Parental Intrusive Homework Support and Math Achievement: Does the Child’s Mindset Matter?

Daeun Park; Elizabeth A Gunderson; Erin A Maloney; Eli Tsukayama; Sian L Beilock; Angela L Duckworth; Susan C Levine

doi:10.1037/dev0001522

. Author manuscript; available in PMC: 2024 Feb 2.

Published in final edited form as: Dev Psychol. 2023 May 11;59(7):1249–1267. doi: 10.1037/dev0001522

Parental Intrusive Homework Support and Math Achievement: Does the Child’s Mindset Matter?

Daeun Park ¹, Elizabeth A Gunderson ², Erin A Maloney ³, Eli Tsukayama ⁴, Sian L Beilock ⁵, Angela L Duckworth ^6,⁷, Susan C Levine ⁸

¹Department of Psychology, Sungkyunkwan University

²Department of Psychology, Temple University

³School of Psychology, University of Ottawa

⁴Division of Business Administration, University of Hawaii–West O’ahu

⁵Barnard College, Columbia University

⁶Department of Psychology, University of Pennsylvania

⁷Department of Operations, Information and Decisions, The Wharton School, University of Pennsylvania

⁸Department of Psychology, University of Chicago

Daeun Park served as lead for conceptualization, data curation, formal analysis, investigation, methodology, visualization, writing–original draft, and writing–review and editing and served in a supporting role for project administration. Elizabeth A. Gunderson served in a supporting role for conceptualization, formal analysis, investigation, methodology, project administration, writing–original draft, and writing–review and editing. Erin A. Maloney served in a supporting role for conceptualization, data curation, methodology, project administration, writing–original draft, and writing–review and editing. Eli Tsukayama contributed equally to formal analysis, methodology, and validation and served in a supporting role for conceptualization, writing–original draft, and writing–review and editing. Sian L. Beilock served as lead for funding acquisition, investigation, methodology, project administration, and supervision and served in a supporting role for conceptualization, writing–original draft, and writing–review and editing. Angela L. Duckworth served as lead for funding acquisition, investigation, project administration, and supervision and served in a supporting role for conceptualization, writing–original draft, and writing–review and editing. Susan C. Levine served as lead for funding acquisition, investigation, and project administration and served in a supporting role for conceptualization, writing–original draft, and writing–review and editing.

^✉

Correspondence concerning this article should be addressed to Daeun Park, Department of Psychology, Sungkyunkwan University, Seoul, 03063, South Korea. daeunp@skku.edu

PMCID: PMC10835763 NIHMSID: NIHMS1959552 PMID: 37166869

Abstract

Prior research shows that when parents monitor, check, and assist in completing homework without an invitation, their children’s motivation and academic achievement often decline. We propose that intrusive support from parents might also send the message that children are incompetent, especially if they believe their intelligence is fixed. We tested whether children’s mindsets moderate the negative link between parents’ intrusive homework support and achievement among first- and second-grade students followed for one academic year (Study 1, N = 563) and middle and high school students for two academic years (Study 2, N = 1,613). The samples were obtained from large urban areas in the United States. In both studies, intrusive homework support more strongly predicted a decrease in achievement over time for children with a fixed mindset. These findings suggest that the belief that intellectual ability cannot be changed may exacerbate the detrimental effects of uninvited help on academic work.

Keywords: parental homework help, mindset, achievement, autonomy, longitudinal, cross-sectional model

Editor’s Note. Koraly Pérez-Edgar served as the action editor for this article.—KP-E

When parents help their children with homework, they presumably expect their assistance to support academic achievement (Hoover-Dempsey & Sandler, 1997). However, not all parental homework assistance is beneficial (Maloney et al., 2015). When provided without the invitation to do so, such help is linked to lower standardized test scores and grades in students of all ages from elementary to high school (Cooper et al., 2000). In other words, when parental homework involvement is uninvited, intrusive, and controlled, it can impede children’s sense of autonomy, motivation to do homework, and confidence (Dumont et al., 2012; Ng et al., 2004; Patall et al., 2008; Pomerantz & Eaton, 2000; Pomerantz et al., 2007). Such well-intended yet detrimental involvement is especially concerning because it is so common. Nearly two-thirds of parents provide unconstructive homework assistance (Cooper et al., 2000). Given this prevalence, it is critical to understand the factors that either exacerbate or mitigate parent–child homework help dynamics.

The deleterious consequences of intrusive parental support may be explained, at least in part, by a need for autonomy. According to self-determination theory, autonomy is a core human motive, along with competence and belonging (Deci & Ryan, 1985; Ryan & Deci, 2000). When these basic psychological needs are met, activities are more likely to be intrinsically motivating. Learning environments that promote autonomy—those that provide choice and encourage self-initiated thinking—facilitate students’ intrinsic motivation, school engagement, and academic performance. Thus, when parents allow children to explore their environment, initiate behaviors, and solve problems independently, they believe they can influence their world and improve their sense of autonomy and competence (Pomerantz & Eaton, 2000). By contrast, learning environments that thwart autonomy, such as those that discourage criticism and suppress self-expression, have the opposite effect (see Ryan & Deci, 2020, for a review). Consequently, when parents do not give children an opportunity to solve problems by themselves, they are more likely to quit challenging tasks (Leonard et al., 2021).

What has not been explored in prior research is the moderating influence of children’s implicit beliefs about the stability of intelligence, known as mindsets (Dweck, 1999; Yeager & Dweck, 2012). Individuals with a growth (incremental) mindset believe intelligence is malleable, whereas those with a fixed (entity) mindset believe it cannot change.¹ When given a choice, individuals with a growth mindset tend to choose challenging tasks that offer learning opportunities over easy tasks that ensure success (Hong et al., 1999; Romero et al., 2014). Moreover, a growth mindset motivates mastery-oriented behaviors after failure because additional effort and new strategies are expected to be fruitful (Dweck & Leggett, 1988; Hong et al., 1999). In contrast, when given a choice, individuals with a fixed mindset tend to choose easy tasks over challenging ones (Hong et al., 1999; Romero et al., 2014). Furthermore, for those with a fixed mindset, additional effort, and new strategies are expected to be futile in the wake of failure (Dweck, 1999).

Although one’s beliefs about the malleability of intelligence and the preference for easy versus challenging tasks are related yet distinct constructs, studies have shown that a scale merging the two constructs yields a more robust and reliable measure, especially in young children (Gunderson et al., 2013; Park et al., 2016). Thus, in studies involving younger children, mindsets have been studied as part of a motivational framework that incorporates both children’s beliefs about the nature of intelligence and their behavioral tendency toward challenge seeking (Gunderson et al., 2013; Gunderson, Park, et al., 2018; Gunderson, Sorhagen, et al., 2018). Children’s motivational frameworks were measured by mindset questions and their preference for easy versus challenging tasks. Across two separate longitudinal studies, young elementary school children with a growth-motivational framework performed better than those with a fixed motivational framework, on subsequent achievement tests (Gunderson, Park, et al., 2018; Gunderson, Sorhagen, et al., 2018).

In this study, we examined whether children’s mindsets (including those in the context of motivational frameworks) moderate the negative relationship between intrusive homework support and achievement. Specifically, we hypothesize that intrusive parental homework involvement predicts a decrease in achievement for children with a fixed mindset compared to those with a growth mindset. We suggest that fixed versus growth mindsets lead to different appraisals of uninvited parental homework assistance. Children with a fixed mindset might interpret parental uninvited support as “You think I cannot do this!” whereas children with a growth mindset might interpret the support as “You’re helping me learn!” Consistent with this possibility, Pomerantz and Eaton (2000) found that second- to fifth-graders with a fixed mindset construed parental control, including helping with homework without being asked, as indicative of their incompetence. Even if children believe that adults offering unsolicited help view them as incompetent, this appraisal may differ for children with different mindsets. For example, having a growth mindset may lead children to infer, “Well then, I need to try something different!” Whereas having a fixed mindset may lead them to believe, “Well then, there’s nothing I can do to get better!”

Current Investigation

In two prospective longitudinal studies, we tested the hypothesis that intrusive homework support from parents predicts a greater decrease in academic achievement for children with a fixed mindset than for children with a growth mindset. To examine these dynamics in the early and later stages of formal schooling, we recruited first- and second-graders from Study 1 and eighth-graders from Study 2.² Although most mindset research has focused on adolescents, some studies have measured individual differences among younger elementary school children (Gunderson et al., 2013; Kinlaw & Kurtz-Costes, 2007). Among first- and second-graders, mindsets measured in the context of motivational frameworks predict academic anxiety and achievement (Gunderson, Park, et al., 2018). In addition, parents of younger children are more likely to help with homework in a way that undermines autonomy (Cooper et al., 2000). Because early achievement predicts later achievement (Duncan et al., 2007), contributing to upward or downward learning trajectories, it is critical to understand the moderating factors, particularly those amenable to intervention.

