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There are severe side effects of students getting too much homework in one night.

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How much homework to give? What type of homework?

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How much homework to give? What type of homework?

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"I plan to study the correlation between effort shown on review homework problems and the achievement level on current assessments because I want to find out if spiral review throughout the year is an efficient way to review past material. I want this research to show that students who put in the time and effort to complete the review problems will score higher on current assessments. I want to understand this better so that I can talk to other students who do not put as much time and effort into the review problems and show them that it is worth their time and effort."

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THIS IS COLOR CODED. Each color is a different sub topic that I am bringing up. The main sub topics are at the beginning and are the same color as to my sources.

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

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

Department of Psychology, University of Oviedo, Oviedo, Spain

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.

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.

Methods

Participants

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.

Procedure

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.

Instruments

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.

Results

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.

Funding

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.

References

Cooper, H., Steenbergen-Hu, S., and Dent, A. L. (2012). "Homework," in APA Educational Psychology Handbook, Vol. 3: Application to Learning and Teaching, eds K. R. Harris, S. Graham, and T. Urdan (Washington, DC: American Psychological Association), 475-495.
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# data[].attributes.attachment_link.title
Students' Achievement and Homework Assignment Strategies

# data[].attributes.attachment_link.description
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.

# data[].attributes.attachment_link.readable_text
A new meta-analysis of studies examining the relationship between homework and student achievement looks at 30 years of data involving over 312,000 students worldwide. It was published in the journal Educational Research Review in March.

The researchers noted a split in the findings of the studies reviewed: about as many studies found a positive relationship between homework and student achievement as found a negative relationship. But the researchers wondered if there were confounding factors among the various studies that might explain the disparate results. To look into this hypothesis, they narrowed the more than 8,000 studies under review down to twenty-eight that contained sufficient data to compare along eight variables identified as having the potential to lead to the inconsistent results.

These eight are interesting and worth noting: grade level (could the findings be more consistent in high school vs. elementary school?), subject matter (science vs. math?), homework indicators (homework measured by time on task vs. effort or grade received), publication type (perhaps the bar for publication in a peer-reviewed journal vs. a dissertation lead to inconsistent reporting of findings?), publication year (might the see-sawing reputation of homework as help or hindrance over time lead to inconsistent reporting of findings?), sampling method (random assignment studies vs. others?), geographical region (does regional or national differences in approach to homework explain the inconsistent findings?), and measure of achievement (could the findings be more consistent when achievement is measured by standardized vs. non-standardized assessments?). Whew!

After considering the eight variables, analysts concluded that a small and positive relationship between homework and academic achievement in math and science exists. That relationship is stronger in elementary and high school over middle school (bucking a finding noted in previous meta-analyses) and is stronger for U.S. students than for their Asian counterparts, which the largely China-based researchers attribute to the rise of private tutoring in a number of Asian countries.

With each report of schools and parents opting out of homework so as not to stress out their kids, the arguments begin anew: What is the purpose of homework, how much should be given, what form should it take, how do we know it's valuable? This research creates some coherence between fragmented findings that could be useful in answering these questions. For now, the age-old notion that homework can help continues to ring true.

SOURCE: Huiyong Fan, Jianzhong Xu, Zhihui Cai, Jinbo He, and Xitao Fan " Homework and students' achievement in math and science: A 30-year meta-analysis, 1986-2015," Educational Research Review (March 2017).

# data[].attributes.attachment_link.title
Homework and students' achievement in math and science

# data[].attributes.attachment_link.description
A new meta-analysis of studies examining the relationship between homework and student achievement looks at 30 years of data involving over 312,000 students worldwide. It was published in the journal Educational Research Review in March. The researchers noted a split in the findings of the studies reviewed: about as many studies found a positive relationship between homework and student achievement as found a negative relationship.

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In a randomized field trial with 2,850 seventh-grade mathematics students, we evaluated whether an educational technology intervention increased mathematics learning. Assigning homework is common yet sometimes controversial. Building on prior research on formative assessment and adaptive teaching, we predicted that combining an online homework tool with teacher training could increase learning. The online tool ASSISTments (a) provides timely feedback and hints to students as they do homework and (b) gives teachers timely, organized information about students' work. To test this prediction, we analyzed data from 43 schools that participated in a random assignment experiment in Maine, a state that provides every seventh-grade student with a laptop to take home. Results showed that the intervention significantly increased student scores on an end-of-the-year standardized mathematics assessment as compared with a control group that continued with existing homework practices. Students with low prior mathematics achievement benefited most. The intervention has potential for wider adoption.

Research Design

We investigated what happened when teachers in Maine were newly trained to use ASSISTments and then deployed it with all their students throughout a full year of seventh-grade mathematics instruction. Our main research question was as follows:

We investigated this question through a randomized controlled experiment in which schools were assigned to either an intervention or a business-as-usual condition.

Intervention

Worcester Polytechnic Institute provided the ASSISTments intervention-comprising the technology and closely related professional development-to schools, teachers, and students for this study.

The ASSISTments technology is a web-based platform that is made available to schools without charge. The name "ASSISTments" intends to convey that the technology gives students assistance while providing teachers with assessments of what students know and can do. ASSISTments was developed with funding from the National Science Foundation and the U.S. Department of Education's Institute of Educational Sciences. Three prior research studies involving small numbers of students and few instructional days are described here, and they showed that ASSISTments could have an effect on learning.

In Mendicino et al. (2009), the experiment covered 4 days of instruction and involved 28 students. The control group received a worksheet with problems, whereas subjects in the treatment condition entered their answers online, receiving scaffolding and hints immediately after answering a problem wrong. A pre- and posttest measure was developed by the experimenter in consultation those teaching the content. Posttest results showed a significant effect of condition, d = 0.6. There were two main differences between the groups in how they experienced homework during the study: The treatment group answered homework on a computer and got immediate feedback, whereas the control group neither answered homework on a computer nor got immediate feedback. In a follow-up study with a similar design involving 63 students over 2 instructional days ( Kelly et al., 2013), both groups answered homework on a computer: One group got immediate feedback via computer, and the other group received next-day feedback from a teacher. Students in the computer-based feedback condition had higher posttest scores. In another follow-up study ( n = 172 students; Singh et al. 2011), both groups again answered homework questions on a computer, but the computer-based feedback group received both correctness feedback and tutorial guidance if they got a problem wrong, whereas the control group did not receive computer-based feedback. Again, the students who received computer-based feedback and tutorial guidance had higher posttest scores than the students who entered their answers on a computer without receiving feedback. Overall, these studies offer preliminary evidence for the effectiveness of ASSISTments but conflate the timing of feedback (immediate vs. delayed) and the modality of feedback (from a computer or a teacher). The experiments involved less than a week of instruction, whereas the present study lasted an entire school year.

