Homework Research 2013

Students' Achievement and Homework Assignment Strategies

Rubén Fernández-Alonso,1,2Marcos Álvarez-Díaz,2Javier Suárez-Álvarez,3,* and José Muñiz3

1Department of Education Sciences, University of Oviedo, Oviedo, Spain

2Department of Education, Principality of Asturias Government, Oviedo, Spain

3Department of Psychology, University of Oviedo, Oviedo, Spain

Edited by: José Jesús Gázquez, University of Almería, Spain

Reviewed by: Trude Nilsen, University of Olso, Norway; Eva M. Romera, University of Córdoba, Spain

*Correspondence: Javier Suárez-Álvarez moc.liamg@jzeravlazeraus

This article was submitted to Educational Psychology, a section of the journal Frontiers in Psychology

Author information ►Article notes ►Copyright and License information ►

Received 2016 Nov 16; Accepted 2017 Feb 14.

Copyright © 2017 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.


The optimum time students should spend on homework has been widely researched although the results are far from unanimous. The main objective of this research is to analyze how homework assignment strategies in schools affect students' academic performance and the differences in students' time spent on homework. Participants were a representative sample of Spanish adolescents (N = 26,543) with a mean age of 14.4 (±0.75), 49.7% girls. A test battery was used to measure academic performance in four subjects: Spanish, Mathematics, Science, and Citizenship. A questionnaire allowed the measurement of the indicators used for the description of homework and control variables. Two three-level hierarchical-linear models (student, school, autonomous community) were produced for each subject being evaluated. The relationship between academic results and homework time is negative at the individual level but positive at school level. An increase in the amount of homework a school assigns is associated with an increase in the differences in student time spent on homework. An optimum amount of homework is proposed which schools should assign to maximize gains in achievement for students overall.

Keywords: homework time, equity, compulsory secondary education, hierarchical modeling, adolescents

The role of homework in academic achievement is an age-old debate (Walberg et al., 1985) that has swung between times when it was thought to be a tool for improving a country's competitiveness and times when it was almost outlawed. So Cooper (2001) talks about the battle over homework and the debates and rows continue (Walberg et al., 1985, 1986; Barber, 1986). It is considered a complicated subject (Corno, 1996), mysterious (Trautwein and Köller, 2003), a chameleon (Trautwein et al., 2009b), or Janus-faced (Flunger et al., 2015). One must agree with Cooper et al. (2006) that homework is a practice full of contradictions, where positive and negative effects coincide. As such, depending on our preferences, it is possible to find data which support the argument that homework benefits all students (Cooper, 1989), or that it does not matter and should be abolished (Barber, 1986). Equally, one might argue a compensatory effect as it favors students with more difficulties (Epstein and Van Voorhis, 2001), or on the contrary, that it is a source of inequality as it specifically benefits those better placed on the social ladder (Rømming, 2011). Furthermore, this issue has jumped over the school wall and entered the home, contributing to the polemic by becoming a common topic about which it is possible to have an opinion without being well informed, something that Goldstein (1960) warned of decades ago after reviewing almost 300 pieces of writing on the topic in Education Index and finding that only 6% were empirical studies.

The relationship between homework time and educational outcomes has traditionally been the most researched aspect (Cooper, 1989; Cooper et al., 2006; Fan et al., 2017), although conclusions have evolved over time. The first experimental studies (Paschal et al., 1984) worked from the hypothesis that time spent on homework was a reflection of an individual student's commitment and diligence and as such the relationship between time spent on homework and achievement should be positive. This was roughly the idea at the end of the twentieth century, when more positive effects had been found than negative (Cooper, 1989), although it was also known that the relationship was not strictly linear (Cooper and Valentine, 2001), and that its strength depended on the student's age- stronger in post-compulsory secondary education than in compulsory education and almost zero in primary education (Cooper et al., 2012). With the turn of the century, hierarchical-linear models ran counter to this idea by showing that homework was a multilevel situation and the effect of homework on outcomes depended on classroom factors (e.g., frequency or amount of assigned homework) more than on an individual's attitude (Trautwein and Köller, 2003). Research with a multilevel approach indicated that individual variations in time spent had little effect on academic results (Farrow et al., 1999; De Jong et al., 2000; Dettmers et al., 2010; Murillo and Martínez-Garrido, 2013; Fernández-Alonso et al., 2014; Núñez et al., 2014; Servicio de Evaluación Educativa del Principado de Asturias, 2016) and that when statistically significant results were found, the effect was negative (Trautwein, 2007; Trautwein et al., 2009b; Lubbers et al., 2010; Chang et al., 2014). The reasons for this null or negative relationship lie in the fact that those variables which are positively associated with homework time are antagonistic when predicting academic performance. For example, some students may not need to spend much time on homework because they learn quickly and have good cognitive skills and previous knowledge (Trautwein, 2007; Dettmers et al., 2010), or maybe because they are not very persistent in their work and do not finish homework tasks (Flunger et al., 2015). Similarly, students may spend more time on homework because they have difficulties learning and concentrating, low expectations and motivation or because they need more direct help (Trautwein et al., 2006), or maybe because they put in a lot of effort and take a lot of care with their work (Flunger et al., 2015). Something similar happens with sociological variables such as gender: Girls spend more time on homework (Gershenson and Holt, 2015) but, compared to boys, in standardized tests they have better results in reading and worse results in Science and Mathematics (OECD, 2013a).

On the other hand, thanks to multilevel studies, systematic effects on performance have been found when homework time is considered at the class or school level. De Jong et al. (2000) found that the number of assigned homework tasks in a year was positively and significantly related to results in mathematics. Equally, the volume or amount of homework (mean homework time for the group) and the frequency of homework assignment have positive effects on achievement. The data suggests that when frequency and volume are considered together, the former has more impact on results than the latter (Trautwein et al., 2002; Trautwein, 2007). In fact, it has been estimated that in classrooms where homework is always assigned there are gains in mathematics and science of 20% of a standard deviation over those classrooms which sometimes assign homework (Fernández-Alonso et al., 2015). Significant results have also been found in research which considered only homework volume at the classroom or school level. Dettmers et al. (2009) concluded that the school-level effect of homework is positive in the majority of participating countries in PISA 2003, and the OECD (2013b), with data from PISA 2012, confirms that schools in which students have more weekly homework demonstrate better results once certain school and student-background variables are discounted. To put it briefly, homework has a multilevel nature (Trautwein and Köller, 2003) in which the variables have different significance and effects according to the level of analysis, in this case a positive effect at class level, and a negative or null effect in most cases at the level of the individual. Furthermore, the fact that the clearest effects are seen at the classroom and school level highlights the role of homework policy in schools and teaching, over and above the time individual students spend on homework.

From this complex context, this current study aims to explore the relationships between the strategies schools use to assign homework and the consequences that has on students' academic performance and on the students' own homework strategies. There are two specific objectives, firstly, to systematically analyze the differential effect of time spent on homework on educational performance, both at school and individual level. We hypothesize a positive effect for homework time at school level, and a negative effect at the individual level. Secondly, the influence of homework quantity assigned by schools on the distribution of time spent by students on homework will be investigated. This will test the previously unexplored hypothesis that an increase in the amount of homework assigned by each school will create an increase in differences, both in time spent on homework by the students, and in academic results. Confirming this hypothesis would mean that an excessive amount of homework assigned by schools would penalize those students who for various reasons (pace of work, gaps in learning, difficulties concentrating, overexertion) need to spend more time completing their homework than their peers. In order to resolve this apparent paradox we will calculate the optimum volume of homework that schools should assign in order to benefit the largest number of students without contributing to an increase in differences, that is, without harming educational equity.



The population was defined as those students in year 8 of compulsory education in the academic year 2009/10 in Spain. In order to provide a representative sample, a stratified random sampling was carried out from the 19 autonomous regions in Spain. The sample was selected from each stratum according to a two-stage cluster design (OECD, 2009, 2011, 2014a; Ministerio de Educación, 2011). In the first stage, the primary units of the sample were the schools, which were selected with a probability proportional to the number of students in the 8th grade. The more 8th grade students in a given school, the higher the likelihood of the school being selected. In the second stage, 35 students were selected from each school through simple, systematic sampling. A detailed, step-by-step description of the sampling procedure may be found in OECD (2011). The subsequent sample numbered 29,153 students from 933 schools. Some students were excluded due to lack of information (absences on the test day), or for having special educational needs. The baseline sample was finally made up of 26,543 students. The mean student age was 14.4 with a standard deviation of 0.75, rank of age from 13 to 16. Some 66.2% attended a state school; 49.7% were girls; 87.8% were Spanish nationals; 73.5% were in the school year appropriate to their age, the remaining 26.5% were at least 1 year behind in terms of their age.


Test application, marking, and data recording were contracted out via public tendering, and were carried out by qualified personnel unconnected to the schools. The evaluation, was performed on two consecutive days, each day having two 50 min sessions separated by a break. At the end of the second day the students completed a context questionnaire which included questions related to homework. The evaluation was carried out in compliance with current ethical standards in Spain. Families of the students selected to participate in the evaluation were informed about the study by the school administrations, and were able to choose whether those students would participate in the study or not.