To assess children’s mindsets and parental intrusive homework support, self-report questionnaires were read aloud to the children by a trained researcher in Study 1 and completed on a computer by adolescents in Study 2. Despite the well-known challenges of self-reporting in young children, we chose this approach rather than asking parents directly. Why? Unlike achievement, implicit beliefs can be invisible to observers. Teachers and parents, for example, can guess what a child thinks is true, but the ultimate authority in the question is the child herself. The extent to which homework support is intrusive versus supporting autonomy depends on the child’s point of view (Pomerantz & Eaton, 2000; see also Darling & Steinberg, 1993). In general, children’s reports of parenting are more predictive of their behavioral and emotional outcomes (e.g., delinquency, conduct problems, self-esteem, and internalizing behaviors) than parents’ self-reports about their parenting (Barry et al., 2008; Demo et al., 1987; Frampton et al., 2010).

We focused on math in particular because early achievement in math predicts subsequent achievement and attitudes toward math (Gunderson, Park, et al., 2018; see Levine & Pantoja, 2021, for a review). Further, the negative relationship between parental intrusive homework involvement and achievement is more evident in math than in other subjects (Patall et al., 2008). Finally, as early as first grade, math is commonly assumed to require “innate ability” (Bian et al., 2017; Gunderson et al., 2017). In both samples, objective indicators of math achievement were used because they are preferable to self-reports (Kuncel et al., 2005).

To estimate the moderating influence of children’s mindsets while accounting for bidirectional associations and the rank-order stability of constructs over time, we fit autoregressive cross-lagged panel models (ARCLs). We selected this analytic approach given prior work suggesting reciprocal relationships between parental intrusive homework support and achievement (Cooper et al., 2000; Dumont et al., 2014; Pomerantz & Eaton, 2001) as well as between mindset and achievement (Gunderson, Park, et al., 2018). Furthermore, this longitudinal study design enables us to examine the time-lagged relationships between measures and test whether these relationships vary over time.

Study 1

In a diverse sample of first- and second-grade students, we measured motivational frameworks, math achievement, and parental intrusive homework support in the fall and spring of an academic year. The procedures were approved by the Institutional Review Board of the University of Chicago.

Method

Participants

The participants were 563 first- and second-grade students from 72 classrooms in 23 schools (253 first-grade students; M_age = 7.2) and 349 parents in a large urban area in the United States.³ Data were collected as part of a larger longitudinal study on children’s academic emotions, motivation, and achievement. Data were retained for all the students who provided information on at least one variable. Of the children, 54.2% were female, 40.3% African American, 39.9% White, 9.6% Asian, 1.0% Native American, 7.0% multiracial, and 2.2% other. The primary caregiver’s years of education ranged from 10 to 18 years (M = 14.6 years; 10 years represents “less than high school” and 18 represents “graduate degree”). Family income ranged from less than $15,000 to more than 100,000, with a mean of $49,634 (SD = $35,078), below the median household income of 54,568 in the United States. Of those who completed the parent questionnaire, 314 (90.0%) identified their relationship with their child: 88.5% were mothers, 10.8% were fathers, and 0.7% were grandmothers. For simplicity, we used the word “parents” to refer to whoever completed the questionnaire.

Procedure and Measures

In one-on-one sessions in the fall and spring, an experimenter read the items aloud to the children and recorded their responses. Separately, at the midpoint of the school year, parents were asked to complete and return the questionnaire by mail.

Motivational Framework.

We used a 12-item motivational framework questionnaire adapted from previous studies (Gunderson et al., 2013; Gunderson, Park, et al., 2018; Kinlaw & Kurtz-Costes, 2007). Six items probed the extent to which children believed abilities were fixed or malleable in general (e.g., “Imagine a kid who thinks that people can get smarter if they work really hard. How much do you agree with this kid?”) and in math and reading (e.g., “Imagine a kid who thinks that people have a certain amount of math [reading] ability, and stay pretty much the same. How much do you agree with this kid?’). Six additional items measured the children’s preference for easy or challenging spatial, math, and spelling tasks (e.g., “How much would you like to spell words that are very easy so you can get a lot right?”).

For each question, the children were asked to point to one of five circles that varied in size (a 5-point Likert scale in which the smallest circle represented not at all and the largest represented really a lot; see Appendix A). We reverse-coded the fixed motivational framework items and averaged all the items to form a total score. Higher scores indicate a stronger endorsement of a growth-motivational framework. Cronbach’s αs were .56 in the fall and .67 in the spring. Low internal reliability indicates that children’s motivational frameworks are less coherent at this early stage. Accordingly, Kinlaw and Kurtz-Costes (2007) suggest that children’s mindsets are better related to achievement goals and motivation as they age.

Parental Intrusive Homework Support.

Children’s perceptions of their parents’ intrusive math homework support were assessed using a three-item questionnaire (Bhanot & Jovanovic, 2005; Pomerantz & Eaton, 2000). It measured unsolicited helping (“Your parents help you with your math homework when you didn’t need their help”), monitoring (“Your parents check answers to your math homework to make sure you did them right without asking you first”), and reminding (“Your parents remind you that you need to do your math homework when you get home from school”). Children verbally responded to each item by saying always, sometimes, or never (on a 3-point Likert scale). We reverse-coded all items such that higher scores represented more frequent intrusive homework help from parents.

Similar to the results reported by Bhanot and Jovanovic (2005) and Pomerantz and Eaton (2000), correlations among parental intrusive homework support items (helping, monitoring, and decision making) were small but positive (rs = .07–.18). Following previous research (Bhanot & Jovanovic, 2005; Pomerantz & Eaton, 2000), we report the results of the children’s overall perception of intrusive homework support by averaging the three items. Cronbach’s αs were .28 in the fall and .34 in the spring. It is important to note that low reliability among young children is quite common (Erdley et al., 1997; Giles & Heyman, 2003; Gunderson et al., 2017; Ramirez et al., 2013), and low consistency does not necessarily mean low reliability (Clifton, 2020; McCrae et al., 2011). Regardless, ceteris paribus, noisier measures tend to dampen observed correlations, suggesting that our estimates in Study 1 represent a lower bound for the true relationships among variables. In other words, if we find statistically significant results, this suggests that the results would have been stronger with a more reliable measure.

Additionally, we examined whether parent-reported homework involvement aligned with the child report data. Parents answered one question regarding the frequency of their homework involvement in math (“How often do you help your child with his/her math homework?”) using a 7-point Likert scale ranging from 1 = never to 7 = more than once a day. Although the parent question did not tap into intrusive support per se, we sought a conceptual replication based on past research indicating that two-thirds of parents reported giving their children unconstructive homework help (e.g., inhibiting children’s autonomy; Cooper et al., 2000).

Achievement.

We administered the Applied Problems subtest of the Woodcock-Johnson III Tests of Achievement (Woodcock et al., 2001), a nationally normed standardized test. As specified in the manual, the experimenter orally presented problems assessing math knowledge, calculation, and quantitative reasoning. The assessment continued until the children met both basal (six correct items in a row) and ceiling (six incorrect items in a row) criteria. In this study, children’s raw scores were transformed into W scores, which are recommended for examining changes over time (Woodcock, 1999). Two equivalent alternative forms containing different problems were used in the fall (Form A) and the spring (Form B).

Demographic Covariates.

In all analyses, we controlled for children’s gender, ethnicity, grade level, parents’ highest level of education, and family income, all taken from parent questionnaires. Note that the main findings’ patterns and significance levels remain unchanged whether these covariates are included.

This study was not preregistered. The analysis scripts are available at https://doi.org/10.17605/OSF.IO/C49RA. However, data are unavailable because the city Public School Research Review Board does not allow for sharing data beyond the investigators involved in this research project.