Content in ASSISTments consists of mathematics problems with answers and hints (and, in some cases, more extensive online guidance on how to solve the problem). These mathematics problems are bundled into problem sets that teachers assign to their students. As the students work online, the computer informs them about the correctness of a solution and offers guidance, if available for that problem. Teachers receive reports on how students perform on the assigned problem sets, including information about common wrong answers.

Two types of ASSISTments content were included in the intervention for this study. One type is closely linked to existing textbook homework problems or to related homework problems that teachers write themselves. During the summer, undergraduates entered all the homework problems into ASSISTments from all the mathematics textbooks used in the treatment group; thus, each teacher had access to every homework problem in his or her textbook. The ASSISTments interface enabled teachers to bundle these into problem sets to assign to their students.

The second kind of content was specifically developed for mastery-oriented skill practice and was called "skill builders." Existing skill builders in ASSISTments cover >300 topics in middle school math. Teachers can assign skill builders to students to provide practice problems that focus on a targeted skill until they reach a teacher-defined criterion for correctness (e.g., a streak of three correct answers on similar math problems). Students can be checked at 1- and 2-week intervals for retention of skills demonstrated on past problem sets, which links to the research-based instructional strategy of spaced practice ( Pashler et al., 2007). For both types of content, teachers (rather than the system or intervention developers) decided how much and what type of homework was assigned, and they were asked to do so in accordance with their existing school homework policy.

The intervention also incorporated teacher professional development aimed to increase teachers' readiness to use ASSISTments. The target practices included (a) encouraging students to rework problems they initially got wrong (and to enter revised answers), (b) focusing attention on the homework problems that students did not answer correctly, (c) reviewing correct solution processes for the problems that students found difficult, and (d) discussing common wrong answers to address underlying misunderstandings.

ASSISTments provides teachers with an easily accessible report that summarizes student work for a particular assignment in a grid format (). The rows of the grid correspond to individual students (if teachers display this report publically, they can hide student names). The columns of the grid correspond to individual problems in the assignment and show the percentage correct for each problem, as well any common wrong answers that students gave. The color-coded cells provide information on each student's performance on each problem in a format that focuses on problems that were difficult for many students. In addition, teachers can use information about students to form study groups and address common issues or to work with the students individually. The professional development training helps teachers learn to use this information to make instructional decisions, such as which problem to focus on or which student to spend time with. It also coaches teachers to determine when a topic needs to be retaught or student mastery is high enough to move on. Consequently, teachers were able to personalize ASSISTments for individual students, groups of students, or the whole class; for example, they could assign additional practice to particular students (e.g., by assigning skill builders) or enter other problems or hints to existing problem sets.

Recruitment, School-Level Attrition, and Demographics

We recruited 46 schools to represent the range of school sizes and mathematics achievement levels found throughout Maine. For the recruitment process, we developed a consistent set of message and presentations, which were available on a project website and as handouts. The study was described to teachers as a delayed treatment design: Some schools would be initially assigned to use ASSISTments, whereas others would wait 2 years and then be given training and access to use ASSISTments. We advertised the study to teachers and school principals throughout Maine via a mix of bulk mailings, presentations at statewide events, a webinar hosted by the state's Department of Education, visits to schools, personal communication through existing relationships, and some news coverage. As interested parties were identified, the team visited each school to make a presentation about the study and requirements for participation. Interested schools were required to complete an application form to ensure that they understood all the requirements of participation and that teachers and school leaders fully agreed to participate; in addition, the application form gave the researchers useful demographic data about the schools. All schools that successfully completed an application were accepted into the study and randomly assigned to a condition, as described later. The procedure for recruitment is detailed in an online technical report ( Roschelle et al., 2014).

Attrition is relatively common in experiments that are fielded in schools and that unfold over a school year or longer. As planned and executed, this experiment required that schools remain in the condition for 2 years and provide student data in the second year. We expected some attrition in the design of the study and thus recruited more schools than needed.

The specifics of attrition from 46 to 43 schools are as follows: Most schools were placed into pairs prior to randomization. The pairs were formed by rank ordering schools by prior test scores and making slight adjustments to the resulting order when schools of similar rank were of dissimilar sizes. In one case, a pair was formed, but then one school dropped out, leaving 45 schools. A new pair was formed with the remaining school, and we proceeded with random assignment. One treatment school dropped out after the start of the project, but its matching control school did not drop out, leaving 44 schools. We continued to collect data for the unpaired control school but later excluded it from analysis because it had lost its matching treatment school. The net result was 43 schools in the final analytic sample, consisting of randomly assigned pairs ( n = 40) plus three schools that had never been paired and were individually randomly assigned to treatment ( n = 2) or control ( n = 1). The school-level overall attrition rate was 6.5%, and the school-level differential attrition rate was 3.8%. The What Works Clearinghouse (2008) publishes guidelines on the acceptability of attrition, and these rates are within its published parameters for attrition. In the Data Linking section, we provide related information on student-level attrition.

The overall demographics of the students in our data sample of 43 schools are reported in Table 1. We studied only the seventh-grade students in each school. Both in our sample and in Maine in general, most students are White, and many qualify for free and reduced-price lunch (FRL), a common measure of socioeconomic status. Students with individualized education programs (IEPs), an indicator of the need for special education services, made up about 12% of our sample.

As discussed, schools were placed into pairs with similar prior achievement levels and enrollment sizes, and pairs were randomly assigned to treatment and control. In three cases, no pair was available at the time of randomization, so we randomly and independently assigned each to one of the conditions. Both teacher groups were expected to assign and review mathematics homework for seventh-grade students in accordance with their local school policies. As previously described, the content of the homework in both groups came from the textbooks already in use at each school as well as from skill builders. The treatment group was immediately offered the intervention, comprising both the ASSISTments platform and teacher professional development, as detailed later. The control group was eventually provided with both ASSISTments and the same professional development but after a 2-year delay; thus, it did not have access to ASSISTments while we were collecting data.

Teachers in the treatment group participated in professional development training beginning in the summer preceding the first year of implementation (3 days) and later throughout the school year (approximately 6 hr across three classroom visits from an ASSISTments coach). During the first year of ASSISTments use, the coach worked with teachers to improve their implementation and formative assessment practices. Teachers attended a second professional development workshop in the summer between the first and second years (2 days). The coaching component of the professional development was both in-person and remote. The coach, who was a member of the ASSISTments team, visited every teacher in his or her classroom at least three times (with the exception of a school located on the far north side of the state of Maine). The coach also conducted remote webinars two to three times each year and encouraged all teachers to attend. Last, many teachers took advantage of the opportunity to occasionally email and call the coach for help during the school year.

Teachers used ASSISTments with a new cohort of seventh-grade students during the second school year. Scores from this second cohort of students were used for our analysis of the impact on student learning.