Tests of academic performance

The performance test battery consisted of 342 items evaluating four subjects: Spanish (106 items), mathematics (73 items), science (78), and citizenship (85). 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, 2011). 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, 2011). The mean Cronbach α for the booklets ranged from 0.72 (mathematics) to 0.89 (Spanish). Student scores were calculated adjusting the bank of items to Rasch's IRT model using the ConQuest 2.0 program (Wu et al., 2007) and were expressed in a scale with mean and standard deviation of 500 and 100 points respectively. 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.g., mean, standard deviation) than estimates of scores using methods of maximum likelihood or expected a-posteriori estimations (Mislevy et al., 1992; OECD, 2009; von Davier et al., 2009).

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. 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. The options were recoded as follows: (a) = 0 min.; (b) = 45 min.; (c) = 90 min.; (d) = 150 min.; (e) = 210 min. According to Trautwein and Köller (2003) 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.

Control variables

Four variables were included to describe sociological factors about the students, three were binary: Gender (1 = female); Nationality (1 = Spanish; 0 = other); School type (1 = state school; 0 = private). 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). Three variables were used to gather educational history: Appropriate School Year (1 = being in the school year appropriate to their age; 0 = repeated a school year). 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., 2014). 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. Students scoring highly in this variable are agreeing with statements such as “at school I learn useful and interesting things.” A Confirmatory Factor Analysis was performed using a Maximum Likelihood robust estimation method (MLMV) and the items fit an essentially unidimensional scale: CFI = 0.954; TLI = 0.915; SRMR = 0.037; RMSEA = 0.087 (90% CI = 0.084–0.091).

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.

Data analyses

Firstly the descriptive statistics and Pearson correlations between the variables were calculated. Then, using the HLM 6.03 program (Raudenbush et al., 2004), two three-level hierarchical-linear models (student, school, autonomous community) were produced for each subject being evaluated: a null model (without predictor variables) and a random intercept model in which adjustment variables and homework variables were introduced at the same time. 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, 2013) and estimate the effect of differences in homework time among the students within the same school. The range of missing variable cases was very small, between 1 and 3%. Recovery was carried out using the procedure described in Fernández-Alonso et al. (2012).

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 100 point standard deviation allows the expression of the effect of the variables and the differences between groups as percentage increases in standardized points.


Table ​1 shows the descriptive statistics and the matrix of correlations between the study variables. 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.

Table 1

Descriptive statistics and Pearson correlation matrix between the variables.

Table ​2 shows the distribution of variance in the null model. In the four subjects taken together, 85% of the variance was found at the student level, 10% was variance between schools, and 5% variance between regions. Although the 10% of variance between schools could seem modest, underlying that there were large differences. For example, in Spanish the 95% plausible value range for the school means ranged between 577 and 439 points, practically 1.5 standard deviations, which shows that schools have a significant impact on student results.

Table 2

Distribution of the variance in the null model.

Table ​3 gives the standardized coefficients of the independent variables of the four multilevel models, as well as the percentage of variance explained by each level.

Table 3

Multilevel models for prediction of achievement in four subjects.

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 percentages of total explained variance in Table ​2 were calculated with all variables. However, if the strategy had been to introduce the adjustment variables first and then add in the homework variables, the explanatory gain in the second model would have been about 2% in each subject.

The amount of homework turned out to be positively and significantly associated with the results in the four subjects. In a 100 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 4.1 and 4.8 points more in each subject, with the exception of mathematics where the increase would be around 2.5 points. 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. Figure ​1 shows the relationship between volume of homework (expressed as mean daily minutes of homework by school) and the differences in time spent by students (expressed as the standard deviation from the mean school daily minutes). The correlation between the variables was 0.69 and the regression gradient indicates that schools which assigned 60 min of homework per day had a standard deviation in time spent by students on homework of approximately 25 min, whereas in those schools assigning 120 min of homework, the standard deviation was twice as long, and was over 50 min. So schools which assigned more homework also tended to demonstrate greater differences in the time students need to spend on that homework.

Figure 1

Relationship between school homework volume and differences in time needed by students to complete homework.

Figure ​2 shows the effect on results in mathematics of the combination of homework time, homework amount, and the variance of homework time associated with the amount of homework assigned in two types of schools: in type 1 schools the amount of homework assigned is 1 h, and in type 2 schools the amount of homework 2 h. 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.

Figure 2

Prediction of results for quick and slow students according to school homework size.

Looking at the first standard deviation of student homework time shown in the first graph, it was estimated that in type 1 schools, which assign 1 h of daily homework, a quick student (one who finishes their homework before 85% of their classmates) would spend a little over half an hour (35 min), whereas the slower student, who spends more time than 85% of classmates, would need almost an hour and a half of work each day (85 min). 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 (175 min for a slow student). Figure ​2 shows how the differences in performance would vary within a school between the more and lesser able students according to amount of homework assigned. In type 1 schools, with 1 h of homework per day, the difference in achievement between quick and slow students would be around 5% of a standard deviation, while in schools assigning 2 h per day the difference would be 12%. 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 (20.4 weekly hours rather than 4.1). It seems like a lot of work for such a small gain.

Discussion and conclusions

The data in this study reaffirm the multilevel nature of homework (Trautwein and Köller, 2003) 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., 2000; Dettmers et al., 2010; Scheerens et al., 2013; Fernández-Alonso et al., 2015). In this case it was found that for each additional hour of homework assigned by a school, a gain of 25% of a standard deviation is expected in all subjects except mathematics, where the gain is around 15%. 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, 2007; Trautwein et al., 2009b; Chang et al., 2014; Fernández-Alonso et al., 2016). 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, 2006; Rømming, 2011; OECD, 2013b).

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, 2007; Mourshed et al., 2010), then assigning an excessive volume of homework at those school levels could accentuate differences, affecting students who are slower, have more gaps in their knowledge, or are less privileged, and can make them feel overwhelmed by the amount of homework assigned to them (Martinez, 2011; OECD, 2014b; Suárez et al., 2016). 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, 2.5 times longer, with the additional aggravation of scoring one twentieth of a standard deviation below their quicker classmates. And in a school assigning 120 min of homework per day, a quick student will need 7.5 h per week whereas a slow student will have to triple this time (20 h per week) to achieve a result one eighth worse, that is, more time for a relatively worse result.

It might be argued that the differences are not very large, as between 1 and 2 h of assigned homework, the level of inequality increases 7% on a standardized scale. 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, 2015), 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, 2013) or school grades (Núñez et al., 2014), which would allow adjustment of the data. 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, 2011) and repeating students achieve worse academic results (Ministerio de Educación, 2011). 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. In this way, once the background factors are discounted, the homework variables explain 2% of the total variance, which is similar to estimations from other multilevel studies (De Jong et al., 2000; Trautwein, 2007; Dettmers et al., 2009; Fernández-Alonso et al., 2016). 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. As Trautwein and Lüdtke (2009) noted, the word “effect” must be understood as “predictive effect.” In other words, it is possible to say that the amount of homework is connected to performance; however, it is not possible to say in which direction the association runs. 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., 2006; Trautwein and Lüdtke, 2007). 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., 2010; Chang et al., 2014) which leads us to believe that the results given here would have changed very little even if the homework-related variables had been separated by subject.

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., 2015). 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, 2005; Xu, 2008, 2013; Kitsantas and Zimmerman, 2009; Kitsantas et al., 2011; Ramdass and Zimmerman, 2011; Bembenutty and White, 2013; Xu and Wu, 2013; Xu et al., 2014; Rosário et al., 2015a; Osorio and González-Cámara, 2016; Valle et al., 2016), as well as the role of expectation, value given to the task, and personality traits (Lubbers et al., 2010; Goetz et al., 2012; Pedrosa et al., 2016). 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., 2009a; Dettmers et al., 2010; Patall et al., 2010; Buijs and Admiraal, 2013; Murillo and Martínez-Garrido, 2013; Rosário et al., 2015b). All of these should be considered in a comprehensive model of homework.

In short, the data seem to indicate that in year 8 of compulsory education, 60–70 min of homework a day is a recommendation that, slightly more optimistically than Cooper's (2001) “10 min rule,” gives a reasonable gain for the whole school, without exaggerating differences or harming students with greater learning difficulties or who work more slowly, and is in line with other available evidence (Fernández-Alonso et al., 2015). 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.

Ethics statement

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.

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.


This research was funded by the Ministerio de Economía y Competitividad del Gobierno de España. References: PSI2014-56114-P, BES2012-053488. 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.

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.