Analytic Approach

To test for bidirectional associations among variables, we fitted the ARCL models using Mplus Version 7.4. Additionally, because the participants came from 72 classrooms, we used the Huber–White sandwich estimator to adjust for clustering by classroom (McNeish et al., 2017). The intraclass correlation coefficient for spring achievement was .35, indicating that 35% of the variance was due to classroom variation. In addition, we used dummy variables to adjust for school-fixed effects. The average missing rate for all variables included in the analyses was 9.4%; thus, we used full-information maximum likelihood (FIML) estimation, which is a less biased and more efficient way of dealing with missing values than listwise deletion (Collins et al., 2001; Enders & Bandalos, 2001; Little & Rubin, 2014; Peters & Enders, 2002). For ease of interpretation, we standardized all variables included in the analyses.

As a robustness check, we fitted a separate ordinary least squares (OLS) regression model in which we substituted parent-reported homework support for the child’s self-report measure of parental intrusive homework support. Additionally, to test for reverse causality, we fit an ARCL model predicting parental intrusive homework support from children’s growth-motivational framework and achievement.

Results and Discussion

Preliminary Analyses

As shown in Table 1, there was clear evidence of rank-order stability across the school year for children’s motivational frameworks (r = .47, p < .001), parental intrusive homework support (r = .27, p < .001), and achievement (r = .83, p < .001). At both time points, the growth framework was associated with higher achievement (r = .39, p < .001 in fall; r = .47, p < .001 in spring). In contrast, parental intrusive homework support was associated with lower achievement in the fall (r = −.17, p < .001) and spring (r = −.21, p < .001), respectively. Parent reports of homework involvement and child reports of parental intrusive homework support were positively related to a small degree (r = .17, p < .01 in fall; r = .16, p < .01 in spring).

Table 1.

Study 1 Descriptive Statistics and Zero-Order Correlations

	Variable	Observed range	M	SD	1	2	3	4	5	6
1.	Fall (T1) motivational framework	1.75–5.00	3.22	0.56	—
2.	Fall (T1) parental intrusive homework support	1.00–3.00	2.18	0.51	−.07	—
3.	Fall (T1) math achievement	407.00–515.00	463.12	2.35	.39^***	−.17^***	—
4.	Spring (T2) motivational framework	1.50–5.00	3.39	0.64	.47^***	−.17^***	.46^***	—
5.	Spring (T2) parental intrusive homework support	1.00–3.00	2.12	0.52	−.07	.27 ^***	−.18^***	−.20^***	—
6.	Spring (T2) math achievement	409.00–526.00	474.24	22.65	.39^***	−.23^***	.83^***	.47^***	−.21^***	—
7.	Parental homework report	1.00–7.00	5.44	1.11	−.08	.17^**	−.32^***	−.24^***	.16^**	−.33^***

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Note. T = Time.

p < .05.

^**

p < .01.

^***

p < .001.

Main Analyses

Figure 1 presents the results of the ARCL panel model. For simplicity, only statistically significant paths are represented, and the bolded paths are indicative of our a priori hypothesis, that is, the interactive effects of motivational framework and parental intrusive support on achievement at the later time point. See Appendix B for all the path coefficients.

*Note*. Only statistically significant paths are shown. The bolded path is the hypothesized interaction path, which tests our a priori hypothesis. Covariates are included in the model but are omitted in the figure for presentability. t= time.

* p<.05. * p<.01. *** p<.001.

As predicted, after accounting for demographic covariates and fall achievement, we found a significant Motivational Framework × Parental Homework Support interaction (β = .05, p = .034, 95% CI [0.004, 0.102]). To investigate this interaction, we performed a series of simple slope tests. As shown in Figure 2, the negative effects of parental intrusive homework support on spring achievement were greater for children with a fixed framework (1 SD below the mean; β = −.12, p = .002, [−0.195, −0.043]) than for those with a growth framework (1 SD above the mean; β = −.01, p = .707, [−0.078, 0.053]). When parental intrusive homework support was low (1 SD below the mean), there was no difference in changes in achievement between children with a fixed framework and those with a growth framework (β = .02, p = .550, [−0.052, 0.099]). In contrast, when parental intrusive homework support was high (1 SD above the mean), the achievement of children with a fixed framework performed worse than that of children with a growth framework (β = .13, p = .001, [0.055, 0.203]).

*Note*. To facilitate the interpretation of the graph, we used unstandardized math achievement scores. Lowand high parental intrusive homework support represents 1 SD belowand above the mean on the scale. The fixed and growth-motivational frameworks represent 1 SD below and above the mean on the motivational framework scale, respectively.

These results mean that under high parental intrusive homework support, children with a fixed motivational framework scored 469.69, whereas those with a growth-motivational framework scored 475.51 in the spring. The difference (5.82) was greater than half of the average child’s improvement across the school year (from the fall semester to the spring semester, the mean math improvement was 11.50).

Given the low Cronbach’s α of the parental intrusive homework support measure, we reran the ARCL model separately for each item of the measure.

Reminding.

Parental reminding (“Your parents remind you that you need to do your math homework when you get home from school”) was associated with lower spring math achievement for children with a fixed motivational framework (β = −.11, p = .002, 95% CI [−0.179, −0.040]), but this relation was not significant among those with a growth-motivational framework (β = .02, p = .444, [−0.032, 0.072]). Further analyses indicated that when parental reminding was low, children with a fixed motivational framework performed as well as those with a growth-motivational framework (β = .01, p = .732, [−0.055, 0.078]). By contrast, when parental reminding was high, children with a fixed motivational framework performed worse in spring than those with a growth-motivational framework (β = .14, p < .001, [0.071, 0.211]). The significant interaction term between reminding and motivational framework (β = .07, p = .001, [0.025, 0.104]) confirmed that the negative impact of reminding differed by the motivational framework.

Unsolicited Help.

Parental unsolicited help (“Your parents help you with your math homework when you didn’t need their help”) was related to lower spring math achievement, especially among those with a fixed motivational framework (β = −.09, p = .026, 95% CI [−0.160, −0.010]), but not those with a growth-motivational framework (β = −.03, p = .295, [−0.085, 0.026]). Further analyses indicated that when parental unsolicited help was low, children’s math achievement did not significantly differ regardless of their motivational framework (β = .04, p = .235, [−0.027, 0.112]). In contrast, when parental unsolicited help was high, children with a fixed motivational framework performed significantly worse than those with a growth-motivational framework (β = .10, p = .013, [0.021, 0.175]). Although these simple slope analyses support our hypothesis, the omnibus interaction term between parent unsolicited help and children’s motivational framework was not statistically significant (β = .03, p = .241, [−0.019, 0.074]).

Monitoring.

Parental monitoring (“Your parents check answers to your math homework to make sure you did them right without asking you first”) was not negatively related to children’s spring achievement among children with either a fixed (β = −.03, p = .370, 95% CI [−0.109, 0.041]) or growth (β = −.03, p = .409, [−0.086, 0.035]) motivational framework. When parental monitoring was low, math achievement in children with a fixed motivational framework did not differ significantly from those with a growth-motivational framework (β = .07, p = .087, [−0.011, 0.159]). On the other hand, when parental monitoring was high, children with a fixed motivational framework performed significantly worse than those with a growth-motivational framework (β = .08, p = .025, [0.011, 0.155]). The interaction term between parental monitoring and children’s motivational framework was not statistically significant (β = .00, p = .873, [−0.049, 0.058]).⁴

Together, parental reminding and unsolicited homework help predicted low academic achievement only among those with a fixed motivational framework, not among those with a growth-motivational framework. Additionally, when these types of parental intrusive homework help were high, children with a fixed motivational framework performed significantly worse than those with a growth-motivational framework. In contrast, performance differences were not observed when these types of parental intrusive homework were low.

Unexpectedly, however, parental monitoring did not predict low achievement in children with either a growth or fixed motivational framework. This result is in line with prior research suggesting that the effect of parental homework monitoring on achievement is less detrimental than direct helping behaviors (Silinskas et al., 2015; Viljaranta et al., 2018). Furthermore, a meta-analytic study (Jeynes, 2005) showed that the effect of parental homework checking on achievement among elementary school children was negligible. It might be that when parents check children’s homework, they may come to realize the specific struggles that children face (e.g., having trouble solving fraction problems) and provide help accordingly (e.g., explaining the concept of fractions and how to solve them).

