is there a correlation between homework and academic achievement

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

The PMC website is updating on October 15, 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Front Psychol

Effects of homework creativity on academic achievement and creativity disposition: Evidence from comparisons with homework time and completion based on two independent Chinese samples

Huiyong fan.

1 College of Educational Science, Bohai University, Jinzhou, China

2 Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian, China

Jianzhong Xu

3 Department of Counseling, Educational Psychology, and Foundations, College of Education, Mississippi State University, MS, United States

Shengli Guo

Associated data.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

During the past several decades, the previous studies have been focusing on the related theoretical issues and measuring tool of homework behaviors (mainly including homework time, completion, and homework creativity). However, the effects of these homework behaviors on general creativity remain unknown. Employing a number of questionnaires, this study investigated two samples from middle schools of Mainland China. The results showed that (1) the eight-item version of Homework Creativity Behaviors Scale had acceptable validity and reliability; (2) compared with homework completion and homework time, homework creativity explained less variety of academic achievement (3.7% for homework creativity; 5.4% for completion and time); (3) homework creativity explained more variance of general creativity than that of homework completion and homework time accounted (7.0% for homework creativity; 1.3% for completion and time); and (4) homework creativity was negatively associated with grade level. Contrary to the popular beliefs, homework completion and homework creativity have positive effects on the students’ general creativity. Several issues that need further studies were also discussed.

Introduction

Homework is an important part of the learning and instruction process. Each week, students around the world spend 3–14 hours on homework, with an average of 5 hours a week ( Dettmers et al., 2009 ; OECD, 2014 ). The results of the previous studies and meta-analysis showed that the homework time is correlated significantly with students’ gains on the academic tests ( Cooper et al., 2012 ; Fan et al., 2017 ; Fernández-Alonso et al., 2019 ).

Homework is a multi-faceted process which has many attributes – each attribute can be identified, defined, and measured independently ( Guo and Fan, 2018 ). Some attributes, such as homework time ( Núñez et al., 2013 ; Kalenkoski and Pabilonia, 2017 ), homework frequency ( Fernández-Alonso et al., 2015 ), homework completion ( Rosário et al., 2015 ), homework effort ( Trautwein and Lüdtke, 2007 ; Fernández-Alonso et al., 2015 ), homework purpose ( Trautwein and Lüdtke, 2009 ; Xu, 2010 , 2021 ), homework performance and problems ( Power et al., 2007 ), homework management behavior ( Xu, 2008 ), homework expectation ( Xu, 2017 ), and self-regulation of homework behavior ( Yang and Tu, 2020 ), have been well recorded in the literature, and operationally defined and measured.

Recently, a research community has noticed the “creativity” in homework (in short form, “homework creativity”) who have raised some speculations about its effects on students’ academic achievement and general creativity disposition ( Kaiipob, 1951 ; Beghetto and Kaufman, 2007 ; Kaufman and Beghetto, 2009 ; Guo, 2018 ; Guo and Fan, 2018 ; Chang, 2019 ). However, the scientific measurement of homework creativity has not been examined systematically. The relationship between homework creativity, academic achievement, and general creativity disposition, as well as the grade difference in homework creativity, are still in the state of conjectures consequently.

As a scientific probe to homework creativity, this study included three main sections. In the “Literature Review” section, the conceptualization and relevant measurement of homework creativity were summarized; the relationship between homework behaviors and academic achievements, general creativity, and the grade difference in homework behaviors and general creativity were also evaluated. These four main results related to the four research questions were also presented in the body of this article. They are reliability and validity of homework creativity behavior scale (HCBS), the relationships between the scores of HCBS and those of general creativity and academic achievement, and the grade effects of scores of HCBS. In the “Discussion” section, the scientific contributions and interpretations of the findings of this study were elaborated.

Homework creativity

Conceptual background of homework creativity.

As an attribute of homework process, homework creativity refers to the novelty and uniqueness of homework ( Guo and Fan, 2018 ). Specifically, the ways relating to homework creativity with extant theoretical literature are presented below.

First, creativity is a natural part of homework process which serves as a sub-process of learning. Guilford (1950) is the first psychologist who linked creativity with learning, pointing out that the acquisition of creativity is a typical quality of human learning, and that a complete learning theory must take creativity into account.

Second, according to the Four-C Model of Creativity (e.g., Kaufman and Beghetto, 2009 ), the homework creativity can be divided mainly into the category of “Transformative Learning” (Mini-C creativity), which is different from the “Everyday Innovation” (Little-C creativity), “Professional Expertise” (Pro-C creativity), or “Eminent Accomplishments” (Big-C creativity, Beghetto and Kaufman, 2007 ; Kaufman and Beghetto, 2009 ; Kozbelt et al., 2011 ).

The Mini-C is defined as a type of intrapersonal creativity which has personal meaning, not solid contribution or breakthrough in a field ( Beghetto and Kaufman, 2007 , p. 76, Table 1 ). The most important point which distinguishes Mini-C from other types of creativity is the level of novelty of product. The Mini-C creativity involves the personal insight or interpretation which is new to a particular individual, but may be ordinary to others. The Little-C creativity refers to any small, but solid innovation in daily life. The Pro-C creativity is represented in the form of professional contribution which is still not a breakthrough. The Big-C creativity generates a real breakthrough appears in some field which is considered as something new to all human beings. The other difference is related with the subjects of sub-types of creativity. The Mini-C creativity mainly happens in all kinds of students. The Little-C creativity can be widely found in normal people. The Pro-C creativity’s masters are those who are proficient in some field. The Big-C creativity is related frequently with those giants who has made eminent contribution to human being.

Basic information of samples 1 and 2 included.

The Mini-C creativity frequently happens in learning process. When the contribution of the Mini-C creativity grows big enough, it can move into the category of the Little-C creativity, or the Big-C creativity. Most homework creativity is of Mini-C creativity, and of which a small part may grow as the Little-C and Big-C creativities. For example, when students independently find a unique solution to a problem in homework which has scientific meaning, a Little-C or Big-C occurs.

Third, the education researchers have observed homework creativity for many years and been manipulating them in educational practice. Kaiipob (1951) described that homework is a semi-guide learning process in which homework such as composition, report, public speech, difficult and complex exercises, experiments, and making tools and models consumes a lot of time and accelerate the development of students’ creativity disposition (p. 153).

In the recent years, creativity has become a curriculum or instruction goal in many countries (the case of United Kingdom, see Smith and Smith, 2010 ; Chinese case, see Pang and Plucker, 2012 ). Homework is the most important way that accomplish this goal. Considering Chinese in primary and secondary schools in China as an example, the curriculum standards have clearly required homework to cultivate students’ creative spirit, creative thinking, and ability to imagination since the year 2000. The results of Qian’s (2006) investigation revealed that the percent of these creative homework items in each unit fluctuates between 29 and 45%.

Previous instruments of homework behaviors

Those existent instruments measuring homework behavior can be divided into the following two categories: The single-indicator instruments and the multi-dimension instruments ( Guo and Fan, 2018 ). The single-indicator instruments employ only one item to measure homework attributes, such as homework time (e.g., Trautwein and Lüdtke, 2007 ), homework frequency (e.g., De Jong et al., 2000 ), homework completion (e.g., Xu et al., 2019 ), and effort (e.g., Liu et al., 2013 ).

The typical multi-dimension instruments include Homework Process Inventory ( Cooper et al., 1998 ), Homework Purpose Scale ( Xu, 2010 ), Homework Performance Questionnaire ( Pendergast et al., 2014 ), Homework Management Scale (HMS; Xu and Corno, 2003 ), Homework Evaluating Scale ( Fernández-Alonso et al., 2015 ), Homework Problem Checklist ( Anesko et al., 1987 ), Science Homework Scale ( Tas et al., 2016 ), Homework Expectancy Value Scale ( Yang and Xu, 2017 ), and Online Homework Distraction Scale ( Xu et al., 2020 ).

Although the previous tools measured some dimensions of homework ( Guo and Fan, 2018 ), there is hardly any tool that can be employed to gauge the homework creativity. Guo and Fan (2018) extracted several attributes (i.e., time, completion, quality, purpose, effort, creativity, sociality, liking) represented in the existent instruments of homework behaviors, and put forth a multi-faceted model of homework behaviors which intuitionally predicts the existence of homework creativity.

Under the guideline of the multi-faceted model ( Guo and Fan, 2018 ), Guo (2018) developed a multi-dimensional homework behavior instrument, which detected the homework creativity as a dimension in the homework behavior of middle school students. A typical item of homework creativity in Guo (2018) is “The way I do my homework is different from others.” The subscale homework creativity reported by Guo (2018) needs to be improved because it has a small number of items with lower reliability.

Following Guo’s (2018) work, Chang (2019) conducted a new investigation focusing on homework creativity behavior. Using an open-ended questionnaire, a total of 30 students from primary, middle, and high schools were invited to answer this question, that is, “What characteristics can be considered as creative in the process of completing the homework?” Here, “creativity” refers to novelty, uniqueness, and high quality. A group of 23 specific behaviors were reported, among which the top 10 are as follows: Learning by analogy, open minded, one question with multiple solutions, unique solution, summarizing the cause of errors, constructing a personal understanding, analyzing knowledge points clearly, classifying homework contents, making more applications, having rich imagination, and a neat handwriting (see Chang, 2019 , Table 4 , p. 14). Based on these results of open-ended questionnaire, Chang (2019) invented a nine-item scale (see Table 1 and Supplementary Table S3 for details) called as the HCBS which has a good reliability coefficient (α = 0.87).

Regression analyses of homework creative behavior on academic achievement and general creativity.

AA, academic achievement; WCAPt, total score of WCAP; TWk, time spent on homework in week days; TWw, time spent on homework in weekend; HCp, homework completion; HCb, homework creativity behavior.

Previous studies on the relationship between homework behaviors and academic achievement

In the literature, homework behaviors is one cluster of variables typically including homework time, homework completion, effort, purpose, frequency, etc. Academic achievement is an outcome of homework which is operationally measured using the scores on the standardized tests, or non-standardized tests (including final examinations, or teachers’ grades, or estimations by participants themselves, those forms were used widely in the literature, see Fan et al., 2017 ). Academic achievement may be affected by a lot of factors inherited in the process of learning (see Hattie, 2009 for an overview of its correlates). The relationship between homework behaviors and academic achievement is one of the most important questions in homework field, because it is related to the effectiveness of homework ( Cooper et al., 2006 , 2012 ; Fan et al., 2017 ).

Most of the previous studies focused on the relationship between homework time and academic achievement. Cooper et al. (2006) synthesized the primary studies published from 1989 to 2003, and found that the correlation between homework time of America students and their academic achievement was about 0.15. Fan et al. (2017) reviewed those individual studies published before June 2015, and reported that the averaged correlation between homework time of international students and their science, technology, engineering, and mathematics (STEM) academic achievement was about 0.20. Fernández-Alonso et al. (2017) investigated a representative sample of Spanish students (more than 26,000), and the results of multi-level analysis indicated that the correlation between homework time and academic achievement was negative at student level, but positive at school level ( r = 0.16). Fernández-Alonso et al. (2019) took a survey on a big sample from 16 countries from Latin America, and reported that the relationship between homework time and academic achievement was very weak. Valle et al. (2019) analyzed the homework time, time management, and achievement of 968 Spain students finding that homework time management was positively related to academic achievement. Taken all these together, we will find that the homework has some small significant correlations with academic achievement, the average r = 0.15.

The correlation between homework completion and academic achievement has also been investigated for decades. Based on a review of 11 primary studies, Fan et al. (2017) reported a high correlation of 0.59 between them. Rosário et al. (2015) investigated 638 students, and demonstrated a correlation of 0.22 between amount of homework completed and math test scores. Xu et al. (2019) took a survey using a sample of 1,450 Chinese eighth graders, and found that the correlations between homework completion and the gains in math test scores ranged from 0.25 to 0.28. Dolean and Lervag (2022) employed the Randomized Controlled Trial design, and demonstrated that amount of homework completed has immediate effect on writing competency in which the effect of moderate amount of homework can last for 4 months. Integrating the aforementioned results, we can find that the averaged correlation between homework completion and academic achievement was higher than that between homework time with academic achievement.

Homework effort was also found to be correlated with academic achievement. Fan et al. (2017) reviewed four primary studies and returned that a medium correlation ( r = 0.31) between homework effort and academic achievement. Two recent investigations showed that this relationship is positively and reciprocally related ( r = 0.41–0.42) ( Xu, 2020 ; Xu et al., 2021 ).

The effect of homework purpose was also correlated with the academic achievement. Fan et al. (2017) summarized four existent primary studies and reported an averaged correlation of 0.11 between them. Later, Rosário et al. (2015) found a similar correlation coefficient of these two variables on a sample of 638 students. Xu’s (2018) investigation revealed that the correlation between purpose and academic achievement was about 0.40. Sun et al. (2021) investigated a larger sample ( N = 1,365), and found that the subscales of homework purpose had different correlation patterns with academic achievement (academic purpose is 0.40, self-regulatory purpose is 0.20, and approval-seeking purpose is 0.10).

Considering the case of homework creativity, there is only one study preliminarily investigated its relationship with academic achievement. Guo (2018) investigated a sample of 1,808 middle school students, and reported a significant correlation between homework creativity and academic achievement ( r = 0.34, p < 0.05).

Previous studies on the relationship between homework behaviors and general creativity

General creativity refers to the psychological attributes which can generate novel and valuable products ( Kaufman and Glăveanu, 2019 ; Sternberg and Karami, 2022 ). These psychological attributes typically included attitude (e.g., willing to take appropriate risk), motivations (e.g., intrinsic motivation, curiosity), abilities (e.g., divergent thinking), and personality (e.g., independence) ( Kaufman and Glăveanu, 2019 ; Long et al., 2022 ). These attributes can be assessed independently, or in the form of grouping ( Plucker et al., 2019 ; Sternberg, 2019 ). For instance, the divergent thinking was measured independently ( Kaufman et al., 2008 ). Also, the willing to take appropriate risk was measured in tools contain other variables ( Williams, 1979 ). There are many studies examined the relationship between learning and general creativity in the past several decades indicating that the correlation between them was around 0.22 (e.g., Gajda et al., 2017 ; Karwowski et al., 2020 ).

Regarding the relationship between homework behaviors and general creativity, there are few studies which presented some contradictory viewpoints. Kaiipob (1951) posited that homework could accelerate development of students’ general creativity disposition, because the tasks in homework provide opportunities to exercise creativity. Cooper et al. (2012) argued that homework can diminish creativity. Furthermore, Zheng (2013) insisted that homework will reduce curiosity and the ability to challenging – the two core components of creativity. The preliminary results of Chang (2019) indicated that the score of HCBS is significantly correlated with scores of a test of general creativity, Williams’ creativity packet ( r = 0.25–0.33, p < 0.05).

Previous studies on the relationship between homework behaviors and homework creativity

In Guo and Fan’s (2018) theoretical work, homework creativity was combined from two independent words, homework and creativity, which was defined as a new attribute of homework process and was considered as a new member of homework behaviors. Up till now, there are two works providing preliminary probe to the relationship between homework behaviors and homework creativity. Guo (2018) investigated a sample of 1808 middle school students, and found that homework creativity was correlated significantly with liking ( r = 0.33), correctness ( r = 0.47), completion ( r = 0.57), and purpose ( r = 0.53). Based on another sample of Chinese students (elementary school students, N = 300; middle school students, N = 518; high school students, N = 386), Chang (2019) showed that the score of homework creativity was correlated significantly with homework time ( r = 0.11), completion ( r = 0.39), correctness ( r = 0.63), effort ( r = 0.73), social interaction ( r = 0.35), quality ( r = 0.69), interpersonal relation purpose ( r = 0.17), and purpose of personal development ( r = 0.41).

Previous studies on grade differences of homework behaviors and general creativity

Grade differences of homework behaviors.

As a useful indicator, homework time was recorded frequently (e.g., Cooper et al., 2006 ; Fan et al., 2017 ). A recent meta-analysis included 172 primary studies (total N = 144,416) published from 2003 to 2019, and demonstrated that time Chinese K-12 students spent on homework increased significantly along with increasing of grades ( Zhai and Fan, 2021 , October).

Regarding homework managing time, some studies reported the grade difference was insignificant. Xu (2006) surveyed 426 middle school students and found that there was no difference between middle school students and high school students. Xu and Corno (2003) reported that urban junior school students ( N = 86) had no grade difference in homework Managing time. Yang and Tu (2020) surveyed 305 Chinese students in grades 7–9, and found that in managing time behavior, the grade differences were insignificant. The rest studies showed that the grade effect is significant. A survey by Xu et al. (2014) based on 1799 Chinese students in grades 10 and 11 showed that the higher level the grade, the lower level of time management.

Grade differences of general creativity

The findings from the previous studies suggested that the scores of general creativity deceases as the grade increases except for some dimensions. Kim (2011) reviewed the Torrance Tests of Creative thinking (TTCT) scores change using five datasets from 1974 to 2008, and reported that three dimensions of creative thinking (i.e., “Fluency,” “Originality,” and “Elaboration”) significantly decreased along with grades increase, while the rest dimension (i.e., “Abstractness of titles”) significantly increased when grades increase. Nie and Zheng (2005) investigated a sample of 3,729 participants from grades 3–12 using the Williams’ Creativity Assessment Packet (WCAP), and reported that the creativity scores decreased from grades 9–12. Said-Metwaly et al. (2021) synthesized 41 primary studies published in the past 60 years, and concluded that the ability of divergent thinking had a whole increase tendency from grades 1 to 12 with a decrease tendency from grades 8 to 11 at the same time.

The purpose and questions of this study

What we have known about homework creativity hitherto is nothing except for its notation and a preliminary version of measurement. To get deeper understanding of homework creativity, this study made an endeavor to examine its relationships with relevant variables based on a confirmation of the reliability and validity of HCBS. Specifically, there are four interrelated research questions, as the following paragraphs (and their corresponding hypotheses) described.

(i) What is the reliability and validity of the HCBS?

Because the earlier version of the HCBS showed a good Cronbach α coefficient of 0.87, and a set of well-fitting indices ( Chang, 2019 ), this study expected that the reliability and validity will also behave well in the current conditions as before. Then, we present the first set of hypotheses as follows:

H1a: The reliability coefficient will equal or greater than 0.80.

H1b: The one-factor model will also fit the current data well; and all indices will reach or over the criteria as the expertise suggested.

(ii) What degree is the score of the HCBS related with academic achievement?

As suggested by the review section, the correlations between homework behaviors and academic achievement ranged from 0.15 and 0.59 (e.g., Fan et al., 2017 ), then we expected that the relationship between homework creativity and academic achievement will fall into this range, because homework creativity is a member of homework behaviors.

The results of the previous studies also demonstrated that the correlation between general creativity and academic achievement changed in a range of 0.19–0.24 with a mean of 0.19 ( Gajda et al., 2017 ). Because it can be treated as a sub-category of general creativity, we predicted that homework creativity will have a similar behavior under the current condition.

Taken aforementioned information together, Hypothesis H2 is presented as follows:

H2: There will be a significant correlation between homework creativity and academic achievement which might fall into the interval of 0.15–0.59.

(iii) What degree is the relationship between HCBS and general creativity?

As discussed in the previous section, there are no inconsistent findings about the relationship between the score of HCBS and general creativity. Some studies postulated that these two variables be positive correlated (e.g., Kaiipob, 1951 ; Chang, 2019 ); other studies argued that this relationship be negative (e.g., Cooper et al., 2012 ; Zheng, 2013 ). Because homework creativity is a sub-category of general creativity, we expected that this relationship would be positive and its value might be equal or less than 0.33. Based on those reasoning, we presented our third hypothesis as follows:

H3: The correlation between homework creativity and general creativity would be equal or less than 0.33.