  • Barber B. (1986). Homework does not belong on the agenda for educational reform. Educ. Leadersh.43, 55–57.
  • Barber M., Mourshed M. (2007). How the World's Best-Performing School Systems Come Out on Top. McKinsey and Company. Available online at: http://mckinseyonsociety.com/downloads/reports/Education/Worlds_School_Systems_Final.pdf (Accessed January 25, 2016).
  • Bembenutty H., White M. C. (2013). Academic performance and satisfaction with homework completion among college students. Learn. Individ. Differ.24, 83–88. 10.1016/j.lindif.2012.10.013 [Cross Ref]
  • Buijs M., Admiraal W. (2013). Homework assignments to enhance student engagement in secondary education. Eur. J. Psychol. Educ.28, 767–779. 10.1007/s10212-012-0139-0 [Cross Ref]
  • Chang C. B., Wall D., Tare M., Golonka E., Vatz K. (2014). Relations of attitudes toward homework and time spent on homework to course outcomes: the case of foreign language learning. J. Educ. Psychol.106, 1049–1065. 10.1037/a0036497 [Cross Ref]
  • Cooper H. (1989). Synthesis of research on homework. Educ. Leadersh.47, 85–91.
  • Cooper H. (2001). The Battle Over Homework: Common Ground for Administrators, Teachers, and Parents. Thousand Oaks, CA: Sage.
  • 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. 10.3102/00346543076001001 [Cross Ref]
  • 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., editors. (Washington, DC: American Psychological Association; ), 475–495.
  • Cooper H., Valentine J. C. (2001). Using research to answer practical questions about homework. Educ. Psychol.36, 143–153. 10.1207/S15326985EP3603_1 [Cross Ref]
  • Corno L. (1996). Homework is a complicated thing. Educ. Res.25, 27–30. 10.3102/0013189X025008027 [Cross Ref]
  • 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. 10.1076/1380-3611(200006)6:2;1-E;F130 [Cross Ref]
  • 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. J. Educ. Psychol.102, 467–482. 10.1037/a0018453 [Cross Ref]
  • 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. Eff. Sch. Improv.20, 375–405. 10.1080/09243450902904601 [Cross Ref]
  • Epstein J. L., Van Voorhis F. L. (2001). More than minutes: teachers' roles in designing homework. Educ. Psychol.36, 181–193. 10.1207/S15326985EP3603_4 [Cross Ref]
  • Eurydice (2015). The Structure of the European Education Systems 2015/16: Schematic Diagrams. Luxembourg: Publications Office of the European Union. Available online at: https://webgate.ec.europa.eu/fpfis/mwikis/eurydice/index.php/Publications:The_Structure_of_the_European_Education_Systems_2015/16:_Schematic_Diagrams (Accessed January 25, 2016).
  • Eurydice (2011). Grade Retention during Compulsory Education in Europe: Regulations and Statistics. Luxembourg: Publications Office of the European Union.
  • 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 [Cross Ref]
  • Farrow S., Tymms P., Henderson B. (1999). Homework and attainment in primary schools. Br. Educ. Res. J.25, 323–341. 10.1080/0141192990250304 [Cross Ref]
  • Fernández-Alonso R., Muñiz J. (2011). Diseños de cuadernillos para la evaluación de competencias b1sicas. Aula Abierta39, 3–34.
  • Fernández-Alonso R., Suárez-Álvarez J., Muñiz J. (2012). Imputación de datos perdidos en las evaluaciones diagnósticas educativas. [Imputation methods for missing data in educational diagnostic evaluation].Psicothema24, 167–175. [PubMed]
  • Fernández-Alonso R., Suárez-Álvarez J., Muñiz J. (2014). Tareas escolares en el hogar y rendimiento en matemáticas: una aproximación multinivel con estudiantes de enseñanza primaria. [Homework and academic performance in mathematics: A multilevel approach with primary school student].Rev. Psicol. Educ.9, 15–30.
  • Fernández-Alonso R., Suárez-Álvarez J., Muñiz J. (2015). Adolescents' homework performance in mathematics and science: personal factors and teaching practices. J. Educ. Psychol.107, 1075–1085. 10.1037/edu0000032 [Cross Ref]
  • 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. Rev. Psicod.21, 5–23. 10.1387/RevPsicodidact.13939 [Cross Ref]
  • 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. Lear. Instr.39, 97–106. 10.1016/j.learninstruc.2015.05.008 [Cross Ref]
  • Gershenson S., Holt S. B. (2015). Gender gaps in high school students' homework time. Educ. Res.44, 432–441. 10.3102/0013189X15616123 [Cross Ref]
  • Goetz T., Nett U. E., Martiny S. E., Hall N. C., Pekrun R., Dettmers S., et al. (2012). Students' emotions during homework: structures, self-concept antecedents, and achievement outcomes. Learn. Individ. Differ.22, 225–234. 10.1016/j.lindif.2011.04.006 [Cross Ref]
  • Goldstein A. (1960). Does homework help? A review of research. Elementary Sch. J.60, 212–224. 10.1086/459804 [Cross Ref]
  • Kitsantas A., Cheema J., Ware H. (2011). The role of homework support resources, time spent on homework, and self-efficacy beliefs in mathematics achievement. J. Adv. Acad.22, 312–341. 10.1177/1932202X1102200206 [Cross Ref]
  • Kitsantas A., Zimmerman B. J. (2009). College students homework and academic achievement: the mediating role of self-regulatory beliefs. Metacognition Learn.4, 1556–1623. 10.1007/s11409-008-9028-y [Cross Ref]
  • Kohn A. (2006). Abusing research: the study of homework and other examples. Phi Delta Kappan88, 9–22. 10.1177/003172170608800105 [Cross Ref]
  • Lubbers M. J., Van Der Werf M. P. C., Kuyper H., Hendriks A. A. J. (2010). Does homework behavior mediate the relation between personality and academic performance?Learn. Individ. Differ.20, 203–208. 10.1016/j.lindif.2010.01.005 [Cross Ref]
  • Martinez S. (2011). An examination of Latino students' homework routines. J. Latinos Educ.10, 354–368. 10.1080/15348431.2011.605688 [Cross Ref]
  • Mislevy R. J., Beaton A. E., Kaplan B., Sheehan K. M. (1992). Estimating population characteristics from sparse matrix samples of item responses. J. Educ. Meas.29, 133–161. 10.1111/j.1745-3984.1992.tb00371.x [Cross Ref]
  • Ministerio de Educación (2011). Evaluación General de Diagnóstico 2010. Educación Secundaria Obligatoria. Informe de Resultados. Madrid: Instituto de Evaluación; Available online at: http://www.mecd.gob.es/dctm/ievaluacion/informe-egd-2010.pdf?documentId=0901e72b80d5ad3e (Accessed January 25, 2016).
  • Mourshed M., Chijioke C., Barber M. (2010). How the World's Most Improved School Systems Keep Getting Better. McKinsey and Company. Available online at: http://mckinseyonsociety.com/downloads/reports/Education/How-the-Worlds-Most-Improved-School-Systems-Keep-Getting-Better_Download-version_Final.pdf (Accessed January 25, 2016).
  • Murillo F. J., Martínez-Garrido C. (2013). Homework influence on academic performance. A study of iberoamerican students of primary education. J. Psychodidactics18, 157–171. 10.1387/RevPsicodidact.6156 [Cross Ref]
  • Núñez J. C., Vallejo G., Rosário P., Tuero E., Valle A. (2014). Student, teacher, and school context variables predicting academic achievement in biology: analysis from a multilevel perspective. J. Psychodidactics19, 145–171. 10.1387/RevPsicodidact.7127 [Cross Ref]
  • OECD (2009). PISA Data Analysis Manual: SPSS, 2nd Edn. Paris: OECD Publishing.
  • OECD (2011). School Sampling Preparation Manual. PISA 2012 Main Survey. Paris: OECD Publishing; Available online at: https://www.oecd.org/pisa/pisaproducts/PISA2012MS-SamplingGuidelines-.pdf (Accessed January 6, 2017).
  • OECD (2013a). PISA 2012 Results: What Students Know and Can Do. Student Performance in Mathematics, Reading and Science (Volume I). Paris: OECD Publishing.
  • OECD (2013b). PISA 2012 Results: What Makes Schools Successful? Resources, Policies and Practices (Volume IV). Paris: OECD Publishing.
  • OECD (2014a). PISA 2012 Technical Report. Paris: OECD Publishing; Available online at: http://www.oecd.org/pisa/pisaproducts/PISA-2012-technical-report-final.pdf (Accessed January 25, 2016).
  • OECD (2014b). Does Homework Perpetuate Inequities in Education? PISA in Focus. Paris: OECD Publishing.
  • Osorio A., González-Cámara M. (2016). Testing the alleged superiority of the indulgent parenting style among Spanish adolescents. Psicothema28, 414–420. 10.7334/psicothema2015.314 [PubMed][Cross Ref]
  • Paschal R. A., Weinstein T., Walberg H. J. (1984). The effects of homework on learning: a quantitative synthesis. J. Educ. Res.78, 97–104. 10.1080/00220671.1984.10885581 [Cross Ref]
  • Patall E. A., Cooper H., Wynn S. R. (2010). The effectiveness and relative importance of providing choices in the classroom. J. Educ. Psychol.102, 896–915. 10.1037/a0019545 [Cross Ref]
  • Pedrosa I., Suárez-Álvarez J., García-Cueto E., Muñiz J. (2016). A computerized adaptive test for enterprising personality assessment in youth. Psicothema28, 471–478. 10.7334/psicothema2016.68 [PubMed][Cross Ref]
  • Ramdass D., Zimmerman B. J. (2011). Developing self-regulation skills: the important role of homework. J. Adv. Acad.22, 194–218. 10.1177/1932202X1102200202 [Cross Ref]
  • Raudenbush S. W., Bryk A. S., Cheong Y. F., Congdon R. T. (2004). HLM6: Hierarchical Linear and Nonlinear Modeling. Chicago: Scientific Software International.
  • Rømming M. (2011). Who benefits from homework assignments?Econ. Educ. Rev.30, 55–64. 10.1016/j.econedurev.2010.07.001 [Cross Ref]
  • Rosário P., Núñez J. C., Vallejo G., Cunha J., Nunes T., Mourão R., et al. (2015a). 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 [Cross Ref]
  • Rosário P., Núñez J. C., Vallejo G., Cunha J., Nunes T., Suárez N., et al. . (2015b). The effects of teachers' homework follow-up practices on students' EFL performance: a randomized-group design. Front. Psychol.6:1528. 10.3389/fpsyg.2015.01528 [PMC free article][PubMed][Cross Ref]
  • Servicio de Evaluación Educativa del Principado de Asturias (2016). La relación entre el tiempo de deberes y los resultados académicos [The Relationship between Homework Time and Academic Performance]. Informes de Evaluación, 1. Oviedo: Consejería de Educación y Cultura del Gobierno del Principado de Asturias.
  • 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. Enschede: University of Twente. Available online at: http://doc.utwente.nl/86371/ (Accessed January 25, 2016).
  • Suárez-Álvarez J., Fernández-Alonso R., Muñiz J. (2014). Self-concept, motivation, expectations and socioeconomic level as predictors of academic performance in mathematics. Learn. Indiv. Diff.30, 118–123. 10.1016/j.lindif.2013.10.019 [Cross Ref]
  • Suárez N., Regueiro B., Epstein J. L., Piñeiro I., Díaz S. M., Valle A. (2016). Homework involvement and academic achievement of native and immigrant students. Front. Psychol.7:1517. 10.3389/fpsyg.2016.01517 [PMC free article][PubMed][Cross Ref]
  • Trautwein U. (2007). The homework–achievement relation reconsidered: differentiating homework time, homework frequency, and homework effort. Learn. Instr.17, 372–388. 10.1016/j.learninstruc.2007.02.009 [Cross Ref]
  • Trautwein U., Köller O. (2003). The relationship between homework and achievement: still much of a mystery. Educ. Psychol. Rev.15, 115–145. 10.1023/A:1023460414243 [Cross Ref]
  • Trautwein U., Köller O., Schmitz B., Baumert J. (2002). Do homework assignments enhance achievement? A multilevel analysis in 7th grade mathematics. Contemp. Educ. Psychol.27, 26–50. 10.1006/ceps.2001.1084 [Cross Ref]
  • Trautwein U., Lüdtke O., Schnyder I., Niggli A. (2006). Predicting homework effort: support for a domain-specific, multilevel homework model. J. Educ. Psychol.98, 438–456. 10.1037/0022-0663.98.2.438 [Cross Ref]
  • 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 [Cross Ref]
  • 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 [Cross Ref]
  • Trautwein U., Niggli A., Schnyder I., Lüdtke O. (2009a). Between-teacher differences in homework assignments and the development of students' homework effort, homework emotions, and achievement. J. Educ. Psychol.101, 176–189. 10.1037/0022-0663.101.1.176 [Cross Ref]
  • Trautwein U., Schnyder I., Niggli A., Neumann M., Lüdtke O. (2009b). Chameleon effects in homework research: the homework–achievement association depends on the measures used and the level of analysis chosen. Contemp. Educ. Psychol.34, 77–88. 10.1016/j.cedpsych.2008.09.001 [Cross Ref]
  • Valle A., Pan I., Regueiro B., Suárez N., Tuero E., Nunes A. R. (2015). Predicting approach to homework in primary school students. Psicothema27, 334–340. 10.7334/psicothema2015.118 [PubMed][Cross Ref]
  • Valle A., Regueiro B., Núñez J. C., Rodríguez S., Piñero I., Rosário P. (2016). Academic goals, student homework engagement, and academic achievement in elementary school. Front. Psychol.7:463. 10.3389/fpsyg.2016.00463 [PMC free article][PubMed]