As a robustness check, we confirmed these findings in an OLS regression model predicting changes in achievement from the motivational framework, parent-reported (as opposed to child-reported) homework involvement, and their interactions. Results were strikingly similar to those in the primary ARCL model: A simple slopes analysis revealed that, indeed, among children with a fixed motivational framework, high parent-reported homework involvement was related to lower spring math achievement (β = −.12, p = .001, 95% CI [−0.198, −0.048]), but this relationship was not significant among those with a growth-motivational framework (β = −.03, p = .205, [−0.087, 0.019]). Furthermore, at a low level of parent-reported homework involvement, children with a fixed motivational framework performed as well as those with a growth-motivational framework (β = .03, p = .265, [−0.025, 0.091]). By contrast, at a high level of parent-reported homework involvement, children with a fixed motivational framework performed worse than those with a growth-motivational framework (β = .12, p = .002, [0.044, 0.200]). We found a significant interaction between parent-reported homework involvement and children’s motivational framework (β = .04, p = .028, [0.005, 0.084]).

Likewise, we fit an additional ARCL model to address the possibility of reverse causality, namely that the motivational framework moderates the influence of achievement on changes in parental intrusive support rather than the framework moderating the effects of parental intrusive support on achievement. In contrast to our primary ARCL model, the interaction term (motivational framework × achievement) on changes in parental intrusive homework support was not statistically significant (β = −.02, p = .706, 95% CI [−0.104, 0.070]; see Appendix D).

In sum, the findings from Study 1 suggest that the effect of parental homework support, whether reported by children or parents, on children’s achievement depends on children’s beliefs about the malleability of intelligence, and the achievement motivation correlates with such beliefs. Even after controlling for children’s prior knowledge at the beginning of the school year, a high level of parental intrusive homework support was related to lower achievement at the end of the school year. However, this negative correlation was not observed among children with a growth-motivational framework.

Study 2

Study 2 was a replication and extension of Study 1, in which we examined the interactive effects of mindset and parental intrusive homework support on achievement in older students, namely adolescents in eighth grade. In addition to confirming the interactive effect, we examined the effect over an extended period (two academic years) and used an alternative measure of achievement, grade point averages (GPAs), obtained from school records. GPAs reflect exam scores, homework completion, and class participation and are better indicators of student effort than standardized achievement test scores (Duckworth et al., 2012). The procedures were approved by the Institutional Review Board of the University of Pennsylvania.

Method

Participants

A total of 1,613 eighth-grade students (M_age = 13.9) from seven middle schools in large urban areas in the United States entered one of six high schools in the second year of the study. Data were collected as part of a larger longitudinal study on children’s character development. Data were retained for all students who provided information on at least one variable included in the analysis. According to school records, 49.0% were female, 49.8% were African American, 23.1% were White, 15.3% were Hispanic, 10.7% were Asian, 1.1% were multiracial or other, and 67.5% of the students qualified for free or reduced-price lunch.

Procedure and Measures

The data were collected during the fall (T1) and spring (T2) semesters of eighth grade and the fall (T3) and spring (T4) semesters of ninth grade. Students completed the online measures under the supervision of teachers or researchers. To maximize the sample size, we included students who participated in at least one of the four waves of data collection.

Growth Mindset.

Three items from Dweck (1999) were used: “You have a certain amount of intelligence, and you really can’t do much to change it,” “Your intelligence is something about you that you can’t change very much,” and “You can learn new things, but you can’t really change your basic intelligence.” Participants responded on a 6-point Likert scale ranging from 1 = strongly disagree to 6 = strongly agree. All items were reverse-coded so that a higher score indicated a stronger endorsement of a growth mindset. The observed αs were .81 (T1), .86 (T2), .89 (T3), and .89 (T4).

Parental Intrusive Homework Support.

The same three items as in Study 1 were used, except that the items were about general homework, not specific to math (“My parents helped me with my homework when I didn’t need their help,” “My parents checked answers to my homework to make sure I did them right without asking me first,” and “My parents reminded me that I needed to do my homework when I got home from school”). The participants responded on a 5-point Likert scale ranging from 1 = never to 5 = always. Unlike among younger children, the correlations among the three items were moderate to high (rs = .33–.81). As in Study 1, we averaged these three items. The observed αs were .71 (T1), .77 (T2), .78 (T3), and .78 (T4).

Academic Achievement.

We collected math GPA for each semester from school records. To standardize the grading systems across schools, we standardized the GPA within each school and then across the full sample (see Park et al., 2017 for a similar method).

Covariates.

Gender, race/ethnicity, free/reduced-price lunch status (a proxy for socioeconomic status), and school affiliation were included as demographic covariates. Notably, the patterns and significance levels of the main findings remained unchanged regardless of whether these covariates were included.

This study was not preregistered. The study data and analysis scripts are available at https://doi.org/10.17605/OSF.IO/C49RA.

Analytic Approach

As in Study 1, we ran the ARCL models. As Study 2 involved more than two time points, we used the model comparison approach (Bollen, 1989) to determine whether the modeled effects were consistent. The goal was to select the most parsimonious and best-fitting model. For the baseline model (Model 1), all autoregressive and cross-lagged paths are allowed to vary across the four waves. In Model 2, the cross-lagged paths are constrained to be equal across time, and in Model 3, the autoregressive paths are constrained to be equal across time. In Model 4, both cross-lagged and autoregressive paths are constrained. Given that the chi-square difference test is sensitive to sample size, we chose the final model based on the comparative fit index (CFI, ΔCFI ≤ .01; Chen, 2007; Cheung & Rensvold, 2002), root mean square error of approximation (RMSEA, ΔRMSEA ≤ .015; Chen, 2007), and Bayesian information criterion (BIC).

Although students were nested within math classrooms, they changed their math classroom every semester, and to our knowledge, there is no established technique to deal with clusters that change over time. Thus, we used fixed effects to account for the multilevel structure by dummy coding the school variables and including them in all analyses. The average missing rate for all variables included in the analyses was 12.2%, and we used FIML estimation. For ease of interpretation, we standardized all variables included in the analyses.

Results and Discussion

Preliminary Analyses

As shown in Table 2, there was evidence of rank-order stability across the school year for growth mindset (rs = .38–.58, ps < .001), parental intrusive homework support (rs = .52–.68, ps < .001), and math GPA (rs = .41–.70, ps < .001). Likewise, as in Study 1, and at each of the four time points in this study, a growth mindset was associated with math achievement, indexed in this study by higher GPA (rs = .10–.16, ps < .001). In addition, as in Study 1, parental intrusive homework support was associated with lower math achievement, which was indexed by lower GPA at each time point (rs = −.12 to −.19, ps < .001).

Table 2.

Study 2 Descriptive Statistics and Zero-Order Correlations

	Variable	Observed range	M	SD	1	2	3	4	5	6	7	8	9	10	11
1.	Growth mindset T1	1.00–6.00	4.01	1.28	—
2.	Parental intrusive homework support T1	1.00–5.00	3.00	1.09	−.10^***	—
3.	Math GPA T1	−3.78–2.20	0.00	1.00	.10^***	−.13^***	—
4.	Growth mindset T2	1.00–6.00	4.05	1.33	.45^***	−.16^***	.10^**	—
5.	Parental intrusive homework support T2	1.00–5.00	2.79	1.15	−.11^***	−.60^***	−.15^***	−.12^***	—
6.	Math GPA T2	−3.39–2.78	0.00	1.00	.05	−.06	.70^***	.11^***	−.12^***	—
7.	Growth mindset T3	1.00–6.00	4.09	1.32	.40^***	−.09^**	.15^***	.58^***	−.08^*	.14^***	—
8.	Parental intrusive homework support T3	1.00–5.00	2.80	1.12	−.11^**	.57^***	−.21^***	−.13^***	.66^***	−.17^***	−.19^***	—
9.	Math GPA T3	−5.17–2.27	0.00	1.00	.13^***	−.16^***	.48^***	.13^***	−.18^***	.44^***	.16^***	−.19^***	—
10.	Growth mindset T4	1.00–6.00	4.08	1.34	.38^***	−.09^*	.14^***	.50^***	−.09^*	.13^***	.55^***	−.12^***	.17^***	—
11.	Parental intrusive homework support T4	1.00–5.00	2.68	1.12	−.13^***	.52^***	−.18^***	−.14^***	.63^***	−.14^***	−.15^***	.68^***	−.17^***	−.14^***	—
12.	Math GPA T4	−8.13–2.09	0.00	1.00	.05	−.16^***	.41^***	.09^**	−.14^***	.43^***	.14^***	−.16^***	.62^***	.13^***	−.12^***

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Note. T= Time; GPA = grade point average.

p<.05.

^**

p<.01.

^***

p<.001.

Main Analyses

Table 3 presents the fit statistics of the ARCL panel models. Based on the CFI, RMSEA, standardized root mean square residual (SRMR), and BIC indices, we chose Model 2 (constraining cross-lagged paths) as our final model. Figure 3 shows the statistically significant paths, and the bolded paths represent our a priori hypotheses. See Appendix E for all the path coefficients.