(iv) What effect does grade have on the HCBS score?

Concerning the grade effect of homework behaviors, the previous findings were contradictory ( Xu et al., 2014 ; Zhai and Fan, 2021 , October). However, the general creativity decreased as the level of grade increases from grade 8 to grade 11 ( Kim, 2011 ; Said-Metwaly et al., 2021 ). Taken these previous findings and the fact that repetitive exercises increase when grades go up ( Zheng, 2013 ), we were inclined to expect that the level of homework creativity is negative correlated with the level of grade. Thus, we presented our fourth hypothesis as follows:

H4: The score of HCBS might decrease as the level of grades goes up.

Materials and methods

Participants.

To get more robust result, this study investigated two convenient samples from six public schools in a medium-sized city in China. Among them, two schools were of high schools (including a key school and a non-key school), and the rest four schools were middle schools (one is key school, and the rest is non-key school). All these schools included here did not have free lunch system and written homework policy. Considering the students were mainly prepared for entrance examination of higher stage, the grades 9 and 12 were excluded in this survey. Consequently, students of grades 7, 8, 10, and 11 were included in our survey. After getting permission of the education bureau of the city investigated, the headmasters administrated the questions in October 2018 (sample 1) and November 2019 (sample 2).

A total of 850 questionnaires were released and the valid number of questionnaires returned is 639 with a valid return rate of 75.18%. Therefore, there were 639 valid participants in sample 1. Among them, there were 273 boys and 366 girls (57.2%); 149 participants from grade 7 (23.31%), 118 from grade 8 (18.47%), 183 from grade 10 (28.64%), and 189 from grade 11 (29.58%); the average age was 15.25 years, with a standard deviation (SD) of 1.73 years. See Table 1 for the information about each grade.

Those participants included received homework assignments every day (see Table 1 for the distribution of homework frequency). During the working days, the averaged homework time was 128.29 minutes with SD = 6.65 minutes. In the weekend, the average homework time was 3.75 hours, with SD = 0.22 hours. The percentage distribution here is similar with that of a national representative sample ( Sun et al., 2020 ), because the values of Chi-squared (χ 2 ) were 7.46 (father) and 8.46 (mother), all p -values were above 0.12 (see Supplementary Table S1 for details).

Another package of 850 questionnaires were released. The valid number of questionnaires returned is 710 with a valid return rate of 83.53%. Among them, there were 366 girls (51.50%); 171 participants from grade 7 (24.23%), 211 from grade 8 (26.06%), 190 from the grade 10 (22.96%), and 216 from grade 11 (26.76%); the average age was 15.06 years, with SD = 1.47 years.

Those participants included received homework assignments almost each day (see Table 1 for details for the distribution of homework frequency). During the working days, the averaged homework time was 123.02 minutes with SD = 6.13 minutes. In weekend, the average homework time was 3.47 hours, with SD = 0.21 hours.

The percentage distribution here is insignificantly different from that of a national representative sample ( Sun et al., 2020 ), because the values of χ 2 were 5.20 (father) and 6.05 (mother), p -values were above 0.30 (see Supplementary Table S1 for details).

Instruments

The homework creativity behavior scale.

The HCBS contains nine items representing students’ creativity behaviors in the process of completing homework (for example, “I do my homework in an innovative way”) ( Chang, 2019 , see Supplementary Table S3 for details). The HCBS employs a 5-point rating scale, where 1 means “completely disagree” and 5 means “completely agree.” The higher the score, the stronger the homework creative behavior students have. The reliability and validity of the HCBS can be found in Section “Reliability and validity of the homework creativity behavior scale” (see Table 2 and Figures 1 , ​ ,2 2 for details).

Results of item discrimination analysis and exploratory factor analysis.

**p < 0.01, two side-tailed. The same for below.

a Correlations for sample 1; b Correlations for sample 2. c Seventh item should be removed away according to the results of CFA (see section “Reliability and validity of the HCBS” for details).

Parallel analysis scree plots of the HCBS data.

The standardized solution for HCBS eight-item model. hcb, homework creativity behavior; it 1∼9, item1 ∼6, 8∼9.

Homework management scale

The HMS contains 22 items describing specific behaviors related to self-management in homework (for example, “I will choose a quiet place to do my homework” or “Tell myself to calm down when encountering difficulties”) ( Xu and Corno, 2003 ; Xu, 2008 ). The HMS employs a 5-point Likert scale, ranging from 1 (completely disagree) to 5 (completely agree). All items can be divided into five dimensions, i.e., arranging environment, managing time, focusing attention, monitoring motivation, and monitoring and controlling emotion. Among them, the monitoring and controlling emotion dimension adopts a method of reverse scoring.

Except for the internal consistency of arranging environment in sample 1, which is 0.63, the internal consistency coefficients of the five dimensions based two samples in this study are all greater than 0.7, ranging from 0.70 to 0.79. The Cronbach’s coefficients of the overall HMS-based two samples are 0.88 and 0.87, respectively. The ω coefficients of the dimensions of HMS ranged from 0.64 to 0.80. The ω coefficients of the HMS total scores were 0.88 and 0.87 for samples 1 and 2, respectively. Those reliability coefficients were acceptable for research purpose ( Clark and Watson, 1995 ; Peterson and Kim, 2013 ).

Williams’ creativity assessment packet

The WCAP including a total of 40 items is a revised version to measure general disposition of creativity (for example, “I like to ask some questions out of other’s expectation” or “I like to imagine something novel, even if it looks useless”) ( Williams, 1979 ; Wang and Lin, 1986 ; Liu et al., 2016 ). The WCAP uses a 3-point Likert scales, in which 1 = disagree, 2 = uncertain, and 3 = agree. The higher WCAP score, the higher is the general creativity level. All items of WCAP can be scattered into four dimensions: adventure, curiosity, imagination, and challenge ( Williams, 1979 ; Wang and Lin, 1986 ; Liu et al., 2016 ). In this study, the Cronbach’s α coefficients of adventure, curiosity, imagination, challenge, and total scale are 0.62, 0.71, 0.78, 0.64, and 0.90, respectively. The ω coefficients were in sequence 0.61, 0.70, 0.77, 0.63, and 0.90 for adventure, curiosity, imagination, challenge, and the total score of WCAP. The correlations between the four dimensions of WCAP are between 0.47 and 0.65. The patterns of reliability coefficients and correlations between dimensions are similar to those results reported by the previous studies ( Williams, 1979 ; Wang and Lin, 1986 ; Liu et al., 2016 ) which stand acceptable reliability and validity ( Clark and Watson, 1995 ; Peterson and Kim, 2013 ).

Homework indicators

Homework time.

The participants were asked to report the time spent on homework in the past week. This technique has been employed widely in many international survey programs, such as PISA from OECD (e.g., Trautwein and Lüdtke, 2007 ). The items are as follows: (1) “Every day, from Monday to Friday, in last week, how many minutes you spent on homework?” The options are as follows: (A) 0–30 min; (B) 31–60 min (C) 61–90 min (D) 91–120 min; (E) 121–180 min; (F) 181 min or more. (2) “In last weekend, how many hours you spent on homework?” The options are as follows: (A) 0–1 h; (B) 1.1–3 h; (C) 3.1–5 h; (D) 5.1–7 h; (E) 7.1 h or more.

Homework completion

The homework completion is a useful indicator demonstrated in the previous studies ( Welch et al., 1986 ; Austin, 1988 ; Swank, 1999 ; Pelletier, 2005 ; Wilson, 2010 ), and had large correlation with achievement, as a meta-analytic results suggested ( Fan et al., 2017 ). In the survey of this study, the participants were also asked to estimate a percent of the completion of homework in the past week and fill in the given blank space. It includes three items which are as follows: “What is the percentage of Chinese/Maths/English homework assignment you completed in the last week?” “Please estimate and write a number from 0 to 100 in the blank space.”

Academic achievement

To record the academic achievement, an item required participants to make a choice based on their real scores of tests, not estimate their tests scores. The item is, “In the last examination, what is the rank of your score in your grade?” (A) The first 2%; (B) The first 3–13%; (C) The first 14–50%; (D) The first 51–84%; (E) The last 16%. The options here correspond to the percentage in the normal distribution, it is convenient to compute a Z -score for each student.

The method employed here is effective to retrieve participants’ test scores. First, the self-report method is more effective than other method under the condition of anonymous investigation. To our knowledge, participants do not have the will to provide their real information in the real name format. Second, this method transforms test scores from different sources into the same space of norm distribution which benefits the comparisons. Third, the validity of this method has been supported by empirical data. Using another sample ( N = 234), we got the academic achievement they reported and real test scores their teacher recorded. The correlation between ranks self-reported and the real scores from Chinese test were r = 0.81, p < 0.001; and the correlation coefficient for mathematics was also large, i.e., r = 0.79, p < 0.001.

Data collection procedure

There are three phases in data collection. The first one is the design stage. At this stage, the corresponding author of this study designed the study content, prepared the survey tools, and got the ethical approve of this project authorized from research ethic committee of school the corresponding author belongs to.

The second stage is to releasing questionnaire prepared. The questionnaire was distributed and retrieved by the head master of those classes involved. Neither the teachers nor the students knew the purpose of this research. During this stage, students can stop answering at any time, or simply withdraw from the survey. None of the teachers and students in this study received payment.

The third stage is the data entry stage. At this stage, the corresponding author of this study recruited five volunteers majored in psychology and education, and explained to them the coding rules, missing value processing methods, identification of invalid questionnaires, and illustrated how to deal with these issues. The volunteers used the same data template for data entry. The corresponding author of this study controlled the data entry quality by selective check randomly.

Data analysis strategies

R packages employed.

The “psych” package in R environment ( R Core Team, 2019 ) was employed to do descriptive statistics, correlation analysis, mean difference comparisons, exploratory factor analysis (EFA), reliability Analysis ( Revelle, 2022 ); and the “lavaan” package was used in confirmatory factor analysis (CFA) and measurement invariance test ( Rosseel, 2012 ); and the “semPlot” package was employed to draw the picture of CFA’s outputs ( Epskamp et al., 2022 ).

Analysis strategies of exploratory factor analysis and reliability

Sample 1 was used for item analysis, EFA, reliability analysis. In EFA, factors were extracted using maximum likelihood, and the promax method served as the rotation method. The number of factors were determined according to the combination of the results from screen plot, and the rule of Eigenvalues exceeding 1.0, and parallel analysis ( Luo et al., 2019 ).

The Cronbach’s α and MacDonald’s ω test were employed to test the reliability of the scale. The rigorous criteria that α ≥ 0.70 ( Nunnally and Bernstein, 1994 ) and ω ≥ 0.7 ( Green and Yang, 2015 ) were taken as acceptable level of the reliability of HCBS.

Analysis strategies of confirmatory factor analysis

As suggested by Hu and Bentler (1999) , two absolute goodness-of-fit indices, namely, the root mean square error of approximation (RMSEA) and the standardized root mean square residual (SRMR), and two relative goodness-of-fit indices, namely, comparative fit index (CFI) and Tucker–Lewis Index (TLI) were recruited as fitting indicators. The absolute goodness-of-fit indices are less than 0.08, and the relative goodness-of-fit indices greater than 0.90 are considered as a good fit. The CFA was conducted using the second sample.

Strategies for measurement invariance

Measurement invariance testing included four models, they are Configural invariance (Model 1), which is to test whether the composition of latent variables between different groups is the same; Weak invariance (Factor loading invariance, Model 2), which is to test whether the factor loading is equal among the groups; Intercept invariance (Model 3), that is, whether the intercepts of the observed variables are equal; Strict equivalent (Residual Variance invariance, Model 4), that is, to test whether the error variances between different groups are equal ( Chen, 2007 ; Putnick and Bornstein, 2016 ).

Since the χ 2 test will be affected easily by the sample size, even small differences will result in significant differences as the sample size will increase. Therefore, this study used the changes of model fitting index CFI, RMSEA, and SRMR (ΔCFI, ΔRMSEA, and ΔSRMR) to evaluate the invariance of the measurement. When ΔCFI ≤ 0.010, ΔRMSEA ≤ 0.015, and ΔSRMR ≤ 0.030 (for metric invariance) or 0.015 (for scalar or residual invariance), the invariance model is considered acceptable ( Cheung and Rensvold, 2002 ; Chen, 2007 ; Putnick and Bornstein, 2016 ).

Strategies of controlling common methods biases

The strategy of controlling common methods biases is mainly hided in the directions. Each part of the printed questionnaire had a sub-direction which invites participants answer the printed questions honestly. The answer formats between any two neighboring parts were different from each other which requested participants change their mind in time. For example, on some part, the answering continuum varied from “1 = totally disagreed” to “5 = total agreed,” while the answering continuum on the neighboring part is the from “5 = totally disagreed” to “1 = total agreed.” Additionally, according to the suggestion of the previous studies, the one factor CFA model and the bi-factor model can be used to detect the common methods biases (e.g., Podsakoff et al., 2012 ).

Detection of common method biases

The fitting results of the one-common-factor model using CFA technique were as follows: χ 2 = 15,073, df = 3320, p < 0.001; χ 2 / df = 4.54, CFI = 0.323, TLI = 0.306, RMSEA = 0.071, 90% CI: 0.070–0.072, and SRMR = 0.101. The results of the bi-factor model under CFA framework were presented as follows: χ 2 = 2,225.826, df = 117, p < 0.001; χ 2 / df = 19.024, CFI = 0.650, TLI = 0.543, RMSEA = 0.159, 90% CI: 0.154–0.164, and SRMR = 0.127. These poor indices of the two models suggested that the one-common-factor model failed to fit the data well and that the biases of common method be ignored ( Podsakoff et al., 2012 ).

Reliability and validity of the homework creativity behavior scale

Item analysis.

Based on the sample 1, the correlation coefficients between the items of the HCBS were between 0.34 and 0.64, p -values were below 0.01. The correlations between the items and the total score of HCBS vary from 0.54 to 0.75 ( p -values are below 0.01). On the condition of sample 2, the correlations between the items fluctuate between 0.31 and 0.58, the correlation coefficients between the items and the total score of the HCBS change from 0.63 to 0.75 ( p -values were below 0.01). All correlation coefficients between items and total score are larger than those between items and reached the criterion suggested ( Ferketich, 1991 ; see Table 2 for details).

Results of exploratory factor analysis

The EFA results (based on sample 1) showed that the KMO was 0.89, and the χ 2 of Bartlett’s test = 1,666.07, p < 0.01. The rules combining eigenvalue larger than 1 and the results of parallel analysis (see Figure 1 for details) suggested that one factor should be extracted. The eigenvalue of the factor extracted was 3.63. The average variance extracted was 0.40. This factor accounts 40% variance with factor loadings fluctuating from 0.40 to 0.76 (see Table 2 ).

Results of confirmatory factor analysis

In the CFA situation (based on sample 2) the fitting indices of the nine-item model of the HCBS are acceptable marginally, they are χ 2 = 266.141; df = 27; χ 2 / df = 9.857; CFI = 0.904; TLI = 0.872; RMSEA = 0.112; 90% CI: 0.100–0.124; SRMR = 0.053.

The modification indices of item 7 were too big (MI value = 74.339, p < 0.01), so it is necessary to consider to delete item 7. Considering its content of “I designed a neat, clean and clear homework format by myself,” item 7 is an indicator of strictness which is weakly linked with creativity. Therefore, the item 7 should be deleted.

After removing item 7, the fitting results were, χ 2 = 106.111; df = 20; χ 2 / df = 5.306; CFI = 0.957; TLI = 0.939; RMSEA = 0.078; 90% CI: 0.064–0.093; SRMR = 0.038). The changes of the fitting indices of the two nested models (eight-item vs. nine-item models) are presented as follows: Δχ 2 = 160.03, Δ df = 7, χ 2 (α = 0.01, df = 7) = 18.48, p < 0.05. After deleting item 7, both CFI and TLI indices increased to above 0.93, and RMSEAs decreased below 0.08 which suggested that the factor model on which eight items loaded fitted the data well. The average variance extracted was 0.50 which is adequate according to the criteria suggested by Fornell and Larcker (1981) . The standardized solution for the eight-item model of the HCBS was shown in Figure 2 .

Correlations between the homework creativity behavior scale and similar concepts

The results showed that the score of the HCBS was significantly correlated with the total score and four dimensions of WCAP and their correlation coefficients ranged from 0.20 to 0.29, p -values were below 0.01. Similarly, the correlations between the score of the HCBS and the scores of arranging environment, managing time, motivation management, and controlling emotion, and total score of the HMS ranged from 0.08 to 0.22, p -values were 0.01; at the meanwhile, the correlation between the score of HCBS and the distraction dimension of the HMS was r = –0.14, p -values were 0.01. The HCBS score was also significantly related to homework completion ( r = 0.18, p < 0.01), but insignificantly related to homework time (see Table 3 for details).

Correlation matrix between variables included and the corresponding descriptive statistics.

About correlation between variables, the results of sample 1 and sample 2 were presented in the lower, upper triangle, respectively.

a In analyses, grades 7, 8, 10, and 11 were valued 1, 2, 3, and 4, respectively.

b TWk, the time spent on homework in the weekend; TWw, the time spent on homework from Monday to Friday; HCp, homework completion; HMSt, total score of homework management scale; AE, arrange environment; MT, manage time; MM, monitor motivation; CE, control emotion; FA, focus attention; WCAPt, WCAP total score; AD, adventure; CU, curiosity; IM, imagination; CH, challenging; HCb, homework creativity behavior; AA, academic achievement.

c Since sample 1 did not answer the WCAP, so the corresponding cells in the lower triangle are blank. *p < 0.05, two side-tailed, the same for below.

d Since there is only one item from variable 1 to 4, the α and ω coefficients cannot be computed.

Correlations between the homework creativity behavior scale and distinct concepts

The correlation analysis results demonstrated that both the correlation coefficients between the score of HCBS and the time spent on homework in week days, and time spent on in weekend days were insignificant ( r -values = 0.02, p -values were above 0.05), which indicated a non-overlap between two distinct constructs of homework creativity and time spent on homework.

Reliability analyses

The results revealed that both the Cronbach’s α coefficients of sample 1 and sample 2 were 0.86, which were greater than a 0.70 criteria the previous studies suggest ( Nunnally and Bernstein, 1994 ; Green and Yang, 2015 ).

Effect of homework creativity on academic achievement

The results (see Table 4 ) of hierarchical regression analyses demonstrated that (1) gender and grade explained 0.8% variation of the score of academic achievement. This number means closing to zero because the regression equation failed to pass the significance test; (2) homework time and completion explained 5.4% variation of academic achievement; considering the β coefficients of the time spent on homework is insignificant, this contribution should be attributed to homework completion totally, and (3) the score of the HCBS explained 3.7% variation of the academic achievement independently.

Effect of homework creativity on general creativity

The results showed the following (see Table 4 for details):

(1) Gender and grade explained 1.3% variation of the total score of general creativity (i.e., the total score of WACP); homework time and completion explained 1.3% variation of the total score of general creativity disposition; and the score of the HCBS independently explained 7.0% variation of the total score of general creativity.

(2) Gender and grade explained 1.7% variation of the adventure score, and homework time and completion explained 1.6% variation of the adventure score, and the score of the HCBS independently explained 6.4% variation of the adventure score.

(3) Gender and grade explained 2.4% variation of the curiosity score, and homework time and completion explained 1.1% variation of the curiosity score, and the score of the HCBS independently explained 5.1% variation of the curiosity score.

(4) Gender and grade explained 0.3% variation of the imagination score, homework time completion explained 0.3% variation of the imagination score. The real values of the two “0.3%” are zeros because both the regression equations and coefficients failed to pass the significance tests. Then the score of the HCBS independently explained 4.4% variation of the imagination score.

