definition of homework by different authors

Homework: Facts and Fiction

• Living reference work entry
• First Online: 09 November 2021
• Cite this living reference work entry

• Rubén Fernández-Alonso 4 , 5 &
• José Muñiz 6  

Part of the book series: Springer International Handbooks of Education ((SIHE))

241 Accesses

4 Citations

Homework is a universal student practice. Despite this universality, the role that homework plays in student academic performance is complex and open to various interpretations. This chapter reviews the current available evidence about the relationships between homework and achievement. We begin by examining the differences between countries and follow that by reviewing the influence of variables related to student homework behavior, teaching practices around assigning homework, and the role of the family in helping with homework. The results indicate that the relationship between time spent on homework and school results is curvilinear, and the best results are seen to be associated with moderate amounts of daily homework. With regard to student homework behavior, there is abundant evidence indicating that the “how” is much more important than the “how much.” Commitment and effort, the emotions prompted by the task, and autonomous working are three key aspects in predicting academic achievement. Effective teaching practice around homework is determined by setting it daily and systematic review. Although family involvement in the educational process is desirable, in the case of homework, direct help has doubtful effects on student achievement.

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

Access this chapter

Institutional subscriptions

Bembenutty, H., & White, M. C. (2013). Academic performance and satisfaction with homework completion among college students. Learning and Individual Differences, 24 , 83–88. https://doi.org/10.1016/j.lindif.2012.10.013

Article   Google Scholar  

Borgonovi, F., & Montt, G. (2012). Parental involvement in selected PISA countries and economies . OECD Education Working Papers, 73. OECD Publishing. https://doi.org/10.1787/5k990rk0jsjj-en .

Castro, M., Expósito-Casas, E., López-Martín, E., Lizasoain, L., Navarro-Asencio, E., & Gaviria, J. L. (2015). Parental involvement on student achievement: A meta-analysis. Educational Research Review, 14 , 33–46. https://doi.org/10.1016/j.edurev.2015.01.002

Cooper, H. (1989). Homework . Longman. https://doi.org/10.1037/11578-000

Book   Google Scholar  

Cooper, H., Robinson, J. C., & Patall, E. A. (2006). Does homework improve academic achievement? A synthesis of research, 1987–2003. Review of Educational Research, 76 , 1–62. https://doi.org/10.3102/00346543076001001

Cooper, H., & Valentine, J. C. (2001). Using research to answer practical questions about homework. Educational Psychologist, 36 (3), 143–153. https://doi.org/10.1207/S15326985EP3603_1

Corno, L. (1996). Homework is a complicated thing. Educational Researcher, 25 (8), 27–30. www.jstor.org/stable/1176489

De Jong, R., Westerhof, K. J., & Creemers, B. P. M. (2000). Homework and student math achievement in junior high schools. Educational Research and Evaluation, 6 (2), 130–157. https://doi.org/10.1076/1380-3611(200006)6:2;1-E;F130

Dettmers, S., Trautwein, U., Lüdtke, M., Kunter, M., & Baumert, J. (2010). Homework works if homework quality is high: Using multilevel modeling to predict the development of achievement in mathematics. Journal of Educational Psychology, 102 , 467–482. https://doi.org/10.1037/a0018453

Dettmers, S., Trautwein, U., & Lüdtke, O. (2009). The relationship between homework time and achievement is not universal: Evidence from multilevel analyses in 40 countries. School Effectiveness and School Improvement, 20 (4), 375–405. https://doi.org/10.1080/09243450902904601

Dettmers, S., Trautwein, U., Lüdtke, O., Goetz, T., Pekrun, R., & Frenzel, A. (2011). Students‘ emotions during homework in mathematics: Testing a theoretical model of antecedents and achievement outcomes. Contemporary Educational Psychology, 36 , 25–35. https://doi.org/10.1016/j.cedpsych.2010.10.001

Dumont, H., Trautwein, U., Nagy, G., & Nagengast, B. (2014). Quality of parental homework involvement: Predictors and reciprocal relations with academic functioning in the reading domain. Journal of Educational Psychology, 102 , 144–161. https://doi.org/10.1037/a0034100

Epstein, J. L. (1988). Homework practices, achievements, and behaviors of elementary school students . Center for Research on Elementary and Middle Schools, Johns Hopkins University. Retrieved December 13, 2020, from: https://eric.ed.gov/?id=ED301322