Table 3.

Study 2 Model Fit Indices

Model	Model fit index
Model	χ²	df	CFI	RMSEA	SRMR	BIC
Model 1: Baseline	70.785	36	0.994	0.024	0.010	102,862.168
Model 2: Time-invariant cross-lagged paths	123.231	60	0.989	0.026	0.016	102,737.353
Model 3: Time-invariant autoregressive paths	232.946	48	0.967	0.049	0.018	102,935.699
Model 4: Time-invariant autoregressive and cross-lagged paths	277.835	72	0.963	0.042	0.021	102,803.327

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Note. CFI = comparative fit index; RMSEA = root mean square error of approximation; SRMR = standardized root mean square residual; BIC = Bayesian information criterion.

*Note*. We included only statistically significant paths. Bolded paths are the interaction paths that test our a priori hypothesis. Second- and third-order autoregressive paths are included in the model but are omitted in the figure. Covariates are included in the model but are omitted in the figure for presentability. GPA = grade point average; t = time.

* p<.05. ** p<.01. *** p<.001.

As expected, given our theory and the findings from Study 1, the interaction between mindset and parental intrusive homework support was statistically significant across all time points (average β = .03, p = .040, 95% CI [0.001, 0.052]).

As shown in Figure 4, in parallel with the findings of Study 1, although statistically marginal, the pooled negative effect of parental intrusive homework support on subsequent GPA across the four time points was greater for children with a fixed mindset (1 SD below the mean; β = −.04, p = .051, 95% CI [−0.073, 0.000]) than for children with a growth mindset (1 SD above the mean; β = .02, p = .388, [−0.021, 0.055]). When parental intrusive homework support was low (1 SD below the mean), there was no difference in changes in GPA between children with a fixed mindset and those with a growth mindset (β = −.01, p = .800, [−0.044, 0.034]). In contrast, when parental intrusive homework support was high (1 SD above the mean), the GPA of children with a fixed mindset declined to a greater degree over the semester than children with a growth mindset (β = .05, p = .008, [0.013, 0.084]).⁵ These results indicate that under high parental intrusive homework support, children with a fixed mindset scored −.059, while those with a growth mindset scored .039. The difference between these two values is 0.098, which is almost equal to .10 SD. Again, this effect occurs after accounting for a strong set of demographic covariates and outcomes at the prior time point.

*Note*. The low and high parental intrusive homework support represent 1 pooled SD below and above the pooled mean on the scale, respectively. Fixed and growth mindsets represent 1 pooled SD below and above the pooled mean on the mindset scale, respectively. GPA = grade point average.

We fit an additional ARCL model to address the possibility of reverse causality: mindset moderates the influence of GPA on changes in parental intrusive support rather than mindset moderating the influence of parental intrusive support on changes in GPA. In contrast to our primary ARCL model, the interaction term (Mindset × GPA) on changes in parental intrusive homework support was not statistically significant (β = −.01, p = .366, 95% CI [−0.040, 0.015]; see Appendix D).

General Discussion

In two longitudinal studies, one focused on young elementary school students and the other on middle school children. We found that a growth mindset buffers against the deleterious effects of parental intrusive homework support on academic achievement. In Study 1, as theorized, the negative relationship between parental intrusive homework support and achievement was weaker for children with a growth-motivational framework. The same pattern emerged for adolescents in Study 2, which we followed from the fall of eighth grade to the spring of the ninth grade. To our knowledge, these findings represent the first empirical evidence for the hypothesis that mindsets and parental intrusive homework behavior interact to influence achievement across development.

To contextualize these findings, we divided the students into four categories by crossing mindsets (motivational frameworks) and parental intrusive homework support. As there are no existing guidelines for defining “high (vs. low) mindset” and “high (vs. low) parental intrusive homework support,” we used a middle scale point (2.5 for the motivational framework measure, 3 for the mindset measures; 1.5 for the parental intrusive support measure in Study 1 and 2.5 in Study 2) as cutoffs for low and high groups on the measures, and categorized children into one of four groups (e.g., 2 mindset × 2 parental intrusive homework support). As shown in Table 4, over 39% and 16% of students fall in the “at-risk group” (39.5% and 16.1% in Study 1 and 2, respectively), holding high levels of a fixed mindset and experiencing high levels of intrusive homework support.

Table 4.

Cross-Tabulation of Students Into Low and High Mindset (Motivational Framework) and Parental Intrusive Homework Support

Parental intrusive homework support	Mindset (motivational framework)
Parental intrusive homework support	Fixed	Growth
Study 1
Low	3.24%	5.15%
High	39.50%	52.10%
Study 2
Low	6.08%	33.16%
High	16.08%	44.68%

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In both studies, the observed effects of the interplay between mindset and parental intrusive homework support on achievement would be categorized as “small” in magnitude by conventional standards (Cohen, 1988). However, it is becoming increasingly clear that conventional standards for effect size need updating (Funder & Ozer, 2019; Schäfer & Schwarz, 2019). In particular, it has been suggested that standardized coefficients smaller than .10 in autoregressive models are meaningful, especially when there is strong stability in the outcome variable (Adachi & Willoughby, 2015), as was the case with our outcome variable of achievement. Small effects can be practically meaningful, especially in child development and educational contexts (Funder & Ozer, 2019; McCartney & Rosenthal, 2000) because academic achievement influences consequential life outcomes, including health and longevity (Bradley & Greene, 2013; Kaplan et al., 2014), income (Watts, 2020), and happiness (Bücker et al., 2018). Furthermore, in the ARCL models that we fit, the cross-lagged effects were estimated while controlling for autoregressive effects and concurrent correlations among measures, thus significantly reducing the magnitude of the cross-lagged effects.

Interestingly, intrusive homework support was more strongly related to lower achievement among younger children with a fixed mindset than among older children with a fixed mindset despite the lower internal reliabilities of all questionnaire measures, including parental intrusive support in Study 1. In retrospect, because reliability generally increases with the number of items in a questionnaire (Brown, 1910; Spearman, 1910), we could have doubled or even tripled the number of items that children in Study 1 were asked to answer. However, this design decision would have come at a cost, particularly in terms of time and response burden, and may not have been possible in the field setting in which we conducted our research.

If, in fact, this age effect is confirmed in future research, why might this be? We can only speculate. Separate meta-analytic research suggests that parental involvement is more strongly related to academic achievement in childhood than adolescence (Jeynes, 2005, 2007). By contrast, the influence of peers, rather than parents, is paramount in adolescence (Steinberg, 2014), and teenagers tend to do their homework with their peers or by themselves more than with their parents (Kackar et al., 2011). Another possibility is that the parental intrusive measure in Study 2 was not specific to math; thus, the level of specificity in parental intrusive homework support and achievement outcome did not match.

Two limitations of this study suggest promising directions for future research. First, although our models capitalized on repeated measures over time, the possibility of unobserved third variable confounding remains. This means that causal inferences cannot be drawn. For example, we measured uninvited parental homework involvement, but children with a fixed mindset might perceive any parental involvement as intrusive. While it is theoretically possible that any homework assistance from parents stymies motivation and achievement, it stands to reason that help signaling the child’s lack of competence is especially detrimental. For example, a recent study showed that when an adult takes over a task, young children are less likely to persist on the following task compared to when they provide help in the form of scaffolding (e.g., direct and indirect instructions, pedagogical questions; Leonard et al., 2021). Thus, we speculate that our results are especially relevant to intrusive and unconstructive forms of helping behaviors; however, future research needs to examine both constructive and unconstructive homework-helping behaviors to test this hypothesis.

Likewise, other parenting behaviors or family factors that correlate with intrusive parental support might diminish children’s academic motivation and achievement. Alternatively, children with more of a growth mindset are different in ways that we did not measure (e.g., emotional stability, positivity), which accounts for buffering against parental intrusive support. Happily, it should be possible to gather experimental evidence, given advances in intervention research on both mindsets (Yeager et al., 2019) and parenting (List et al., 2021; Webster-Stratton, 1998). In addition to advancing theory, intervention research has direct practical benefits—opening opportunities to not only examine what happens in healthy child development but also to examine whether it is possible to experimentally create these conditions, and determine whether this results in favorable effects on child outcomes.