(5) Gender and grade explained 0.3% variation of the score of the challenge dimension, homework time and completion explained 2.3% variation of the challenge score, and the score of the HCBS independently explained 4.9% variation of the challenge score.

Grade differences of the homework creativity behavior scale

Test of measurement invariance.

The results of measurement invariance test across four grades indicated the following:

(1) The fitting states of the four models (Configural invariance, Factor loading invariance, Intercept invariance, and Residual variance invariance) were marginally acceptable, because values of CFIs (ranged from 0.89 to 0.93), TLIs (varied from 0.91 to 0.93), RMSEAs (fluctuated from 0.084 to 0.095), and SRMRs (changed from 0.043 to 0.074) located the cutoff intervals suggested by methodologists ( Cheung and Rensvold, 2002 ; Chen, 2007 ; Putnick and Bornstein, 2016 ; see Table 5 for fitting indices, and refer to Supplementary Table S2 for the estimation of parameters).

Fitting results of invariance tests across grades.

(2) When setting factor loadings equal across four grades (i.e., grades 7, 8, 10, and 11), the ΔCFA was –0.006, ΔRMSEA was –0.007, and ΔSRMR was 0.016 which indicated that it passed the test of factor loading invariance. After adding the limit of intercepts equal across four groups, the ΔCFA was –0.008, ΔRMSEA was –0.004, and the ΔSRMR was 0.005 which supported that it passed the test of intercept invariance. At the last step, the error variances were also added as equal, the ΔCFA was –0.027, ΔRMSEA was 0.005, and the ΔSRMR was 0.019 which failed to pass the test of residual variance invariance (see Table 5 for changes of fitting indices). Taking into these fitting indices into account, the subsequent comparisons between the means of factors can be conducted because the residuals are not part of the latent factor ( Cheung and Rensvold, 2002 ; Chen, 2007 ; Putnick and Bornstein, 2016 ).

Grade differences in homework creativity and general creativity

The results of ANOVA showed that there were significant differences in the HCBS among the four grades [ F (3,1345) = 27.49, p < 0.001, η 2 = 0.058, see Table 6 for details]. Further post-test tests returned that the scores of middle school students were significantly higher than those of high school students (Cohen’s d values ranged from 0.46 to 0.54; the averaged Cohen’s d = 0.494), and no significant difference occurs between grades 7 and 8, or between grades 10 and 11. See Figure 3 for details.

Grade differences in HCBS.

***p < 0.001.

The mean differences of the HCBS between the groups of grades.

To address the gap in the previous research on homework creativity, this study examined the psychometric proprieties of the HCBS and its relationship with academic achievement and general creativity. The main findings were (1) Hypotheses H1a and H1b were supported that the reliability and validity of the HCBS were acceptable; (2) Hypothesis H2 was supported that the correlation between the score of the HCBS and academic achievement was significant ( r -values = 0.23–0.26 for two samples); (3) Hypothesis H3 received support that the correlation between the scores of HCBS and WCAP was significant ( r -values = 0.20–0.29 for two samples); and (4) the H4 was supported from the current data that the score of high school students’ was lower than that of the middle school students’ (Cohen’s d = 0.49).

The positive correlations among homework creativity, homework completion, and general creativity

The first key finding should be noted is that the positive correlations with between pairs of homework creativity, homework completion, and general creativity. This result is inconsistent with prediction of an argument that homework diminishes creativity ( Cooper et al., 2012 ; Zheng, 2013 ). Specifically, the correlation between homework completion and curiosity was insignificant ( r = 0.08, p > 0.05) which did not support the argument that homework hurts curiosity of creativity ( Zheng, 2013 ). The possible reason may be homework can provide opportunities to foster some components of creativity by independently finding and developing new ways of understanding what students have learned in class, as Kaiipob (1951) argued. It may be the homework creativity that served as the way to practice the components of general creativity. In fact, the content of items of the HCBS are highly related with creative thinking (refer to Table 2 for details).

Possible reasons of the grade effect of the score of the homework creativity behavior scale

The second key finding should be noted is that the score of the HCBS decreased as the level of grades increased from 7 to 11. This is consistent with the basic trend recorded in the previous meta-analyses ( Kim, 2011 ; Said-Metwaly et al., 2021 ). There are three possible explanations leading to this grade effect. The first one is the repetitive exercises in homework. As Zheng (2013) observed, to get higher scores in the highly competitive entrance examination of high school and college, those Chinese students chose to practice a lot of repetitive exercises. The results of some behavior experiments suggested that repetitive activity could reduce the diverse thinking of subjects’ (e.g., Main et al., 2020 ). Furthermore, the repetitive exercises would lead to fast habituation (can be observed by skin conductance records) which hurts the creative thinking of participants ( Martindale et al., 1996 ). The second explanation is that the stress level in Chinese high schools is higher than in middle school because of the college entrance examination. The previous studies (e.g., Beversdorf, 2018 ) indicated that the high level of stress will trigger the increase activity of the noradrenergic system and the hypothalamic–pituitary–adrenal (HPA) axis which could debase the individual’s performance of creativity. Another likely explanation is the degree of the certainty of the college entrance examination. The level of certainty highly increases (success or failure) when time comes closer to the deadline of the entrance examination. The increase of degree of certainty will lead to the decrease of activity of the brain areas related to curiosity (e.g., Jepma et al., 2012 ).

The theoretical implications

From the theoretical perspective, there are two points deserving to be emphasized. First, the findings of this study extended the previous work ( Beghetto and Kaufman, 2007 ; Kaufman and Beghetto, 2009 ). This study revealed that homework creativity had two typical characteristics, including the personal meaning of students (as represented by the content of items of the HCBS) and the small size of “creativity” and limited in the scope of exercises (small correlations with general creativity). These characteristics are in line with what Mini-C described by the previous studies ( Beghetto and Kaufman, 2007 ; Kaufman and Beghetto, 2009 ). Second, this study deepened our understanding of the relationship between learning (homework is a part of learning) and creativity which has been discussed more than half a century. One of the main viewpoints is learning and creativity share some fundamental similarities, but no one explained what is the content of these “fundamental similarities” (e.g., Gajda et al., 2017 ). This study identified one similarity between learning and creativity in the context of homework, that is homework creativity. Homework creativity has the characteristics of homework and creativity at the same time which served as an inner factor in which homework promote creativity.

The practical implications

The findings in this study also have several potential practical implications. First, homework creativity should be a valuable goal of learning, because homework creativity may make contributions to academic achievement and general creativity simultaneously. They accounted for a total of 10.7% variance of academic achievement and general creativity which are the main goals of learning. Therefore, it is valuable to imbed homework creativity as a goal of learning, especially in the Chinese society ( Zheng, 2013 ).

Second, the items of the HCBS can be used as a vehicle to help students how to develop about homework creativity. Some studies indicated that the creative performance of students will improve just only under the simple requirement of “to be creative please” ( Niu and Sternberg, 2003 ). Similarly, some simple requirements, like “to do your homework in an innovative way,” “don’t stick to what you learned in class,” “to use a simpler method to do your homework,” “to use your imagination when you do homework,” “to design new problems on the basis what learnt,” “to find your own unique insights into your homework,” and “to find multiple solutions to the problem,” which rewritten from the items of the HCBS, can be used in the process of directing homework of students. In fact, these directions are typical behaviors of creative teaching (e.g., Soh, 2000 ); therefore, they are highly possible to be effective.

Third, the HCBS can be used to measure the degree of homework creativity in ordinary teaching or experimental situations. As demonstrated in the previous sections, the reliability and validity of the HCBS were good enough to play such a role. Based on this tool, the educators can collect the data of homework creativity, and make scientific decisions to improve the performance of people’s teaching or learning.

Strengths, limitations, and issues for further investigation

The main contribution is that this study accumulated some empirical knowledge about the relationship among homework creativity, homework completion, academic achievement, and general creativity, as well as the psychometric quality of the HCBS. However, the findings of this study should be treated with cautions because of the following limitations. First, our study did not collect the test–retest reliability of the HCBS. This makes it difficult for us to judge the HCBS’s stability over time. Second, the academic achievement data in our study were recorded by self-reported methods, and the objectivity may be more accurate. Third, the lower reliability coefficients existed in two dimensions employed, i.e., the arrange environment of the HMS (the α coefficient was 0.63), and the adventure of the WCAP (the α coefficient was 0.61). Fourth, the samples included here was not representative enough if we plan to generalize the finding to the population of middle and high school students in main land of China.

In addition to those questions listed as laminations, there are a number of issues deserve further examinations. (1) Can these findings from this study be generalized into other samples, especially into those from other cultures? For instances, can the reliability and validity of the HCBS be supported by the data from other samples? Or can the grade effect of the score of the HCBS be observed in other societies? Or can the correlation pattern among homework creativity, homework completion, and academic achievement be reproduced in other samples? (2) What is the role of homework creativity in the development of general creativity? Through longitudinal study, we can systematically observe the effect of homework creativity on individual’s general creativity, including creative skills, knowledge, and motivation. The micro-generating method ( Kupers et al., 2018 ) may be used to reveal how the homework creativity occurs in the learning process. (3) What factors affect homework creativity? Specifically, what effects do the individual factors (e.g., gender) and environmental factors (such as teaching styles of teachers) play in the development of homework creativity? (4) What training programs can be designed to improve homework creativity? What should these programs content? How about their effect on the development of homework creativity? What should the teachers do, if they want to promote creativity in their work situation? All those questions call for further explorations.

Homework is a complex thing which might have many aspects. Among them, homework creativity was the latest one being named ( Guo and Fan, 2018 ). Based on the testing of its reliability and validity, this study explored the relationships between homework creativity and academic achievement and general creativity, and its variation among different grade levels. The main findings of this study were (1) the eight-item version of the HCBS has good validity and reliability which can be employed in the further studies; (2) homework creativity had positive correlations with academic achievement and general creativity; (3) compared with homework completion, homework creativity made greater contribution to general creativity, but less to academic achievement; and (4) the score of homework creativity of high school students was lower than that of middle school students. Given that this is the first investigation, to our knowledge, that has systematically tapped into homework creativity, there is a critical need to pursue this line of investigation further.

Data availability statement

Ethics statement.

The studies involving human participants were reviewed and approved by the research ethic committee, School of Educational Science, Bohai University. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.

Author contributions

HF designed the research, collected the data, and interpreted the results. YM and SG analyzed the data and wrote the manuscript. HF, JX, and YM revised the manuscript. YC and HF prepared the HCBS. All authors read and approved the final manuscript.

Acknowledgments

We thank Dr. Liwei Zhang for his supports in collecting data, and Lu Qiao, Dounan Lu, Xiao Zhang for their helps in the process of inputting data.

This work was supported by the LiaoNing Revitalization Talents Program (grant no. XLYC2007134) and the Funding for Teaching Leader of Bohai University.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2022.923882/full#supplementary-material

