale consists of 9 items.
  • Internal consistency: full scale, \alpha = .88; inattention subscale, \alpha = .94.
  • Test-retest reliability estimates for the full scale range from .72–.90.
  • In the current sample, a correlation of .74 was found for the inattention subscale at Year 1A and Year 2A.
  • Negative scores indicate stronger attention; lower levels of inattentive behavior, while positive scores indicate weak attention; higher levels of inattentive behavior.

• Measures of math achievement were assessed using subtests from two commonly used batteries:

  • Addition and subtraction probes from AIMSweb® M-CBM, Mathematics Curriculum-Based Measurement, (arithmetic fluency).
    • High test-retest (.87) and inter-rater reliability (.83), moderate alternate form reliability (.66).
    • Forms for grades 1–3 included 60 math fact problems; forms for grade 4 students included 84 math fact problems.
    • Credit is given to each individual correct digit that appears in the solution.
    • 2 min time limit to complete as many problems as they can.
  • Math Calculation subtest from the Woodcock-Johnson - III Tests of Achievement (WJ-IIIACH) (algorithmic computation).
    • Internal consistency reliability is .86 for Math Calculation.
    • Problems start with simple arithmetic and quickly move into borrowing and carrying and to multiplication and division.
    • 7 min time limit.
    • No partial points are given.
  • Previous research has found that addition and subtraction fluency (short time limit for simple arithmetic problems) and math calculation (algorithmic computation, longer time limit) cluster together under the narrow math ability factor, and both are related to perceptual speed.

• Measures of reading achievement:

  • Reading fluency was assessed using the Dynamic Indicators of Basic Early Literacy Skills (DIBELS, 5th ed), Oral Reading Fluency Subtest.
    • Strong concurrent validity (.91–.96) and alternate-form reliability (.89–.96).
    • Students are given 3 grade level passages to read out loud and are instructed to read as accurately as possible and to read as many words as they can within one minute.
    • The median number of errors across the three passages is scored, as is the median number of correct words.
  • One subtest from the Woodcock-Johnson-III Tests of Achievement (WJ-IIIACH), Letter-Word Identification, was used from the “Reading ability” cluster of this battery in order to test fluent single word reading ability.

• Assessment of working memory:

  • The Wechsler Intelligence Scale for Children (WISC-IV), Digit Span Subtests (DS) is a widely-used test of auditory-verbal WM.
    • Internal consistency of .87 and test-retest reliability of .82.
    • Participants listen to and recall a series of digits.
    • In the Digit Span Forward task, participants are asked to recall the digits exactly as heard, while in the Digit Span Backward task, participants are asked to reproduce the digits heard in backward sequence.
  • The Wide Range Assessment of Memory and Learning (WRAML-2), Finger Windows Forward Subtest (FWF) was administered in order to assess visual-spatial WM.
    • High internal consistency (.99)
    • Participants replicate the examiner’s visual sequence, created with a pencil tapping different sequences of ‘windows.’

Statistical Approach

• Missing data was imputed according to the methods suggested by McKnight et al. (2007), when not more than 10–15% of data is missing.
• Relationships between study variables were examined using partial correlations, controlling for age.
• A one-way ANOVA was used to examine sex differences between study variables.
• All mediation models were designed with visual-spatial WM and auditory-verbal WM at Year 1B as parallel mediators between teacher-rated inattention at Year 1A and academic outcomes at Year 2B, with sex as a moderator.
• Moderated mediation analyses were carried out using the PROCESS macro for SPSS (Hayes, 2013; Preacher & Hayes, 2008).
• Year 1A academic scores were added in each analysis as a covariate.
• All analyses were carried out with IBM SPSS version 21.