Academic Goals, Student Homework Engagement, and Academic Achievement in Elementary School

Antonio Valle,1,*Bibiana Regueiro,1José C. Núñez,2Susana Rodríguez,1Isabel Piñeiro,1 and Pedro Rosário3

1Department of Developmental and Educational Psychology, University of A Coruña, A Coruña, Spain

2Department of Psychology, University of Oviedo, Oviedo, Spain

3Departmento de Psicologia Aplicada, Universidade do Minho, Braga, Portugal

Edited by: Jesus De La Fuente, University of Almería, Spain

Reviewed by: Melinda J. Mollette, Gwinnett County Public Schools, USA; Javier Fiz Pérez, Università Europea di Roma, Italy

*Correspondence: Antonio Valle se.cdu@rallav

This article was submitted to Educational Psychology, a section of the journal Frontiers in Psychology

Author information ►Article notes ►Copyright and License information ►

Received 2015 Nov 1; Accepted 2016 Mar 15.

Copyright © 2016 Valle, Regueiro, Núñez, Rodríguez, Piñeiro and Rosário.

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.

This article has been cited by other articles in PMC.


There seems to be a general consensus in the literature that doing homework is beneficial for students. Thus, the current challenge is to examine the process of doing homework to find which variables may help students to complete the homework assigned. To address this goal, a path analysis model was fit. The model hypothesized that the way students engage in homework is explained by the type of academic goals set, and it explains the amount of time spend on homework, the homework time management, and the amount of homework done. Lastly, the amount of homework done is positively related to academic achievement. The model was fit using a sample of 535 Spanish students from the last three courses of elementary school (aged 9 to 13). Findings show that: (a) academic achievement was positively associated with the amount of homework completed, (b) the amount of homework completed was related to the homework time management, (c) homework time management was associated with the approach to homework, (d) and the approach to homework, like the rest of the variables of the model (except for the time spent on homework), was related to the student's academic motivation (i.e., academic goals).

Keywords: homework, academic goals, student homework engagement, approach to homework, academic achievement, elementary school


Literature indicates that doing homework regularly is positively associated with students' academic achievement (Zimmerman and Kitsantas, 2005). Hence, as expected, the amount of homework done is one of the variables that shows a strong and positive relationship with academic achievement (Cooper et al., 2001).

It seems consensual in the literature that doing homework is always beneficial to students, but it is also true that the key for the academic success does not rely on the amount of homework done, but rather on how students engage on homework (Trautwein et al., 2009; Núñez et al., 2015c), and on how homework engagement is related with student motivation (Martin, 2012). There is, therefore, a call to analyze the process of homework rather than just the product; that is, to examine the extent to which the quality of the process of doing homework may be relevant to the final outcome.

Trautwein's model of homework

The model by Trautwein et al. (2006b) is rooted in the motivational theories, namely the theory of the expectancy value (Eccles (Parsons) et al., 1983; Pintrich and De Groot, 1990), and the theory of self-determination (Deci et al., 2002), as well as on theories of learning and instruction (Boekaerts, 1999). Trautwein and colleagues' model analyzes students' related variables in two blocks, as follows: the motivational (aiming at directing and sustaining the behavior) and the cognitive and behavioral implications (cognitions and behaviors related to the moment of doing homework).These two blocks of variables are rooted in the literature. Motivational variables are related with the theory of expectancy-value by Eccles (Parsons) et al. (1983), while the variables addressing students' implication are related with the school engagement framework (e.g., Fredricks et al., 2004). However, as Eccles and Wang (2012) stress, both models are interrelated due to the fact that both variables are closely related and show reciprocal relationships.

Student homework engagement: the interplay between cognitive and behavioral components

Engagement is a relatively new construct with great relevance in the field of psychology and instruction (Fredricks et al., 2004). Generally considered, engagement has been described as the active implication of the person in an activity (Reeve et al., 2004). However, despite the close relation between engagement and motivation, literature clearly differentiates between them (e.g., Martin, 2012), stressing engagement as the behavioral manifestation of motivation (Skinner and Pitzer, 2012), or arguing that motivation is a precursor of engagement rather than part of it. In sum, motivation relates to the “why” whereas the engagement focuses on the “what” of a particular behavior.

Consistent with this perspective, the current research fitted a model with the variable engagement mediating the relationship between motivation and academic achievement (see Eccles and Wang, 2012). Engagement is a complex construct with observational and non-observational aspects (Appleton et al., 2008). Some researchers conceptualize engagement with two dimensions—behavior and emotions (e.g., Marks, 2000)—while others define engagement with four dimensions—academic, behavioral, cognitive, and emotional (e.g., Appleton et al., 2006). In the current study, we followed Fredricks' et al. (2004) conceptualization of engagement as a construct with three dimensions: cognitive (e.g., approaches to learning), behavioral (e.g., student homework behaviors), and emotional (e.g., interest, boredom). For the purpose of the present study, the dimension of emotion was not included in the model (see Figure ​1).

