The items, completed on paper, were in various formats and were subject to binary scoring, except 21 items which were coded on a polytomous scale, between 0 and 2 points Ministerio de Educación, As a single student is not capable of answering the complete item pool in the time given, the items were distributed across various booklets following a matrix design Fernández-Alonso and Muñiz, The mean Cronbach α for the booklets ranged from 0. Student scores were calculated adjusting the bank of items to Rasch's IRT model using the ConQuest 2. The student's scores were divided into five categories, estimated using the plausible values method.

In large scale assessments this method is better at recovering the true population parameters e. A questionnaire was made up of a mix of items which allowed the calculation of the indicators used for the description of homework variables. Daily minutes spent on homework was calculated from a multiple choice question with the following options: a Generally I don't have homework; b 1 h or less; c Between 1 and 2 h; d Between 2 and 3 h; e More than 3 h. According to Trautwein and Köller the average homework time of the students in a school could be regarded as a good proxy for the amount of homework assigned by the teacher. So the mean of this variable for each school was used as an estimator of Amount or volume of homework assigned.

The fourth variable was Socioeconomic and cultural index SECI , which is constructed with information about family qualifications and professions, along with the availability of various material and cultural resources at home. It is expressed in standardized points, N 0,1. The other two adjustment variables were Academic Expectations and Motivation which were included for two reasons: they are both closely connected to academic achievement Suárez-Álvarez et al. Their position as adjustment factors is justified because, in an ex-post facto descriptive design such as this, both expectations and motivation may be thought of as background variables that the student brings with them on the day of the test.

Academic expectations for finishing education was measured with a multiple-choice item where the score corresponds to the years spent in education in order to reach that level of qualification: compulsory secondary education 10 points ; further secondary education 12 points ; non-university higher education 14 points ; University qualification 16 points. Motivation was constructed from the answers to six four-point Likert items, where 1 means strongly disagree with the sentence and 4 means strongly agree. As this was an official evaluation, the tests used were created by experts in the various fields, contracted by the Spanish Ministry of Education in collaboration with the regional education authorities.

Firstly the descriptive statistics and Pearson correlations between the variables were calculated. Then, using the HLM 6. Given that HLM does not return standardized coefficients, all of the variables were standardized around the general mean, which allows the interpretation of the results as classical standardized regression analysis coefficients. Levels 2 and 3 variables were constructed from means of standardized level 1 variables and were not re-standardized. Level 1 variables were introduced without centering except for four cases: study time, motivation, expectation, and socioeconomic and cultural level which were centered on the school mean to control composition effects Xu and Wu, and estimate the effect of differences in homework time among the students within the same school.

Recovery was carried out using the procedure described in Fernández-Alonso et al. The results are presented in two ways: the tables show standardized coefficients while in the figures the data are presented in a real scale, taking advantage of the fact that a scale with a point standard deviation allows the expression of the effect of the variables and the differences between groups as percentage increases in standardized points. As can be seen in the table, the relationship between the variables turned out to be in the expected direction, with the closest correlations between the different academic performance scores and socioeconomic level, appropriate school year, and student expectations. The nationality variable gave the highest asymmetry and kurtosis, which was to be expected as the majority of the sample are Spanish.

Descriptive statistics and Pearson correlation matrix between the variables. β, Standardized weight; SE, Standard Error; SECI, Socioeconomic and cultural index; AC, Autonomous Communities. The results indicated that the adjustment variables behaved satisfactorily, with enough control to analyze the net effects of the homework variables. This was backed up by two results, firstly, the two variables with highest standardized coefficients were those related to educational history: academic expectations at the time of the test, and being in the school year corresponding to age.

Motivation demonstrated a smaller effect but one which was significant in all cases. Secondly, the adjustment variables explained the majority of the variance in the results. The amount of homework turned out to be positively and significantly associated with the results in the four subjects. In a point scale of standard deviation, controlling for other variables, it was estimated that for each 10 min added to the daily volume of homework, schools would achieve between 4. In other words, an increase of between 15 and 29 points in the school mean is predicted for each additional hour of homework volume of the school as a whole. This school level gain, however, would only occur if the students spent exactly the same time on homework as their school mean.