Epstein, J. L., & Van Voorhis, F. L. (2001). More than minutes: Teachers’ roles in designing homework. Educational Psychologist, 36 (3), 181–193. https://doi.org/10.1207/S15326985EP3603_4

Falch, T., & Rønning, M. (2012). Homework assignment and student achievement in OECD countries . Statistics Norway discussion papers, 711. Statistics Norway. Retrieved April 15, 2020, from: http://hdl.handle.net/10419/192693

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. Educational Research Review, 20 , 35–54. https://doi.org/10.1016/j.edurev.2016.11.003

Farrow, S., Tymms, P., & Henderson, B. (1999). Homework and attainment in primary schools. British Educational Research Journal, 25 (3), 323–341. https://doi.org/10.1080/0141192990250304

Fernández-Alonso, R., Álvarez-Díaz, M., Suárez-Álvarez, J., & Muñiz, J. (2017). Students’ achievement and homework assignment strategies. Frontiers in Psychology, 8 , 286. https://doi.org/10.3389/fpsyg.2017.00286

Fernández-Alonso, R., Álvarez-Díaz, M., Woitschach, P., Suárez-Álvarez, J., & Cuesta, M. (2017). Parental involvement and academic performance: Less control and more communication. Psicothema, 29 , 453–461. https://doi.org/10.7334/psicothema2017.181

Fernández-Alonso, R., Suárez-Álvarez, J., & Muñiz, J. (2015). Adolescents’ homework performance in mathematics and science: Personal factors and teaching practices. Journal of Educational Psychology, 107 (4), 1075–1085. https://doi.org/10.1037/edu0000032

Fernández-Alonso, R., Suárez-Álvarez, J., & Muñiz, J. (2016). Homework and performance in mathematics: The role of the teacher, the family and the student’s background. Revista de Psicodidáctica, 21 (1), 5–23. https://doi.org/10.1387/RevPsicodidact.13939

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. Frontiers in Psychology, 10 , 95. https://doi.org/10.3389/fpsyg.2019.00095

Flunger, B., Trautwein, U., Nagengast, B., Lüdtke, O., Niggli, A., & Schnyder, I. (2015). The Janus-faced nature of time spent on homework: Using latent profile analyses to predict academic achievement over a school year. Learning and Instruction, 39 , 97–106. https://doi.org/10.1016/j.learninstruc.2015.05.008

Flunger, B., Trautwein, U., Nagengast, B., Lüdtke, O., Niggli, A., & Schnyder, I. (2017). A person-centered approach to homework behavior: Students’ characteristics predict their homework learning type. Contemporary Educational Psychology, 48 , 1–15. https://doi.org/10.1016/j.cedpsych.2016.07.002

Flunger, B., Trautwein, U., Nagengast, B., Lüdtke, O., Niggli, A., & Schnyder, I. (2019). Using multilevel mixture models in educational research: An illustration with homework research. The Journal of Experimental Education. https://doi.org/10.1080/00220973.2019.1652137

Goetz, T., Nett, U. E., Martiny, S. E., Hall, N. C., Pekrun, R., Dettmers, S., & Trautwein, U. (2012). Students’ emotions during homework: Structures, self-concept antecedents, and achievement outcomes. Learning and Individual Differences, 22 , 225–234. https://doi.org/10.1016/j.lindif.2011.04.006

Gustafsson, J. E. (2013). Causal inference in educational effectiveness research: A comparison of three methods to investigate effects of homework on student achievement. School Effectiveness and School Improvement, 24 (3), 275–295. https://doi.org/10.1080/09243453.2013.806334

Hattie, J. (2009). Visible learning. A synthesis of over 800 meta-analyses relating to achievement . Routledge.