Figure 1

General model hypothesized to explain the relationship between academic motivation, student homework engagement, and academic achievement.

Cognitive homework engagement

In the past few decades, a robust body of research has been addressing the relationship between the way students deal with their learning process and academic outcomes (Marton and Säljö, 1976a,b; Struyven et al., 2006; Rosário et al., 2010a, 2013a). Marton and Säljö (1976a,b) examined how students studied an academic text and found two ways of approaching the task: a surface and a deep approach. The surface approach is characterized by learning the contents aiming at achieving goals that are extrinsic to the learning content. In contrast, the deep approach is characterized by an intrinsic interest in the task and students are likely to be focused on understanding the learning content, relating it to prior knowledge and to the surrounding environment (Entwistle, 2009; Rosário et al., 2010b). The metaphor “surface vs. deep” constitutes an easy to perceive conceptual framework, both in the classroom setting and in other educational settings (i.e., doing homework at home), and has been shown to be a powerful tool for parents, teachers, and students when conceptualizing the ways students approach school tasks (Entwistle, 1991; Rosário et al., 2005). The core of the concept of approaches to studying (or to learning) is the metacognitive connection between an intention to approach a task and a strategy to implement it (Rosário et al., 2013b).

The process of doing homework focuses on what students do when completing homework, that is, how they approach their work and how they manage their personal resources and settings while doing homework. It is likely that students' approaches to homework may influence not only the final homework outcome but also the quality of that process. Students who adopt a deep approach are likely to engage their homework with the intention of deepening their understanding of the knowledge learned in class. In this process, students often relate the homework exercises to prior knowledge and monitor their mastery of the content learned. This process involves intrinsic intention to understand the ideas and the use of strategies to build meaning (Cano et al., 2014). In contrast, students who approach homework with a surface approach are likely to do homework with extrinsic motivation (e.g., rewards of their parents, fear of upsetting their teacher). Their goals may target finishing homework as soon as and with the less effort possible to be able to do more interesting activities. Students using this approach are more likely to do homework to fulfill an external obligation (e.g., hand in homework in class and get a grade), than for the benefits for learning.

Behavioral homework engagement

Findings from prior research indicate that the more the implication of students in doing their homework the better the academic achievement (Cooper et al., 2006). Following Trautwein et al. (2006b), our conceptualization of student homework engagement includes behaviors related with the amount of homework done, time spent on homework, and homework time management (e.g., concentration). In the present investigation, these three variables were included in the model (see Figure ​1).

Extant findings on the relationship between the amount of homework done and academic achievement are in need of further clarification. Some authors argue for a strong and positive relationship (e.g., Cooper et al., 2006), while others found that this relationship is higher throughout schooling (Cooper et al., 2001; Zimmerman and Kitsantas, 2005). Authors explained this last finding arguing that the load of homework assigned by teachers vary throughout schooling, and also that the cognitive competencies of students are likely to vary with age (Muhlenbruck et al., 2000). More recently, Núñez et al. (2015c) found that the relationship between these two variables varied as a function of the age of the students enrolled. Particularly, this relationship was found to be negative in elementary school, null in junior high school, and positive in high school.

Moreover, the relationship between the amount of homework done and academic achievement relates, among other factors, with the students' age, the quality of the homework assigned, the type of assessment, and the nature of the feedback provided. For example, some students may always complete their homework and get good grades for doing it, which does not mean that these students learn more (Kohn, 2006). In fact, more important than the quantity of the homework done, is the quality of that work (Fernández-Alonso et al., 2014).

Another variable included in the model was the time spent on homework. Findings on the relationship between time spent on homework and academic achievement are mixed. Some studies found a positive relationship (Cooper et al., 2001, 2006) while others found a null or a negative one (Trautwein et al., 2006b, 2009). In 2009, Dettmers, Trautwein and Lüdtke conducted a study with data from the PISA 2003 (Dettmers et al., 2009). Findings on the relationship between the number of hours spent on homework and academic achievement in mathematics show that the students in countries with higher grades spend fewer hours doing homework than students in countries with low academic grades. At the student level, findings showed a negative relationship between time spent on homework and academic achievement in 12 out of 40 countries.

The relationship between the amount of homework done, time dedicated to homework, and academic achievement was hypothesized to be mediated by the homework time management. Xu (2007) was one of the pioneers examining the management of the time spent on homework. Initially, Xu (2007) did not find a relationship between time management and academic achievement (spend more time on homework is not equal to use efficient strategies for time management). Latter, Xu (2010) found a positive relationship between students' grade level, organized environment, and homework time management. More recently, Núñez et al. (2015c) found that effective homework time management affects positively the amount of homework done, and, consequently, academic achievement. This relationship is stronger for elementary students when compared with students in high school.

Academic motivation and student homework engagement relationship

Literature has consistently shown that a deep approach to learning is associated positively with the quality of the learning outcomes (Rosário et al., 2013b; Cano et al., 2014; Vallejo et al., 2014). The adoption of a deep approach to homework depends on many factors, but students self-set goals and their motives for doing homework are among the most critical motivational variables when students decide to engage in homework.

Literature on achievement motivation highlights academic goals as an important line of research (Ng, 2008). In the educational setting, whereas learning goals focus on the comprehension and mastery of the content, performance goals are more focused on achieving a better performance than their colleagues (Pajares et al., 2000; Gaudreau, 2012).

Extant literature reports a positive relationship between adopting learning goals and the use of cognitive and self-regulation strategies (Elliot et al., 1999; Núñez et al., 2013). In fact, students who value learning and show an intention to learn and improve their competences are likely to use deep learning strategies (Suárez et al., 2001; Valle et al., 2003a,b, 2015d), which are aimed at understanding the content in depth. Moreover, these learning-goal oriented students are likely to self-regulate their learning process (Valle et al., 2015a), put on effort to learn, and assume the control of their learning process (Rosário et al., 2016). These students persist much longer when they face difficult and challenging tasks than colleagues pursuing performance goals. The former also use more strategies oriented toward the comprehension of content, are more intrinsically motivated, and feel more enthusiasm about academic work. Some researchers also found positive relationships between learning goals and pro-social behavior (e.g., Inglés et al., 2013).

Reviewing the differentiation between learning goals and performance goals, Elliot and colleagues (Elliot and Church, 1997; Elliot, 1999; Elliot et al., 1999) proposed a three-dimensional framework for academic goals. In addition to learning goals, performance goals were differentiated as follows: (a) performance-approach goals, focused on achieving competence with regard to others; and (b) performance-avoidance goals, aimed at avoiding incompetence with regard to others. Various studies have provided empirical support for this distinction within performance goals (e.g., Wolters et al., 1996; Middleton and Midgley, 1997; Skaalvik, 1997; Rodríguez et al., 2001; Valle et al., 2006). Moreover, some authors proposed a similar differentiation for learning goals (Elliot, 1999). The rationale was as follows: learning goals are characterized by high engagement in academic tasks, so an avoidance tendency in such goals should reflect avoidance of this engagement. Hence, students who pursue a work avoidance goal are likely to avoid challenging tasks and to put on effort to do well, only doing the bare minimum to complete the task. In general, learning goals are associated with a large amount of positive results in diverse motivational, cognitive, and achievement outcomes, whereas performance goals have been linked to less adaptive outcomes, or even to negative outcomes (Valle et al., 2009).

Aims of this study

Several relationships between motivational, cognitive, and behavioral variables involving self-regulated learning in the classroom have recently been studied (Rosário et al., 2013a). However, there is a lack of knowledge of the relationships between these variables throughout the process of doing homework.

The principal purpose of this work (see Figure ​1) is to analyze how student homework engagement (cognitive and behavioral) mediates motivation and academic performance. This study aims to provide new information about an issue that is taken for granted, but which, as far as we know, lacks empirical data. The question is: to what extent students acknowledge homework as a good way to acquire competence, improve their skills and performance? Our working hypothesis is that student value homework in this regard. Therefore, we hypothesized that the more students are motivated to learn, the more they will be involved (cognitively and behaviorally) in their homework, and the higher their academic achievement.

To address this goal, we developed a path analysis model (see Figure ​1) in which we hypothesized that: (a) the student's motivational level is significantly related to their cognitive homework engagement (i.e., the approach to studying applied to homework), and their behavioral homework engagement (i.e., amount of time spent and homework time management, and amount of homework completed); (b) student's cognitive and behavioral homework engagement are positively associated with academic achievement; and (c) cognitive and behavioral homework engagement are related (the more deep cognitive engagement, the more time spent and time management, and the more amount of homework is done).



The study enrolled 535 students, aged between 9 and 13 (M = 10.32, SD = 0.99), of four public schools, from the last three years of the Spanish Elementary Education (4th, 5th, and 6th grade level), of whom 49.3% were boys. By grade, 40.4% (n = 216) were enrolled in the 4th grade, 35.1% (n = 188) in the 5th grade, and 24.5% (n = 131) in the 6th grade.