• Anesko K. M., Schoiock G., Ramirez R., Levine F. M. (1987). The homework problem checklist: Assessing children’s homework difficulties. Behav. Assess. 9 179–185. 10.1155/2020/1250801 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Austin C. A. (1988). Homework as a parental involvement strategy to improve the achievement of first grade children: Dissertation abstracts international, 50/03, 622. Doctoral dissertation. Memphis, TN: Memphis State University. [ Google Scholar ]
• Beghetto R. A., Kaufman J. C. (2007). Toward a broader conception of creativity: A case for mini-c creativity. Psycho. Aesthetics Creat. Arts 1 73–79. [ Google Scholar ]
• Beversdorf D. Q. (2018). “ Stress, pharmacology, and creativity ,” in The cambridge handbook of the neuroscience of creativity , eds Jung R. E., Vartanian O. V. (Cambridge: Cambridge University Press.), 73–91. 10.1017/9781316556238.006 [ CrossRef ] [ Google Scholar ]
• Chang Y. (2019). An investigation on relationship between homework and creativity of elementary and middle school students. Master thesis. Liaoning Jinzhou: Bohai University. [ Google Scholar ]
• Chen F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Struct. Equ. Modeling 14 464–504. 10.1080/10705510701301834 [ CrossRef ] [ Google Scholar ]
• Cheung G. W., Rensvold R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Struct. Equ. Modeling 9 233–255. [ Google Scholar ]
• Clark L. A., Watson D. (1995). Constructing validity: Basic issues in objective scale development. Psychological Assessment 7 309–319. 10.1037/1040-3590.7.3.309 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cooper H., Lindsay J. J., Nye B., Greathouse S. (1998). Relationships among attitudes about homework, amount of homework assigned and completed, and student achievement. J. Educ. Psychol. 90 70–83. 10.1037//0022-0663.90.1.70 [ CrossRef ] [ Google Scholar ]
• Cooper H., Robinson J. C., Patall E. A. (2006). Does homework improve academic achievement? A synthesis of research, 1987–2003. Rev. Educ. Res. 76 1–62. [ Google Scholar ]
• Cooper H., Steenbergen-Hu S., Dent A. L. (2012). “ Homework ,” in APA educational psychology handbook, Vol.3. Application to learning and teaching , eds Harris K. R., Graham S., Urdan T. (Washington DC: American Psychological Association; ), 475–495. [ Google Scholar ]
• De Jong R., Westerhof K. J., Creemers B. P. M. (2000). Homework and student math achievement in junior high schools. Educ. Res. Eval. 6 130–157. [ Google Scholar ]
• Dettmers S., Trautwein U., Lüdtke O. (2009). The relationship between homework time and achievement is not universal: Evidence from multilevel analyses in 40 countries. Sch. Effect. Sch. Improv. 20 375–405. 10.1080/09243450902904601 [ CrossRef ] [ Google Scholar ]
• Dolean D. D., Lervag A. (2022). Variations of homework amount assigned in elementary school can impact academic achievement. J. Exp. Educ. 90 280–296. 10.1080/00220973.2020.1861422 [ CrossRef ] [ Google Scholar ]
• Epskamp S., Stuber S., Nak J., Veenman M., Jorgensen T. D. (2022). semPlot: Path diagrams and visual analysis of various sem packages’ output. R package Version 1.1.5. Availabl eonline at: https://cran.r-project.org/web/packages/semPlot/index.html (accessed July 18, 2022). [ Google Scholar ]
• Fan H., Xu J., Cai Z., He J., Fan X. (2017). Homework and students’ achievement in math and science: A 30-year meta-analysis, 1986–2015. Educ. Res. Rev. 20 35–54. 10.1016/j.edurev.2016.11.003 [ CrossRef ] [ Google Scholar ]
• Ferketich S. (1991). Focus on psychometrics. Aspects of item analysis. Res. Nurs. Health 14 165–168. 10.1002/nur.4770140211 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Fernández-Alonso R., Álvarez-Díaz M., Suárez-Álvarez J., Muñiz J. (2017). Students’ achievement and homework assignment strategies. Front. Psychol. 8 : 286 . 10.3389/fpsyg.2017.00286 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Fernández-Alonso R., Suárez-álvarez J., Javier M. (2015). Adolescents’ homework performance in mathematics and science: Personal factors and teaching practices. J. Educ. Psychol. 107 1075–1085. 10.1037/edu0000032 [ CrossRef ] [ Google Scholar ]
• Fernández-Alonso R., Woitschach P., Álvarez-Díaz M., González-López A. M., Cuesta M., Muñiz J. (2019). Homework and academic achievement in latin america: A multilevel approach. Front. Psychol. 10 : 95 . 10.3389/fpsyg.2019.00095 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Fornell C., Larcker D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 18 39–50. 10.1177/002224378101800104 [ CrossRef ] [ Google Scholar ]
• Gajda A., Karwowski M., Beghetto R. A. (2017). Creativity and academic achievement: A meta-analysis. J. Educ. Psychol. 109 269–299. 10.1037/edu0000133 [ CrossRef ] [ Google Scholar ]
• Green S. B., Yang Y. (2015). Evaluation of dimensionality in the assessment of internal consistency reliability: Coefficient alpha and omega coefficients. Educ. Meas. Issues Pract. 34 14–20. 10.1111/emip.12100 [ CrossRef ] [ Google Scholar ]
• Guilford J. P. (1950). Creativity. Am. Psychol. 5 444–454. 10.1037/h0063487 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Guo L. (2018). The compilation of homework behavior questionnaire for junior middle school students. Master thesis. Liaoning Jinzhou: Bohai University. [ Google Scholar ]
• Guo L., Fan H. (2018). Analysis and prospect of homework instruments in primary and middle schools. Educ. Sci. Res. 3 48–53. [ Google Scholar ]
• Hattie J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. London: Routledge. [ Google Scholar ]
• Hu L. T., Bentler P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct. Equ. Modeling 6 1–55. 10.1080/10705519909540118 [ CrossRef ] [ Google Scholar ]
• Jepma M., Verdonschot R. G., van Steenbergen H., Rombouts S. A. R. B., Nieuwenhuis S. (2012). Neural mechanisms underlying the induction and relief of perceptual curiosity. Front. Behav. Neurosci. 6 : 2012 . 10.3389/fnbeh.2012.00005 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kaiipob I. A. (1951). Pedagogy (Shen yingnan, Nan zhishan et al, translated into chinese). Beijing: People’s Education Press, 150–155. [ Google Scholar ]
• Kalenkoski C. M., Pabilonia S. W. (2017). Does high school homework increase academic achievement? Educ. Econ. 25 45–59. 10.1080/09645292.2016.1178213 [ CrossRef ] [ Google Scholar ]
• Kaufman J. C., Beghetto R. A. (2009). Beyond big and little: The Four-C model of creativity. Rev. Gen. Psychol. 13 1–12. 10.1037/a0013688 [ CrossRef ] [ Google Scholar ]
• Kaufman J. C., Glăveanu V. P. (2019). “ A review of creativity theories: What questions are we trying to answer? ,” in Cambridge handbook of creativity , 2nd Edn, eds Kaufman J. C., Sternberg R. J. (New York, NY: Cambridge University Press; ), 27–43. [ Google Scholar ]
• Kaufman J. C., Plucker J. A., Baer J. (2008). Essentials of creativity assessment. Hoboken, NJ: John Wiley & Sons. [ Google Scholar ]
• Karwowski M., Jankowska D. M., Brzeski A., Czerwonka M., Gajda A., Lebuda I., et al. (2020). Delving into creativity and learning. Creat. Res. J. 32 4–16. 10.1080/10400419.2020.1712165 [ CrossRef ] [ Google Scholar ]
• Kim K. H. (2011). The creativity crisis: The decrease in creative thinking scores on the torrance tests of creative thinking. Creat. Res. J. 23 285–295. 10.1080/10400419.2011.627805 [ CrossRef ] [ Google Scholar ]
• Kozbelt A., Beghetto R. A., Runco M. A. (2011). “ Theories of creativity ,” in The cambridge handbook of creativity , eds Kaufman J. C., Sternberg R. J. (New York, NY: Cambridge University Press; ), 20–47. [ Google Scholar ]
• Kupers E., van Dijk M., Lehmann-Wermser A. (2018). Creativity in the here and now: A generic, micro-developmental measure of creativity. Front. Psychol. 9 : e2095 . 10.3389/fpsyg.2018.02095 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Liu X.-L., Liu L., Qiu Y.-X., Jin Y., Zhou J. (2016). Reliability and validity of williams creativity assessment packet. J. Sch. Stud. 13 51–58. 10.3969/j.issn.1005-2232.2016.03.007 [ CrossRef ] [ Google Scholar ]
• Liu Y., Gong S., Cai X. (2013). Junior-high school students’ homework effort and its influencing factors. Adv. Psychol. Sci. 21 1422–1429. 10.3724/SP.J.1042.2013.01422 [ CrossRef ] [ Google Scholar ]
• Long H., Kerr B. A., Emler T. E., Birdnow M. (2022). A critical review of assessments of creativity in education. Rev. Res. Educ. 46 288–323. 10.3102/0091732X221084326 [ CrossRef ] [ Google Scholar ]
• Luo L., Arizmendi C., Gates K. M. (2019). Exploratory factor analysis (EFA) programs in R. Struct. Equ. Modeling 26 819–826. 10.1080/10705511 [ CrossRef ] [ Google Scholar ]
• Main K. J., Aghakhani H., Labroo A. A., Greidanus N. S. (2020). Change it up: Inactivity and repetitive activity reduce creative thinking. J. Creat. Behav. 54 395–406. 10.1002/jocb.373 [ CrossRef ] [ Google Scholar ]
• Martindale C., Anderson K., Moor K., West A. (1996). Creativity, oversensitivity and rate of habituation. Pers. Individ. Diff. 20 423–427. 10.1016/0191-8869(95)00193-X [ CrossRef ] [ Google Scholar ]
• Nie Y., Zheng X. (2005). A study on the developmental characteristics of children’s and adolescent’s creative personality. Psychol. Sci. 28 356–361. 10.16719/j.cnki.1671-6981.2005.02.024 [ CrossRef ] [ Google Scholar ]
• Niu W., Sternberg R. J. (2003). Societal and school influences on student creativity: The case of China. Psychol. Sch. 40 103–114. 10.1002/pits.10072 [ CrossRef ] [ Google Scholar ]
• Núñez J. C., Suárez N., Cerezo R., González-Pienda J., Valle A. (2013). Homework and academic achievement across Spanish Compulsory Education. Educ. Psychol. 35 1–21. 10.1080/01443410 [ CrossRef ] [ Google Scholar ]
• Nunnally J. C., Bernstein I. H. (1994). Psychometric theory , 3rd Edn. New York, NY: McGraw-Hill. [ Google Scholar ]
• OECD (2014). Does homework perpetuate inequities in education? Pisa in Focus, No. 46. Paris: OECD Publishing, 10.1787/5jxrhqhtx2xt-en [ CrossRef ] [ Google Scholar ]
• Pang W., Plucker J. A. (2012). Recent transformations in China’s economic, social, and education policies for promoting innovation and creativity. J. Creat. Behav. 46 247–273. 10.1002/jocb.17 [ CrossRef ] [ Google Scholar ]
• Pendergast L. L., Watkins M. W., Canivez G. L. (2014). Structural and convergent validity of the homework performance questionnaire. Educ. Psychol. 34 291–304. 10.1080/01443410.2013.785058 [ CrossRef ] [ Google Scholar ]
• Pelletier R. (2005). The predictive power of homework assignments on student achievement in grade three (Order No. 3169466). Available from proquest dissertations & theses global. (305350863). Available online at: http://search.proquest.com/docview/305350863?accountid¼12206 [ Google Scholar ]
• Peterson R., Kim Y. (2013). On the relationship between coefficient alpha and composite reliability. J. Appl. Psychol. 98 194–198. 10.1037/a0030767 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Plucker J. A., Makel M. C., Qian M. (2019). “ Chapter3: assessment of creativity ,” in The cambridge handbook of creativity , 2nd Edn, eds Kaufman J. C., Sternberg R. J. (Cambridge University Press: New York, NY; ), 44–68. [ Google Scholar ]
• Podsakoff P. M., Mac Kenzie S. B., Podsakoff N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annu. Rev. Psychol. 63 539–569. 10.1146/annurev-psych-120710-100452 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Power T. J., Dombrowski S. C., Watkins M. W., Mautone J. A., Eagle J. W. (2007). Assessing children’s homework performance: Development of multi-dimensional, multi-informant rating scales. J. Sch. Psychol. 45 333–348. 10.1016/j.jsp.2007.02.002 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Putnick D. L., Bornstein M. H. (2016). Measurement invariance conventions and reporting: The state of the art and future directions for psychological research. Dev. Rev. 41 71–90. 10.1016/j.dr.2016.06.004 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Qian A. (2006). Research on the creative thought ability training in the language teaching material work system. Ph.D. thesis. Jiangsu Nanjing: Nanjing Normal University. [ Google Scholar ]
• R Core Team (2019). R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. [ Google Scholar ]
• Revelle W. (2022). Psych: Procedures for psychological, psychometric, and personality research. Evanston, IL: Northwestern University. [ Google Scholar ]
• Rosário P., Núñez J., Vallejo G., Cunha J., Nunes T., Mourão R., et al. (2015). Does homework design matter? The role of homework’s purpose in student mathematics achievement. Contemp. Educ. Psychol. 43 10–24. 10.1016/j.cedpsych.2015.08.001 [ CrossRef ] [ Google Scholar ]
• Rosseel Y. (2012). Lavaan: An R package for structural equation modeling. J. Stat. Softw. 48 : 97589 . 10.3389/fpsyg.2014.01521 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Said-Metwaly S., Fernández-Castilla B., Kyndt E., Van den Noortgate W., Barbot B. (2021). Does the fourth-grade slump in creativity actually exist? A meta-analysis of the development of divergent thinking in school-age children and adolescents. Educ. Psychol. Rev. 33 275–298. 10.1007/s10648-020-09547-9 [ CrossRef ] [ Google Scholar ]
• Smith J. K., Smith L. F. (2010). “ Educational creativity ,” in The cambridge handbook of creativity , eds Kaufman J. C., Sternberg R. J. (New York, NY: Cambridge University Press; ), 250–264. [ Google Scholar ]
• Soh K.-C. (2000). Indexing creativity fostering teacher behavior: A preliminary validation study. J. Creat. Behav. 34 118–134. 10.1002/j.2162-6057.2000.tb01205.x [ CrossRef ] [ Google Scholar ]
• Sternberg R. J. (2019). Measuring creativity: A 40+ year retrospective. J. Creat. Behav. 53 600–604. 10.1002/jocb.218 [ CrossRef ] [ Google Scholar ]
• Sternberg R. J., Karami S. (2022). An 8P theoretical framework for understanding creativity and theories of creativity. J. Creat. Behav. 56 55–78. 10.1002/jocb.516 [ CrossRef ] [ Google Scholar ]
• Sun M., Du J., Xu J. (2021). Are homework purposes and student achievement reciprocally related? A longitudinal study. Curr. Psychol. 40 4945–4956. 10.1007/s12144-019-00447-y [ CrossRef ] [ Google Scholar ]
• Sun L., Shafiq M. N., McClure M., Guo S. (2020). Are there educational and psychological benefits from private supplementary tutoring in Mainland China? Evidence from the China Education Panel Survey, 2013–15. Int. J. Educ. Dev. 72 : 102144 . [ Google Scholar ]
• Swank A. L. G. (1999). The effect of weekly math homework on fourth grade student math performance. Master of arts action research project. Knoxville, TN: Johnson Bible College. [ Google Scholar ]
• Tas Y., Sungur S., Oztekin C. (2016). Development and validation of science homework scale for middle-school students. Int. J. Sci. Math. Educ. 14 417–444. 10.1007/s10763-014-9582-5 [ CrossRef ] [ Google Scholar ]
• Trautwein U., Lüdtke O. (2007). Students’ self-reported effort and time on homework in six school subjects: Between-student differences and within-student variation. J. Educ. Psychol. 99 432–444. 10.1037/0022-0663.99.2.432 [ CrossRef ] [ Google Scholar ]
• Trautwein U., Lüdtke O. (2009). Predicting homework motivation and homework effort in six school subjects: The role of person and family characteristics, classroom factors, and school track. Learn. Instr. 19 243–258. 10.1016/j.learninstruc.2008.05.001 [ CrossRef ] [ Google Scholar ]
• Valle A., Piñeiro I., Rodríguez S., Regueiro B., Freire C., Rosário P. (2019). Time spent and time management in homework in elementary school students: A person-centered approach. Psicothema 31 422–428. 10.7334/psicothema2019.191 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wang M., Lin X. (1986). Research on the revised williams creative aptitude test. Bull. Spec. Educ. 2 231–250. [ Google Scholar ]
• Welch W. W., Walberg H. J., Fraser B. J. (1986). Predicting elementary science learning using national assessment data. J. Res. Sci. Teach. 23 699–706. 10.1002/tea.3660230805 [ CrossRef ] [ Google Scholar ]
• Williams F. E. (1979). Assessing creativity across Williams “CUBE” model. Gifted Child Q. 23 748–756. 10.1177/001698627902300406 [ CrossRef ] [ Google Scholar ]
• Wilson J. L. (2010). The impact of teacher assigned but not graded compared to teacher assigned and graded chemistry homework on the formative and summative chemistry assessment scores of 11th-grade students with varying chemistry potential (Order No. 3423989). Available from proquest dissertations & theses global. (759967221). Available online at: https://www.proquest.com/docview/759967221 (accessed July 18, 2022). [ Google Scholar ]
• Xu J. (2006). Gender and homework management reported by high school students. Educ. Psychol. 26 73–91. 10.1080/01443410500341023 [ CrossRef ] [ Google Scholar ]
• Xu J. (2008). Validation of scores on the homework management scale for high school students. Educ. psychol. Meas. 68 304–324. 10.1177/0013164407301531 [ CrossRef ] [ Google Scholar ]
• Xu J. (2010). Homework purpose scale for high school students: A validation study. Educ. Psychol. Meas. 70 459–476. 10.1177/0013164409344517 [ CrossRef ] [ Google Scholar ]
• Xu J. (2017). Homework expectancy value scale for high school students: Measurement invariance and latent mean differences across gender and grade level. Learn. Individ. Diff. 60 10–17. 10.1016/j.lindif.2017.10.003 [ CrossRef ] [ Google Scholar ]
• Xu J. (2018). Reciprocal effects of homework self-concept, interest, effort, and math achievement. Contemp. Educ. Psychol. 55 42–52. 10.1016/j.cedpsych.2018.09.002 [ CrossRef ] [ Google Scholar ]
• Xu J. (2020). Longitudinal effects of homework expectancy, value, effort, and achievement: An empirical investigation. Int. J. Educ. Res. 99 : 101507 . 10.1016/j.ijer.2019.101507 [ CrossRef ] [ Google Scholar ]
• Xu J. (2021). Math homework purpose scale: Confirming the factor structure with high school students. Psychology in the Schools 58 1518–1530. 10.1002/pits.22507 [ CrossRef ] [ Google Scholar ]
• Xu J., Corno L. (2003). Family help and homework management reported by middle school students. Elem. Sch. J. 103 503–518. 10.1086/499737 [ CrossRef ] [ Google Scholar ]
• Xu J., Du J., Cunha J., Rosrio P. (2021). Student perceptions of homework quality, autonomy support, effort, and math achievement: Testing models of reciprocal effects. Teach. Teach. Educ. 108 : 103508 . 10.1016/j.tate.2021.103508 [ CrossRef ] [ Google Scholar ]
• Xu J., Du J., Liu F., Huang B. (2019). Emotion regulation, homework completion, and math achievement: Testing models of reciprocal effects. Contemp. Educ. Psychol. 59 : 101810 . 10.1016/j.cedpsych.2019.101810 [ CrossRef ] [ Google Scholar ]
• Xu J., Núñez J., Cunha J., Rosário P. (2020). Validation of the online homework distraction scale. Psicothema 32 469–475. 10.7334/psicothema2020.60 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Xu J., Yuan R., Xu B., Xu M. (2014). Modeling students’ managing time in math homework. Learn. Individ. Differences 34 33–42. 10.1016/j.lindif.2014.05.011 [ CrossRef ] [ Google Scholar ]
• Yang F., Tu M. (2020). Self-regulation of homework behavior: Relating grade, gender, and achievement to homework management. Educ. Psychol. 40 392–408. 10.1080/01443410.2019.1674784 [ CrossRef ] [ Google Scholar ]
• Yang F., Xu J. (2017). Homework expectancy value scale: Measurement invariance and latent mean differences across gender. J. Psychoeduc. Assess. 36 863–868. 10.1177/0734282917714905 [ CrossRef ] [ Google Scholar ]
• Zhai J., Fan H. (2021). “ The changes in primary and middle school students’ homework time in china: A cross-temporal meta-analysis ,” in Paper presented at the meeting of the 23rd national academic conference of psychology , Huhhot. [ Google Scholar ]
• Zheng Y. (2013). Problems and causes of China’s education. Beijing: China CITIC Press, 125. [ Google Scholar ]

Duke Study: Homework Helps Students Succeed in School, As Long as There Isn't Too Much

The study, led by professor Harris Cooper, also shows that the positive correlation is much stronger for secondary students than elementary students

• Share this story on facebook
• Share this story on twitter
• Share this story on reddit
• Share this story on linkedin
• Get this story's permalink
• Print this story

It turns out that parents are right to nag: To succeed in school, kids should do their homework.

Duke University researchers have reviewed more than 60 research studies on homework between 1987 and 2003 and concluded that homework does have a positive effect on student achievement.

Harris Cooper, a professor of psychology, said the research synthesis that he led showed the positive correlation was much stronger for secondary students --- those in grades 7 through 12 --- than those in elementary school.

READ MORE: Harris Cooper offers tips for teaching children in the next school year in this USA Today op-ed published Monday.

"With only rare exception, the relationship between the amount of homework students do and their achievement outcomes was found to be positive and statistically significant," the researchers report in a paper that appears in the spring 2006 edition of "Review of Educational Research."

Cooper is the lead author; Jorgianne Civey Robinson, a Ph.D. student in psychology, and Erika Patall, a graduate student in psychology, are co-authors. The research was supported by a grant from the U.S. Department of Education.

While it's clear that homework is a critical part of the learning process, Cooper said the analysis also showed that too much homework can be counter-productive for students at all levels.

"Even for high school students, overloading them with homework is not associated with higher grades," Cooper said.

Cooper said the research is consistent with the "10-minute rule" suggesting the optimum amount of homework that teachers ought to assign. The "10-minute rule," Cooper said, is a commonly accepted practice in which teachers add 10 minutes of homework as students progress one grade. In other words, a fourth-grader would be assigned 40 minutes of homework a night, while a high school senior would be assigned about two hours. For upper high school students, after about two hours' worth, more homework was not associated with higher achievement.

The authors suggest a number of reasons why older students benefit more from homework than younger students. First, the authors note, younger children are less able than older children to tune out distractions in their environment. Younger children also have less effective study habits.

But the reason also could have to do with why elementary teachers assign homework. Perhaps it is used more often to help young students develop better time management and study skills, not to immediately affect their achievement in particular subject areas.

"Kids burn out," Cooper said. "The bottom line really is all kids should be doing homework, but the amount and type should vary according to their developmental level and home circumstances. Homework for young students should be short, lead to success without much struggle, occasionally involve parents and, when possible, use out-of-school activities that kids enjoy, such as their sports teams or high-interest reading."

Cooper pointed out that there are limitations to current research on homework. For instance, little research has been done to assess whether a student's race, socioeconomic status or ability level affects the importance of homework in his or her achievement.

This is Cooper's second synthesis of homework research. His first was published in 1989 and covered nearly 120 studies in the 20 years before 1987. Cooper's recent paper reconfirms many of the findings from the earlier study.

Cooper is the author of "The Battle over Homework: Common Ground for Administrators, Teachers, and Parents" (Corwin Press, 2001).

Link to this page

Copy and paste the URL below to share this page.

• Corpus ID: 146553654

The relationship between homework and academic achievement

• David Córdoba
• Published 1 August 2013

2 Citations

Middle school students' perceptions regarding the motivation and effectiveness of homework., phenomenological study of middle school teacher practices regarding homework in an eastern north carolina rural community, 95 references, homework practices and academic achievement: the mediating role of self-efficacy and perceived responsibility beliefs, classwork and homework in early adolescence: the ecology of achievement.

• Highly Influential

The Relationship Between Homework and Achievement—Still Much of a Mystery

Looking at homework differently, meanings of homework and implications for practice, the forgotten voices in homework: views of students, does homework improve academic achievement a synthesis of research, 1987–2003, homework and attainment in primary schools, parental involvement, homework, and tv time: direct and indirect effects on high school achievement, homework as the job of childhood, related papers.

Showing 1 through 3 of 0 Related Papers

SiOWfa15: Science in Our World: Certainty and Controversy

The course website and blog for the fall 2015 instance of penn state's sc200 course.

Does Homework Promote Academic Achievement?

We all hate homework. It’s tedious, frustrating, time-consuming, and downright horrible. Sometimes we get points for doing homework and doing well which is always a good reason for getting it done, but could success on homework be the reason for fantastic final grades?

Let’s establish the basics of what we are trying to find here. The x-variable is doing your homework while the y-variable is earning excellent grades. Confounding, z-variables , could include personality traits, lack of procrastination habits, natural ability to succeed in school, etc. Our null hypothesis  is that doing your homework does not improve your final grade . Our alternative hypothesis is that doing your homework does improve your final grade and promotes academic achievement.

Harris Cooper , a professor of psychology and neuroscience at Duke University, and his colleagues compiled an analysis of dozens of studies done on homework in order to come to a conclusion on whether homework is effective. If it is effective, how much homework is too much, and what is the appropriate amount to give out to students?

Many of the studies done on this question examine students who are assigned homework with students who are not assigned homework but are still similar in other ways. Interestingly, many of the results found that homework can improve test scores at the end of a topic. “Students assigned homework in 2nd grade did better on math, 3rd and 4th graders did better on English skills and vocabulary, 5th graders on social studies, 9th through 12th graders on American history, and 12th graders on Shakespeare.” ( Cooper )

Some studies do not attempt to control for student differences. 35 studies suggest that 77% find the correlation between homework and and academic achievement to be positive; however, they fail to make this correlation among elementary students. One possible solution to control for student differences would be to randomly distribute the students based on similarities so that on average, both the homework group and the non-homework group are about the same in terms of similarities, i.e. learning disabilities, gender, and prior achievement in school. Additionally, Cooper says an explanation for why there is not a correlation among elementary students could be because they do not have well developed study habits and because they get distracted easily.

In short, Cooper suggests that through his analysis, homework is in fact beneficial to students . Not only can it have positive effects on overall grades, but it can also have other benefits such as developing responsible character traits, maturing cognitive capacities, fostering independent learning habits, and growing of good study habits. Cooper, along with most educators, says homework should not exceed 10-20 minutes for children K-2, 30-60 minutes a day for grades 3-6, and varying times depending on the subjects for middle school and high school students.

Some feel that homework can have many negative effects such as developing a disinterest in school among students, homework denies children of leisure time and takes them away from extra-curricular activities which also teach important life skills. It is important to allow teachers and administrators to have flexibility to account for the differences in some students and their families; however, sticking to the prescribed regiment is most effective for most students.

Rival ACC school, the University of Virginia, has a much different take on homework than Cooper. Co-authors Adam Maltese, assistant professor of science education at Indiana University, Robert H. Tai, associate professor of science education at the University of Virginia’s Curry School of Education, and Xitao Fan, dean of education at the University of Macau, conducted their own studies and published “ When Is Homework Worth the Time ?”

Because the paper is twenty-two pages long, I will summarize the findings. If you would like to, the full report can be read here . 18,000, tenth grade students’s survey and transcript data were observed in the study collected from 1990 to 2002 by the National Center for Education Statistics . Unlike many studies done on homework and final grades, Maltese, Tai, and Fan found that time spent on homework did not effect the final course grade among those who did and did not do their homework. Conversely, they did find a correlation between time spent on homework and success on standardized test scores. Maltese says, “Our results hint that maybe homework is not being used as well as it could be.” In order to be more effective with homework, teachers should assign homework which is useful, sort of a quality over quantity  type of thing. Rather than give a designated amount of homework, give assignments which will keep the students engaged for a short period of time and allow for a greater chance of retaining that information. In effect, this will also allow for appropriate amounts of time to be allocated towards extracurricular activities which teach young people other valuable lessons while also learning from engaging homework.

