Neurocognitive mechanisms explaining the role of math attitudes in predicting children's improvement in multiplication skill

Abstract

Enhancing student's math achievement is a significant educational challenge. Numerous studies have shown that math attitudes can predict improvement in math performance, but no study has yet revealed the underlying neurocognitive mechanisms explaining this effect. To answer this question, 50 children underwent functional magnetic resonance imaging (fMRI) when they were 11 (time 1; T1) and 13 (time 2; T2) years old. Children solved a rhyming judgment and a single-digit multiplication task inside the scanner at T1. The rhyming task was used to independently define a verbal region of interest in the left inferior frontal gyrus (IFG). We focused on this region because of previous evidence showing math attitudes-related effects in the left IFG for children with low math skill (Demir-Lira et al., 2019). Children completed standardized testing of math attitudes at T1 and of multiplication skill both at T1 and T2. We performed a cluster-wise regression analysis to investigate the interaction between math attitudes and improvement in multiplication skill over time while controlling for the main effects of these variables, intelligence, and accuracy on the task. This analysis revealed a significant interaction in the left IFG, which was due to improvers with positive math attitudes showing enhanced activation. Our result suggests that IFG activation, possibly reflecting effort invested in retrieving multiplication facts, is one of the possible neurocognitive mechanism by which children with positive math attitudes improve in multiplication skill. Our finding suggests that teachers and parents can help children do better in math by promoting positive math attitudes.

Keywords: Attitudes; Children; Longitudinal; Math; Multiplication; functional magnetic resonance imaging.

Figure 1.

Tasks solved inside the scanner. (A) The rhyming judgment task was used to localize verbal regions of the brain in inferior frontal cortex. In this task, participants had to respond to whether pairs of words rhymed or not. (B) The control condition common to all tasks solved inside the scanner, in which participants had to press a button when the blue square turned red. Multiplication task: Single-digit multiplication verification task, including (C) small and (D) large multiplication problems.

Figure 2.

Covariates in the cluster-wise regression analyses. Illustration of the covariates of interest and covariates of no interest (i.e. controls) included in the two cluster-wise regression analyses carried out to study the association between math attitudes and improvement in multiplication skill while children solved small and large multiplication problems inside the scanner. All variables were continuous. For the two regressions, the covariate of interest was the interaction between math attitudes and improvement in multiplication skill over time. The main effects of math attitudes, the main effect of improvement in multiplication skill, and children’s full IQ were included as covariates of no interest in the two regressions. When we studied this interaction with small multiplication task solving, we included accuracy in small multiplication problems solved inside the scanner as the fourth covariate of no interest. When the interaction was studied with large multiplication problems inside the scanner, accuracy in solving large problems was included as the fourth covariate of no interest.

Figure 3.

Visualization of the region of interest. Region of interest resulting from constraining brain activation elicited by comparing all word pairs of the rhyming judgment localizer task with the control condition within the anatomical left inferior frontal gyrus (IFG).

Figure 4.

Math attitudes and improvement groups. (A) Illustration of the math attitudes raw scores at T1 for the math attitudes subtest of the Test of Mathematical Abilities (Brown, Cronin, & Bryant, 2012), separately for the groups of children with negative and positive math attitudes (created based on median-split). (B) Illustration of the changes over time in the raw scores for the Comprehensive Mathematical Abilities Test (Hresko, Schlieve, Herron, Swain, & Sherbenou, 2003) used to measure multiplication skill, separately for improvers and non-improvers children (created based on median-split). Error bars show standard error of the mean.

Figure 5.

Clusters showing significance for the interaction and the follow-up analyses. (A) Cluster showing significance for the interaction between math attitudes and improvement in multiplication skill over time (i.e. in blue; K=58). (B) Cluster found for the follow-up analysis showing math attitudes-related activation only for improvers (i.e. in green; K=57). (C) Cluster found by Demir-Lira and colleagues (2019) showing an interaction between math attitudes and math skill at T1 (i.e. in cyan; K=127). (D) Overlap of the three clusters: Cluster showing the follow-up activation for improvers (i.e. in green; K=57), overlaid on the cluster showing the interaction of math attitudes by improvement in multiplication skill (i.e. in blue; K=58), and on the Demir-Lira et al. (2019)’ cluster showing the interaction of math attitudes by math skill at T1 (i.e. in cyan; K=124).

Figure 6.

Association between math attitudes and brain activation for the improvers group shown for illustration purposes. Scatterplot showing the positive correlation between math attitudes, in the X-axis, and the average brain activation extracted from the cluster showing significance in the follow-up analysis (i.e. K=57; in green in Figure 5B), in the Y-axis. Error bars show standard error of the mean.