As the regression coefficient of student homework time is negative and the variable is centered on the level of the school, the model predicts deterioration in results for those students who spend more time than their class mean on homework, and an improvement for those who finish their homework more quickly than the mean of their classmates. Furthermore, the results demonstrated a positive association between the amount of homework assigned in a school and the differences in time needed by the students to complete their homework. The correlation between the variables was 0. So schools which assigned more homework also tended to demonstrate greater differences in the time students need to spend on that homework. Relationship between school homework volume and differences in time needed by students to complete homework.

The result in mathematics was used as a dependent variable because, as previously noted, it was the subject where the effect was smallest and as such is the most conservative prediction. With other subjects the results might be even clearer. Prediction of results for quick and slow students according to school homework size. In type 2 schools, where the homework amount is 2 h a day, the differences increase from just over an hour 65 min for a quick student to almost 3 h min for a slow student. On the other hand, the slow student in a type 2 school would score 6 points more than the quick student in a type 1 school. However, to achieve this, the slow student in a type 2 school would need to spend five times as much time on homework in a week It seems like a lot of work for such a small gain.

The data in this study reaffirm the multilevel nature of homework Trautwein and Köller, and support this study's first hypothesis: the amount of homework mean daily minutes the student spends on homework is positively associated with academic results, whereas the time students spent on homework considered individually is negatively associated with academic results. These findings are in line with previous research, which indicate that school-level variables, such as amount of homework assigned, have more explanatory power than individual variables such as time spent De Jong et al.

On the basis of this evidence, common sense would dictate the conclusion that frequent and abundant homework assignment may be one way to improve school efficiency. However, as noted previously, the relationship between homework and achievement is paradoxical- appearances are deceptive and first conclusions are not always confirmed. Analysis demonstrates another two complementary pieces of data which, read together, raise questions about the previous conclusion. In the first place, time spent on homework at the individual level was found to have a negative effect on achievement, which confirms the findings of other multilevel-approach research Trautwein, ; Trautwein et al.

Furthermore, it was found that an increase in assigned homework volume is associated with an increase in the differences in time students need to complete it. Taken together, the conclusion is that, schools with more homework tend to exhibit more variation in student achievement. These results seem to confirm our second hypothesis, as a positive covariation was found between the amount of homework in a school the mean homework time by school and the increase in differences within the school, both in student homework time and in the academic results themselves. The data seem to be in line with those who argue that homework is a source of inequity because it affects those less academically-advantaged students and students with greater limitations in their home environments Kohn, ; Rømming, ; OECD, b.

This new data has clear implications for educational action and school homework policies, especially in compulsory education. If quality compulsory education is that which offers the best results for the largest number Barber and Mourshed, ; Mourshed et al. The data show that in a school with 60 min of assigned homework, a quick student will need just 4 h a week to finish their homework, whereas a slow student will spend 10 h a week, 2. And in a school assigning min of homework per day, a quick student will need 7. But this percentage increase has been estimated after statistically, or artificially, accounting for sociological and psychological student factors and other variables at school and region level. The adjustment variables influence both achievement and time spent on homework, so it is likely that in a real classroom situation the differences estimated here might be even larger.

This is especially important in comprehensive education systems, like the Spanish Eurydice, , in which the classroom groups are extremely heterogeneous, with a variety of students in the same class in terms of ability, interest, and motivation, in which the aforementioned variables may operate more strongly. The results of this research must be interpreted bearing in mind a number of limitations. The most significant limitation in the research design is the lack of a measure of previous achievement, whether an ad hoc test Murillo and Martínez-Garrido, or school grades Núñez et al. In an attempt to alleviate this, our research has placed special emphasis on the construction of variables which would work to exclude academic history from the model.