All of this raises the question: what is the most effective type of homework assignment? I certainly feel as though this question can best be answered based on each individual person. Because some people are inherently auditory, visual, or hands-on learners, one standard type of homework cannot be called the best . I believe in order to really get the best result from everyone, each person would require their own homework regiment. Seeing as though some schools have entire graduating classes of well-over 2,000 students , creating an individualized homework regiment for each student is simply impossible. So what basic principles should teachers and administrators use to create effect homework?

The Association for Supervision and Curriculum Development ( ASCD ) attempted to tackle this tricky question with their “ Five Hallmarks of Good Homework .” The first principle is purpose . Students must be given a clear end goal to their assignment such as giving simple division problems in order to understand the concept of division or writing sentences using certain vocabulary words so that students can understand the context of those specific vocabulary words. In addition, ASCD says practice is most effective when given in small doses over long periods of time, concurrent with Maltese, Tai, and Fan. The second principle is efficiency . ASCD says projects which involve cutting, gluing, and constructing are often extremely inefficient even though the teacher has great intentions when they assign them because they are fun and creative. Instead, rather than making a poster, students should be tasked to put themselves in the perspective of their project. For example, ASCD suggests if students are tasked with a history assignment, they should be asked to create a diary entry as if they were the person who experienced what they are trying to learn (writing about what it was like to immigrate from another country, writing about what World War 2 was like, etc.). The third principle is ownership . One of the easiest ways to promote ownership is by giving flexibility. Instead of prescribing a common book for the class to read, teachers could allow students to find their own sources such as magazines and academic journals which are still relevant to the topic. This keeps the students engaged and interested in what they are learning. “Instead of worrying about whether students did the reading, we should be focusing on whether the reading did them any good” ( ASCD ). The fourth principle is competence . Because, each student is different, they should be allowed to work together if they choose to and receive help on assignments. Students often get discouraged when forced to work alone and are more likely not to complete a task. The fifth, and final, principle is aesthetic appeal . First impressions are extremely important to students. As soon as they see the requirements and details of an assignment, they make a snap decision about whether they are going to do it or not and, if they are going to do it, how well they are going to do it. Students are more inclined to complete an assignment which are visually uncluttered with few information on the page. Lots of room to write answers and the use of graphics and clip art on the page are also quite appealing to students. Visuals are just as important to the student as knowing they have little work to do.

Take home message: homework is beneficial to the student in more ways than just improving final grades but only when allocated effectively . In my opinion, and I think most would agree, there need to be more studies done on the effectiveness of homework. Preferably, some kind of experimental study would be conducted to almost definitively prove that effective homework benefits the student in multiple ways. Of course, a double-blind placebo would be out of the question because the student would know if they are doing their homework or not. Maybe a single-blind study could be effective where the students are randomly placed into two groups, homework and no homework. The teacher would not know who is and who is not doing their homework, but would still assign regular assignments to the class. The students either do or do not complete their homework, and at the end of the semester or grading period, examine the results of how many students received good or bad marks on their final reports. Of course, this study would flawed in that if a student gets placed into the group who does not do their homework but normally would have done their homework and their grade suffers from not doing it, that is infringing on the student’s ability and right to learn, and compromises their own responsibility for their grades; however, at this point, this is the closest I could get to an appropriate experimental study. Any other suggestions would be greatly appreciated in the comments.

Picture Links:

http://cdn.kidspot.com.au/wp-content/uploads/2013/02/hateshomework-600×420.jpg

http://familyfieldguide.com/wp-content/uploads/2013/08/boy-doing-homework.jpg

http://www.primaryteaching.co.uk/prodimg/B4_1_Zoom.jpg

One thought on “ Does Homework Promote Academic Achievement? ”

Very interesting post. In high school, I was adamantly anti-homework, generally equating homework with meaningless busywork, but your post got me thinking, and I came up with an idea for a study that sort of expands on your idea. Here’s what I’ve got: a class made specially for the study is split into two groups via random assignment, and one group is assigned homework while the other is told not to do the homework. The study will include a random sample of students of the same grade level or year, and everyone will come to class as required and will be encouraged to be active in the classroom and really pay attention to what is being taught (the teacher will find a way to get around to this somehow). Besides the homework, the only real assignments given are in-class quizzes and a final at the end of the semester, which is when the grades of those who did and didn’t do the homework will be compared. The homework, of course, will be GOOD homework, as determined by the five hallmarks you went over in your post. Also, this class will institute a NO-STUDY policy. That’s important. It will be physically impossible to study for the tests anyway, because there is nothing that students can read or study from at home — no handouts, nothing. (The entire curriculum may as well be completely fabricated.) This study is far from a perfect setup and I’m sure it contains some major flaws in reasoning, the most obvious of which is the question of the students’ drive and motivation to actually try on the homework in the first place (since this won’t be a class that they’re technically graded on, so it may not be a true measure of their aptitude and ability… but that’s still better than the alternative). Anyhow, I could see it turning up some interesting results. Given that the homework demonstrates the strongest possible examples of the five hallmarks of good homework, and the students assigned homework put forth their best effort on the homework assignments, I think that the homework-assigned group could receive better overall grades than the no-homework group.

Comments are closed.

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

Download Free PDF

The Impact of Homework Time on Academic Achievement

This study takes advantage of nationally representative panel data on student behavior and academic performance to test two possible policy reforms. First, I examine a policy that increases the amount of homework that students complete. Second, I examine the impact of increasing the amount of homework assigned. Previous studies have not been able to consistently estimate the impact of homework because of important omitted variables and measurement error, which strongly bias the estimated impact of homework time. This paper, however, uses an instrumental variables approach with student fixed effects to account for both time-varying and time-invariant unobserved characteristics and inputs. This approach produces estimates of the impact of homework time on academic achievement that are much larger than those of previous studies. Additionally, these findings suggest that assigning additional homework primarily improves the achievement of low performing students and students in low performing schools. Thus, assigning more homework could help close the gap in achievement between high and low performing students.

Free related PDFs Related papers

In the literature on the impact of homework there is little empirical support for assigning homework to elementary school students. Nevertheless, the practice has become more common, despite popular resistance among many parents and popular media. We examine the effects of both assigning homework and time spent on homework on mathematics and reading achievement using nationally representative longitudinal data on elementary school students. In order to control for important unobserved characteristics and inputs we use empirical specifications that include student fixed effects. We find that this approach consistently indicates that homework has a positive impact on academic achievement, and that less sophisticated empirical approaches will produce misleading results. Additionally, we find that the impact of homework is not uniform across the population, but that some minority groups and low income students get more benefit from homework, indicating that increasing homework assigned could be a valuable policy for decreasing the black-white as well as the high and low-income achievement gap.

Journal of Labor Research, 2011

School Psychology Quarterly, 2004

Children’s Homework Time—Do Parents ’ Investments Make a Difference? This article describes the homework time of 2024 children in school grades 1 through 12, using time diary data from a national dataset, the Panel Study of Income Dynamics-Child Development Supplement (PSID-CDS). As part of the PSID-CDS, time diary data were collected for one randomly selected weekday and weekend day. Data were analyzed with an investment model perspective, where parental time, money, and human capital were expected to influence children’s and adolescents ’ homework time. About 2/3 of children did any homework. Logistic regression revealed that ethnicity was the primary predictor of whether or not children in elementary school and junior high school did any homework, although some investment model variables, in particular time (number of children in household) was significant for elementary school children, and money (family income) was influential for junior high school students. All three investme...

Education and Urban Society, 2015

As schools aim to raise student academic achievement levels and districts wrangle with decreased funding, it is essential to understand the relationship between learning time and academic achievement. Using regression analysis and a data set drawn from California’s elementary school sites, we find a statistically significant and positive relationship between the number of instructional minutes in an academic year and school-site standardized test scores. Fifteen more minutes of school a day at a school site (or about an additional week of classes over an academic year) relates to an increase in average overall academic achievement of about 1%, and about a 1.5% increase in average achievement for disadvantaged students. This same increase in learning time yields the much larger 37% gain in the average growth of socioeconomically disadvantage achievement from the previous academic year. Placing this impact in the context of other influences found important to academic achievement, similar increases in achievement only occur with an increase of fully credentialed teachers by nearly 7 percentage points. These findings offer guidance regarding the use of extended learning time to increase academic performance. Moreover, they suggest caution in reducing instructional time as the default approach to managing fiscal challenges.

Journal of Educational Psychology, 1986

The main purpose of this study was to determine the effect of homework assignments on students' academic achievement. This meta-analysis sought an answer to the research question: "What kind of effect does homework assignment have on students' academic achievement levels?" In this research, meta-analysis was adopted to determine the effect of homework assignments on students' academic achievement. The effect sizes of the studies included in the meta-analysis were compared with regard to their methodological characteristics (research design, sample size, and publication bias) and substantive characteristics (course type, grade level, duration of implementation, instructional level, socioeconomic status, and setting). At the end of the research, it was revealed that homework assignments had a small effect size (d = 0.229) on students' academic achievement levels. Lastly, it was seen that there was not a significant difference with regard to the effect sizes of the studies with respect to all variables, except the course type variable in the research.

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

Theory Into Practice, 2004

Economics of Education Review, 1987

PROSPECTS, 2004

Frontiers in Psychology, 2019

Social Psychology of Education, 1999

Contemporary Educational Psychology, 2002

International Journal of Behavioral Development, 2013

Http Dx Doi Org 10 1080 09645292 2011 585794, 2011

Frontiers in Psychology, 2017

Educational Psychology, 2014

Psicología Educativa: Revista de los Psicólogos de la Educación, 2021

Related topics

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

• Open access
• Published: 27 September 2024

Inhibitory control and academic achievement – a study of the relationship between Stroop Effect and university students’ academic performance

• Martin Dvorak 1  

BMC Psychology volume  12 , Article number:  498 ( 2024 ) Cite this article

Metrics details

While previous research has identified executive functions as predictors of academic performance in school children, similar studies conducted among adults show mixed results. One of the reasons given for executive functions having a limited effect on academic achievements in adulthood is that they are usually fully developed by that time. Since these executive functions are at their peak at that age, the individual differences in these as well as their influence on academic performance in adults are harder to trace. The paper describes a study conducted among 107 university students the goal of which was to find out whether there is any relationship between the adult students’ inhibitory control values measured with the Stroop Test and their academic achievements. Although the results indicate a weak correlation between the Stroop Effect and the students’ academic performance of low statistical significance, which seems to confirm the outcomes of the previous studies focusing on adults, the study reveals an unexpected statistically significant correlation between the students’ grade averages and the number of their incorrect color identifications. This phenomenon appears to be worth pursuing in future research since it suggests the existence of another, relatively quickly measurable, variable possibly reflecting other predictors of academic performance in adults such as a degree of their manifested conscientiousness, their ability to concentrate on an assigned, relatively short, one-off task and their attitude to fulfilling this task. The Stroop Test, despite not being originally designed for this purpose, might thus be used as a simple tool suitable for providing information about these variables via the subject’s number of color identification errors. Such information can subsequently inform the activities that educators may include in their curricula to foster conscientiousness and concentration in the students lacking these.

Peer Review reports

Introduction

As documented by previous research, academic performance as the “level of knowledge demonstrated in an area or subject compared to the norm for the particular age and level of education” [ 46 ] has been affected by a myriad of factors. These are socioeconomic [ 66 ], student-related (e.g. students’ self-control and class attendance) [ 25 , 29 ], or psychosocial [ 60 ]. Another factor documented as affecting academic performance is a complex of executive functions which appear to play a significant role in language development as well as the processing and organization of received information [ 57 ]. The processing of received information is done either through automatic attention or controlled attention. In the case of the former, the attention responses direct attention automatically to a target regardless of concurrent inputs or memory load. In the case of the latter, an active attention of the subject is required, which also makes the information processing limited in terms of processing capacity [ 63 ].

Executive functions encompass cognitive skills related to attention control, i.e. the process by which attention is selectively directed to specific aspects of a representation, particularly in misleading situations [ 11 ]. One of the attention control mechanisms can be switching attention between tasks where, in the case of a card-sort test, for instance, the subject must switch between different rules by which they sort cards (e.g. first by their shape and then by their color). Another mechanism is the inhibition of attention when it comes to the stimuli that need to be ignored. This inhibition is activated in, for instance, multilinguals when they need to suppress their temptation to use one (or more) of their languages not needed or inappropriate for a given situation [ 11 , 13 , 77 , etc.]. Other researchers [ 2 , 53 , etc.] work with the terms of inhibition (the ability to suppress dominant responses, a term synonymous to the attention inhibition mentioned above), shifting (the ability to switch over between tasks, a term synonymous to switching attention mentioned above) and monitoring (the ability to update information in the working memory). The working memory as a function that enables individuals to temporarily remember information while competitively processing information [ 54 ] has been mentioned as a factor influencing school performance even more than intelligence with the latter predicting a wide range of indicators of academic success [ 48 , 52 ].

Effect of executive functions on academic performance

Multiple studies emphasize the fact that educational research should pay more attention to executive functions since these represent essential ingredients for successful academic functioning [ 75 ] and since they also appear to be connected with school dysfunction as deficits in them have been associated with disabilities in mathematics and reading [ 51 , 70 ]. According to Pascual [ 57 ], who also mentions cognitive flexibility, i.e. the ability to temporarily manipulate information, and planning, the executive functions represent “distinct, but related, higher-order neurocognitive processes that control thought and behaviors aimed at achieving an objective goal” (p. 2). The existence of relationship between executive functions and academic achievement is also supported by other studies most of which investigate this relationship in children of pre-school or early-school age (e.g. [ 4 , 9 , 19 , 28 , 76 , etc.]) or those that study it in the context of learning disabilities [ 3 , 37 , 49 , 64 ].

Some studies also stress the fact that the positive contribution of executive functions to academic performance is domain-dependent, i.e. that certain executive functions contribute to gaining certain knowledge or skill more than others. Thus, inhibition, for instance, appears to be beneficial when it comes to mathematics and science [ 57 ]. Similarly, Gerst [ 38 ] found a direct relationship between inhibition and the ability to conduct mathematical calculations in children aged 10–11.

As there seems to be a variation in the way in which younger and older children solve calculations due to the age-related shift from the procedural-based processing in arithmetic tasks to more memory-based [ 72 , 7 , 16 , etc.] it is also different types of interference that appear to disrupt children at different ages. In a dual-task study McKenzie et al. [ 50 ], for instance, found out that the mathematical processing of 6-year-old children was disrupted only by a visuo-spatial passive interference task whereas in the 8-year-old ones it was disrupted by both a visuo-spatial and a phonological interference task. In this respect, the type of information processing deployed in problem solving appears to determine the type of irrelevant stimuli that need to be suppressed through inhibition for the students to complete an arithmetic task efficiently.

The executive function of inhibition is usually defined as the ability to suppress dominant but irrelevant responses and prioritize important information instead. This way “it moderates behavior, suppresses impulsive reactions to a stimulus, and enables an appropriate and thoughtful response” [ 57 ]. Cognitive inhibition is thus responsible for planning, analyzing and choosing the most appropriate response.

Negative effects of a low degree of inhibitory control on academic performance

The relationship between poor academic performance and poor performance in tasks requiring the inhibition of irrelevant information has been pointed out by multiple studies. Espy et al. [ 33 ], investigating how working memory and inhibitory control affect arithmetic competency, identified differences in the ability to inhibit irrelevant stimuli as a factor responsible for unique variance in mathematical skills. In mathematics, inhibitory control is used to inhibit information that should not be maintained in working memory for upcoming responding [ 27 ]. Similarly, Espy et al. point out that to flexibly shift responding in the face of conflicting rules requires maintaining the rule in mind and inhibiting prepotent, previous responses [ 33 ]. Passolunghi & Lanfranchi [ 55 ] mention inhibition as a factor influencing performance at the numerical competence test.

The importance of the role that inhibition plays in reading and listening comprehension has been pointed out by studies focusing on children. Passolunghi et al. [ 56 ], for instance, stress that groups of poor problem solvers tend to perform poorly in a working memory test requiring inhibition of irrelevant information and that this condition appears to be related to poor recall of critical information and greater recall of to-be-inhibited information. In addition, the process of reading often involves exposure to visual distractions such as images, graphs, etc. present in the very text as well as external physical distractors in the environment in which the reading takes place. In such situations, the inhibitory control helps the reader to stay focused on the written content. Similarly, De Beni et al. [ 26 ], showed that the “poor comprehenders” had a significantly lower performance in the listening span test associated with a higher number of intrusions. These intrusions can be background noise or competing sounds that need to be ignored for the listener to focus on and understand what a speaker is saying. In addition, both reading and listening often involve interpretation of figurative language, where the inhibition of the literal, irrelevant information enables the reader or listener to grasp the relevant meaning [ 39 ].

Inhibitory control as a facilitator of learning

The positive correlation between a degree of inhibitory control and academic achievement has been documented by other studies as well. Duckworth et al. [ 29 ], for instance, stress behavioral inhibition (self-control) as affecting academic performance. St Clair-Thompson & Gathercole [ 62 ] identify inhibition as a factor associated with achievement in English, mathematics, and science in 11- and 12-year-old children while Blair & Razza [ 14 ] point out that the inhibitory control correlated with both early math and reading ability in their study conducted among 3- to 5-year-old children. Privitera et al. [ 59 ] give a reason for why the improved inhibitory control leads to greater academic performance; the students with improved inhibitory control can focus on tasks both within and outside of the classroom better, ignoring the ever-growing number of distractions present in their environments. The authors also claim that this improved focus may result in superior academic performance. Irvan & Tsapali [ 44 ] point out the positive effect of improved inhibitory control on academic performance stating that the inhibition as an executive function appears particularly crucial for young children growing up and learning as they are exposed to constant distractions vying for their attention. Blair & Razza [ 14 ] also suggest that curricula designed to improve self-regulation skills and enhance early academic abilities may be most effective in helping children succeed in school.

Age-dependent effect of executive functions on academic performance

On the other hand, some sources conclude that the relationship between executive functions, with inhibitory control representing one of them, and academic performance appears to depend on age. Bryce et al. [ 15 ] focused on the relationship between executive functions and metacognitive skills, which they have identified as most significant predictors of educational achievements in their study groups of 5- and 7-year-old children. Their results indicate that executive functions appear to be more related to metacognitive skills in 5-year-olds than in 7-year-olds. In the study conducted among subjects aged 5–17, Best et al. [ 10 ] analyzed a varying correlation between executive functions and academic achievement in relation to age concluding that the correlation is strongest at the ages of 6, 8–9 and subsequently appears to be of somewhat consistent strength in the late childhood and adolescence. This conclusion partly contradicts the findings of Altemeier et al. [ 1 ], who claim that the effect of executive functions on academic performance may be more evident earlier in schooling, when academic skills are less automatic and require more effortful planning to execute. Similarly, some other studies point out inhibition as the strongest predictor of academic success at children’s early age such as that by Senn et al. [ 61 ]. They found out that while working memory contributed to academic success to a greater extent in older children, the inhibitory control did this in younger ones. Other authors [ 8 , 42 , etc.] mention the complete maturation of inhibitory processes by around the age of 12. The decrease in the potential of executive functions to predict academic performance during secondary education and even more so during university studies has also been touched upon by Pascual & Robres [ 57 ].

Methods used to measure the inhibitory control and the role of anterior cingulate

Scientists have developed several methods to measure inhibitory control, whose choice partly depends on the type of inhibition that is being targeted. Response inhibition, a term referring to the process of countermanding a prepotent motor response, has generally been assessed using non-selective stopping tasks such as the stop signal, go/no-go, and anti-saccade tasks. These tests require participants to intermittently suppress a motor response to a given presentation of a conditional stimulus or cue [ 6 , 20 , 71 , 73 ]. Attentional inhibition, which refers to the ability to resist interference from stimuli in the external environment, has been investigated using visual matching tasks requiring participants to judge whether target and comparison stimuli are the same or different and, at the same time, requiring them to ignore task-irrelevant distracters [ 36 , 68 , 71 ].