In the control group, teachers continued with their existing homework practices without any specific teacher training beyond that offered by their districts. Control group teachers were not able to use ASSISTments with seventh-grade students. Teachers knew that after the 2-year experiment ended, they would be invited to attend professional development training for ASSISTments and use it without cost. Thus, via a delayed treatment design, the study provided all schools with a similar incentive while maintaining an intervention contrast during the first 2 years.

To measure students' levels of achievement prior to intervention, we obtained data from the state of Maine under an approved institutional review board and data security plan. The data included students' achievement on the New England Common Assessment Program (NECAP) test for reading and mathematics. The data also included demographic indicators, such as gender, FRL status, and IEP status. During the fielding of this experiment, the state of Maine phased out NECAP but was not yet administering Smarter Balanced, the replacement Common Core examination. Hence, we could not rely on a state-administered test as an outcome measure. To measure students' levels of achievement at the end of the intervention, we administered the TerraNova standardized test. We chose TerraNova because it has well-established and reputable psychometric qualities. The developers of ASSISTments did not make any changes to the content of their intervention to align with TerraNova, nor was TerraNova selected with an expectation that there was an especially strong alignment with ASSISTments.

Mathematics scores from the NECAP test were used as the measure of students' knowledge. NECAP was developed in response to the federal No Child Left Behind Act of 2001 (2002). The NECAP test is considered a well-designed assessment with strong technical qualities. Maine administered the NECAP test once each year from Grades 3 through 8 to comply with the act. NECAP data were collected by the state of Maine for students when they were in sixth grade, which was the year before they were first exposed to the intervention. Due to the transition from NECAP to Common Core-based testing, the last available scores for our students were sixth-grade scores; we used the most recent available data.

Scores from the TerraNova Common Core assessment mathematics test were used as a primary outcome measure to provide data on student achievement in mathematics concepts, processes, and skills, and the test was administered at the end of students' seventh-grade year. This achievement test is published by Data Recognition Corporation CTB and is nationally normed.

According to the publisher, TerraNova's content is aligned with the Common Core Standards for Mathematics and represents mathematics curriculum taught throughout the United States. During the time of this experiment, schools in Maine were transitioning from prior instructional frameworks to the Common Core. The TerraNova content is appropriate for the transitional period because it is largely consistent with existing instructional practices but focuses on grade-level expectations in the Common Core. The TerraNova mathematics test at Level 17 is appropriate for Grade 7; it has 37 items and requires 1.5 hr to administer. This instrument combines selected-response items with sections of constructed-response items that allow students to produce short and extended responses. It generates norm-referenced achievement scores, criterion-referenced objective mastery scores, and achievement-level information that includes five levels of proficiency. We used the norm-referenced achievement scores, which are units of a single equal-interval scale that extends across 12 years of schooling.

Teachers were trained to administer the TerraNova mathematics test at the end of the second year of implementation in both cohorts of schools. Training and administration of the test occurred near the end of the school year. The test booklets were then returned to the test publisher for scoring. The test publisher (blind to condition) scored the test booklets and reported the scale scores to us.

The electronic records collected by the ASSISTments system are a source of implementation data in the treatment group. For each classroom, the system collects data for each student on each day at an individual action level (e.g., when a student enters an answer or requests a hint). In addition to data about student usage, the system collects data on teacher usage. Using these data, we confirmed that all treatment group teachers used ASSISTments, and we found no evidence that control teachers were using the system.

Data Analysis

Data Linking and Student-Level Attrition

Students and parents were first notified about the study when students entered seventh grade and were exposed to the intervention (treatment group only). Teachers, however, began the study with a warm-up year, while student participants were still in sixth grade. Hence, for the purpose of counting the students who left a study school or joined a study school, we began with the student population as of the first semester of sixth grade. Although we have no evidence of this occurring, it is theoretically possible that a sixth-grade student or parent would have learned about the seventh-grade study and decided to change schools, and this conservative method of determining the populations would count such students as joining or leaving the study.

In the first semester of sixth grade, 3,035 students were in a study school. Of these, 2,728 completed the study by taking a TerraNova assessment at the end of seventh grade. Hence, 307 exited the study because they left a study school or skipped the TerraNova assessment. Of the 307 leavers, 178 were in the control group and 129 in the treatment group. The student-level attrition is thus 10.1% overall, with 5.6% differential attrition between groups. Compared with the What Works Clearinghouse (2008) model for differential attrition, this level of attrition fits within the zone considered to be acceptable with regard to potential bias.

In addition, 122 students (61 in the control group and 61 in the treatment group) joined a study school, had available NECAP scores, and completed the study by taking the TerraNova assessment. (Later we report an analysis of whether our findings are sensitive to the addition of joiners and further justify our decision to include them in our analysis.) Thus, our total study sample size was 2,850 (the sum of 122 and 2,728) with 1,621 in the treatment group and 1,229 in the control group.

Baseline Equivalence of Groups

Analyses found no difference between the treatment and control groups in various demographic and predictor variables. Multilevel logistic models found no control-versus-treatment condition differences in student-level FRL status, γ = 0.012, t(21) = 0.121, p = .905; non-White/non-Hispanic heritage, γ = −0.219, t(21) = −0.809, p = .427; IEP status, γ = −0.013, t(21) = −0.115, p = .910; and student sex, γ = 0.100, t(21) = 1.747, p = .095. Multilevel regression found no treatment condition differences in prior year math performance, γ = 0.194, t(21) = 0.463, p = .648, or prior year reading performance, γ = −0.139, t(21) = −0.177, p = .861.

Use of ASSISTments

We tracked usage of ASSISTments directly via system log files. Students in the ASSISTments condition used the system, on average, for 967 mathematics problems and for 14 hr in the course of a school year. We had expected 18 to 24 hr of use in the course of the year, by estimating that homework assignments could be completed in about 10 min and would be assigned three to four nights each week.

Analytic Models

To estimate the impacts of the intervention on student learning, we developed analytic models that reflect the clustering of students. We primarily report results from a hierarchical linear regression model (HLM) with two levels: students nested within schools. In most of the schools in our sample, there is only one teacher for seventh-grade mathematics; thus, in these schools, there is no distinction between the school and teacher level. Furthermore, the two- and three-level models produced similar statistical results. In our primary HLM, we included the following variables as student-level covariates: prior NECAP mathematics (PriorMath), NECAP reading scores (PriorRead), FRL status, and IEP status. The school-level covariates included are the school's mean prior NECAP mathematics score (SchMath) and 20 school-pair variables (SchPair), indicating which school pair a school belonged to. The three schools not paired before randomization were treated as their own cluster.

All analyses were conducted with students' TerraNova-scaled scores as the dependent (outcome) variable. The two-level HLM that predicts student TerraNova performance is as follows:

Trx is a binary variable to indicate whether the school is in the treatment condition ( Trx = 1) or control condition ( Trx = 0), and γ ₀₁ is the estimate of the average adjusted difference between the treatment and control conditions in spring TerraNova scores.