Google Scholar  

Hill, N. E., & Tyson, D. F. (2009). Parental involvement in middle school: A meta-analytic assessment of the strategies that promote achievement. Developmental Psychology, 45 (3), 740–763. https://doi.org/10.1037/a0015362

Hoover-Dempsey, K. V., Battiato, A. C., Walker, J. M. T., Reed, R. P., DeJong, J. M., & Jones, K. P. (2001). Parental involvement in homework. Educational Psychologist, 36 , 195–210. https://doi.org/10.1207/S15326985EP3603_5

Jerrim, J., Lopez-Agudo, L. A., & Marcenaro-Gutierrez, O. D. (2020). The association between homework and primary school children’s academic achievement. International evidence from PIRLS and TIMSS. European Journal of Education, 00 , 1–13. https://doi.org/10.1111/ejed.12374

Kim, S. w., & Hill, N. E. (2015). Including fathers in the picture: A meta-analysis of parental involvement and students’ academic achievement. Journal of Educational Psychology, 107 (4), 919–934. https://doi.org/10.1037/edu0000023

Kitsantas, A., Cheema, J., & Ware, H. (2011). Mathematics achievement: The role of homework and self-efficacy beliefs. Journal of Advanced Academics, 22 (2), 310–339. https://doi.org/10.1177/1932202X1102200206

Kitsantas, A., & Zimmerman, B. J. (2009). College students homework and academic achievement: The mediating role of self-regulatory beliefs. Metacognition and Learning, 4 (2), 1556–1623. https://doi.org/10.1007/s11409-008-9028-y

Lee, J., & Stankov, L. (2018). Non-cognitive predictors of academic achievement: Evidence from TIMSS and PISA. Learning and Individual Differences, 65 , 50–64. https://doi.org/10.1016/j.lindif.2018.05.009

Moroni, S., Dumont, H., Trautwein, U., Niggli, A., & Baeriswyl, F. (2015). The need to distinguish between quantity and quality in research on parental involvement: The example of parental help with homework. Journal of Educational Research, 108 (5), 417–431. https://doi.org/10.1080/00220671.2014.901283

Murillo, F. J. (2003). La investigación sobre eficacia escolar en Iberoamérica. Revisión Internacional sobre el estado del arte [Research on school effectiveness in Latin America. An international review] . Convenio Andrés Bello. Retrieved December 13, 2020, from: http://convenioandresbello.org/cab/educacion/eficacia-escolar-en-iberoamerica/

Núñez, J. C., Suárez, N., Cerezo, R., González-Pienda, J., Rosário, P., Mourão, R., & Valle, A. (2015). Homework and academic achievement across Spanish compulsory education. Educational Psychology, 35 (6), 726–746. https://doi.org/10.1080/01443410.2013.817537

Núñez, J. C., Suárez, N., Rosário, P., Vallejo, G., Cerezo, R., & Valle, A. (2015). Teachers’ feedback on homework, homework-related behaviors, and academic achievement. The Journal of Educational Research, 108 , 204–216. https://doi.org/10.1080/00220671.2013.878298

OECD. (2013). PISA 2012 results: What makes schools successful? Resources, policies and practices (volume IV) . OECD Publishing. https://doi.org/10.1787/9789264201156-en

Paschal, R. A., Weinstein, T., & Walberg, H. J. (1984). The effects of homework on learning: A quantitative synthesis. The Journal of Educational Research, 78 (2), 97–104. https://doi.org/10.1080/00220671.1984.10885581

Patall, E. A., Cooper, H., & Robinson, J. C. (2008). Parent involvement in homework: A research synthesis. Review of Educational Research, 78 (4), 1039–1101. https://doi.org/10.3102/0034654308325185

Pomerantz, E. M., Moorman, E. A., & Litwack, S. D. (2007). The how, whom, and why of parents’ involvement in children’s academic lives: More is not always better. Review of Educational Research, 77 (3), 373–410. https://doi.org/10.3102/003465430305567

Ramdass, D., & Zimmerman, B. J. (2011). Developing self-regulation skills: The important role of homework. Journal of Advanced Academics, 22 , 194–218. https://doi.org/10.1177/1932202X1102200202

Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods . Sage.