The use of the repetition of school year variable was unavoidable because Spain has one of the highest levels of repetition in the European Union Eurydice, and repeating students achieve worse academic results Ministerio de Educación, Similarly, the expectation and motivation variables were included in the group of adjustment factors assuming that in this research they could be considered background variables. On the other hand, the statistical models used to analyze the data are correlational, and as such, one can only speak of an association between variables and not of directionality or causality in the analysis.

Another aspect to be borne in mind is that the homework time measures are generic -not segregated by subject- when it its understood that time spent and homework behavior are not consistent across all subjects Trautwein et al. Nonetheless, when the dependent variable is academic results it has been found that the relationship between homework time and achievement is relatively stable across all subjects Lubbers et al. Future lines of research should be aimed toward the creation of comprehensive models which incorporate a holistic vision of homework. It must be recognized that not all of the time spent on homework by a student is time well spent Valle et al. In addition, research has demonstrated the importance of other variables related to student behavior such as rate of completion, the homework environment, organization, and task management, autonomy, parenting styles, effort, and the use of study techniques Zimmerman and Kitsantas, ; Xu, , ; Kitsantas and Zimmerman, ; Kitsantas et al.

Along the same lines, research has also indicated other important variables related to teacher homework policies, such as reasons for assignment, control and feedback, assignment characteristics, and the adaptation of tasks to the students' level of learning Trautwein et al. All of these should be considered in a comprehensive model of homework. These results have significant implications when it comes to setting educational policy in schools, sending a clear message to head teachers, teachers and those responsible for education. The results of this research show that assigning large volumes of homework increases inequality between students in pursuit of minimal gains in achievement for those who least need it. Therefore, in terms of school efficiency, and with the aim of improving equity in schools it is recommended that educational policies be established which optimize all students' achievement.

This study was carried out in accordance with the recommendations of the University of Oviedo with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the University of Oviedo. RF and JM have designed the research; RF and JS have analyzed the data; MA and JM have interpreted the data; RF, MA, and JS have drafted the paper; JM has revised it critically; all authors have provided final approval of the version to be published and have ensured the accuracy and integrity of the work.

This research was funded by the Ministerio de Economía y Competitividad del Gobierno de España. References: PSIP, BES We would like to express our utmost gratitude to the Ministerio de Educación Cultura y Deporte del Gobierno de España and to the Consejería de Educación y Cultura del Gobierno del Principado de Asturias, without whose collaboration this research would not have been possible. 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.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation. Front Psychol. Published online Mar 7. doi: PMCID: PMC Rubén Fernández-Alonso 1 Department of Education Sciences, University of Oviedo, Oviedo, Spain 2 Department of Education, Principality of Asturias Government, Oviedo, Spain Find articles by Rubén Fernández-Alonso. Marcos Álvarez-Díaz 2 Department of Education, Principality of Asturias Government, Oviedo, Spain Find articles by Marcos Álvarez-Díaz. Javier Suárez-Álvarez 3 Department of Psychology, University of Oviedo, Oviedo, Spain Find articles by Javier Suárez-Álvarez. José Muñiz 3 Department of Psychology, University of Oviedo, Oviedo, Spain Find articles by José Muñiz. Author information Article notes Copyright and License information Disclaimer. Edited by: José Jesús Gázquez, University of Almería, Spain.

Reviewed by: Trude Nilsen, University of Olso, Norway; Eva M. Romera, University of Córdoba, Spain. liamg jzeravlazeraus. This article was submitted to Educational Psychology, a section of the journal Frontiers in Psychology. Received Nov 16; Accepted Feb Copyright © Fernández-Alonso, Álvarez-Díaz, Suárez-Álvarez and Muñiz. This is an open-access article distributed under the terms of the Creative Commons Attribution License CC BY. The use, distribution or reproduction in other forums is permitted, provided the original author s or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice.