Using a separate model, we also analyzed whether ASSISTments benefited students with low or high prior mathematics achievement differently. Using the sixth-grade NECAP mathematics scores, we divided students into groups with scores at or below the median (low prior achievement) or above the median (high prior achievement). An HLM used the treatment condition and the binary prior-math-performance variable as predictors of seventh-grade TerraNova scores, controlling for student-level FRL status, student-level IEP status, school-level prior-year mean NECAP mathematics scores, and the 20 school-pair variables. This model evaluated the interaction between prior achievement status and being in the treatment condition.

We first provide the results from fitting the data to the primary HLM and then consider the impact on students with low prior mathematics achievement.

Modeling Student Achievement

We modeled student achievement and tested statistical significance using the primary HLM described above. Two- and three-level models gave similar statistical results. We report the results for the simpler two-level model.

When we controlled for student-level covariates (prior math and reading achievement, FRL status, and IEP status), the treatment students' adjusted mean TerraNova scale score was 8.84 points higher than the control students' model-adjusted mean scale score of 681.95. This difference is statistically significant, t(20) = 2.992, p = .007. Further details of the model results appear in Table 2. As a check on the reasonableness of the statistical model, we also examined the unadjusted difference in mean TerraNova scores between groups. The unadjusted difference between the mean scores of the two conditions was slightly greater, 11 points, and also favored the treatment group. We then computed an effect size using the adjusted difference in scaled scores and the pooled standard deviation of the scores in our overall seventh-grade sample. The difference of 8.84 points corresponds to an effect size of 0.18 standard deviation units (we used Hedges's g as our measure of effect size throughout the analyses).

We also examined the sensitivity of the impact findings to the inclusion of 122 students who joined the study after their school was randomly assigned to a treatment condition. To do so, we reanalyzed the data using the same HLM but without these 122 students in the sample. The findings are essentially the same (see Table 3); the adjusted mean difference between groups is 8.57 and remains statistically significant ( p = .01). We report the main findings based on a sample that includes joiners, for three reasons. First, this intervention has low risk for bias due to the inclusion of joiners in the analytic sample. All schools in Maine already send students home with laptops for the purpose of doing homework; thus, laptops were given to students in both conditions. Furthermore, it is unlikely that a student would change schools because a teacher in one school subject (math) uses a particular application for homework; in general, students are tolerant of a variety of applications that their teachers ask them to use on the school-assigned laptop and are unlikely to change schools just to obtain or avoid a particular application used only in mathematics classes. Second, we did not detect a threat to internal validity due to the presence of joiners. The joiners were equally distributed across treatment groups; their average prior math test scores were also similar across groups, t(120) = 1.085, p = .280; and the impact results are very similar with and without joiners in the sample. Third, with regard to external validity, school officials are typically concerned about impacts for all their students who complete a particular school year, not only those students who stayed in the same school for 2 years in a row.

Students With Low and High Prior Achievement

For those students with NECAP scores at or below the median, the mean score on TerraNova in the treatment group was 13.35 points higher than that in the control group. For the students who started above the median, the mean score in the treatment group was 5.84 points higher than that in the control group (see Table 4). The interaction is statistically significant, t(2770) = 2.432, p = .015; the effect of the ASSISTments intervention is greater for lower-performing students than for higher-performing students (see ). We calculated effect sizes for each group using the pooled standard deviation for the total sample. The corresponding effect sizes were .29 for those students at or below the median and .12 for those students above the median.

Discussion

Compared with the business-as-usual control group, we found that achievement was higher in the schools that used the online homework intervention comprising ASSISTments and related teacher professional development. Furthermore, the intervention provided a greater benefit to students with lower, rather than higher, prior mathematics achievement.

To interpret this effect size of 0.18 standard deviations, we considered standard guidance ( Lipsey et al., 2012). We noted that the gains due to the intervention were greater in magnitude than the difference in scores between students with and without FRL status; thus, the effect is meaningful relative to the size of achievement gaps among students with different levels of socioeconomic status. The magnitude of the effect can also be described in terms of an improvement index: Students at the 50th percentile without the intervention would improve to the 58th percentile if they received the ASSISTments treatment. (An improvement index is the expected percentile gain for the average student in the control group-the student who scored at the 50th percentile on the outcome measure-if that student had attended a school where the intervention was implemented.)

We also examined how the difference in means on the TerraNova translates to differences on a grade-level scale. Terra Nova scores are scaled such that the mean score in a nationally normed sample increases by a modest number of scale points per school year. For example, the mean scale scores for the test publisher's nationally normed sample at the beginning of sixth, seventh, eighth, and ninth grades are 655, 670, 678, and 690, respectively (an average difference of 11.66 points per year). In the context of these modest expected differences, the measured difference of 8.84 scale score points is meaningful.

This intervention was designed for use by all students in a school. Some interventions target particular identified populations, such as students with needs for special education, students whose performances are below grade level, or students with poor language skills. In contrast, this intervention followed typical homework policies, which apply to all students. A notable effect size is less likely when the treated population is inclusive, as some students in the population will probably do well without any specific intervention. Indeed, we found that the benefit was smaller for students with above-median prior mathematics achievement. It is reasonable to expect that students with high prior mathematics achievement may be doing well on homework without any special supports. Yet school stakeholders are frequently concerned about fairness; thus, schools often prefer interventions that can be offered to all students and that have the potential of benefiting all students. Hence, a desirable feature of this experiment is that the intervention was assigned to all seventh-grade students in a school and that a meaningful impact was nonetheless obtained.

There were several limitations of this study. First, the state of Maine provides laptops to all seventh-grade students; other settings might have less or variable access to technology. Inequitable access to technology within the treatment condition might result in smaller effects. Second, Maine is more rural and homogeneous than many other states and regions; results could vary in other regions. Third, teachers had one school year to improve their quality of implementation before we measured impact in a second school year. Implementations that seek results quickly or provide less teacher support may result in smaller or different effects.

Within the scope of this report, we are also limited in the degree to which we can identify specific mechanisms within the intervention that were most closely related to the effects observed. The project collected several additional data sources that provide insight on possible mechanisms, but the analysis and integration of this additional qualitative and quantitative data require further work. For example, in further analysis we plan to use additional data from teacher interviews, surveys, and instructional logs to consider whether variations in the type and amount of homework might explain effects. It is possible that students spent more or less time on homework in the treatment condition (i.e., when using ASSISTments) than the business-as-usual condition. We do not expect this to be the case, because the amount of homework assigned was set by school policies on homework and we found the policies to be similar among schools in our sample ( Fairman, Porter, & Fisher, 2015). It could also be that the content of the homework was different; however, this is also unlikely because teachers in both conditions assigned homework from their textbooks. We will be examining the extent to which the additional skill builder content offered in the ASSISTments treatment group (but not the control group) might explain the findings.