Rosário, P., Cunha, J., Nunes, T., Nunes, A. R., Moreira, T., & Núñez, J. C. (2019). “Homework should be…but we do not live in an ideal world”: Mathematics teachers’ perspectives on quality homework and on homework assigned in elementary and middle schools. Frontiers in Psychology, 10 , 224. https://doi.org/10.3389/fpsyg.2019.00224

Rosário, P., Mourão, R., Baldaque, M., Nunes. T., Núñez, J. C., González-Pienda, J. A., Cerezo, R., & Valle A. (2009). Homework, self-regulated learning and Math achievement. Revista de Psicodidáctica, 14 (2), 179–192. Retrieved December 13, 2020, from: https://ojs.ehu.eus/index.php/psicodidactica/article/view/721

Sammons, P., Hillman, J., & Mortimore, P. (1995). Key characteristics of effective schools: A review of school effectiveness research . Office for Standards in Education. Retrieved December 13, 2020, from: https://eric.ed.gov/?id=ED389826

Scheerens, J. (2016). Educational effectiveness and ineffectiveness. A critical review of the knowledge base . Springer. https://doi.org/10.1007/978-94-017-7459-8

Scheerens, J., Hendriks, M., Luyten, H., Sleegers, P., & Cees, G. (2013). Productive time in education. A review of the effectiveness of teaching time at school, homework and extended time outside school hours . University of Twente. Retrieved April 15, 2020, from: https://research.utwente.nl/en/publications/productive-time-in-education-a-review-of-the-effectiveness-of-tea

Scheerens, J., Witziers, B., & Steen, R. (2013). A meta-analysis of school effectiveness studies. Revista de Educación, 361 , 619–645. https://doi.org/10.4438/1988-592X-RE-2013-361-235

Shell, D. F., Brooks, D. W., Trainin, G., Wilson, K. M., Kauffman, D. F., & Herr, L. M. (2010). The unified learning model . Springer. https://doi.org/10.1007/978-90-481-3215-7

Sirin, S. R. (2005). Socioeconomic status and academic achievement: A Meta-Analytic review of research. Review of Educational Research, 75 (3), 417–453. https://doi.org/10.3102/00346543075003417

Stoeger, H., & Ziegler, A. (2008). Evaluation of a classroom based training to improve self-regulation in time management tasks during homework activities with fourth graders. Metacognition and Learning, 3 , 207–230. https://doi.org/10.1007/s11409-008-9027-z

Tan, C. Y., Lyu, M., & Peng, B. (2019). Academic benefits from parental involvement are stratified by parental socioeconomic status: A Meta-analysis. Parenting , 1–47. https://doi.org/10.1080/15295192.2019.1694836

Tan, C. Y., Peng, B., & Lyu, M. (2019). What types of cultural capital benefit students’ academic achievement at different educational stages? Interrogating the meta-analytic evidence. Educational Research Review , 100289. https://doi.org/10.1016/j.edurev.2019.100289

Trautwein, U. (2007). The homework–achievement relation reconsidered: Differentiating homework time, homework frequency, and homework effort. Learning and Instruction, 17 , 372–388. https://doi.org/10.1016/j.learninstruc.2007.02.009

Trautwein, U., & Köller, O. (2003). The relationship between homework and achievement: Still much of a mystery. Educational Psychology Review, 15 , 115–145. https://doi.org/10.1023/A:1023460414243

Trautwein, U., Köller, O., Schmitz, B., & Baumert, J. (2002). Do homework assignments enhance achievement? A multilevel analysis in 7th grade mathematics. Contemporary Educational Psychology, 27 , 26–50. https://doi.org/10.1006/ceps.2001.1084

Trautwein, U., & Lüdtke, O. (2007). Students’ self-reported effort and time on homework in six school subjects: Between-student differences and within-student variation. Journal of Educational Psychology, 99 , 432–444. https://doi.org/10.1037/0022-0663.99.2.432

Trautwein, U., Niggli, A., Schnyder, I., & Lüdtke, O. (2009). Between-teacher differences in homework assignments and the development of students’ homework effort, homework emotions, and achievement. Journal of Educational Psychology, 101 , 176–189. https://doi.org/10.1037/0022-0663.101.1.176

Trautwein, U., Schnyder, I., Niggli, A., Neumann, M., & Lüdtke, O. (2009). Chameleon effects in homework research: The homework–achievement association depends on the measures used and the level of analysis chosen. Contemporary Educational Psychology, 34 , 77–88. https://doi.org/10.1016/j.cedpsych.2008.09.001

UNESCO, & LLECE. (2000). Primer Estudio Internacional Comparativo sobre lenguaje, matemática y factores asociados, para alumnos del tercer y cuarto grado de la educación básica [First international comparative study on language, mathematics and associated factors, for third and fourth grade students of basic education] . UNESCO.