Learning goals

The level and type of motivation for academic learning was assessed with the Academic Goals Instrument (Núñez et al., 1997). Although, this instrument allows differentiating a broad range of academic goals, for the purposes of this work, we only used the subscale of learning goals (i.e., competence and control). The instrument is rated on a 5-point Likert-type scale, with responses ranging from one (not at all interested) to five (absolutely interested in learning and acquiring competence and control in the different subjects). An example item is: “I make an effort in my studies because performing the academic tasks allows me to increase my knowledge.” The reliability of the scale is good (α = 0.87).

Approach to homework

To measure the process of approaching homework, we adapted the Students' Approaches to Learning Inventory (Rosário et al., 2010a, 2013a), taking into account both the students' age and the homework contexts. This instrument is based on voluminous literature on approaches to learning (e.g., Biggs et al., 2001; Rosário et al., 2005), and provides information about two ways of approaching homework. For the purpose of this research, we only used the deep approach (e.g., “Before starting homework, I usually decide whether what was taught in class is clear and, if not, I review the lesson before I start”). Students respond to the items on a 5-point Likert-type scale ranging from one (not at all deep approach) to five (completely deep approach). The reliability of the scale is good (α = 0.80).

Time spent on homework, homework time management, and amount of homework completed

To measure these three variables, we used the Homework Survey (e.g., Rosário et al., 2009; Núñez et al., 2015a,b; Valle et al., 2015b,c). To measure the time spent on homework, students responded to three items (in general, in a typical week, on a typical weekend) with the general formulation, “How much time do you usually spend on homework?,” with the response options 1, <30 min; 2, 30 min to 1 h; 3, 1 h to an hour and a half; 4, 1 h and a half to 2 h; 5, more than 2 h. Homework time management was measured through the responses to three items (in general, in a typical week, on a typical weekend) in which they were asked to indicate how they managed the time normally spent doing homework, using the following scale: 1, I waste it completely (I am constantly distracted by anything); 2, I waste it more than I should; 3, regular; 4, I manage it pretty much; 5, I optimize it completely (I concentrate and until I finish, I don't think about anything else). Finally, the amount of homework completed by students (assigned by teachers) was assessed through responses to an item about the amount of homework usually done, using a 5-point Likert-type scale (1, none; 2, some; 3, one half; 4, almost all; 5, all).

Academic achievement

Assessment of academic achievement was assessed through students' report card grades in Spanish Language, Galician Language, English Language, Knowledge of the Environment, and Mathematics. Average achievement was calculated with the mean grades in these five areas.


Data of the target variables was collected during regular school hours, by research assistants, after obtaining the consent of the school administration and of the teachers and students. Prior to the application of the questionnaires, which took place in a single session, the participants were informed about the goals of the project, and assured that data was confidential and used for research purposes only.

Data analysis

The model was fit with AMOS 18 (Arbuckle, 2009). The data were previously analyzed and individual cases presenting a significant number of missing values were eliminated (2.1%), whereas the rest of the missing values were replaced by the mean. Taking into account the analysis of the characteristics of the variables (e.g., skewness and kurtosis in Table ​1), we used the maximum likelihood method to fit the model and estimate the values of the parameters.

Table 1

Means, standard deviations, skewness, kurtosis, and correlation matrix of the target variables.

A series of goodness-of-fit statistics were used to analyze our model. Beyond chi-square (χ2) and its associated probability (p), the information provided by the goodness-of-fit index (GFI) and the adjusted goodness-of-fit index (AGFI; Jöreskog and Sörbom, 1983); the comparative fit index (CFI) (Bentler, 1990); and the root mean square error of approximation (RMSEA; Browne and Cudeck, 1993) was used. According to these authors, the model fits well when GFI and AGFI > 0.90, CFI > 0.95, and RMSEA ≤ 0.05.


Descriptive analysis

The relations between the variables included in the model as well as the descriptive statistics are shown in Table ​1. All the variables were significantly and positively related, except for the time spent on homework, which was only related to the amount of homework done. According to the value of the means of these variables, students in the last years of elementary school: (a) reported a high level of motivation to learn and mastery; (b) used preferentially a deep approach to homework; (c) did the homework assigned by the teachers most of the times; (d) usually spent about an hour a day on homework; (e) reported to manage their study time effectively; and (f) showed a medium-high level of academic achievement.

Evaluation and re-specification of the initial model

The data obtained indicated that the initial model (see Figure ​1) presented a poor fit to the empirical data: χ2 = 155.80, df = 8, p < 0.001, GFI = 0.917, AGFI = 0.783, TLI = 0.534, CFI = 0.751, RMSEA = 0.186, 90% CI (0.161, 0.212), p < 0.001. Analysis of the modification indexes revealed the need to include three direct effects initially considered as null, and to eliminate a finally null effect (included in the initial model as significant). The strategy adopted to modify the initial model involved including and estimating the model each time a new effect was included. The final model comprised three effects (academic goals on homework time management, on amount of homework done, and on academic achievement) and the elimination of the initially established effect of the approach to studying on the time spent doing homework. The inclusion or elimination of the effects in the model was determined accounting for their statistical and theoretical significance. The final model resulting from these modifications is shown in Figure ​2, with an adequate fit to the empirical data: χ2 = 12.03, df = 6, p = 0.061, GFI = 0.993, AGFI = 0.974, TLI = 0.975, CFI = 0.990, RMSEA = 0.043, 90% CI (0.000, 0.079), p = 0.567.

Figure 2

The results of the fit of the hypothesized model (standardized outcomes): Relations in dashed lines were found to be statistically significant, but this was not established in the initial model.

Assessment of the relationships on the final model

Table ​2 presents the data obtained for the relationships considered in the final model (see also Figure ​2).

Table 2

Fit of the hypothesized model (standardized outcomes): final model of student engagement in homework.

The data from Table ​2 and Figure ​2 indicates that the majority of the relationships between the variables are consistent with the hypotheses. First, we found a statistically significant association between the learning goals (i.e., competence and control), the approach to homework (b = 0.50, p < 0.001), two of the variables associated with engagement in homework (the amount of homework done [b = 0.27, p < 0.001], homework time management [b = 0.30, p < 0.001]), and academic achievement (b = 0.34, p < 0.001). These results indicate that the more oriented students are toward learning goals (i.e., competence and control), the deeper the approach to homework, the more homework is completed, the better the homework time management, and the higher the academic achievement.

Second, a statistically significant association between the deep approach and homework time management (b = 0.30, p < 0.001) and the amount of homework done (b = 0.09, p < 0.05) was found. These results reflect that the deeper the students' approach to homework, the better the management of the time spent on homework, and the more the homework done. Third, there was a statistically significant association between homework time management, time spent on homework, and the amount of homework done (b = 0.23, p < 0.001, and b = 0.10, p < 0.01, respectively). These results confirm, as expected, that the more time students spent doing homework and the better students manage their homework time, the more homework they will do. Four, we found a statistically significant relation between the amount of homework done and academic achievement (b = 0.20, p < 0.001). This indicates that the more homework students complete the better their academic achievement.

In summary, our findings indicate that: (a) academic achievement is positively associated with the amount of homework completed; (b) the amount of homework done is related to homework time management; (c) homework time management is associated with how homework is done (approach to homework); and (d) consistent with the behavior of the variables in the model (except for the time spent on homework), how homework is done (i.e., approach to homework) is explained to a great extent (see total effects in Table ​3) by the student's type of academic motivation.

Table 3

Standardized direct, indirect, and total effects for the final model.

Finally, taking into account both the direct effects (represented in Figure ​2) and the indirect ones (see Table ​3), the model explained between 20 and 30% of the variance of the dependent variables (except for the time spent on homework, which is not explained at all): approach to homework (24.7%), time management (26.9%), amount of homework done (24.4%), and academic achievement (21.6%).


Consistent with prior research (e.g., Cooper et al., 2001), our findings showed that students' academic achievement in the last years of elementary education is closely related to the amount of homework done. In addition, the present study also confirms the importance of students' effort and commitment to doing homework (Trautwein et al., 2006a,b), showing that academic achievement is also related with students' desire and interest to learn and improve their skills. Therefore, when teachers assign homework, it is essential to attend to students' typical approach to learning, which is mediated by the motivational profile and by the way students solve the tasks proposed (Hong et al., 2004). The results of this investigation suggest that the adoption of learning goals leads to important educational benefits (Meece et al., 2006), among which is doing homework.

Importantly, our study shows that the amount of homework done is associated not only with the time spent, but also with the time management. Time spent on homework should not be considered an absolute indicator of the amount of homework done, because students' cognitive skills, motivation, and prior knowledge may significantly affect the time needed to complete the homework assignment (Regueiro et al., 2015). For students, managing homework time is a challenge (Corno, 2000; Xu, 2008), but doing it correctly may have a positive influence on their academic success (Claessens et al., 2007), on homework completion (Xu, 2005), and on school achievement (Eilam, 2001).