In further analysis, we plan to look at the extent to which changes in student learning were mediated by changes in teacher behavior, by analyzing and integrating additional data from observations, interviews, surveys, and instructional logs. Students may receive a direct benefit from the feedback and hints that they receive in ASSISTments as they do homework, regardless of whether their teacher's behavior is different during classroom instruction; however, teachers can use the reports generated by ASSISTments to adapt their teaching based on their students' work. There is some preliminary indication that teachers did change their behavior, but due to the complexity of analyzing and integrating the multiple qualitative and quantitative data sources to address this issue, comprehensive analysis of this issue will await a subsequent research effort.

Conclusion

In this study, an online mathematics homework intervention produced a positive impact on students' mathematics achievement at the end of a school year. Students with low, rather than high, prior achievement benefited more. The intervention provided students with personalized feedback and hints immediately-more typically, students wait until the next day to know what they did right and wrong and to get help. When students struggled, they had additional opportunities to work toward mastery in supplementary problem sets. The intervention also enabled formative assessment practices for teachers, such as adapting their discussions of homework to fit students' needs. Specific professional development was provided to teachers to enable them to enact these adaptive practices. Formative assessment is a frequently recommended practice; this intervention provided a practical, targeted way to do it.

Our findings are relevant to many schools because mathematics homework is a commonplace school policy and school stakeholders often critique the value of existing homework practices. This study demonstrates one way to improve the value of homework. Furthermore, the tested intervention fit within common school policies for homework and did not require change in instructional policies, such as the school's choice of textbook or pacing through topics. Thus, the intervention was targeted and modest: Teachers were not expected to change their entire repertoire of classroom routines. Although reformers often promote more ambitious changes to classroom routines (e.g., "flipped classrooms"), such changes in teaching practice require much more intensive teacher training and can meet resistance. Here, teachers continued to make the majority of instructional decisions following their own approach in accordance with existing school policy. An intervention that mostly fits existing school policies is typically easier for schools to adopt.

Interventions like this one can also bring new personalized options to schools. Schools tend to have a uniform homework policy for all students, and teachers can assign mathematics homework to all students in ASSISTments. However, students' assignments need not be identical. Students who show mastery of a particular topic after just a few minutes could perhaps do only a few problems that are likely to be discussed with the full class the next day (so they get some practice and can participate in class discussion). Other students may benefit from more intensive practice opportunities as well as specific skill builder exercises or other resources. ASSISTments and similar technology could help mathematics teachers assign some problems uniformly while personalizing some assignments so that particular students have additional opportunities to learn. Although the potential to combine elements of uniformity and personalization seems promising, an empirical basis for recommending exactly how to optimize this mix of homework assignments remains a topic for further research.

Acknowledgements

This material is based on work supported by the Institute of Educational Sciences of the U.S. Department of Education under Grant R305A120125. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of Institute of Educational Sciences. The data reported in the paper may be obtained from the State of Maine Department of Education. We thank the Department of Education for providing access to the data. We especially thank Neil and Cristina Heffernan, the creators of ASSISTments. ASSISTments is available as a free public service of Worcester Polytechnic Institute at http://assistments.org. None of the authors has a financial interest in ASSISTments. We also thank the students, teachers, and schools who participated in the study, as well as the project staff from SRI International, University of Maine, and Worcester Polytechnic Institute.

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Online Mathematics Homework Increases Student Achievement - Jeremy Roschelle, Mingyu Feng, Robert F. Murphy, Craig A. Mason, 2016

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In a randomized field trial with 2,850 seventh-grade mathematics students, we evaluated whether an educational technology intervention increased mathematics learning. Assigning homework is common yet sometimes controversial. Building on prior research on formative assessment and adaptive teaching, we predicted that combining an online homework tool with teacher training could increase learning.

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Any parent who has battled with a child over homework night after night has to wonder: Do those math worksheets and book reports really make a difference to a student's long-term success? Or is homework just a headache-another distraction from family time and downtime, already diminished by the likes of music and dance lessons, sports practices, and part-time jobs?

Allison, a mother of two middle-school girls from an affluent Boston suburb, describes a frenetic afterschool scenario: "My girls do gymnastics a few days a week, so homework happens for my 6th grader after gymnastics, at 6:30 p.m. She doesn't get to bed until 9. My 8th grader does her homework immediately after school, up until gymnastics. She eats dinner at 9:15 and then goes to bed, unless there is more homework to do, in which case she'll get to bed around 10." The girls miss out on sleep, and weeknight family dinners are tough to swing.

Parental concerns about their children's homework loads are nothing new. Debates over the merits of homework-tasks that teachers ask students to complete during non-instructional time-have ebbed and flowed since the late 19th century, and today its value is again being scrutinized and weighed against possible negative impacts on family life and children's well-being.

Are American students overburdened with homework? In some middle-class and affluent communities, where pressure on students to achieve can be fierce, yes. But in families of limited means, it's often another story. Many low-income parents value homework as an important connection to the school and the curriculum-even as their children report receiving little homework. Overall, high-school students relate that they spend less than one hour per day on homework, on average, and only 42 percent say they do it five days per week. In one recent survey by the National Assessment of Educational Progress (NAEP), a minimal 13 percent of 17-year-olds said they had devoted more than two hours to homework the previous evening (see Figure 1).

Recent years have seen an increase in the amount of homework assigned to students in grades K-2, and critics point to research findings that, at the elementary-school level, homework does not appear to enhance children's learning. Why, then, should we burden young children and their families with homework if there is no academic benefit to doing it? Indeed, perhaps it would be best, as some propose, to eliminate homework altogether, particularly in these early grades.

On the contrary, developmentally appropriate homework plays a critical role in the formation of positive learning beliefs and behaviors, including a belief in one's academic ability, a deliberative and effortful approach to mastery, and higher expectations and aspirations for one's future. It can prepare children to confront ever-more-complex tasks, develop resilience in the face of difficulty, and learn to embrace rather than shy away from challenge. In short, homework is a key vehicle through which we can help shape children into mature learners.
The Homework-Achievement Connection
A narrow focus on whether or not homework boosts grades and test scores in the short run thus ignores a broader purpose in education, the development of lifelong, confident learners. Still, the question looms: does homework enhance academic success? As the educational psychologist Lyn Corno wrote more than two decades ago, "homework is a complicated thing." Most research on the homework-achievement connection is correlational, which precludes a definitive judgment on its academic benefits. Researchers rely on correlational research in this area of study given the difficulties of randomly assigning students to homework/no-homework conditions. While correlation does not imply causality, extensive research has established that at the middle- and high-school levels, homework completion is strongly and positively associated with high achievement. Very few studies have reported a negative correlation.