Second, further research is needed to establish the mediators, moderators, and boundary conditions of the relationships identified here. Do appraisals like “My parents are helping me because I can’t do this myself!” and “Oh, I must not be smart enough!” constitute the mechanism by which intrusive parenting and fixed mindsets conspire to undermine achievement? Is the pernicious influence of parental intrusive homework support blunted when students attend schools that are especially supportive of challenge seeking (Yeager et al., 2019)?

One can also ask: Do parental characteristics predict types of homework involvement? Possibly, Rattan et al. (2012) suggested that when a student fails at a task, an instructor with a fixed mindset tends to make a hasty judgment about the student’s low ability and provides reassuring feedback (e.g., “It’s okay, not everyone is good at math”), which reduces student motivation. Children with a fixed mindset may have parents with a fixed mindset; thus, when parents help with their children’s homework, they may be more likely to demonstrate unconstructive homework-helping behaviors, such as performance-oriented instruction or acting controlling and impatient, which in turn lowers the children’s math competence and leads them to adopt a fixed mindset.

Another important question concerns external validity and, in particular, the combination of intrusive parenting and fixed mindset in non-WEIRD (Henrich et al., 2010) cultures. Asian parents tend to have more controlling and intrusive parenting behaviors than parents from western countries (Chao, 1994; Chiu, 1987; Kelley & Tseng, 1992; Lin & Fu, 1990), and parental control is perceived to be normal in Asia (Chao, 1995). Thus, uninvited homework assistance may be less detrimental in cultures where greater control over children’s schoolwork is common and widely accepted.

Very recently, there has been a call for interventionists to join a “heterogeneity revolution” (Bryan et al., 2021). We agree and believe that this argument applies equally to observational research. Researchers must pursue a more nuanced understanding of the dynamics of development, including how risk (e.g., fixed mindset and intrusive homework support) and promotion factors (e.g., growth mindset and autonomy-supportive homework support) interact over time. In this spirit, the current research highlights the pernicious combination of intrusive parenting and a fixed mindset in childhood and adolescence. Pragmatically, our findings convey a message for parents: while the impulse to help our children is instinctive, intervening without an invitation to do so—particularly when intellectual ability is assumed to be fixed—may do more harm than good in a Western society.

Public Significance Statement.

Across two prospective longitudinal studies, the present study, for the first time, showed that intrusive homework support predicted lower math achievement among children with a fixed mindset but not among children with a growth mindset. These findings show how well-intended parental help can backfire, particularly for children who believe that ability is fixed.

Acknowledgments

This research was supported by the Institute for Education Sciences (Grant R305A110682 to Sian Beilock and Susan Levine); the National Science Foundation Science of Learning Center, the Spatial Intelligence and Learning Center (Grant SBE-1041707 to Sian Beilock and Susan Levine); the National Science Foundation (Grant CAREER DRL-0746970 to Sian Beilock); the John Templeton Foundation, the Walton Family Foundation, and the National Institute on Aging (Grant R24-AG048081-01 to Angela Duckworth). The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies. Data and analytic script can be found at https://doi.org/10.17605/OSF.IO/C49RA.

Appendix A

Study 1 Mindset Scale

Materials:

Circle scale (as depicted below).

graphic file with name nihms-1959552-f0005.jpg

Experimenter:

“I’m going to ask you some questions, and you’ll use these circles to answer. The smallest circle [point] means ‘not at all,’ this circle [point] means ‘a little,’ this circle [point] means ‘medium,’ this circle [point] means ‘kind of a lot,’ and the biggest circle [point] means ‘really a lot.’ You can point to any one of these circles to answer the questions. Let’s try one. Imagine a boy who thinks ice cream is tasty. How much do you agree with this boy? [Give feedback.] Okay, let’s try another one. How much do you like playing sports? [Give feedback.] Okay, now let’s do some more” (Table A1 and Appendix B).

Table A1.

Motivational Framework Measure

Response

Question

1. How much would you like to do mazes that are very easy so you can get a lot right? [show child picture of easy maze] [show circles]
2. Imagine a kid who thinks that people can get smarter if they work really hard. How much do you agree with this kid? [show circles]
3. Imagine a kid who thinks that people have a certain amount of math ability, and stay pretty much the same. How much do you agree with this kid? [show circles]
4. How much would you like to spell words that are very hard so you can learn more about spelling? [show circles]
5. Imagine a kid who thinks that people can get better at reading if they work really hard. How much do you agree with this kid? [show circles]
6. How much would you like to do math problems that are very easy so you can get a lot right? [show circles]
7. How much would you like to do mazes that are very hard so you can learn more about doing mazes? [show child picture of hard maze] [show circles]
8. How much would you like to spell words that are very easy so you can get a lot right? [show circles]
9. Imagine a kid who thinks that people can get better at math if they work really hard. How much do you agree with this kid? [show circles]
10. Imagine a kid who thinks that a person is a certain amount smart, and stays pretty much the same. How much do you agree with this kid? [show circles]
11. How much would you like to do math problems that are very hard so you can learn more about doing math? [show circles]
12. Imagine a kid who thinks that people have a certain amount of reading ability, and stay pretty much the same. How much do you agree with this kid? [show circles]

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Appendix B

All Path Coefficients From the Autoregressive Cross-Lagged Model in Study 1

Path
Time 1 (T1)	Time 2 (T2)	Standardized coefficient	p-value
Motivational framework T1	→ Motivational framework T2	0.353	<.001
Parental intrusive homework support T1	→ Motivational framework T2	−0.075	.066
Motivational Framework T1 × Parental Intrusive Homework Support T1	→ Motivational framework T2	−0.011	>.70
Math achievement T1	→ Motivational framework T2	0.327	<.001
Motivational framework T1	→ Parental intrusive homework support T2	0.005	<.90
Parental intrusive homework support T1	→ Parental intrusive homework support T2	0.218	<.001
Motivational Framework T1 × Parental Intrusive Homework Support T1	→ Parental intrusive homework support T2	0.106	.001
Math achievement T1	→ Parental intrusive homework support T2	−0.108	.036
Motivational framework T1	→ Motivational Framework T2 × Parental Intrusive Homework Support T2	−0.020	>.50
Parental intrusive homework support T1	→ Motivational Framework T2 × Parental Intrusive Homework Support T2	0.163	.001
Motivational Framework T1 × Parental Intrusive Homework Support T1	→ Motivational Framework T2 × Parental Intrusive Homework Support T2	0.153	<.001
Math achievement T1	→ Motivational Framework T2 × Parental Intrusive Homework Support T2	−0.028	>.60
Motivational framework T1	→ Math achievement T2	0.076	.008
Parental intrusive homework support T1	→ Math achievement T2	−0.066	.012
Motivational Framework T1 × Parental Intrusive Homework Support T1	→ Math achievement T2	0.053	.034
Math achievement T1	→ Math achievement T2	0.714	<.001

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Appendix C

ARCL Model With the Mindset and the Challenge-Seeking Aspects of Measure Separately

Given the low Cronbach’s α of the motivational framework measure, we reran the ARCL model separately with the mindset aspect of the measure (e.g., “Imagine a kid who thinks that people can get smarter if they work really hard. How much do you agree with this kid?”) and the challenge-seeking aspect of the measure (e.g., “How much would you like to spell words that are very easy so you can get a lot right?”).

Mindset (α = .43 in the fall, .56 in the spring): Overall parental intrusive homework support was associated with lower spring math achievement for children with a fixed mindset (β = −.11, p = .002, 95% CI [−0.183, −0.040]), but this relation was not significant among those with a growth mindset (β = −.03, p = .423, [−0.088, 0.037]). Furthermore, when overall parental intrusive homework support was low, children with a fixed mindset performed as well as those with a growth mindset (β = −.01, p = .676, [−0.071, 0.046]). In contrast, when overall parental intrusive homework was high, children with a fixed mindset performed worse than those with a growth mindset (β = .07, p = .057, [−0.026, 0.149]). The marginally significant interaction term between mindset and overall parental intrusive homework support (β = .04, p = .054, [−0.001, 0.082]) confirmed that the negative impact of overall parental intrusive homework support differed by mindset.