Response inhibition and attentional inhibition are also commonly measured with Stimulus-Response Compatibility tasks, such as the Eriksen Flanker (Flanker), Simon, and Stroop tasks [ 32 , 65 , 69 , 71 ]. The Stroop task (test) is utilized for comparing reaction times to stimuli in the condition where this control is not deployed (congruent condition) with the reaction times requiring inhibiting irrelevant stimuli (incongruent condition). The test has been shown to activate either the left dorsolateral prefrontal cortex or anterior cingulate for cognitive inhibition [ 74 ]. As Imbrosciano & Berlach [ 43 ] point out, anterior cingulate is considered to be responsible for selecting an appropriate response when the brain is exposed to two conflicting conditions. Bush et al. [ 17 ] hypothesize that anterior cingulate dysfunction is responsible for producing core features of ADHD, namely inattention and impulsivity. Anterior cingulate activation has been linked to detection of conflict and its resolution [ 18 ] as well as to academic results in college students [ 40 ]. The last-named researchers investigated the activity of anterior cingulate in connection with the error-related negativity (ERN, an electrophysiological signal associated with the anterior cingulate monitoring process, occurring approximately 100 ms after an error is made) and found a correlation between the magnitude of ERN and undergraduate students’ academic performance suggesting that the error detection mechanism is stronger in the students who perform better at university. Veroude et al. [ 74 ] observed a positive correlation between average course grades and the activation of anterior cingulate cortex in freshmen enrolled in a medical college during cognitive inhibition on the Stroop task finding no relationship between the course grades and activation of the left dorsolateral prefrontal cortex. Similarly, there are other studies which suggest a link between inhibitory control and academic performance associating the activation of anterior cingulate with cognitive control across tasks (e.g [ 31 , 34 ]).

The aim of the study

The aim of the study described in this paper was to find out whether there is any relationship between adult students’ inhibitory control measured with the Stroop Test and their academic achievement. To measure the degree of inhibitory control, a computerized version of the Stroop Test was used.

Based on the previous research (e.g. [ 29 , 40 , 55 , 57 , 74 ] the initial hypothesis was that there might be a relationship between inhibitory control and academic performance. In this respect, the participants with a higher degree of inhibitory control (lower Stroop Effect) were expected to be those with a higher grade average and lower failure rate than those indicating a lower degree of inhibitory control (higher Stroop Effect). On the other hand, if the correlation between the two variables were to be found, it was not expected to be significantly high since the previous research studying this phenomenon in relation to age points to the inhibitory control exerting its influence on academic performance chiefly at an individual’s young age [ 1 , 4 , 9 , 19 , 28 , 76 ].

Participants

107 students studying at (undisclosed) University, Stockholm, Sweden, (29 males, 78 females, mean age = 25.83 years, SD age = 6.32 years, age range = 19–52 years) participated in the study. Originally, 110 students were involved in the study, but 3 of them were removed as outliers due to the overwhelming majority of their grades being at the extreme ends of the grading scale, i.e. either VGs or Us, and only few Gs (for more information on the grading scale, see the next section). This was done to exclude the students whose extraordinary performance in certain academic subjects might be due to either their extra talents for, or their exceptional motivation to study, these subjects. The participants were recruited from students each of whom was enrolled in one of three teacher education programs, i.e. either primary ( N  = 21), secondary ( N  = 51), or upper secondary ( N  = 35). The reason why the participants were recruited from this group was that most of the courses they study within these programs are somewhat similar in terms of contents. Besides, the students are also assessed in these courses mainly by the same teachers. The original idea was to recruit the highest number of volunteers enrolled in the three programs who were, at the same time, studying the courses given by the department in which the study was conducted. Nevertheless, the final number of the participants was determined by their willingness to participate in the study and it was also restricted by the fact that all of them had to be tested on campus in a computer laboratory within the limited time of the project. The volunteers had no neurological or psychiatric disorders. All the participants signed an informed consent with their participation in the study.

Data collection and analysis

Information about the participants’ age and sex was collected via questionnaires distributed among the participants prior to the execution of the Stroop Test. The students’ university grade averages were computed based on their past course grades and their calculation included a computational model (see below) used in another study [ 30 ] researching the effect of mother tongue proficiency on the students’ academic performance.

The study was conducted in an institution using the grading scale consisting of three grades, i.e. VG, G, and U, a system commonly used in Swedish universities. According to this system, VG represents “passed with distinction,” G denotes “passed,” and U denotes “failed.” To facilitate a statistical analysis, these grades were assigned numerical values of 4, 2, and 0, respectively. This approach mirrors the GPA calculation method, where the highest grade corresponds to 4, the middle one to 2, and the fail grade to 0. Each student’s failure rate, expressed as a percentage, was computed as the ratio of their fail grades (Us) to the total number of grades received. Approximately 30 grades, encompassing both courses and graded modules, were considered for the computation of grade averages and failure rates per student. In instances where a student received multiple fail grades for the same course or module, each of these was included as a distinct grade in the calculation.

To measure the participants’ inhibitory control, a computerized version of the Stroop Test available at https://www.psytoolkit.org/ was used. The task was performed in English since the students represent a relatively uniform group when it comes to their English knowledge, which is at the C1 level of Common European Framework of Reference for languages. Moreover, English represents the language that all the participants have studied in a language instructional setting and thus the color identification rule in the Stroop test had to be followed in the context of their knowledge previously adopted at school. In this respect, the experiment made the participants deploy controlled information processing [ 63 ] through the application of a new cognitive concept requiring the inhibition of the semantic contents they have learnt at school before. This way an attempt was made at inducing the situation activating those cognitive processes that resemble the ones which are in operation in school environments when new concepts are learnt or when adjustments are made to the already acquired knowledge.

The task consisted of two conditions on which the participants were tested: (a) congruent trials, where the names of colors displayed on the screen matched the colors these were displayed in, (b) incongruent trails, where the names of colors displayed on the screen did not match the colors these were displayed in. For each trial type the students were instructed to identify the color of the word as quickly as possible by pressing a corresponding key on their keyboards. The keys the subjects were instructed to press were those that bore the initial letters of the names of the colors the words were displayed in. Therefore, when the word “red”, for instance, got displayed in blue, the students were supposed to press the b key (“b” standing for “blue”). The explicit instruction given to the students was to disregard the meaning of the words and focus solely on the color in which these words were displayed.

There were four colors used in the test (red, yellow, blue and green) and the students were instructed to press the r , y , b and g keys, respectively, to indicate these. Before each of the words was presented in the middle of the screen against the black background (for up to 2 s or until the participant responded), a fixation cross was displayed in the same position for 200 milliseconds for the participant to know where the word would appear. Once the participant made their choice, either a word “correct” or “wrong” popped up for 500 milliseconds depending on whether their choice had been correct or not. The computer script in which the test was run measured the participants’ reaction times in both the congruent and incongruent conditions and counted the errors they made when indicating a wrong color. The Stroop test was run under these conditions twice – once as a practice session with thirty trials, whose purpose was to make sure all the participants understood what they were supposed to do as well as to enable them to practice the key-color associations, and then as the test itself with 120 test trials. Half of the test trials were in the congruent condition and the other half in the incongruent one. The congruent and incongruent conditions were mixed and presented to the subjects randomly.

The main Stroop effect size was calculated for the individual participants according to the following formula using the reaction times recorded for congruent as well as incongruent trials:

The reaction times used in the formula above were collected together with the numbers of incorrect color identifications from files saved on a server once the tests had been completed.

Subsequently, Pearson’s bivariate correlation analysis was conducted on the collected data to find out the correlation coefficients between the participants’ grade averages (as well as failure rates) and their Stroop Effect values. Similar analyses were also done for their reaction times in congruent and incongruent conditions as well as their number of Stroop Test mistakes, i.e. the situations where a color was not correctly identified.

As the study group the analysis was conducted with consisted of three sub-groups of students (each sub-group consisting of students enrolled in one of the three teacher training programs), One-Way ANOVA was used to compare these sub-groups for Stroop Effect, reaction times for congruent trials, reaction times for incongruent trials, and number of color identification errors. Since the sub-groups were of unequal sizes, the test of homogeneity of variances was run on all the variables with the subsequent Tukey HSD post hoc test to identify the significance of the differences between the sub-groups.

The students’ ( N  = 107) mean Stroop Effect, mean reaction times (in ms) in congruent and incongruent conditions, the mean number of errors as well as the mean number of times the participants exceeded the time limit are given in Table  1 below. The table also lists students’ grade average and fail rate average as well as standard deviations for each variable.

Pearson’s bivariate correlation analysis shows a very weak negative correlation of low statistical significance, r (107) = − 0.13, p  = .20, between the Stroop Effect and participants’ grade averages and basically no correlation between the former and participants’ fail rate, r (107) = 0.04, p  = .68, (see Table  2 below). Similarly, there seems to be no statistically significant relationship between the participants’ grade averages and their reaction times in congruent ( r (107) = − 0.06, p  = .58) and incongruent ( r (107) = − 0.10, p  = .31) conditions as well as no significant relationship between the grade averages and the number of times the participants exceeded the time limit for color identification ( r (107) = − 0.07, p  = .46). The only statistically significant relationship detected appears to be the one between the students’ grade averages and the number of mistakes (incorrect color identifications) made during the Stroop Test with a weak negative correlation, r (107) = − 0.21, p  = .03. Scatter plots for Stroop Effect values and the number of errors made during the test in relation to the students’ grades are shown in Figs.  1 and 2 below. The diagonal lines in the scatter plots represent identity lines indicating the points in which the values of the variables correlate perfectly while the distances of the data points from these lines represent degrees of correlation; the closer the points are to the line, the stronger the correlation between the variables. Tables 3 and 4 show the comparison of measured variables across the educational programs and related post hoc Tukey HSD, respectively.

Grade average – Stroop Effect scatter plot

Grade average – Stroop Test errors scatter plot

The Tukey HSD post hoc test (see Table  4 below) shows the comparison of the distribution of the values within the sub-groups, where the only statistically significant Stroop Effect mean difference of 74.65 ms ( p  = .002) can be found between the primary and upper-secondary programs (the students of the latter indicating a lower mean).

The goal of this study was to find out whether there was some relationship between the inhibitory control of the students studying at (undisclosed) University, Stockholm, Sweden, measured with the Stroop Test and their academic performance. Based on the previous research focusing on links between school performance and executive functions [ 51 , 70 , 75 ], the hypothesis was that the participants with a higher grade average and lower fail rate might tend to manifest a higher degree of inhibitory control indicated with a lower Stroop Effect. Nevertheless, this correlation was expected to be weak in the study group consisting of university students since the other research into the area shows the strongest correlation between the inhibitory control and the academic performance in subjects at their early age [ 8 , 10 , 15 , 42 , 61 ].

The results show that although there is a very weak negative correlation ( r (107) = − 0.13) between the Stroop Effect and participants’ grade averages, which might suggest some effect of the degree of their inhibitory control on their school performance that is in line with the previous research focusing on this phenomenon, this relationship has not turned out to be statistically significant ( p  = .20). As regards the students’ failure rates, these turned out to be completely independent of the Stroop Effect values.

As regards the comparison of the distribution of all the observed values within the different sub-groups of students depending on what program they study, the only statistically significant difference was found in Stroop Effect (mean difference of 74.65 ms ( p  = .002)) between the primary and upper-secondary programs, with the latter indicating a lower Stroop effect.

Another statistically significant relationship detected was the one between the students’ grade averages and the number of mistakes (incorrect color identifications) made during the Stroop Test with a weak negative correlation ( r (107) = − 0.21, p  = .03) indicating that the students performing worse academically (having lower grades) made more mistakes during the test. This relationship may suggest that the degree of conscientiousness the students approach the assigned task of Stroop Test with might be in direct proportion to the degree of conscientiousness they approach their university studies with in general. That is [ 41 ], point out that conscientiousness predicts better performance on the Stroop task in terms of fewer errors and diminished incongruency effects. They even suggest that this personality trait may promote certain attentional processes even as cognitive capacities decline at a later age. Similarly, other studies [ 45 , 67 ], deploying other attention control tasks, also found a relationship between conscientiousness and cognitive performance. Finally, as the myriad of studies shows conscientiousness, defined as dependability and will to achieve, as being in direct relationship with academic performance as well [ 21 , 23 , 24 , 58 , etc.], the results of the study described in this paper might be indicative of the newly found relationship between the number of color identification errors (as a factor reflecting this conscientiousness) and academic performance, albeit this relationship has not been studied before.

One of the reasons why the weak correlation between the students’ Stroop Effect and their school performance shows low statistical significance may be that the grade averages had been calculated from the grades the students obtained for a wide range of school subjects ranging from mathematics, to languages, history, etc. That is, the meta-analysis conducted by Pascual & Robres [ 57 ] shows that the degree to which executive functions affect school performance depends on the subject studied. This phenomenon can be observed, for example, in the relationship between mathematics and the visuo-spatial aspect of working memory. Similar observations have also been made when it comes to other executive functions, which appear to be more related to performance in mathematics than in a language, for instance. Moreover, the meta-analysis points out that most studies identify working memory as a better predictor of school performance than inhibition and that executive functions represent an important predictor of academic performance and future learning problems at an early age. However, the predictive capacity of executive functions in relation to academic performance seems to decrease during secondary education and even more so during university education, which might be the case with the study described in this article. The reason for this phenomenon could be minimal individual differences in executive functioning in certain age groups. Bialystok [ 12 ], for instance, in her study of the Stroop task performance, mentions no differences in Stroop Effect among university undergraduates giving the cognitive performance in this age group being at its peak as a reason for the phenomenon. Likewise, Comalli et al. [ 22 ] demonstrated that older adults and children indicate longer response latencies than young adults. The aforementioned factors are also suspected of being the reasons why no correlation has been found between the students’ Stroop Effect and their failure rates.

The fact that the relationship between the students’ Stroop Effect and school performance shows low statistical significance might also be due to the Stroop Test activating different regions of the brain in different individuals – operations that have been documented as correlating with school grades. That is, Veroude et al. [ 74 ] report a significant main effect of cognitive inhibition being observed in the left dorsolateral prefrontal cortex, but not so much in the dorsal anterior cingulate cortex (ACC). However, they report the activation of ACC for the “incongruent” condition being associated with higher grades. In this respect, they found an association with achievement only in the situations where the Stroop Test activated dorsal ACC, indicating that “involvement of this region can potentially predict differences in education success.” (p. 104). Other studies also show the involvement of both the ACC and dorsolateral prefrontal cortex during the Stroop task (e.g [ 47 ]). even though the ACC does not seem to be necessary for cognitive control as patients with damage to this region perform normally on the Stroop Test [ 35 ]. As the study described in this paper did not include functional magnetic resonance imaging, it was not possible to find out in which situations the inhibitory control in the subjects in incongruent conditions resulted from the activation of the ACC and in which situations it resulted from the activation of the dorsolateral prefrontal cortex. Hence, it is also impossible to assess the extent to which the activation of the former or the latter for inhibitory control might influence the subjects’ grades. Not distinguishing between these two conditions could thus have been one of the reasons for the weak p-value of the results and thus it might be desirable to differentiate between them in future research.

Finally, as the current study suggests a link between the number of mistakes made during the Stroop Test and the students’ grade averages, the potential of the test to be used to measure the degree of their manifested conscientiousness and the ability to concentrate on an assigned, relatively short, one-off task should be studied further. The results of such further testing might provide clues regarding to what extent these characteristics can be viewed as predictors of academic performance.

Overall, this study has shown that there was a weak correlation of low statistical significance between the participants’ grade averages and the inhibitory control measured with the Stroop Test. It has also shown no relationship between their failure rates and inhibitory control.

These findings suggest that differences in the impact of inhibitory control on cognitive functioning among young adults might be much smaller, if any, than in children or older people. This fact seems to be in line with the findings of previous studies which point out that individual differences in executive functions are greatest while these functions are either under development, i.e. in children, or when they are in decline, i.e. in the elderly.

The study has also revealed that the students with lower grades made more color identification errors than those with higher grades. This phenomenon is worth pursuing in the future since the Stroop Test, or any other test where subjects need to follow a relatively simple rule, might be indicative (via their error rates) of their conscientiousness, a way in which they approach a certain assigned task or a degree of their ability to handle the task. Consequently, these findings can offer educators insights into their students’ specific weaknesses in these domains, empowering them to address these areas through tailored teaching approaches, such as individualized activities.

Data availability

The data pertinent to this study and used in the analysis are enclosed in a separate file uploaded at the submission of the paper.

Altemeier L, Jones J, Abbott RD, Berninger VW. Executive functions in becoming writing readers and reading writers: note taking and report writing in third and fifth graders. Dev Neuropsychol. 2006;29:161–73.

Article   PubMed   Google Scholar  

Antón E, García YF, Carreiras M, Dunabeitia JA. Does bilingualism shape inhibitory control in the elderly? J Mem Lang. 2016;90:147–60.

Article   Google Scholar  

Alloway T. Working memory, but not IQ, predicts subsequent learning in children with learning difficulties. Eur J Psychol Assess. 2009;25(2):92–8.

Alloway T, Bibile V, Lau G. Computerized working memory training: can it lead to gains in cognitive skills in students? Comput Hum Behav. 2013;29(3):632–8.

Anderson P. Assessment and development of executive function (EF) during childhood. Child Neuropsychol. 2002;8:71–82.

Aron AR, Robbins TW, Poldrack RA. Inhibition and the right inferior frontal cortex: one decade on. Trends Cogn Sci. 2014;18:177–85.

Barrouillet P, Le´pine R. Working memory and children’s use of retrieval to solve addition problems. J Exp Child Psychol. 2005;91(3):183–204.

Bédard AC, Nichols S, Barbosa JA, Schachar R, Logan GD, Tannock R. The development of selective inhibitory control across the life span. Dev Neuropsychol. 2002;21:93–111.

Berg D. Working memory and arithmetic calculation in children: the contributory roles of processing speed, short-term memory, and reading. J Exp Child Psychol. 2008;99:288–308.

Best JR, Miller PH, Naglieri JA. Relations between executive function and academic achievement from ages 5 to 17 in a large, representative national sample. Learn Individ Differ. 2011;21:327–36.

Article   PubMed   PubMed Central   Google Scholar  

Bialystok E, Martin MM. Attention and inhibition in bilingual children: evidence from the dimensional change card sort task. Dev Sci. 2004;7(3):325–39.

Bialystok E, Martin M, Viswanathan M. Bilingualism across the lifespan: the rise and fall of inhibitory control. Int J Biling. 2005;9(1):103–19.

Bialystok E, Craik FIM, Luk G. Cognitive control and lexical access in younger and older bilinguals. J Exp Psychol Learn Mem Cogn. 2008;34(4):859–73.

Blair C, Razza RP. Relating effortful control, executive function, and false belief understanding to emerging math and literacy ability in kindergarten. Child Dev. 2007;78:647–63.

Bryce D, Whitebread D, Szucs D. The relationships among executive functions, metacognitive skills, and educational achievement in 5 and 7-year-old children. Metacognition Learn. 2015;10:181–98.

Bull R, Espy K. Working memory, executive functioning, and children’s mathematics. Educ Psychol. 2006;93–123.

Bush G, Frazier JA, Rauch SL, Seidman LJ, Whalen PJ, Jenike MA, Rosen BR, Biederman J. Anterior cingulate cortex dysfunction in attention-deficit/hyperactivity disorder revealed by MRI and the counting Stroop. Biol Psychiatry. 1999;45(12):1542–52.