As noted above, findings on the homework-achievement connection at the elementary level are mixed. A small number of experimental studies have demonstrated that elementary-school students who receive homework achieve at higher levels than those who do not. These findings suggest a causal relationship, but they are limited in scope. Within the body of correlational research, some studies report a positive homework-achievement connection, some a negative relationship, and yet others show no relationship at all. Why the mixed findings? Researchers point to a number of possible factors, such as developmental issues related to how young children learn, different goals that teachers have for younger as compared to older students, and how researchers define homework.

Certainly, young children are still developing skills that enable them to focus on the material at hand and study efficiently. Teachers' goals for their students are also quite different in elementary school as compared to secondary school. While teachers at both levels note the value of homework for reinforcing classroom content, those in the earlier grades are more likely to assign homework mainly to foster skills such as responsibility, perseverance, and the ability to manage distractions.

Most research examines homework generally. Might a focus on homework in a specific subject shed more light on the homework-achievement connection? A recent meta-analysis did just this by examining the relationship between math/science homework and achievement. Contrary to previous findings, researchers reported a stronger relationship between homework and achievement in the elementary grades than in middle school. As the study authors note, one explanation for this finding could be that in elementary school, teachers tend to assign more homework in math than in other subjects, while at the same time assigning shorter math tasks more frequently. In addition, the authors point out that parents tend to be more involved in younger children's math homework and more skilled in elementary-level than middle-school math.

In sum, the relationship between homework and academic achievement in the elementary-school years is not yet established, but eliminating homework at this level would do children and their families a huge disservice: we know that children's learning beliefs have a powerful impact on their academic outcomes, and that through homework, parents and teachers can have a profound influence on the development of positive beliefs.
How Much Is Appropriate?
Harris M. Cooper of Duke University, the leading researcher on homework, has examined decades of study on what we know about the relationship between homework and scholastic achievement. He has proposed the "10-minute rule," suggesting that daily homework be limited to 10 minutes per grade level. Thus, a 1st grader would do 10 minutes each day and a 4th grader, 40 minutes. The National Parent Teacher Association and the National Education Association both endorse this guideline, but it is not clear whether the recommended allotments include time for reading, which most teachers want children to do daily.

For middle-school students, Cooper and colleagues report that 90 minutes per day of homework is optimal for enhancing academic achievement, and for high schoolers, the ideal range is 90 minutes to two and a half hours per day. Beyond this threshold, more homework does not contribute to learning. For students enrolled in demanding Advanced Placement or honors courses, however, homework is likely to require significantly more time, leading to concerns over students' health and well-being.

Notwithstanding media reports of parents revolting against the practice of homework, the vast majority of parents say they are highly satisfied with their children's homework loads. The National Household Education Surveys Program recently found that between 70 and 83 percent of parents believed that the amount of homework their children had was "about right," a result that held true regardless of social class, race/ethnicity, community size, level of education, and whether English was spoken at home.
Learning Beliefs Are Consequential
As noted above, developmentally appropriate homework can help children cultivate positive beliefs about learning. Decades of research have established that these beliefs predict the types of tasks students choose to pursue, their persistence in the face of challenge, and their academic achievement. Broadly, learning beliefs fall under the banner of achievement motivation, which is a constellation of cognitive, behavioral, and affective factors, including: the way a person perceives his or her abilities, goal-setting skills, expectation of success, the value the individual places on learning, and self-regulating behavior such as time-management skills. Positive or adaptive beliefs about learning serve as emotional and psychological protective factors for children, especially when they encounter difficulties or failure.

Motivation researcher Carol Dweck of Stanford University posits that children with a "growth mindset"-those who believe that ability is malleable-approach learning very differently than those with a "fixed mindset"-kids who believe ability cannot change. Those with a growth mindset view effort as the key to mastery. They see mistakes as helpful, persist even in the face of failure, prefer challenging over easy tasks, and do better in school than their peers who have a fixed mindset. In contrast, children with a fixed mindset view effort and mistakes as implicit condemnations of their abilities. Such children succumb easily to learned helplessness in the face of difficulty, and they gravitate toward tasks they know they can handle rather than more challenging ones.

Of course, learning beliefs do not develop in a vacuum. Studies have demonstrated that parents and teachers play a significant role in the development of positive beliefs and behaviors, and that homework is a key tool they can use to foster motivation and academic achievement.
Parents' Beliefs and Actions Matter
It is well established that parental involvement in their children's education promotes achievement motivation and success in school. Parents are their children's first teachers, and their achievement-related beliefs have a profound influence on children's developing perceptions of their own abilities, as well as their views on the value of learning and education.

Parents affect their children's learning through the messages they send about education, whether by expressing interest in school activities and experiences, attending school events, helping with homework when they can, or exposing children to intellectually enriching experiences. Most parents view such engagement as part and parcel of their role. They also believe that doing homework fosters responsibility and organizational skills, and that doing well on homework tasks contributes to learning, even if children experience frustration from time to time.

Many parents provide support by establishing homework routines, eliminating distractions, communicating expectations, helping children manage their time, providing reassuring messages, and encouraging kids to be aware of the conditions under which they do their best work. These supports help foster the development of self-regulation, which is critical to school success.

Self-regulation involves a number of skills, such as the ability to monitor one's performance and adjust strategies as a result of feedback; to evaluate one's interests and realistically perceive one's aptitude; and to work on a task autonomously. It also means learning how to structure one's environment so that it's conducive to learning, by, for example, minimizing distractions. As children move into higher grades, these skills and strategies help them organize, plan, and learn independently. This is precisely where parents make a demonstrable difference in students' attitudes and approaches to homework.

Especially in the early grades, homework gives parents the opportunity to cultivate beliefs and behaviors that foster efficient study skills and academic resilience. Indeed, across age groups, there is a strong and positive relationship between homework completion and a variety of self-regulatory processes. However, the quality of parental help matters. Sometimes, well-intentioned parents can unwittingly undermine the development of children's positive learning beliefs and their achievement. Parents who maintain a positive outlook on homework and allow their children room to learn and struggle on their own, stepping in judiciously with informational feedback and hints, do their children a much better service than those who seek to control the learning process.

A recent study of 5th and 6th graders' perceptions of their parents' involvement with homework distinguished between supportive and intrusive help. The former included the belief that parents encouraged the children to try to find the right answer on their own before providing them with assistance, and when the child struggled, attempted to understand the source of the confusion. In contrast, the latter included the perception that parents provided unsolicited help, interfered when the children did their homework, and told them how to complete their assignments. Supportive help predicted higher achievement, while intrusive help was associated with lower achievement.