Challenge seeking (α = .49 in the fall, .63 in the spring): Overall parental intrusive homework support was associated with lower spring math achievement for children with a low level of challenge seeking (β = −.11, p = .003, 95% CI [−0.186, −0.038]), but this relation was not significant among those with a high level of challenge seeking (β = −.02, p = .594, [−0.085, 0.048]). Further analyses indicated that when overall parental intrusive homework support was low, children with a low level of challenge seeking performed as well as those with a high level of challenge seeking (β = .05, p = .193, [−0.023, 0.112]). In contrast, when overall parental intrusive homework was high, children with a lower level of challenge seeking performed worse than those with a higher level of challenge seeking (β = .14, p < .001, [0.066, 0.210]). The marginally significant interaction term between levels of challenge seeking and overall parental intrusive homework support (β = .05, p = .059, [−0.002, 0.095]) confirmed the negative impact of overall parental intrusive homework support differed by children’s challenge-seeking tendency.

Together, these results suggest that uninvited homework involvement by parents is detrimental, especially for children who construe intelligence as a fixed ability and prefer an easy task to a hard one. The lower Cronbach’s αs and marginally significant interaction terms support the idea that merging the two constructs yields a more robust and reliable measure in younger children. Thus, we recommend using the entire motivational framework scale rather than a single subcomponent for future studies.

Appendix D

Reverse Causality Model

Parents may be more likely to intrusively involve themselves in their children’s homework when they are low achieving and display helpless responses (which are commonly observed in people with a fixed mindset). We examined the interactive effects of children’s motivational framework and achievement on parental intrusive homework support. The interaction effect (Motivational Framework × Achievement) on parental intrusive homework support was not statistically significant in Study 1 (β = −.02, p = .706, 95% CI [−0.104, 0.070] and in Study 2, β = −.01, p = .366, [−0.040, 0.015] (Figures D1 and D2 and Appendix E).

Figure D1 — *Note*. Only statistically significant paths are shown. Covariates are included in the model but are omitted in the figure for presentability; t = time.

* p < .05. ** p < .01. *** p < .001.

Figure D2 — *Note*. Only statistically significant paths are shown. Covariates are included in the model but are omitted in the figure for presentability. GPA = grade point average; t = time.

* p < .05. ** p < .01. *** p < .001.

Appendix E

All Path Coefficients From the Autoregressive Cross-Lagged Model in Study 2

T1→T2				T1, T2→T3				T1, T2, T3→T4
Path		Standardized coefficient	p-value	Path		Standardized coefficient	p-value	Path		Standardized coefficient	p-value
								Growth mindset T1	→ Growth mindset T4	0.122	<.001
				Growth mindset T1	→ Growth mindset T3	0.175	<.001	Growth mindset T2	→ Growth mindset T4	0.209	<.001
Growth mindset T1	→ Growth mindset T2	0.45	<.001	Growth mindset T2	→ Growth mindset T3	0.483	<.001	Growth mindset T3	→ Growth mindset T4	0.371	<.001
Parental intrusive homework support T1	→ Growth mindset T2	−0.036	.019	Parental intrusive homework support T2	→ Growth mindset T3	−0.036	.019	Parental intrusive homework support T3	→ Growth mindset T4	−0.036	.019
Growth mindset T1 × Parental Intrusive Homework Support T1	→ Growth mindset T2	0.022	.123	Growth Mindset T2 × Parental Intrusive Homework Support T2	→ Growth mindset T3	0.022	.123	Growth Mindset T3 × Parental Intrusive Homework Support T3	→ Growth mindset T4	0.022	.123
Math GPA T1	→ Growth mindset T2	0.058	<.001	Math GPA T2	→ Growth mindset T3	0.058	<.001	Math GPA T3	→ Growth mindset T4	0.058	<.001
								Parental intrusive homework support T1	→ Parental intrusive homework support T4	0.09	.006
				Parental intrusive homework support T1	→ Parental intrusive homework support T3	0.259	<.001	Parental intrusive homework support T2	→ Parental intrusive homework support T4	0.287	<.001
Parental intrusive homework support T1	→ Parental intrusive homework support T2	0.581	<.001	Parental intrusive homework support T2	→ Parental intrusive homework support T3	0.492	<.001	Parental intrusive homework support T3	→ Parental intrusive homework support T4	0.419	<.001
Growth mindset T1	→ Parental intrusive homework support T2	−0.039	.004	Growth mindset T2	→ Parental intrusive homework support T3	−0.039	.004	Growth mindset T3	→ Parental intrusive homework support T4	−0.039	<.004
Growth Mindset T1 × Parental intrusive homework support T1	→ Parental intrusive homework support T2	0.015	>.20	Growth Mindset T2 × Parental Intrusive Homework Support T2	→ Parental intrusive homework support T3	0.015	>.20	Growth Mindset T3 × Parental Intrusive Homework Support T3	→ Parental intrusive homework support T4	0.015	>.20
Math GPA T1	→ Parental intrusive homework support T2	−0.042	.003	Math GPA T2	→ Parental intrusive homework support T3	−0.042	.003	Math GPA T3	→ Parental intrusive homework support T4	−0.042	.003
								Growth Mindset T1 × Parental Intrusive Homework Support T1	→ Growth mindset T4 × Parental Intrusive Homework Support T4	0.094	.011
				Growth Mindset T1 × Parental Intrusive Homework Support T1	→ Growth Mindset T3 × Parental Intrusive Homework Support T1	0.047	>.20	Growth Mindset T2 × Parental Intrusive Homework Support T1	→ Growth Mindset T4 × Parental Intrusive Homework Support T1	0.122	.002
Growth Mindset T1 × Parental Intrusive Homework Support T1	→ Growth Mindset T2 × Parental Intrusive Homework Support T2	0.344	<.001	Growth Mindset T2 × Parental Intrusive Homework Support T2	→ Growth Mindset T3 × Parental Intrusive Homework Support T3	0.361	<.001	Growth Mindset T3 × Parental Intrusive Homework Support T3	→ Growth Mindset T4 × Parental Intrusive Homework Support T4	0.376	<.001
Growth mindset T1	→ Growth Mindset T2 × Parental Intrusive Homework Support T2	0.02	>.20	Growth mindset T2	→ Growth Mindset T3 × Parental Intrusive Homework Support T3	0.02	>.20	Growth mindset T3	→ Growth Mindset T4 × Parental Intrusive Homework Support T4	0.02	>.20
Parental intrusive homework support T1	→ Growth Mindset T2 × Parental Intrusive Homework Support T2	0.013	>.40	Parental Intrusive Homework Support T2	→ Growth Mindset T3 × Parental Intrusive Homework Support T3	0.013	>.40	Parental intrusive homework support T3	→ Growth Mindset T4 × Parental Intrusive Homework Support T4	0.013	>.40
Math GPA T1	→ Growth Mindset T2 × Parental Intrusive Homework Support T2	0.021	>.20	Math GPA T2	→ Growth Mindset T3 × Parental Intrusive Homework Support T3	0.021	>.20	Math GPA T3	→ Growth Mindset T4 × Parental Intrusive Homework Support T4	0.021	>.20
								Math GPA T1	→ Math GPA T4	0.04	>.20
				Math GPA T1	→ Math GPA T3	0.298	<.001	Math GPA T2	→ Math GPA T4	0.179	<.001
Math GPA T1	→ Math GPA T2	0.686	<.001	Math GPA T2	→ Math GPA T3	0.206	<.001	Math GPA T3	→ Math GPA T4	0.517	<.001
Growth mindset T1	→ Math GPA T2	0.022	>.10	Growth mindset T2	→ Math GPA T3	0.022	>.10	Growth mindset T3	→ Math GPA T4	0.022	>.10
Parental intrusive homework support T1	→ Math GPA T2	−0.01	>.40	Parental Intrusive Homework Support T2	→ Math GPA T3	−0.01	>.40	Parental intrusive homework support T3	→ Math GPA T4	−0.01	>.40
Growth Mindset T1 × Parental Intrusive Homework Support T1	→ Math GPA T2	0.027	.04	Growth mindset T2 × Parental Intrusive Homework Support T2	→ Math GPA T3	0.027	.04	Growth Mindset T3 × Parental Intrusive Homework Support T3	→ Math GPA T4	0.027	.04

Open in a new tab

Note. GPA = grade point average; T = time.

Appendix F

Itemized Analyses for the Parental Intrusive Homework Support Scale in Study 2

As in Study 1, we reran the ARCL model separately with each item of the measure and found similar results across the three models.