Carter CS, Van Veen V. ACC and conflict detection: an update of theory and data. Cogn Affect Behav Neurosci. 2007;7(4):367–79.

Cartwright K. Insights from cognitive neuroscience: the importance of executive function for early reading development and education. Early Educ Dev. 2012;23(1):24–36.

Chambers CD, Garavan H, Bellgrove MA. Insights into the neural basis of response inhibition from cognitive and clinical neuroscience. Neurosci Biobehav Rev. 2009;33:631–46.

Chamorro-Premuzic T. Creativity versus conscientiousness: which is a better predictor of student performance? Appl Cogn Psychol. 2006;20(4):521–31.

Comalli PE Jr, Wapner S, Werner H. Interference effects of Stroop color-word test in childhood, adulthood, and aging. J Genet Psychol. 1962;100:47–53.

Conrad N, Patry MW. Conscientiousness and academic performance: a mediational analysis. Int J Scholarsh Teach Learn. 2012;6(1).

Costa PT Jr. Revised NEO personality inventory and NEO five-factor inventory. Prof Man.

Crede M, Roch SG, Kieszczynka UM. Class attendance in college: a meta-analytic review of the relationship of class attendance with grades and student characteristics. Rev Educ Res. 2010;80(2):272–95.

De Beni R, Palladino P, Pazzaglia F, Cornoldi C. Increases in intrusion errors and working memory deficit of poor comprehenders. Q J Exp Psychol. 1998;51:305–20.

Diamond A. Development of the ability to use recall to guide action, as indicated by infants’ performance on AB. Child Dev. 1985;56:868–83.

Diamond A. Activities and programs that improve children’s executive functions. Curr Dir Psychol Sci. 2012;21(5):335–41.

Duckworth AL, Taxer JL, Eskreis-Winkler L, Galla BM, Gross JJ. Self-control and academic achievement. Annu Rev Psychol. 2019;70:373–99.

Dvorak M. The varying relationship between perceived oral and written mother tongue proficiency and academic performance in native multilingual students at their secondary school and university. In: 13th International Conference The Future of Education: 2023; Bologna, Italy: Bologna: Pixel International Conferences; 2023;24–27.

Egner T, Etkin A, Gale S, Hirsch J. Dissociable neural systems resolve conflict from emotional versus nonemotional distractors. Cereb Cortex. 2007;18:1475–84.

Eriksen BA, Eriksen CW. Effects of noise letters upon the identification of a target letter in a nonsearch task. Attent Percept Psychophys. 1974;16:143–9.

Espy KA, McDiarmid MM, Cwik MF, Stalets MM, Hamby A, Senn TE. The contribution of executive functions to emergent mathematical skills in preschool children. Dev Neuropsychol. 2004;26:465–86.

Evers EA, Van der Veen FM, Van Deursen JA, Schmitt JA, Deutz NEP, Jolles J. The effect of acute tryptophan depletion on the BOLD response during performance monitoring and response inhibition in healthy male volunteers. Psychopharmacology. 2006;187:200–8.

Fellows LK, Farah MJ. Is anterior cingulate cortex necessary for cognitive control? Brain. 2005;128(4):788–96.

Friedman NP, Miyake A. The relations among inhibition and interference control functions: a latent-variable analysis. J Exp Psychol Gen. 2004;133:101–35.

Gathercole S, Alloway T, Willis C, Adams A. Working memory in children with reading disabilities. J Exp Child Psychol. 2006;93(3):265–81.

Gerst EH, Cirino PT, Fletcher JM, Yoshida H. Cognitive and behavioral rating measures of executive function as predictors of academic outcomes in children. Child Neuropsychol. 2017;23:381–407.

Glucksberg S, Newsome MR, Goldvarg Y. Inhibition of the literal: filtering Metaphor-Irrelevant Information during Metaphor Comprehension. Metaphor Symbol. 2001;16(3–4):277–98.

Hirsh JB, Inzlicht M. Error-related negativity predicts academic performance. Psychophysiology. 2010;47:192–6.

Huff MJ, Gretz MR, Keefer LA. Conscientiousness predicts performance on the Stroop task but not other attentional control tasks in older and younger adults. Imagin Cogn Pers. 2023;43(2):150–68.

Huizinga M, van der Molen MW. Age-group differences in set-switching and set-maintenance on the Wisconsin Card sorting Task. Dev Neuropsychol. 2007;31:193–215.

Imbrosciano A, Berlach RG. The Stroop test and its relationship to academic performance and general behaviour of young students. Teach Dev. 2005;9:1.

Google Scholar  

Irvan R, Tsapali M. The role of Inhibitory Control in Achievement in Early Childhood Education. Camb Educational Res E-J. 2020;7:168–90.

Jackson JD, Balota DA. Mind-wandering in younger and older adults: converging evidence from the sustained attention to Response Task and reading for comprehension. Psychol Aging. 2012;27(1):106–19.

Jiménez M. Competencia social: intervención preventiva en la escuela [Social competence: preventive intervention at school]. Infanc Soc. 2000;24:21–48.

Laird AR, McMillan KM, Lancaster JL, Kochunov P, Turkeltaub PE, Pardo JV, Fox PT. A comparison of label-based review and ALE meta-analysis in the Stroop task. Hum Brain Mapp. 2005;25:6–21.

Lee K, Lee Pe M, Ang SJ, Stankov L. Do measures of working memory predict academic proficiency better than measures of intelligence? Psychol Sci. 2009;51(4):403–19.

Locascio G, Mahone EM, Eason SH, Cutting LE. Executive dysfunction among children with reading comprehension deficits. J Learn Disabil. 2010;43(5):44.

McKenzie B, Bull R, Gray C. The effects of phonological and visual-spatial interference on children’s arithmetical performance. Educ Child Psychol. 2003;20(3):93–108.

McLean JF, Hitch GJ. Working memory impairments in children with specific arithmetic learning difficulties. J Exp Child Psychol. 1999;74:240–60.

Mellanby J, Martin M, O’Doherty J. The ‘gender gap’ in final examination results at Oxford University. Br J Psychol. 2000;91(3):377–90.

Miyake A, Friedman NP. The nature and organization of individual differences in executive functions: four general conclusions. Curr Dir Psychol Sci. 2012;21(1):8–14.

Miyake A, Shah P. Models of working memory: mechanisms of active maintenance and executive control. Cambridge: University; 1999.

Book   Google Scholar  

Passolunghi MC, Lanfranchi S. Domain-specific and domain-general precursors of mathematical achievement: a longitudinal study from kindergarten to first grade. Br J Educ Psychol. 2012;82:42–63.

Passolunghi MC, Cornoldi C, De Liberto S. Working memory and intrusions of irrelevant information in a group of specific poor problem solvers. Mem Cogn. 1999;27:779–90.

Pascual CA, Muñoz MN, Robres QA. The relationship between executive functions and academic performance in primary education: review and meta-analysis. Front Psychol. 2019;10:1582.

Poropat AE. A meta-analysis of the five-factor model of personality and academic performance. Psychol Bull. 2009;135(2):322–38.

Privitera AJ, Zhou Y, Xie X. Inhibitory control as a significant predictor of academic performance in Chinese high schoolers. Child Neuropsychol. 2023;29(3):457–73.

Robbins SB, Lauver K, Le H, Davis D, Langley R, Carlstrom A. Do psychosocial and study skill factors predict college outcomes? A meta-analysis. Psychol Bull. 2004;130(2):261–88.

Senn TE, Espy KA, Kaufmann PM. Using path analysis to understand executive function organization in preschool children. Dev Neuropsychol. 2004;26:445–64.

St Clair-Thompson HL, Gathercole SE. Executive functions and achievements in school: shifting, updating, inhibition, and working memory. Q J Exp Psychol. 2006;59:745–59.

Schneider W, Shiffrin RM. Controlled and Automatic Human Information Processing: I. Detection, Search, and attention. Psychol Rev. 1977;84(1).

Schuchardt K, Mähler C, Hasselhorn M. Working memory deficits in children with specific learning disorders. J Learn Disabil. 2008;41(6):514–23.

Simon JR. Reactions toward the source of stimulation. J Exp Psychol. 1969;81:174–6.

Sirin SR. Socioeconomic status and academic achievement: a meta-analytic review of research. Rev Educ Res. 2005;75(3):417–53.

Soubelet A. Age-cognition relations and the personality trait of conscientiousness. J Res Pers. 2011;45(6):529–34.

Stahl C, Voss A, Schmitz F, Nuszbaum M, Tuscher O, Lieb K, et al. Behavioral components of impulsivity. J Exp Psychol Gen. 2014;143:850–86.

Stroop JR. Studies of interference in serial verbal reactions. J Exp Psychol. 1935;18:643–62.

Swanson HL. Reading comprehension and working memory in skilled readers: is the phonological loop more important than the executive system? J Exp Child Psychol. 1999;72:1–31.

Tiego J, Testa R, Bellgrove MA, Pantelis C, Whittle S. A hierarchical model of inhibitory control. Front Psychol. 2018;9:1339.

Titz C, Karbach J. Working memory and executive functions: effects of training on academic achievement. Psychol Res. 2014;78:852–68.

Verbruggen F, Logan GD. Response inhibition in the stop-signal paradigm. Trends Cogn Sci. 2008;12:418–24.

Veroude K, Jolles J, Knezevic M, Vos CMP, Croiset G, Krabbendam L. Anterior cingulate activation during cognitive control relates to academic performance in medical students. Trends Neurosci Educ. 2013;2:100–6.

Visu-Petra L, Cheie L, Benga O, Miclea M. Cognitive control goes to school: the impact of executive functions on academic performance. Proc Soc Behav Sci. 2011;11:240–4.

Willoughby MT, Kupersmidt JB, Voegler-Lee ME. Is preschool executive function causally related to academic achievement? Child Neuropsychol. 2012;18(1):79–91.

Yow WQ, Li X. Balanced bilingualism and early age of second language acquisition as the underlying mechanisms of a bilingual executive control advantage: why variations in bilingual experiences matter. Front Psychol. 2015;6:164.

Download references

Open access funding provided by Södertörn University. The research project described in the paper was partially funded by the School of Teacher Education, Södertörn University, Stockholm.

Open access funding provided by Södertörn University.

Author information

Authors and affiliations.

Södertörn University, Stockholm, Sweden

Martin Dvorak

You can also search for this author in PubMed   Google Scholar

Contributions

The sole author, Martin Dvorak, has conducted the collection of data, its analysis and all the other procedures described in this paper.

Corresponding author

Correspondence to Martin Dvorak .

Ethics declarations

Ethics approval and consent to participate.

The study was conducted according to the guidelines of the 1964 Declaration of Helsinki (as revised in 2000). All the subjects participated in the research voluntarily and signed the informed consent. The need for ethics approval has been deemed unnecessary according to Swedish national regulations (Lag (2003:460) om etikprövning av forskning som avser människor), as no information collected or processed during the analysis represents data stipulated in the law as those requiring ethics approval.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Dvorak, M. Inhibitory control and academic achievement – a study of the relationship between Stroop Effect and university students’ academic performance. BMC Psychol 12 , 498 (2024). https://doi.org/10.1186/s40359-024-01984-3

Download citation

Received : 08 September 2023

Accepted : 05 September 2024

Published : 27 September 2024

DOI : https://doi.org/10.1186/s40359-024-01984-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Academic performance
• Executive functions
• Inhibitory control
• Stroop Test
• Manifested conscientiousness

BMC Psychology

ISSN: 2050-7283

• General enquiries: [email protected]

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Journal Proposal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Examining the relationship between broadband access, parent technology beliefs, and student academic outcomes.

1. Introduction

2. literature review, 3. research questions, 4. the edconnect study, 5. culturally responsive evaluation framework, 6. methodology, study setting and data sources, 7.1. rq 1: how do households’ participation in a free community broadband program relate to their children’s math and ela achievement, 7.2. rq 2: how do parents’ beliefs and use of technology relate to students’ math and ela achievement, controlling for household participation in a free community broadband program, 7.3. rq 3: how do students’ personal and household characteristics relate to their math and ela achievement, given their parents’ beliefs and use of technology, and their households participation in a free community broadband program, 8. discussion, 8.1. program participation, 8.2. parents’ beliefs and use of technology, 8.3. racialized/ethnic identity and systemic inequality, 9. conclusions and limitations, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

• BroadbandUSA. Available online: https://broadbandusa.ntia.doc.gov/funding-programs/digital-equity-act-programs (accessed on 17 March 2024).
• Murray, S.P.; Portman, S.R.; King, S.A.; Siefer, A. Digital Equity Act. Available online: https://www.digitalequityact.org/ (accessed on 13 November 2022).
• National Center for Education Statistics (NCES). Students Access to the Internet and Digital Devices at Home. 2021. Available online: https://nces.ed.gov/blogs/nces/post/students-access-to-the-internet-and-digital-devices-at-home (accessed on 17 March 2024).
• Chandra, S.; Chang, A.; Day, L.; Fazlullah, A.; Liu, J.; McBride, L.; Weiss, D. Closing the K–12 Digital Divide in the Age of Distance Learning ; Common Sense and Boston Consulting Group: Boston, MA, USA, 2020. [ Google Scholar ]
• National Center for Education Statistics (NCES). Children’s internet access at home. In Condition of Education ; U.S. Department of Education, Institute of Education Sciences: Washington, DC, USA, 2023. Available online: https://nces.ed.gov/programs/coe/indicator/cch/home-internet-access (accessed on 18 March 2024).
• United States Census Bureau. B28002: Presence and Types of Internet Subscriptions in Household. Census.Gov. Available online: https://data.census.gov/table?q=B28002:%20Presence%20and%20Types%20of%20Internet%20Subscriptions%20in%20Household (accessed on 17 March 2024).
• Le, V.; Moya, G. On the Wrong Side of the Digital Divide: Life without Internet Access, and Why We Must Fix It in the Age of COVID-19 ; The Greenlining Institute: Oakland, CA, USA, 2020. Available online: https://greenlining.org/publications/online-resources/2020/on-the-wrong-side-of-the-digital-divide/ (accessed on 17 March 2024).
• Goldberg, R. Digital Divide among School-Age Children Narrows, but Millions Still Lack Internet Connections ; U.S. Department of Commerce, National Telecommunications and Information Administration (NTIA): Washington, DC, USA, 2018. Available online: https://www.ntia.doc.gov/blog/2018/digital-divide-among-school-age-children-narrows-millions-still-lack-internet-connections (accessed on 17 March 2024).
• Goolsbee, A.; Guryan, J. The impact of internet subsidies in public schools. Rev. Econ. Stat. 2006 , 88 , 336–347. [ Google Scholar ] [ CrossRef ]
• KewalRamani, A.; Zhang, J.; Wang, X.; Rathbun, A.; Corcoran, L.; Diliberti, M.; Zhang, J. Student Access to Digital Learning Resources outside of the Classroom (NCES 2017-098) ; U.S. Department of Education: Washington, DC, USA; National Center for Education Statistics: Washington, DC, USA, 2018. Available online: https://nces.ed.gov/PUBSEARCH/pubsinfo.asp?pubid=2017098 (accessed on 17 March 2024).
• Galperin, H.; Bar, F.; Le, A.M.; Daum, K. COVID-19 and the Distance Learning Gap: Connected Cities and Inclusive Growth Policy Briefs ; USC Annenberg Research Network on International Communication: Los Angeles, CA, USA, 2020. Available online: https://arnicusc.org/covid-19-and-the-distance-learning-gap/ (accessed on 17 March 2024).
• Hampton, K.N.; Fernandez, L.; Robertson, C.T.; Bauer, J.M. Broadband and Student Performance Gaps ; James, H., Mary, B., Eds.; Quello Center, Michigan State University: East Lansing, MI, USA, 2020. [ Google Scholar ] [ CrossRef ]
• Pseudonym Department of Education. Statewide Assessment Dashboard. Available online: https://tdepublicschools.ondemand.sas.com/state/assessment (accessed on 18 March 2024).
• Farkas, G. The Black-White test score gap. Contexts 2004 , 3 , 12–19. [ Google Scholar ] [ CrossRef ]
• Jencks, C.; Phillips, M. The Black-White test score gap: Why it persists and what can be done. Brook. Rev. 1998 , 16 , 24–27. [ Google Scholar ] [ CrossRef ]
• Rothstein, J.; Wozny, N. Permanent income and the Black-White test score gap. J. Hum. Resour. 2013 , 48 , 510–544. [ Google Scholar ] [ CrossRef ]
• Shores, K.; Kim, H.E.; Still, M. Categorical inequality in Black and White: Linking disproportionality across multiple educational outcomes. Am. Educ. Res. J. 2020 , 57 , 2089–2131. [ Google Scholar ] [ CrossRef ]
• Hampton, K.N.; Robertson, C.T.; Fernandez, L.; Shin, I.; Bauer, J.M. How variation in internet access, digital skills, and media use are related to rural student outcomes: GPA, SAT, and educational aspirations. Telemat. Inform. 2021 , 63 , 101666. [ Google Scholar ] [ CrossRef ]
• Hampton, K.N.; Hales, G.E.; Bauer, J.M. Broadband and Student Performance Gaps after the COVID-19 Pandemic ; James, H., Mary, B., Eds.; Quello Center, Michigan State University: East Lansing, MI, USA, 2023. [ Google Scholar ]
• DiMaggio, P.; Hargittai, E.; Celeste, C.; Shafer, S. Digital inequality: From unequal access to differentiated use. In Social Inequality ; Kathryn, N., Ed.; Russell Sage Foundation: New York, NY, USA, 2004; pp. 355–400. [ Google Scholar ]
• Helsper, E.J. A corresponding fields model for the links between social and digital exclusion. Commun. Theory 2012 , 22 , 403–426. [ Google Scholar ] [ CrossRef ]
• Engin, G. An examination of primary school students’ academic achievements and motivation in terms of parents’ attitudes, teacher motivation, teacher self-efficacy and leadership approach. Int. J. Progress. Educ. 2020 , 16 , 257–276. [ Google Scholar ] [ CrossRef ]
• Henderson, A.T.; Mapp, K.L. A New Wave of Evidence: The Impact of School, Family, and Community Connections on Student Achievement ; Southwest Educational Development Laboratory: Austin, TX, USA, 2002. [ Google Scholar ]
• Kösterelioğlu, İ. Effects of parenting style on students’ achievement goal orientation: A study on high school students. Educ. Policy Anal. Strateg. Res. 2018 , 13 , 91–107. [ Google Scholar ] [ CrossRef ]
• Caldarulo, M.; Mossberger, K.; Howell, A. Community-wide broadband adoption and student academic achievement. Telecommun. Policy 2023 , 47 , 102445. [ Google Scholar ] [ CrossRef ]
• Anthony, C.G.; Ogg, J. Parent involvement, approaches to learning, and student achievement: Examining longitudinal mediation. Sch. Psychol. 2019 , 34 , 376–385. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Topor, D.R.; Keane, S.P.; Shelton, T.L.; Calkins, S.D. Parent involvement and student academic performance: A multiple mediational analysis. J. Prev. Interv. Community 2010 , 38 , 183–197. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Veas, A.; Castejón, J.-L.; Miñano, P.; Gilar-Corbí, R. Relationship between parent involvement and academic achievement through metacognitive strategies: A multiple multilevel mediation analysis. Br. J. Educ. Psychol. 2019 , 89 , 393–411. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Katz, V.; Rideout, V. Learning at Home While Under-Connected Lower-Income Families during the COVID-19 Pandemic ; New America: Washington, DC, USA, 2021. Available online: https://www.newamerica.org/education-policy/reports/learning-at-home-while-underconnected/ (accessed on 18 March 2024).
• Nobis, M.J.; Caparroso, C.L. Bridging the gap: Examining parental involvement strategies and their impact on homework completion rates in mathematics. Alifmatika: J. Pendidik. Dan Pembelajaran Mat. 2024 , 6 , 1–13. [ Google Scholar ] [ CrossRef ]
• National Center for Education Statistics (NCES). ACS-ED District Demographic Dashboard 2018-22 ; Hamilton County School District: Tennessee, TN, USA. Available online: https://nces.ed.gov/Programs/Edge/ACSDashboard/4701590I (accessed on 17 March 2024).
• Bebell, D.; Xin, Z.; Cleveland, G.; Russell, M.; Ellis, J. Exploring parents’ access, beliefs, and use of educational technology across a community-wide broadband initiative. Comput. Sch. 2023 , 1–24. [ Google Scholar ] [ CrossRef ]
• Bebell, D.; Xin, Z.; Cleveland, G.; Russell, M. Parent use of and beliefs about technology for education and parenting: Year 1 results from the HCS EdConnect study. J. Res. Technol. Educ. 2023 . [ Google Scholar ] [ CrossRef ]
• Hood, S.; Hopson, R.K.; Kirkhart, K.E. Culturally responsive evaluation. In Handbook of Practical Program Evaluation , 4th ed.; Newcomer, K.E., Hatry, H.P., Wholey, J.S., Eds.; Jossey-Bass: San Francisco, CA, USA, 2015; pp. 281–317. [ Google Scholar ] [ CrossRef ]
• U.S. Department of Education. Department Hosts Raising the Bar: Literacy Math Series to Address Academic Recovery ; U.S. Department of Education: Washington, DC, USA. Available online: https://www.ed.gov/news/press-releases/department-hosts-raising-bar-literacy-math-series-address-academic-recovery (accessed on 18 October 2022).
• Pseudonym Department of Education. TCAP English Language Arts. Available online: https://www.tn.gov/education/districts/lea-operations/assessment/tnready/tnready-ela.html (accessed on 9 September 2023).
• Pseudonym Department of Education. TCAP Math. Available online: https://www.tn.gov/education/districts/lea-operations/assessment/tnready/tnready-math.html (accessed on 9 September 2023).