Parents' attitudes and emotions during homework time can support the development of positive attitudes and approaches in their children, which in turn are predictive of higher achievement. Children are more likely to focus on self-improvement during homework time and do better in school when their parents are oriented toward mastery. In contrast, if parents focus on how well children are doing relative to peers, kids tend to adopt learning goals that allow them to avoid challenge.
Homework and Social Class
Social class is another important element in the homework dynamic. What is the homework experience like for families with limited time and resources? And what of affluent families, where resources are plenty but the pressures to succeed are great?

Etta Kralovec and John Buell, authors of The End of Homework, maintain that homework "punishes the poor," because lower-income parents may not be as well educated as their affluent counterparts and thus not as well equipped to help with homework. Poorer families also have fewer financial resources to devote to home computers, tutoring, and academic enrichment. The stresses of poverty-and work schedules-may impinge, and immigrant parents may face language barriers and an unfamiliarity with the school system and teachers' expectations.

Yet research shows that low-income parents who are unable to assist with homework are far from passive in their children's learning, and they do help foster scholastic performance. In fact, parental help with homework is not a necessary component for school success.

Brown University's Jin Li queried low-income Chinese American 9th graders' perceptions of their parents' engagement with their education. Students said their immigrant parents rarely engaged in activities that are known to foster academic achievement, such as monitoring homework, checking it for accuracy, or attending school meetings or events. Instead, parents of higher achievers built three social networks to support their children's learning. They designated "anchor" helpers both inside and outside the family who provided assistance; identified peer models for their children to emulate; and enlisted the assistance of extended kin to guide their children's educational socialization. In a related vein, a recent analysis of survey data showed that Asian and Latino 5th graders, relative to native-born peers, were more likely to turn to siblings than parents for homework help.

Further, research demonstrates that low-income parents, recognizing that they lack the time to be in the classroom or participate in school governance, view homework as a critical connection to their children's experiences in school. One study found that mothers enjoyed the routine and predictability of homework and used it as a way to demonstrate to children how to plan their time. Mothers organized homework as a family activity, with siblings doing homework together and older children reading to younger ones. In this way, homework was perceived as a collective practice wherein siblings could model effective habits and learn from one another.

In another recent study, researchers examined mathematics achievement in low-income 8th-grade Asian and Latino students. Help with homework was an advantage their mothers could not provide. They could, however, furnish structure (for example, by setting aside quiet time for homework completion), and it was this structure that most predicted high achievement. As the authors note, "It is . . . important to help [low-income] parents realize that they can still help their children get good grades in mathematics and succeed in school even if they do not know how to provide direct assistance with their child's mathematics homework."

The homework narrative at the other end of the socioeconomic continuum is altogether different. Media reports abound with examples of students, mostly in high school, carrying three or more hours of homework per night, a burden that can impair learning, motivation, and well-being. In affluent communities, students often experience intense pressure to cultivate a high-achieving profile that will be attractive to elite colleges. Heavy homework loads have been linked to unhealthy symptoms such as heightened stress, anxiety, physical complaints, and sleep disturbances. Like Allison's 6th grader mentioned earlier, many students can only tackle their homework after they do extracurricular activities, which are also seen as essential for the college résumé. Not surprisingly, many students in these communities are not deeply engaged in learning; rather, they speak of "doing school," as Stanford researcher Denise Pope has described, going through the motions necessary to excel, and undermining their physical and mental health in the process.

Fortunately, some national intervention initiatives, such as Challenge Success (co-founded by Pope), are heightening awareness of these problems. Interventions aimed at restoring balance in students' lives (in part, by reducing homework demands) have resulted in students reporting an increased sense of well-being, decreased stress and anxiety, and perceptions of greater support from teachers, with no decrease in achievement outcomes.

What is good for this small segment of students, however, is not necessarily good for the majority. As Jessica Lahey wrote in Motherlode, a New York Times parenting blog, "homework is a red herring" in the national conversation on education. "Some otherwise privileged children may have too much, but the real issue lies in places where there is too little. . . . We shouldn't forget that."

My colleagues and I analyzed interviews conducted with lower-income 9th graders (African American, Mexican American, and European American) from two Northern California high schools that at the time were among the lowest-achieving schools in the state. We found that these students consistently described receiving minimal homework-perhaps one or two worksheets or textbook pages, the occasional project, and 30 minutes of reading per night. Math was the only class in which they reported having homework each night. These students noted few consequences for not completing their homework.

Indeed, greatly reducing or eliminating homework would likely increase, not diminish, the achievement gap. As Harris M. Cooper has commented, those choosing to opt their children out of homework are operating from a place of advantage. Children in higher-income families benefit from many privileges, including exposure to a larger range of language at home that may align with the language of school, access to learning and cultural experiences, and many other forms of enrichment, such as tutoring and academic summer camps, all of which may be cost-prohibitive for lower-income families. But for the 21 percent of the school-age population who live in poverty-nearly 11 million students ages 5-17-homework is one tool that can help narrow the achievement gap.
Community and School Support
Often, community organizations and afterschool programs can step up to provide structure and services that students' need to succeed at homework. For example, Boys and Girls and 4-H clubs offer volunteer tutors as well as access to computer technology that students may not have at home. Many schools provide homework clubs or integrate homework into the afterschool program.

Home-school partnerships have succeeded in engaging parents with homework and significantly improving their children's academic achievement. For example, Joyce Epstein of Johns Hopkins University has developed the TIPS model (Teachers Involve Parents in Schoolwork), which embraces homework as an integral part of family time. TIPS is a teacher-designed interactive program in which children and a parent or family member each have a specific role in the homework scenario. For example, children might show the parent how to do a mathematics task on fractions, explaining their reasoning along the way and reviewing their thinking aloud if they are unsure.

Evaluations show that elementary and middle-school students in classrooms that have adopted TIPS complete more of their homework than do students in other classrooms. Both students and parent participants show more positive beliefs about learning mathematics, and TIPS students show significant gains in writing skills and report-card science grades, as well as higher mathematics scores on standardized tests.

Another study found that asking teachers to send text messages to parents about their children's missing homework resulted in increased parental monitoring of homework, consequences for missed assignments, and greater participation in parent-child conferences. Teachers reported fewer missed assignments and greater student effort in coursework, and math grades and GPA significantly improved.
Homework Quality Matters
Teachers favor homework for a number of reasons. They believe it fosters a sense of responsibility and promotes academic achievement. They note that homework provides valuable review and practice for students while giving teachers feedback on areas where students may need more support. Finally, teachers value homework as a way to keep parents connected to the school and their children's educational experiences.

While students, to say the least, may not always relish the idea of doing homework, by high school most come to believe there is a positive relationship between doing homework and doing well in school. Both higher and lower achievers lament "busywork" that doesn't promote learning. They crave high-quality, challenging assignments-and it is this kind of homework that has been associated with higher achievement.