Reminding

Unlike younger children, parental reminding was not associated with lower spring math achievement for older children with a fixed mindset (β = −.02, p = .423, 95% CI [−0.055, 0.023]). Interestingly, this relation was marginally significant but in the opposite direction among those with a growth mindset (β = .03, p = .080, [−0.004, 0.070]), suggesting that parental reminding to do homework seems to be beneficial for children with a growth mindset. Further analysis indicated that when parental reminding was low, children with a fixed mindset performed as well as those with a growth mindset (β = .001, p = .971, [−0.038, 0.040]). In contrast, when parental reminding was high, children with a fixed mindset performed worse than those with a growth mindset (β = .05, p < .01, [0.013, 0.087]). The interaction term between reminding and mindset was marginal (β = .03, p = .075, [−0.002, 0.052]), partially confirming that the negative impact of reminding differed according to mindset.

Unsolicited Help

Parental unsolicited help was related to lower spring math achievement, especially among those with a fixed mindset (β = −.03, p = .068, 95% CI [−0.071, 0.003]), but not those with a growth mindset (β = .02, p = .252, [−0.016, 0.062]). Further analyses indicated that when unsolicited help was low, the math achievement of children with a fixed mindset did not differ from those with a growth mindset (β = −.01, p = .729, [−0.046, 0.032]). In contrast, when unsolicited help was high, children with a fixed mindset performed significantly worse than those with a growth mindset (β = .05, p = .007, [0.014, 0.087]). The omnibus interaction term between unsolicited parental help and children’s mindset was statistically significant (β = .03, p < .05, [0.003, 0.055]).

Monitoring

Parental monitoring was negatively related to spring achievement among children with a fixed mindset (β = −.05, p < .01, 95% CI [−0.086, −0.012]) but not among those with a growth mindset (β = −.02, p = .422, [−0.055, 0.023]). However, when parent monitoring was low, the math achievement of children with a fixed mindset did not differ from those with a growth mindset (β = .00, p = .887, [−0.036, 0.042]). On the other hand, when parental monitoring was high, children with a fixed mindset performed significantly worse than those with a growth mindset (β = .04, p = .046, [0.001–0.072]). The interaction term between parental monitoring and children’s mindset was not statistically significant (β = .02, p = .200, [−0.009, 0.042]).

Unlike younger children, reminding did not lower the achievement of older children with a fixed mindset and heightened the achievement of older children with a growth mindset. As for younger children, unsolicited help decreased the achievement of older children with a fixed mindset but not those with a growth mindset. In older children, monitoring did not lower the achievement of those with either a fixed or growth mindset. Importantly, however, we observed consistent results across items and age groups: regardless of its type, when parental intrusive support was low, the math achievement of children with either a fixed or growth mindset did not differ. However, when parental intrusive support was high, children with a fixed mindset underperformed compared to those with a growth mindset. Together, these results suggest that the types and degree to which intrusive helping influences achievement may differ by the developmental stage of children, but regardless of its type, high levels of intrusive homework help lower the achievement of children with a fixed mindset (framework) more so than those with a growth mindset (framework).

Footnotes

Mindsets vary along a continuous dimension, with a strong growth mindset at one end and a strong fixed mindset at the other. However, for simplicity, we use the terms “fixed mindset” and “growth mindset” interchangeably with “higher level of growth (fixed) mindset.”

The K–12 education system in the United States refers to kindergarten to 12th grade, which is divided into three stages: elementary school (Grades K–5, typically ages 5–11 years), middle school (Grades 6–8, typically ages 11–14 years), and high school (Grades 9–12, typically ages 14–18 years).

Using G*power, we performed post hoc power analyses using observed effects (Cohen’s f², Cohen, 1988) with an α = .05 and 80% power. In Study 1, we would have required a sample size of 1,023 to detect the observed f² = 0.0077, suggesting that Study 1 was underpowered. For Study 2, because we had multiple time points (i.e., more than two), power analyses were a little more complicated. While our primary effect of interest was a path/regression coefficient, f² is based on differences in R². To our knowledge, there is no easy way to pool R² estimates, so we manually pooled the differences and used those estimates for this power analysis. We would have required a sample size of 1,298 to detect the estimated f² = 0.0061 in Study 2.

⁴

We also reran the ARCL model separately with the mindset and the challenge seeking aspects of the measure and found a similar pattern of results (see Appendix C).

⁵

Like Study 1, we reran the ARCL model separately with each item of the measure and found similar results across the three models (see Appendix F).

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[R3] Bhanot R, & Jovanovic J (2005). Do parents’ academic gender stereotypes influence whether they intrude on their children’s homework? Sex Roles, 52(9-10), 597–607. 10.1007/s11199-005-3728-4 [DOI] [Google Scholar]

[R4] Bian L, Leslie SJ, & Cimpian A (2017). Gender stereotypes about intellectual ability emerge early and influence children’s Interests. Science, 355(6323), 389–391. 10.1126/science.aah6524 [DOI] [PubMed] [Google Scholar]

[R5] Bollen KA (1989). Structural equations with latent variables. Wiley. [Google Scholar]

[R6] Bradley BJ, & Greene AC (2013). Do health and education agencies in the United States share responsibility for academic achievement and health? A review of 25 years of evidence about the relationship of adolescents’ academic achievement and health behaviors. Journal of Adolescent Health, 52(5), 523–532. 10.1016/j.jadohealth.2013.01.008 [DOI] [PubMed] [Google Scholar]

[R7] Brown W. (1910). Some experimental results in the correlation of mental abilities. British Journal of Psychology, 3(3), 296–322. 10.1111/j.2044-8295.1910.tb00207.x [DOI] [Google Scholar]

[R8] Bryan CJ, Tipton E, & Yeager DS (2021). Behavioural science is unlikely to change the world without a heterogeneity revolution. Nature Human Behaviour, 5(8), 980–989. 10.1038/s41562-021-01143-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[R9] Bücker S, Nuraydin S, Simonsmeier BA, Schneider M, & Luhmann M (2018). Subjective well-being and academic achievement: A meta-analysis. Journal of Research in Personality, 74, 83–94. 10.1016/j.jrp.2018.02.007 [DOI] [Google Scholar]

[R10] Chao RK (1994). Beyond parental control and authoritarian parenting style: Understanding chinese parenting through the cultural notion of training. Child Development, 65(4), 1111–1119. 10.1111/cdev.1994.65.issue-4 [DOI] [PubMed] [Google Scholar]

[R11] Chao RK (1995). Chinese and European American cultural models of the self reflected in mothers’ child rearing beliefs. Ethos, 23(3), 328–354. 10.1525/eth.1995.23.3.02a00030 [DOI] [Google Scholar]

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PERMALINK

Parental Intrusive Homework Support and Math Achievement: Does the Child’s Mindset Matter?

Daeun Park

Elizabeth A Gunderson

Erin A Maloney

Eli Tsukayama

Sian L Beilock

Angela L Duckworth

Susan C Levine

Abstract

Current Investigation

Study 1

Method

Participants

Procedure and Measures

Motivational Framework.

Parental Intrusive Homework Support.

Achievement.

Demographic Covariates.

Analytic Approach

Results and Discussion

Preliminary Analyses

Table 1.

Main Analyses

Figure 1. Study 1 Autoregressive Cross-Lagged Panel Model Results.

Figure 2. Study 1 Spring Math Achievement Scores as a Function of Parental Intrusive Homework Support and Students’ Mindsets.

Reminding.

Unsolicited Help.

Monitoring.

Study 2

Method

Participants

Procedure and Measures

Growth Mindset.

Parental Intrusive Homework Support.

Academic Achievement.

Covariates.

Analytic Approach

Results and Discussion

Preliminary Analyses

Table 2.

Main Analyses

Table 3.

Figure 3. Study 2 Autoregressive Cross-Lagged Panel Model Results.

Figure 4. Study 2 Pooled Mean of Standardized Math GPA as a Function of Parental Intrusive Homework Support and Students’ Mindsets.

General Discussion

Table 4.

Public Significance Statement.

Acknowledgments

Appendix A

Study 1 Mindset Scale

Materials:

Experimenter:

Table A1.

Appendix B

All Path Coefficients From the Autoregressive Cross-Lagged Model in Study 1

Appendix C

ARCL Model With the Mindset and the Challenge-Seeking Aspects of Measure Separately

Appendix D

Reverse Causality Model

Figure D1. Reverse-Causation Autoregressive Cross-Lagged Panel Model for Study 1.

Figure D2. Reverse-Causation Autoregressive Cross-Lagged Panel Model for Study 2.

Appendix E

All Path Coefficients From the Autoregressive Cross-Lagged Model in Study 2

Appendix F

Itemized Analyses for the Parental Intrusive Homework Support Scale in Study 2

Reminding

Unsolicited Help

Monitoring

Footnotes

References

ACTIONS

PERMALINK

RESOURCES

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