UNESCO-OREALC, & LLECE. (2016). Informe de resultados del Tercer Estudio regional Comparativo y Explicativo. Factores Asociados [Report of the third regional comparative and explanatory study. Effectiveness factors] . UNESCO.

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 (4), 422–428. https://doi.org/10.7334/psicothema2019.191

Wilder, S. (2014). Effects of parental involvement on academic achievement: A meta-synthesis. Educational Review, 66 (3), 377–397. https://doi.org/10.1080/00131911.2013.780009

Wingard, L., & Forsberg, L. (2009). Parent involvement in children’s homework in American and Swedish dual-earner families. Journal of Pragmatics, 41 , 1576–1595. https://doi.org/10.1016/j.pragma.2007.09.010

Xu, J. (2013). Why do students have difficulties completing homework? The need for homework management. Journal of Education and Training Studies, 1 (1), 98–105. https://doi.org/10.11114/jets.v1i1.78

Xu, J., & Wu, H. (2013). Self-regulation of homework behavior: Homework management at the secondary school level. The Journal of Educational Research, 106 (1), 1–13. https://doi.org/10.1080/00220671.2012.658457

Xu, J., Yuan, R., Xu, B., & Xu, M. (2014). Modeling students’ time management in math homework. Learning and Individual Differences, 34 , 33–42. https://doi.org/10.1016/j.lindif.2014.05.011

Zimmerman, B. J., & Kitsantas, A. (2005). Homework practices and academic achievement: The mediating role of self-efficacy and perceived responsibility beliefs. Contemporary Educational Psychology, 30 , 397–417. https://doi.org/10.1016/j.cedpsych.2005.05.003

Download references

Author information

Authors and affiliations.

Department of Education, Government of Principado de Asturias, Oviedo, Spain

Rubén Fernández-Alonso

University of Oviedo, Oviedo, Spain

Nebrija University, Madrid, Spain

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Rubén Fernández-Alonso .

Editor information

Editors and affiliations.

University of Oslo, Blindern, Oslo, Norway

Trude Nilsen

IEA Hamburg, Hamburg, Hamburg, Germany

Agnes Stancel-Piątak

University of Gothenburg, Gothenburg, Sweden

Jan-Eric Gustafsson

Section Editor information

CEMO, University of Oslo, Oslo, Norway

Ronny Scherer

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this entry

Cite this entry.

Fernández-Alonso, R., Muñiz, J. (2021). Homework: Facts and Fiction. In: Nilsen, T., Stancel-Piątak, A., Gustafsson, JE. (eds) International Handbook of Comparative Large-Scale Studies in Education. Springer International Handbooks of Education. Springer, Cham. https://doi.org/10.1007/978-3-030-38298-8_40-1

Download citation

DOI : https://doi.org/10.1007/978-3-030-38298-8_40-1

Received : 31 January 2021

Accepted : 03 February 2021

Published : 09 November 2021

Publisher Name : Springer, Cham

Print ISBN : 978-3-030-38298-8

Online ISBN : 978-3-030-38298-8

eBook Packages : Springer Reference Education Reference Module Humanities and Social Sciences Reference Module Education

• Publish with us

Policies and ethics

• Find a journal
• Track your research

Definitions of homework, types of homework, and ratings of the importance of homework among psychologists with cognitive behavior therapy and psychoanalytic theoretical orientations

Affiliation.

• 1 School of Psychological Science, La Trobe University, Victoria, Australia. [email protected]
• PMID: 20527055
• DOI: 10.1002/jclp.20699

A random sample of 827 psychologists were surveyed to assess their definitions of homework, use of homework tasks, and perceived importance of homework. Theoretical orientation distinguished practitioners' responses. Cognitive-behavioral therapists defined homework as being closer to empirically supported therapy, whereas psychodynamic therapists rated homework as less characteristic of a process that embraces client responsibility and adaptive skills. Cognitive-behavior therapists did not limit their choices to activity-based tasks, and psychodynamic therapists reported using behavioral tasks "sometimes." Monitoring dreams and conscious thought were also used among the entire sample surveyed. Psychodynamic therapists rated homework as "somewhat" or "moderately" important, whereas cognitive-behavior therapists more often rated homework as "very important." Data suggest some homework may be common to different psychotherapeutic approaches. Findings are discussed in the context of recent theoretical work on homework in psychotherapy and recommendations for future research.