Click here to enlarge figure

Share and Cite

Xin, Z.; Bebell, D.; Cleveland, G. Examining the Relationship between Broadband Access, Parent Technology Beliefs, and Student Academic Outcomes. Educ. Sci. 2024 , 14 , 1057. https://doi.org/10.3390/educsci14101057

Xin Z, Bebell D, Cleveland G. Examining the Relationship between Broadband Access, Parent Technology Beliefs, and Student Academic Outcomes. Education Sciences . 2024; 14(10):1057. https://doi.org/10.3390/educsci14101057

Xin, Zhexun, Damian Bebell, and Gareth Cleveland. 2024. "Examining the Relationship between Broadband Access, Parent Technology Beliefs, and Student Academic Outcomes" Education Sciences 14, no. 10: 1057. https://doi.org/10.3390/educsci14101057

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

The relationship between academic stress and educational anxiety: learning anxiety and learning weariness as mediators

• Published: 25 September 2024

Cite this article

• Lei Han   ORCID: orcid.org/0000-0002-4199-5476 1 ,
• Xinhang Gao   ORCID: orcid.org/0009-0004-3796-5516 1 ,
• Xujie Wang 2 &
• Wentao Ren   ORCID: orcid.org/0009-0001-2893-3414 1  

42 Accesses

Explore all metrics

Parental educational anxiety is a result of the fierce competition in Chinese education. When their children face a series of academic problems, parents inevitably feel anxious about their children's educational and future development. Therefore, the aim of this study was to investigate the effects of academic stress on parental educational anxiety and the mediating effects of learning anxiety and learning weariness in junior high school students. A total of 467 students from two junior high schools in China and one of their parents (934 participants in total) were invited to complete a questionnaire. SEM analysis revealed that academic stress had a direct ( β  = 0.25, p  < 0.001) and indirect relationship with educational anxiety through the mediators of learning anxiety ( β  = 0.16, p  = 0.001) and learning weariness ( β  = 0.04, p  = 0.039). However, the chain mediating role of learning anxiety and learning weariness in the relationship between academic stress and educational anxiety was not significant ( β  = 0.01, p  = 0.284). Moreover, the academic stress, learning anxiety and learning weariness of junior high school students were associated with parental educational anxiety, but there was no necessary link between learning anxiety and learning weariness. Regular assessment of the academic stress and academic problems faced by junior high school students and the development of effective interventions are important for alleviating parental educational anxiety. Teachers should pay more attention to students' academic stress rather than focusing only on their grades and provide students with relevant education and assistance to alleviate their learning anxiety, reduce their learning weariness, and prevent or alleviate educational anxiety in parents.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Sources of academic stress among Iranian adolescents: a multilevel study from Qazvin City, Iran

Coping with Test Anxiety and Academic Performance in High School and University: Two Studies in Brazil

Academic Stress in the Final Years of School: A Systematic Literature Review

Data availability.

The data analysed in the current study are available at the Open Science Framework ( https://osf.io/gnq67/?view_only=2c840bc30c2a4de6a4340e20ba1f9ffb ).

Bai, Y., Liu, X., Zhang, B., Fu, M., Huang, N., Hu, Q., & Guo, J. (2022). Associations of youth mental health, parental psychological distress, and family relationships during the COVID-19 outbreak in China. BMC Psychiatry , 22 . https://doi.org/10.1186/s12888-022-03938-8

Baker, T. B., Piper, M. E., McCarthy, D. E., Majeskie, M. R., & Fiore, M. C. (2004). Addiction motivation reformulated: An affective processing model of negative reinforcement. Psychological Review, 111 (1), 33. https://doi.org/10.1037/0033-295X.111.1.33

Article   PubMed   Google Scholar  

Belsky, J., & Pluess, M. (2009). Beyond diathesis stress: Differential susceptibility to environmental influences. Psychological Bulletin, 135 (6), 885. https://doi.org/10.1037/a0017376

Bhujade, V. M. (2017). Depression, anxiety and academic stress among college students: A brief review. Indian Journal of Health & Wellbeing, 8 (7), 748–751.

Google Scholar  

Bowen, M. (1993). Family therapy in clinical practice . Jason Aronson.

Chen, Y., Huang, R., Lu, Y., & Zhang, K. (2021). Education fever in China: Children’s academic performance and parents’ life satisfaction. Journal of Happiness Studies, 22 (2), 927–954. https://doi.org/10.1007/s10902-020-00258-0

Article   Google Scholar  

Chen, G., Oubibi, M., Liang, A., & Zhou, Y. (2022). Parents’ educational anxiety under the “double reduction” policy based on the family and students’ personal factors. Psychology Research and Behavior Management, 15 , 2067–2082. https://doi.org/10.2147/PRBM.S370339

Article   PubMed   PubMed Central   Google Scholar  

Cheng, X. Y., & Fu, M. H. (2021). The relationship between parental educational anxiety and elementary school students’ academic emotions: The mediating role of parental educational involvement. Mental Health Education in Primary and Secondary School, 20 , 9–14.

Coyne, J. C., & Downey, G. (1991). Social factors and psychopathology: Stress, social support, and coping processes. Annual Review of Psychology, 42 (1), 401. https://doi.org/10.1146/annurev.ps.42.020191.002153

Crosnoe, R., & Benner, A. D. (2015). Children at school. In R. M. Lerner (Ed.), Handbook of child psychology and developmental science (7th ed., Vol. 4, pp. 268–304). Wiley.

Fu, A., Nie, J., Li, Y., Jin, B., & Cui, J. (2002). A correlation research on interventions in middle school students’ hatred for schooling and learning efficiency. Journal of Psychological Science, 25 , 22–23. https://doi.org/10.16719/j.cnki.1671-6981.2002.01.008

Gao, X. (2023). Academic stress and academic burnout in adolescents: A moderated mediating model. Frontiers in Psychology, 14 , 1133706. https://doi.org/10.3389/fpsyg.2023.1133706

Gibbons, I. R. (2022). Parent anxiety, parental psychological control, and adolescent anxiety: Mediation and bidirectional relationships (Doctoral dissertation, Brigham Young University).

Gonzálvez, C., Inglés, C. J., Fernández-Sogorb, A., Sanmartín, R., Vicent, M., & García-Fernández, J. M. (2020). Profiles derived from the school refusal assessment scale-revised and its relationship to anxiety. Educational Psychology, 40 (6), 767–780. https://doi.org/10.1080/01443410.2018.1530734

Hafeez, M., Saira, S., & Ijaz, A. (2022). Relationship between parental anxiety and students’ academic stress at secondary level. International Journal of Learning and Teaching, 14 (1), 12–28. https://doi.org/10.18844/ijlt.v14i1.6271

Hao, Z., Jin, L., Huang, J., & Wu, H. (2022). Stress, academic burnout, smartphone use types and problematic smartphone use: The moderation effects of resilience. Journal of Psychiatric Research, 150 , 324–331. https://doi.org/10.1016/j.jpsychires.2022.03.019

Hooda, M., & Saini, A. (2017). Academic anxiety: An overview. Educational Quest, 8 (3), 807–810. https://doi.org/10.5958/2230-7311.2017.00139.8

Huang, J., Cao, M., Zhu, D., & You, X. (2018). Contagion of academic anxiety among intimate senior high school students. Journal of Psychological Science , (6), 1382. https://doi.org/10.16719/j.cnki.1671-6981.20180614

Iida, M., Gleason, M., Green-Rapaport, A. S., Bolger, N., & Shrout, P. E. (2017). The influence of daily coping on anxiety under examination stress: A model of interindividual differences in intraindividual change. Personality and Social Psychology Bulletin, 43 (7), 907–923. https://doi.org/10.1177/0146167217700605

Jin, N. N. (2015). Review of domestic research on education anxiety. Journal of Beijing Institute of Education , (03), 31–35. https://doi.org/10.16398/j.cnki.jbjieissn1008-228x.2015.03.007

Jongerden, L., Simon, E., Bodden, D. H. M., Dirksen, C. D., & Bögels, S. M. (2015). Factors associated with the referral of anxious children to mental health care: The influence of family functioning, parenting, parental anxiety and child impairment. International Journal of Methods in Psychiatric Research, 24 (1), 46–57. https://doi.org/10.1002/mpr.1457

Kim, Y., Kwak, K., & Lee, S. (2016). Does optimism moderate parental achievement pressure and academic stress in Korean children? Current Psychology, 35 (1), 39–43. https://doi.org/10.1007/s12144-015-9355-5

Li, C., Zhang, X., & Cheng, X. (2022). Associations among academic stress, anxiety, extracurricular participation, and aggression: An examination of the general strain theory in a sample of Chinese adolescents.  Current Psychology: A Journal for Diverse Perspectives on Diverse Psychological Issues . https://doi.org/10.1007/s12144-022-03204-w

Little, T. D., Cunningham, W. A., Shahar, G., & Widaman, K. F. (2002). To parcel or not to parcel: Exploring the question, weighing the merits. Structural Equation Modeling: A Multidisciplinary Journal, 9 , 151–173. https://doi.org/10.1207/S15328007SEM0902_1

Liu, Q., Hong, X., & Wang, M. (2022). Parental educational anxiety during children’s transition to primary school in China. International Journal of Environmental Research and Public Health, 19 (23), 15479. https://doi.org/10.3390/ijerph192315479

Liu, Z., Xie, Y., Sun, Z., Liu, D., Yin, H., & Shi, L. (2023). Factors associated with academic burnout and its prevalence among university students: A cross-sectional study. BMC Medical Education , 23 (1). https://doi.org/10.1186/s12909-023-04316-y

Luo, X., He, H., & Pan, Y. (2021). Relationship between study weariness, self-compassion and problem behaviors of left-behind adolescents. Chinese Journal of School Health , (07), 1059–1063. https://doi.org/10.16835/j.cnki.1000-9817.2021.07.023

Mikolajczak, M., Raes, M. E., Avalosse, H., & Roskam, I. (2022). Exhausted parents: Sociodemographic, child-related, parent-related, parenting and family-functioning correlates of parental burnout. In  Key Topics in Parenting and Behaviour  (pp. 57–69). Cham: Springer Nature Switzerland. https://doi.org/10.1007/s10826-017-0892-4

Minamitani, N., & Matsumoto, Y. (2018). Developmental trial of a cognitive behaviour therapy program for parents of junior high students exhibiting school refusal: Evidence based on a small sample from a metropolitan area in Japan. School Health, 14 , 1–11. https://doi.org/10.20812/jash.SH_086

Muthén, L. K., & Muthén, B. O. (2017). Mplus user’s guide (8th ed.). Chapman and Hall/CRC.

Nasser-Abu Alhija, F., & Wisenbaker, J. (2006). A monte carlo study investigating the impact of item parceling strategies on parameter estimates and their standard errors in CFA. Structural Equation Modeling: A Multidisciplinary Journal, 13 (2), 204–228. https://doi.org/10.1207/s15328007sem1302_3

Núñez-Regueiro, F., & Núñez-Regueiro, S. (2021). Identifying salient stressors of adolescence: A systematic review and content analysis. Journal of Youth and Adolescence, 50 (12), 2533–2556. https://doi.org/10.1007/s10964-021-01492-2

Peng, X., Cai, T., Gui, T., & Fu, J. (2021). Moderating effect of psychological Sushi on relationship between study stress and suicidal ideation in adolescents. Journal of Psychological Science, 11 , 919–924.

Pinquart, M. (2019). Meta-analysis of anxiety in parents of young people with chronic health conditions. Journal of Pediatric Psychology, 44 (8), 959–969. https://doi.org/10.1093/jpepsy/jsz024

Poole, K. L., Van Lieshout, R. J., McHolm, A. E., Cunningham, C. E., & Schmidt, L. A. (2018). Trajectories of social anxiety in children: Influence of child cortisol reactivity and parental social anxiety. Journal of Abnormal Child Psychology, 46 (6), 1309–1319. https://doi.org/10.1007/s10802-017-0385-3

Rosenthal, L., Moro, M. R., & Benoit, L. (2020). Migrant parents of adolescents with school refusal: A qualitative study of parental distress and cultural barriers in access to care. Frontiers in Psychiatry, 10 , 942. https://doi.org/10.3389/fpsyt.2019.00942

Silver, A. M., Elliott, L., & Libertus, M. E. (2021). When beliefs matter most: Examining children’s math achievement in the context of parental math anxiety. Journal of Experimental Child Psychology, 201 , 104992. https://doi.org/10.1016/j.jecp.2020.104992

Staudt, M. (2014). The needs of parents of youth who are truant: Implications for best practices. Best Practice in Mental Health, 10 (1), 47–53.

Surging news. (2021). CCTV Survey: Children's education anxiety up to 36% of major family difficulties in 2020. Baidu. Retrieved April 27, 2021, from: https://baijiahao.baidu.com/s?id=1698156916458803127&wfr=spider&for=pc

Tian, Q., Deng, S. C., & Guo, J. (2012). The influence of self-determination motivation on test anxiety: Procrastinations as a different mediator. Journal of Psychological Science, 35 (5), 1096. https://doi.org/10.16719/j.cnki.1671-6981.2012.05.019

Trevethan, M., Jain, A. T., Shatiyaseelan, A., Luebbe, A. M., & Raval, V. V. (2022). A longitudinal examination of the relation between academic stress and anxiety symptoms among adolescents in India: The role of physiological hyperarousal and social acceptance. International Journal of Psychology, 57 (3), 401–410. https://doi.org/10.1002/ijop.12825

Wu, Y., Schulz, L. E., Frank, M. C., & Gweon, H. (2021). Emotion as information in early social learning. Current Directions in Psychological Science, 30 (6), 468–475. https://doi.org/10.1177/09637214211040779

Wu, K., Wang, F., Wang, W., & Li, Y. (2022). Parents’ education anxiety and children’s academic burnout: The role of parental burnout and family function. Frontiers in Psychology, 12 , 764824. https://doi.org/10.3389/fpsyg.2021.764824

Xu, J., Cao, J., Cui, L., & Zhu, P. (2010). Preliminary compilation of study stress questionnaire for middle school students. Chinese Journal of School Health, 1 , 68–69. https://doi.org/10.16835/j.cnki.1000-9817.2010.01.032

Xu, F., Cui, W., & Lawrence, P. J. (2020). The intergenerational transmission of anxiety in a Chinese population: The mediating effect of parental control. Journal of Child and Family Studies, 29 (6), 1669–1678. https://doi.org/10.1007/s10826-019-01675-3

Yan, N., & Ansari, A. (2017). Bidirectional relations between intrusive caregiving among parents and teachers and children’s externalizing behaviour problems. Early Childhood Research Quarterly, 41 , 13–20. https://doi.org/10.1016/j.ecresq.2017.05.004

Yu, S., Zheng, J., Xu, Z., & Zhang, T. (2022). The transformation of parents’ perception of education involution under the background of “double reduction” policy: The mediating role of education anxiety and perception of education equity. Frontiers in Psychology, 13 , 800039. https://doi.org/10.3389/fpsyg.2022.800039

Zhao, Y. (2019). Establishment and application of junior middle school Students’ learning weariness scale. Journal of Shanghai Educational Research, 10 , 27–30. https://doi.org/10.16194/j.cnki.31-1059/g4.2019.10.006

Zhao, J. X., Zhao, J. X., & Wang, M. F. (2018). The transmission of anxiety from left-behind women to children: Moderated mediating effect. Psychological Development and Education, 34 (6), 86–93. https://doi.org/10.16187/j.cnki.issn1001-4918.2018.06.11

Zheng, Q., Wen, N., Xu, F., & Zhu, J. H. (2004). Exploration on and modification of structure of mental health test (MHT). Chinese Journal of Applied Psychology, 02 , 3–7.

Download references

Acknowledgements

The author would like to acknowledge all the participants in the study. We are very grateful to the editor and reviewers for their work as well as their suggestions for this paper.

This study was funded by the National Natural Science Foundation of China (62077034), Taishan Scholar Project of Shandong Province (tsqn202306153), Jinan City School Integration Development Strategy Project (JNSX2023037), and Haiyou Innovation and Research Team on Cyberpsychology. Funds were used to support data collection.

Author information

Authors and affiliations.

School of Psychology, Shandong Normal University, 1 Daxue Road, Changqing District, Jinan, Shandong, China

Lei Han, Xinhang Gao & Wentao Ren

Bishan Bashu Secondary School, Xinyan Road, Biquan Street, Bishan District, Chongqing, China

You can also search for this author in PubMed   Google Scholar

Contributions

Lei Han: Conceptualization, Resources, Supervision, Writing—review & editing.

Xinhang Gao: Investigation, Writing—original draft, Writing—review & editing.

Wentao Ren: Software, Investigation, Writing—review & editing.

Xujie Wang: Software, Investigation, Writing—review & editing.

Corresponding authors

Correspondence to Xujie Wang or Wentao Ren .

Ethics declarations

Informed consent.

Written informed consent to participate in this study was provided by all individual participants’ legal guardians/next of kin.

Human participants and animal

The study involving human participants was reviewed and approved by the Ethical Committee of the School of Psychology, Shandong Normal University (SDNU2024053).

Conflict of interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Lei Han is the first authorship.

Xinhang Gao is the second authorship.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Han, L., Gao, X., Wang, X. et al. The relationship between academic stress and educational anxiety: learning anxiety and learning weariness as mediators. Curr Psychol (2024). https://doi.org/10.1007/s12144-024-06738-3

Download citation

Accepted : 17 September 2024

Published : 25 September 2024

DOI : https://doi.org/10.1007/s12144-024-06738-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Academic stress
• Learning anxiety
• Learning weariness
• Parental educational anxiety
• Junior high school student
• Find a journal
• Publish with us
• Track your research