Despite, that previous studies reported a positive relationship between the time spent on homework and academic achievement (Cooper et al., 2006), the present research shows that time spent on homework is not a relevant predictor of academic achievement. Other studies have also obtained similar results (Trautwein et al., 2009; Núñez et al., 2015a), indicating that time spent on homework is negatively associated to academic achievement, perhaps because spending a lot of time on homework may indicate an inefficient working style and lack of motivation (Núñez et al., 2015a). Besides, our data indicates that spending more time on homework is positively associated to the amount of homework done.

Although, some studies have found that students who spend more time on homework also tend to report greater commitment to school work (Galloway et al., 2013), our findings indicated that spending more time doing homework was not related to a deeper engagement on the task. A possible explanation may be that using a deep approach to school tasks subsumes engaging in homework with the aim of practicing but also to further extend the content learned in class. This approach does not depends on the time spent doing homework, rather on the students' motives for doing homework.

Another important contribution of this study concerns learning-oriented goals—usually associated with positive outcomes in motivational, cognitive, and achievement variables (Pajares et al., 2000). Results indicate that the motivation to increase competence and learning is also related to approaching homework deeply and to manage homework efficiently. Consistent with previous findings (Xu, 2005), these results provide additional empirical support to time management goals (Pintrich, 2004).

There is a robust relationship between learning-oriented goals and a deep approach, and between a deep approach and the amount of homework done. All this indicates that these results are in line with prior research, meaning that the adoption of a deep approach to learning is related with high quality academic achievement (Lindblom-Ylänne and Lonka, 1999; Rosário et al., 2013b).

Educational implications and study limitations

One of the major limitations of this study lies in the type of research design used. We used a cross-sectional design to examine the effects among the variables within a path analysis model. However, to establish a cause-effect relationship a temporal sequence between two variables is needed a requirement that can only be met with longitudinal designs. Future studies should consider address this limitation.

Despite the above limitation, our results can be considered relevant and show important educational implications. It is essential for teachers and school administrators to be sensitized about the effects of teachers' homework follow-up practices on students' homework engagement (Rosário et al., 2015), and of these variables in students' school engagement and academic success. Likewise, research on students' learning should be undertaken from the perspective of the learners to understand how students use their knowledge and skills to do homework and to solve problems posed therein. On the other hand, research should examine in-depth the use of learning strategies during homework, as well as how students' motivations at an early age may foster homework completion and increase the quality of school outcomes. For this last purpose, teachers should pay attention not only to the acquisition of curricular content but also to the development of the appropriate thinking skills and self-regulated learning strategies (Rosário et al., 2010b; Núñez et al., 2013). Finally, the amount of homework done and its positive relationship with academic achievement should be considered as a final outcome of a process rooted on a comprehensive and meaningful learning. Students motivated to learn are likely to approach homework deeply and manage homework time efficaciously. As a result, they tend to do more homework and outperform. In sum, is doing homework a good way to acquire competence, improve skills, and outperform? Our data suggest a positive answer.

Author contributions

AV and BR Collect data, data analysis, writing the paper. JN and PR data analysis, writing the paper. SR and IP writing the paper.

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.


This work was developed through the funding of the research project EDU2013-44062-P, of the State Plan of Scientific and Technical Research and Innovation 2013-2016 (MINECO) and to the financing received by one of the authors in the FPU program of the Ministry of Education, Culture, and Sport.