• Cognitive Behavioral Therapy / education*
• Middle Aged
• Psychoanalysis / education*
• Psychoanalytic Theory*
• Psychoanalytic Therapy / education*
• Psychology / methods*
• Surveys and Questionnaires

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

Preview improvements coming to the PMC website in October 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 [ 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 ]

• More from M-W
• To save this word, you'll need to log in. Log In

Definition of homework

Examples of homework in a sentence.

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'homework.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

1662, in the meaning defined at sense 1

Dictionary Entries Near homework

Cite this entry.

“Homework.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/homework. Accessed 25 Apr. 2024.

Kids Definition

Kids definition of homework, more from merriam-webster on homework.

Thesaurus: All synonyms and antonyms for homework

Nglish: Translation of homework for Spanish Speakers

Britannica English: Translation of homework for Arabic Speakers

Britannica.com: Encyclopedia article about homework

Subscribe to America's largest dictionary and get thousands more definitions and advanced search—ad free!

Can you solve 4 words at once?

Word of the day, tendentious.

See Definitions and Examples »

Get Word of the Day daily email!

Popular in Grammar & Usage

More commonly misspelled words, commonly misspelled words, how to use em dashes (—), en dashes (–) , and hyphens (-), absent letters that are heard anyway, how to use accents and diacritical marks, popular in wordplay, 10 words from taylor swift songs (merriam's version), 9 superb owl words, 'gaslighting,' 'woke,' 'democracy,' and other top lookups, 10 words for lesser-known games and sports, your favorite band is in the dictionary, games & quizzes.

Orders of are accepted for more complex assignment types only (e.g. Dissertation, Thesis, Term paper, etc.). Special conditions are applied to such orders. That is why please kindly choose a proper type of your assignment.

We do not tolerate any form of plagiarism and use modern software to detect any form of it

Alexander Freeman

Finished Papers

Andre Cardoso

What if I can’t write my essay?

Can i hire someone to write essay.

Student life is associated with great stress and nervous breakdowns, so young guys and girls urgently need outside help. There are sites that take all the responsibility for themselves. You can turn to such companies for help and they will do all the work while clients relax and enjoy a carefree life.

Take the choice of such sites very seriously, because now you can meet scammers and low-skilled workers.

On our website, polite managers will advise you on all the details of cooperation and sign an agreement so that you are confident in the agency. In this case, the user is the boss who hires the employee to delegate responsibilities and devote themselves to more important tasks. You can correct the work of the writer at all stages, observe that all special wishes are implemented and give advice. You pay for the work only if you liked the essay and passed the plagiarism check.

We will be happy to help you complete a task of any complexity and volume, we will listen to special requirements and make sure that you will be the best student in your group.

Emery Evans

There are questions about essay writing services that students ask about pretty often. So we’ve decided to answer them in the form of an F.A.Q.

Is essay writing legitimate?

As writing is a legit service as long as you stick to a reliable company. For example, is a great example of a reliable essay company. Choose us if you’re looking for competent helpers who, at the same time, don’t charge an arm and a leg. Also, our essays are original, which helps avoid copyright-related troubles.

Are your essay writers real people?

Yes, all our writers of essays and other college and university research papers are real human writers. Everyone holds at least a Bachelor’s degree across a requested subject and boats proven essay writing experience. To prove that our writers are real, feel free to contact a writer we’ll assign to work on your order from your Customer area.

Is there any cheap essay help?

You can have a cheap essay writing service by either of the two methods. First, claim your first-order discount – 15%. And second, order more essays to become a part of the Loyalty Discount Club and save 5% off each order to spend the bonus funds on each next essay bought from us.

Can I reach out to my essay helper?

Contact your currently assigned essay writer from your Customer area. If you already have a favorite writer, request their ID on the order page, and we’ll assign the expert to work on your order in case they are available at the moment. Requesting a favorite writer is a free service.

Tinggalkan Balasan Batalkan balasan

Alamat email Anda tidak akan dipublikasikan. Ruas yang wajib ditandai *

Finished Papers

(415) 397-1966

Check your email for notifications. Once your essay is complete, double-check it to see if it falls under your expectations and if satisfied-release the funds to your writer. Keep in mind that our essay writing service has a free revisions policy.

Customer Reviews