No use, distribution or reproduction is permitted which does not comply with these terms. Abstract The optimum time students should spend on homework has been widely researched although the results are far from unanimous. Keywords: homework time, equity, compulsory secondary education, hierarchical modeling, adolescents. Procedure Test application, marking, and data recording were contracted out via public tendering, and were carried out by qualified personnel unconnected to the schools. Instruments Tests of academic performance The performance test battery consisted of items evaluating four subjects: Spanish items , mathematics 73 items , science 78 , and citizenship Homework variables A questionnaire was made up of a mix of items which allowed the calculation of the indicators used for the description of homework variables.

Data analyses Firstly the descriptive statistics and Pearson correlations between the variables were calculated. Table 1 Descriptive statistics and Pearson correlation matrix between the variables. Mathematics — 2. Spanish 0. Sciences 0. Citizenship 0. SEC 0. Spanish national 0. Appropriate school year 0. Expectations 0. Motivation 0. Homework time 0. School SEC 0. AC SEC 0. Open in a separate window. Table 2 Distribution of the variance in the null model. Variance Mathematics Sciences Spanish Citizenship Level 1 0. Table 3 Multilevel models for prediction of achievement in four subjects.

Mathematics Sciences Spanish Citizenship β SE β SE β SE β SE CONTROL VARIABLES Level 1 student SECI 0. Figure 1. Figure 2. Discussion and conclusions The data in this study reaffirm the multilevel nature of homework Trautwein and Köller, and support this study's first hypothesis: the amount of homework mean daily minutes the student spends on homework is positively associated with academic results, whereas the time students spent on homework considered individually is negatively associated with academic results. Ethics statement This study was carried out in accordance with the recommendations of the University of Oviedo with written informed consent from all subjects. Author contributions RF and JM have designed the research; RF and JS have analyzed the data; MA and JM have interpreted the data; RF, MA, and JS have drafted the paper; JM has revised it critically; all authors have provided final approval of the version to be published and have ensured the accuracy and integrity of the work.

Funding This research was funded by the Ministerio de Economía y Competitividad del Gobierno de España. Conflict of interest statement 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. References Barber B. Homework does not belong on the agenda for educational reform. How the World's Best-Performing School Systems Come Out on Top. McKinsey and Company. pdf Accessed January 25, Bembenutty H. Academic performance and satisfaction with homework completion among college students.

Homework assignments to enhance student engagement in secondary education. Relations of attitudes toward homework and time spent on homework to course outcomes: the case of foreign language learning. Synthesis of research on homework. The Battle Over Homework: Common Ground for Administrators, Teachers, and Parents. Thousand Oaks, CA: Sage. Does homework improve academic achievement? A synthesis of research, Homework , in APA Educational Psychology Handbook , Vol. Washington, DC: American Psychological Association; , — Using research to answer practical questions about homework. Homework is a complicated thing. Homework and student math achievement in junior high schools. Homework works if homework quality is high: using multilevel modeling to predict the development of achievement in mathematics.

The relationship between homework time and achievement is not universal: evidence from multilevel analyses in 40 countries. More than minutes: teachers' roles in designing homework. Luxembourg: Publications Office of the European Union. Eurydice Grade Retention during Compulsory Education in Europe: Regulations and Statistics. Studies also suggest that young students who are struggling in school take more time to complete homework assignments simply because these assignments are more difficult for them. So, how much homework should students do? The National PTA and the NEA have a parent guide called "Helping Your Child Get the Most Out of Homework. These recommendations are consistent with the conclusions reached by our analysis. Practice assignments do improve scores on class tests at all grade levels.

A little amount of homework may help elementary school students build study habits. Homework for junior high students appears to reach the point of diminishing returns after about 90 minutes a night. For high school students, the positive line continues to climb until between 90 minutes and 2½ hours of homework a night, after which returns diminish. Beyond achievement, proponents of homework argue that it can have many other beneficial effects. They claim it can help students develop good study habits so they are ready to grow as their cognitive capacities mature. It can help students recognize that learning can occur at home as well as at school. Homework can foster independent learning and responsible character traits.