Results

• Teacher-rated inattention, measured at Year 1A, was significantly correlated in the expected direction with WM measures at Year 1B and all academic outcome variables at Year 2B.
• All main study variables were significantly correlated in the expected direction at the .01 level, with the exception of visual-spatial and auditory-verbal WM, which were not significantly correlated.
• A very strong positive relationship was found between reading fluency and word reading and between the math addition and subtraction fluency tests.
• The correlation between math calculation and fluency of math addition and subtraction was lower by comparison, but still strong.
• Parent education was weakly to moderately correlated with all variables, with the exception of visual-spatial WM, which appears to be related to sex but not to parental education in this sample.
• Conversely, auditory-verbal WM was not related to sex but was weakly correlated with parental education.
• Sex was significantly correlated with inattention at the .01 level and with reading fluency, math subtraction, and visual-spatial WM at the .05 level.
• Significant sex differences were found for teacher-rated inattention (boys are rated as more inattentive than girls), reading fluency (girls have higher scores than boys), and math subtraction (boys have higher scores than girls).
• When applying a Bonferroni family-wise correction, with the threshold for significance at an alpha level of .0063, the only remaining sex difference was between teacher-rated inattention.
• Ordinary least squares (OLS) path analysis, revealed that teacher-rated inattention indirectly influenced math addition and subtraction fluency outcomes through its effect on visual-spatial WM, but for boys only.
• Children who displayed lower levels of teacher-rated inattention at Year 1A had higher visual-spatial WM scores at Year 1B, and children with higher visual-spatial WM scores had stronger scores on addition fluency outcomes at Year 2B.
• There was also evidence that teacher-rated inattention influenced addition fluency scores the following year independently of its effect on WM.
• Results were similar for subtraction fluency outcomes.
• The proposed model was also significant for boys’ math calculation outcomes at Year 2B.
• The bias-corrected confidence intervals (BCa CIs) for all three models passed through zero for girls, thus the mediation through WM was not significant for girls’ math fluency or calculation outcomes.
• When predicting reading outcomes, there was no significant direct relationship between teacher-rated inattention and reading fluency or word reading scores one year later.
• There were also no significant mediation effects for reading fluency or word reading.
• Significant predictors of reading fluency at Year 2B were auditory-verbal WM at Year 1B, parental education, and Year 1A reading fluency scores.
• The only significant predictors of word reading were Year 1A word reading scores and age.
• When assessing WM scores at Year 2B, Results replicate the first model in that visual-spatial WM was a significant mediator of the relationship between teacher-rated inattention and math calculation, the confidence interval for the indirect effect for boys was entirely below zero

Discussion

• The study examined the longitudinal relationships between classroom inattention, WM, and math and reading outcomes in a community sample of elementary school children.
• The study found support for a model in which children’s classroom inattention, as rated by teachers at the beginning of the school year, was indirectly associated with math outcomes one year later through visual-spatial WM, but only for boys.
• There was also a significant direct association between teacher-rated inattention and all measured math outcomes the following year.
• The study found that boys' classroom inattention directly influences their math fact fluency and math calculation scores across time, controlling for age, parental education and math scores at Year 1.
• The study found domain differences in a sample of children, representative of the full range of inattentive behaviours in a classroom setting.
• The main finding that visual-spatial WM was a significant mediator of the relationship between teacher-rated inattention and both math fluency and math calculation outcomes is consistent with expectations based on current literature.
• As elementary school children move from using counting based methods for solving math facts to fluent memory-based retrieval, there is a parallel shift from activation in the fronto-parietal WM systems to increased hippocampal activation.
• The results suggest that the influences of inattention on higher-level math outcomes, which require more attention to algorithm and less reliance on fluent retrieval, are partially accounted for by visual-spatial WM.
• The study finds that spatial numerical associations may be represented differently between males and females.
• The current results suggest that although sex differences were not found on visual-spatial WM, this construct plays an important role as a mediator between classroom inattention and math outcomes for boys; boys may rely more on spatial representations of number.
• The direct relationship between inattention and math outcomes was significant for both boys and girls.
• However, results show higher levels of inattention for boys, which is consistent with existing research.
• In terms of sex differences on WM tasks, the current results are in line with findings in clinical ADHD populations.
• The study confirms and replicates the body of literature that positions behavioral inattention as a robust predictor of later math achievement, further specifying that this relationship is robust for arithmetic fluency and algorithmic computation, in typically developing elementary school children.

Limitations

• The study did not have a visual-spatial WM measure that required processing or manipulation; the WRAML measure only taps into short-term storage.
• practical considerations limited data collection time points, such that our mediating variable was collected at 2 time points across two years, whereas the outcome variables were collected at 4 time points.

Conclusions

• Boys’ classroom inattention (as rated by teachers) directly influences their math fact fluency and math calculation scores across time, controlling for age, parental education, and math scores at Year 1.
• Main findings emphasize that for boys, inattention has a direct effect as well as an indirect influence on math fluency and calculation skills through visual-spatial WM in the elementary school grades.