  • Appleton J. J., Christenson S. L., Furlong M. J. (2008). Student engagement with school: critical conceptual and methodological issues of the construct. Psychol. Sch.45, 369–386. 10.1002/pits.20303 [Cross Ref]
  • Appleton J. J., Christenson S. L., Kim D., Reschly A. L. (2006). Measuring cognitive and psychological engagement: validation of the student engagement instrument. J. Sch. Psychol.44, 427–445. 10.1016/j.jsp.2006.04.002 [Cross Ref]
  • Arbuckle J. L. (2009). Amos 18.0 User's Guide. Crawfordville, FL: Amos Development Corporation.
  • Bentler P. M. (1990). Comparative fit indexes in structural models. Psychol. Bull.107, 238–246. 10.1037/0033-2909.107.2.238 [PubMed][Cross Ref]
  • Biggs J., Kember D., Leung D. Y. (2001). The revised two-actor study process questionnaire: R-SPQ-2F. Br. J. Educ. Psychol.71, 133–149. 10.1348/000709901158433 [PubMed][Cross Ref]
  • Boekaerts M. (1999). Self-regulated learning: where are today. Int. J. Educ. Res.31, 445–458. 10.1016/S0883-0355(99)00014-2 [Cross Ref]
  • Browne M. W., Cudeck R. (1993). Alternative ways of assessing model fit, in Testing Structural Equation Models, eds Bollen K., Long J., editors. (Newbury Park, CA: Sage; ), 136–162.
  • Cano F., García A., Justicia F., García-Berbén A. B. (2014). Enfoques de aprendizaje y comprensión lectora: el papel de las preguntas de los estudiantes y del conocimiento previo [Approaches to learning and reading comprehension: the role of students' questions and of prior knowledge]. Rev. Psicodidáctica19, 247–265. 10.1387/RevPsicodidact.10186 [Cross Ref]
  • Claessens B. J. C., van Eerde W., Rutte C. G., Roe R. A. (2007). A review of the time management literature. Pers. Rev.36, 255–276. 10.1108/00483480710726136 [Cross Ref]
  • Cooper H., Jackson K., Nye B., Lindsay J. J. (2001). A model of homework's influence on the performance evaluations of elementary school students. J. Exp. Educ.69, 181–200. 10.1080/00220970109600655 [Cross Ref]
  • 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. 10.3102/00346543076001001 [Cross Ref]
  • Corno L. (2000). Looking at homework differently. Element. Sch. J.100, 529–548. 10.1086/499654 [Cross Ref]
  • Deci E. L., Ryan R. M. (2002). Handbook of Self-Determination Research. New York, NY: University of Rochester Press.
  • 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. Eff. Sch. Improv.20, 375–405. 10.1080/09243450902904601 [Cross Ref]
  • Eccles J., Wang M. T. (2012). Part I Commentary: so what is student engagement anyway?,in Handbook of Research on Student Engagement, eds Christenson S. L., Reschly A. L., Wylie C., editors. (New York, NY: Springer; ), 133–145.
  • Eccles (Parsons) J., Adler T. F., Futterman R., Goff S. B., Kaczala C. M., Meece J. L., et al. (1983). Expectancies, values, and academic choice: origins and changes, in Achievement and Achievement Motivation, ed Spence J., editor. (San Francisco, CA: Freeman; ), 75–146.
  • Eilam B. (2001). Primary strategies for promoting homework performance. Am. Educ. Res. J.38, 691–725. 10.3102/00028312038003691 [Cross Ref]
  • Elliot A. J. (1999). Approach and avoidance motivation and achievement goals. Educ. Psychol.34, 169–189. 10.1207/s15326985ep3403_3 [Cross Ref]
  • Elliot A. J., Church M. A. (1997). A hierarchical model of approach and avoidance achievement motivation. J. Pers. Soc. Psychol.72, 218–232. 10.1037/0022-3514.72.1.218 [Cross Ref]
  • Elliot A. J., McGregor H. A., Gable S. (1999). Achievement goals, study strategies, and exam performance: a mediational analysis. J. Educ. Psychol.91, 549–563. 10.1037/0022-0663.91.3.549 [Cross Ref]
  • Entwistle N. J. (1991). Approaches to learning and perceptions of the learning environment. High. Educ.22, 201–204. 10.1007/BF00132287 [Cross Ref]
  • Entwistle N. J. (2009). Teaching for Understanding at University: Deep Approaches and Distinctive Ways of Thinking. Basingstoke: Palgrave Macmillan.
  • Fernández-Alonso R., Suárez-Álvarez J., Muñiz J. (2014). Tareas escolares en el hogar y rendimiento en matemáticas: una aproximación multinivel con estudiantes de Enseñanza Primaria [Homework and academic performance in mathematics: a multilevel approach with Primary school students]. Rev. Psicol. Educ.9, 15–29.
  • Fredricks J. A., Blumenfeld P. C., Paris A. (2004). School engagement: potential of the concept, state of the evidence. Rev. Educ. Res.74, 59–109. 10.3102/00346543074001059 [Cross Ref]
  • Galloway M., Conner J., Pope D. (2013). Nonacademic effects of homework in privileged, high-performing high schools. J. Exp. Educ.81, 490–510. 10.1080/00220973.2012.745469 [Cross Ref]
  • Gaudreau P. (2012). Goal self-concordance moderates the relationship between achievement goals and indicators of academic adjustment. Learn. Individ. Differ.22, 827–832. 10.1016/j.lindif.2012.06.006 [Cross Ref]
  • Hong E., Milgram R. M., Rowell L. L. (2004). Homework motivation and preference: a learner-centered homework approach. Theory Pract.43, 197–203. 10.1207/s15430421tip4303_5 [Cross Ref]
  • Inglés C. J., Martínez-González A. E., García-Fernández J. M. (2013). Conducta prosocial y estrategias de aprendizaje en una muestra de estudiantes españoles de Educación Secundaria Obligatoria [Prosocial behavior and learning strategies in a sample of Spanish students of Compulsory Secondary Education]. Eur. J. Educ. Psychol.6, 33–53. 10.1989/ejep.v6i1.101 [Cross Ref]
  • Jöreskog K. G., Sörbom D. (1983). LISREL - 6 User's Reference Guide. Mooresville, IN: Scientifi c Software.
  • Kohn A. (2006). Abusing research: the study of homework and other examples. Phi Delta Kappan88, 9–22. 10.1177/003172170608800105 [Cross Ref]
  • Lindblom-Ylänne S., Lonka K. (1999). Individual ways of interacting with the learning environment - are they related to study success?Learn. Instruct.9, 1–18. 10.1016/S0959-4752(98)00025-5 [Cross Ref]
  • Marks H. (2000). Student engagement in instructional activity: patterns in the elementary, middle, and high school years. Am. Educ. Res. J.37, 153–184. 10.3102/00028312037001153 [Cross Ref]
  • Martin A. J. (2012). Motivation and engagement: conceptual, operational, and empirical clarity, in Handbook of Research on Student Engagement, eds Christenson S. L., Reschly A. L., Wylie C., editors. (New York, NY: Springer; ), 303–311.
  • Marton F., Säljö R. (1976a). On qualitative differences in learning. I: outcome and process. Br. J. Educ. Psychol.46, 4–11. 10.1111/j.2044-8279.1976.tb02980.x [Cross Ref]
  • Marton F., Säljö R. (1976b). On qualitative differences in learning. II: outcome as a function of the learner's conception of the task. Br. J. Educ. Psychol.46, 115–127. 10.1111/j.2044-8279.1976.tb02304.x [Cross Ref]
  • Meece J. L., Anderman E. M., Anderman L. H. (2006). Classroom goal structure, student motivation, and academic achievement. Annu. Rev. Psychol.57, 487–503. 10.1146/annurev.psych.56.091103.070258 [PubMed][Cross Ref]
  • Middleton M., Midgley C. (1997). Avoiding the demonstration of lack of ability: an unexplored aspect of goal theory. J. Educ. Psychol.89, 710–718. 10.1037/0022-0663.89.4.710 [Cross Ref]
  • Muhlenbruck L., Cooper H., Nye B., Lindsay J. J. (2000). Homework and achievement: explaining the different strengths of relation at the elementary and secondary school levels. Soc. Psychol. Educ.3, 295–317. 10.1023/A:1009680513901 [Cross Ref]
  • Ng C. H. (2008). Multiple-goal learners and their differential patterns of learning. Educ. Psychol.28, 439–456. 10.1080/01443410701739470 [Cross Ref]
  • Núñez J. C., González-Pienda J. A., González-Pumariega S., García M., Roces C. (1997). Cuestionario Para la Evaluación de Metas Académicas [Academic Goals Assessment Questionnaire]. Department of Psychology, University of Oviedo.
  • Núñez J. C., Suárez N., Cerezo R., González-Pienda J. A., Rosário P., Mourão R., et al. (2015a). Homework and academic achievement across Spanish Compulsory Education. Educ. Psychol.35, 726–746. 10.1080/01443410.2013.817537 [Cross Ref]
  • Núñez J. C., Suárez N., Rosário P., Vallejo G., Cerezo R., Valle A. (2015b). Teachers' feedback on homework, homework-related behaviors and academic achievement. J. Educ. Res.108, 204–216. 10.1080/00220671.2013.878298 [Cross Ref]
  • Núñez J. C., Suárez N., Rosário P., Vallejo G., Valle A., Epstein J. L. (2015c). Relationships between parental involvement in homework, student homework behaviors, and academic achievement: differences among elementary, junior high, and high school students. Metacogn. Learn.10, 375–406. 10.1007/s11409-015-9135-5 [Cross Ref]
  • Núñez J., Rosário P., Vallejo G., González-Pienda J. (2013). A longitudinal assessment of the effectiveness of a school-based mentoring program in middle school. Contemp. Educ. Psychol.38, 11–21. 10.1016/j.cedpsych.2012.10.002 [Cross Ref]
  • Pajares F., Britner S. L., Valiante G. (2000). Relation between achievement goals and self-beliefs of middle school students in writing and science. Contemp. Educ. Psychol.25, 406–422. 10.1006/ceps.1999.1027 [PubMed][Cross Ref]
  • Pintrich P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educ. Psychol. Rev.16, 385–407. 10.1007/s10648-004-0006-x [Cross Ref]
  • Pintrich P. R., De Groot E. V. (1990). Motivational and self-regulated learning components of classroom performance. J. Educ. Psychol.82, 33–40. 10.1037/0022-0663.82.1.33 [Cross Ref]
  • Reeve J., Jang H., Carrell D., Jeon S., Barch J. (2004). Enhancing students' engagement by increasing teachers' autonomy support. Motiv. Emot.28, 147–169. 10.1023/B:MOEM.0000032312.95499.6f [Cross Ref]
  • Regueiro B., Suárez N., Valle A., Núñez J. C., Rosário P. (2015). La motivación e implicación en los deberes escolares a lo largo de la escolaridad obligatoria [Homework motivation and engagement throughout compulsory education]. Rev. Psicodidáctica20, 47–63. 10.1387/RevPsicodidact.12641 [Cross Ref]
  • Rodríguez S., Cabanach R. G., Piñeiro I., Valle A., Núñez J. C., González-Pienda J. A. (2001). Metas de aproximación, metas de evitación y múltiples metas académicas [Approach goals, avoidance goals and multiple academic goals]. Psicothema13, 546–550.
  • Rosário P., González-Pienda J. A., Pinto R., Ferreira P., Lourenço A., Paiva O. (2010a). Efficacy of the program “Testas's (mis)adventures” to promote the deep approach to learning. Psicothema22, 828–834. [PubMed]
  • Rosário P., Mourão R., Baldaque M., Nunes T., Núñez J. C., González-Pienda J. A., et al. (2009). Homework, self-regulation of learning and math performance. Rev. Psicodidáctica14, 179–192.
  • Rosário P., Núñez J. A., Ferrando J. P., Paiva O., Lourenço A., Cerezo R., et al. (2013a). The relationship between approaches to teaching and approaches to studying: a two-level structural equation model for biology achievement in high school. Metacogn. Learn.8, 47–77. 10.1007/s11409-013-9095-6 [Cross Ref]
  • Rosário P., Núñez J. C., González-Pienda J. A., Almeida L., Soares S., Rúbio M. (2005). Academic learning from the perspective of Model 3P of J. Biggs. Psicothema17, 20–30.
  • Rosário P., Núñez J. C., González-Pienda J. A., Valle A., Trigo L., Guimarães C. (2010b). Enhancing self-regulation and approaches to learning in first-year college students: a narrative-based program assessed in the Iberian Peninsula. Eur. J. Psychol. Educ.25, 411–428. 10.1007/s10212-010-0020-y [Cross Ref]
  • Rosário P., Núñez J. C., Vallejo G., Cunha J., Azevedo R., Pereira R., et al. (2016). Promoting Gypsy children school engagement: a story-tool project to enhance self-regulated learning. Contemp. Educ. Psychol. [Epub ahead of print]. 10.1016/j.cedpsych.2015.11.005. [Cross Ref]
  • Rosário P., Núñez J. C., Vallejo G., Cunha J., Nunes T., Suárez N., et al. . (2015). The effects of teachers' homework follow-up practices on students' EFL performance: a randomized-group design. Front. Psychol.6:1528. 10.3389/fpsyg.2015.01528 [PMC free article][PubMed][Cross Ref]
  • Rosário P., Núñez J., Valle A., González-Pienda J., Lourenço A. (2013b). Grade level, study time, and grade retention and their effects on motivation, self-regulated learning strategies, and mathematics achievement: a structural equation model. Eur. J. Psychol. Educ.28, 1311–1331. 10.1007/s10212-012-0167-9 [Cross Ref]
  • Skaalvik E. (1997). Self- enhancing and self-defeating ego orientation: relations with task and avoidance orientation, achievement, self- perceptions, and anxiety. J. Educ. Psychol.89, 71–81. 10.1037/0022-0663.89.1.71 [Cross Ref]
  • Skinner E. A., Pitzer J. R. (2012). Developmental dynamics of student engagement, coping, and everyday resilience, in Handbook of Research on Student Engagement, eds Christenson S. L., Reschly A. L., Wylie C., editors. (New York, NY: Springer; ), 21–44.
  • Struyven K., Dochy F., Janssens S., Gielen S. (2006). On the dynamics of students' approaches to learning: the effects of the teaching/learning environment. Learn. Instr.16, 279–294. 10.1016/j.learninstruc.2006.07.001
Categories: 1

0 Replies to “Homework Research 2013”

Leave a comment

L'indirizzo email non verrà pubblicato. I campi obbligatori sono contrassegnati *