And it can give parents an opportunity to see what's going on at school and let them express positive attitudes toward achievement. Opponents of homework counter that it can also have negative effects. They argue it can lead to boredom with schoolwork, since all activities remain interesting only for so long. Homework can deny students access to leisure activities that also teach important life skills. Parents can get too involved in homework -- pressuring their child and confusing him by using different instructional techniques than the teacher. My feeling is that homework policies should prescribe amounts of homework consistent with the research evidence, but which also give individual schools and teachers some flexibility to take into account the unique needs and circumstances of their students and families.

In general, teachers should avoid either extreme. Deadline extended to October 24 to nominate individuals and teams.

Self-efficacy for learning, although moderately correlated with perceptions of responsibility, predicted course grades more strongly than the latter variable. No gender differences were found for any of the variables, a finding that extends prior research based on high school girls. This is a preview of subscription content, access via your institution. Rent this article via DeepDyve. Bandura, A. Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall. Google Scholar. Guide for constructing self-efficacy scales. Urdan Eds. Greenwich, CT: Information Age. Boekaerts, M. Handbook of self-regulation: Theory, research, and applications. San Diego, CA: Academic. Campbell, J. NAEP trends in academic progress: Three decades of student performance.

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Parents' reported involvement in students' homework: strategies and practices. The Elementary School Journal , 95 5 , — Joreskog, J. Chicago: Scientific Software International. Karabenick, S. Relationship of academic help seeking to the use of learning strategies and other instrumental achievement behavior in college students. Journal of Educational Psychology , 83 , — Keith, T. Longitudinal effects of in-school and out-of-school homework on high school grades. School Psychology Quarterly , 19 , — Newman, R. Academic help-seeking: A strategy of self-regulated learning.

Zimmerman Eds. National Center for Higher Education Management Systems NCHEMS. Pajares, F. Response format in writing self-efficacy assessment: greater discrimination increases prediction. Measurement and Evaluation in Counseling and Development , 38 4 , — Pintrich, P. New directions in college teaching and learning: Understanding self-regulated learning. San Francisco, CA: Jossey-Bass. Pressley, M. Advanced educational psychology for educators, researchers, and policy makers. New York: Harper Collins. Schunk, D. Self-regulation of learning and performance: Issues and educational applications. Hillsdale, NJ: Erlbaum. Self-regulated learning: From teaching to self-reflective practice.

New York: Guilford. Trautwein, U. The relationship between homework and achievement—still much of a mystery. Educational Psychology Review , 15 , — Do homework assignments enhance achievement? A multilevel analysis in 7th-grade mathematics. Contemporary Educational Psychology , 27 , 26— Predicting homework effort: support for a domain-specific, multilevel homework model. Journal of Educational Psychology , 98 , — Valentine, J. The relation between self-beliefs and academic achievement: a meta-analytic review. Educational Psychologist , 39 2 , — Warton, P. Learning about responsibility: lessons from homework. The British Journal of Educational Psychology , 67 , — United States: William Collins.

Winne, P. Contemporary Educational Psychology , 27 , — Measuring self-regulated learning. Boekaerts, P. Zeidner Eds. Orlando, FL: Academic. Xu, J. Gender and homework management reported by high school students. Educational Psychology , 26 1 , 73— Zimmerman, B. Dimensions of academic self-regulation: A conceptual framework for education. Becoming a self-regulated learner: an overview. Theory into Practice , 41 2 , 64— Subotnik Eds. Investigating self-regulation and motivation: historical background, methodological developments, and future prospects. American Educational Research Journal , 45 1 , — Self-motivation for academic attainment: The role of self-efficacy beliefs and personal goal setting.

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The relation between goal orientation and students' motivational beliefs and self- regulated learning. Therefore, in terms of school efficiency, and with the aim of improving equity in schools it is recommended that educational policies be established which optimize all students' achievement. It can help students recognize that learning can occur at home as well as at school. Trautwein, U. A review of the time management literature. Pressley, M. Biggs, J.

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