What constitutes high-quality homework? Assignments that are developmentally appropriate and meaningful and that promote self-efficacy and self-regulation. Meaningful homework is authentic, allowing students to engage in solving problems with real-world relevance. More specifically, homework tasks should make efficient use of student time and have a clear purpose connected to what they are learning. An artistic rendition of a period in history that would take hours to complete can become instead a diary entry in the voice of an individual from that era. By allowing a measure of choice and autonomy in homework, teachers foster in their students a sense of ownership, which bolsters their investment in the work.

High-quality homework also fosters students' perceptions of their own competence by 1) focusing them on tasks they can accomplish without help; 2) differentiating tasks so as to allow struggling students to experience success; 3) providing suggested time frames rather than a fixed period of time in which a task should be completed; 4) delivering clearly and carefully explained directions; and 5) carefully modeling methods for attacking lengthy or complex tasks. Students whose teachers have trained them to adopt strategies such as goal setting, self-monitoring, and planning develop a number of personal assets-improved time management, increased self-efficacy, greater effort and interest, a desire for mastery, and a decrease in helplessness.
Excellence with Equity
Currently, the United States has the second-highest disparity between time spent on homework by students of low socioeconomic status and time spent by their more-affluent peers out of the 34 OECD-member nations participating in the 2012 Program for International Student Assessment (PISA) (see Figure 2). Noting that PISA studies have consistently found that spending more time on math homework strongly correlates with higher academic achievement, the report's authors suggest that the homework disparity may reflect lower teacher expectations for low-income students. If so, this is truly unfortunate. In and of itself, low socioeconomic status is not an impediment to academic achievement when appropriate parental, school, and community supports are deployed. As research makes clear, low-income parents support their children's learning in varied ways, not all of which involve direct assistance with schoolwork. Teachers can orient students and parents toward beliefs that foster positive attitudes toward learning. Indeed, where homework is concerned, a commitment to excellence with equity is both worthwhile and attainable.

In affluent communities, parents, teachers, and school districts might consider reexamining the meaning of academic excellence and placing more emphasis on leading a balanced and well-rounded life. The homework debate in the United States has been dominated by concerns over the health and well-being of such advantaged students. As legitimate as these worries are, it's important to avoid generalizing these children's experiences to those with fewer family resources. Reducing or eliminating homework, though it may be desirable in some advantaged communities, would deprive poorer children of a crucial and empowering learning experience. It would also eradicate a fertile opportunity to help close the achievement gap.
Janine Bempechat is clinical professor of human development at the Boston University Wheelock College of Education and Human Development. An unabridged version of this article is available here.

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Why it improves learning, and how parents can help

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Many teachers and parents believe that homework helps students build study skills and review concepts learned in class. Others see homework as disruptive and unnecessary, leading to burnout and turning kids off to school. Decades of research show that the issue is more nuanced and complex than most people think: Homework is beneficial, but only to a degree. Students in high school gain the most, while younger kids benefit much less.

The National PTA and the National Education Association support the " 10-minute homework guideline "-a nightly 10 minutes of homework per grade level. But many teachers and parents are quick to point out that what matters is the quality of the homework assigned and how well it meets students' needs, not the amount of time spent on it.

The guideline doesn't account for students who may need to spend more-or less-time on assignments. In class, teachers can make adjustments to support struggling students, but at home, an assignment that takes one student 30 minutes to complete may take another twice as much time-often for reasons beyond their control. And homework can widen the achievement gap, putting students from low-income households and students with learning disabilities at a disadvantage.

However, the 10-minute guideline is useful in setting a limit: When kids spend too much time on homework, there are real consequences to consider.

Small Benefits for Elementary Students

As young children begin school, the focus should be on cultivating a love of learning, and assigning too much homework can undermine that goal. And young students often don't have the study skills to benefit fully from homework, so it may be a poor use of time (Cooper, 1989; Cooper et al., 2006; Marzano & Pickering, 2007). A more effective activity may be nightly reading, especially if parents are involved. The benefits of reading are clear: If students aren't proficient readers by the end of third grade, they're less likely to succeed academically and graduate from high school (Fiester, 2013).

For second-grade teacher Jacqueline Fiorentino, the minor benefits of homework did not outweigh the potential drawback of turning young children against school at an early age, so she experimented with dropping mandatory homework. "Something surprising happened: They started doing more work at home," Fiorentino writes. "This inspiring group of 8-year-olds used their newfound free time to explore subjects and topics of interest to them." She encouraged her students to read at home and offered optional homework to extend classroom lessons and help them review material.

Moderate Benefits for Middle School Students

As students mature and develop the study skills necessary to delve deeply into a topic-and to retain what they learn-they also benefit more from homework. Nightly assignments can help prepare them for scholarly work, and research shows that homework can have moderate benefits for middle school students (Cooper et al., 2006). Recent research also shows that online math homework, which can be designed to adapt to students' levels of understanding, can significantly boost test scores (Roschelle et al., 2016).

There are risks to assigning too much, however: A 2015 study found that when middle school students were assigned more than 90 to 100 minutes of daily homework, their math and science test scores began to decline (Fernández-Alonso, Suárez-Álvarez, & Muñiz, 2015). Crossing that upper limit can drain student motivation and focus. The researchers recommend that "homework should present a certain level of challenge or difficulty, without being so challenging that it discourages effort." Teachers should avoid low-effort, repetitive assignments, and assign homework "with the aim of instilling work habits and promoting autonomous, self-directed learning."

In other words, it's the quality of homework that matters, not the quantity. Brian Sztabnik, a veteran middle and high school English teacher, suggests that teachers take a step back and ask themselves these five questions:

How long will it take to complete?

Have all learners been considered?

Will an assignment encourage future success?

Will an assignment place material in a context the classroom cannot?

Does an assignment offer support when a teacher is not there?

More Benefits for High School Students, but Risks as Well

By the time they reach high school, students should be well on their way to becoming independent learners, so homework does provide a boost to learning at this age, as long as it isn't overwhelming (Cooper et al., 2006; Marzano & Pickering, 2007). When students spend too much time on homework-more than two hours each night-it takes up valuable time to rest and spend time with family and friends. A 2013 study found that high school students can experience serious mental and physical health problems, from higher stress levels to sleep deprivation, when assigned too much homework (Galloway, Conner, & Pope, 2013).

Homework in high school should always relate to the lesson and be doable without any assistance, and feedback should be clear and explicit.

Teachers should also keep in mind that not all students have equal opportunities to finish their homework at home, so incomplete homework may not be a true reflection of their learning-it may be more a result of issues they face outside of school. They may be hindered by issues such as lack of a quiet space at home, resources such as a computer or broadband connectivity, or parental support (OECD, 2014). In such cases, giving low homework scores may be unfair.

Since the quantities of time discussed here are totals, teachers in middle and high school should be aware of how much homework other teachers are assigning. It may seem reasonable to assign 30 minutes of daily homework, but across six subjects, that's three hours-far above a reasonable amount even for a high school senior. Psychologist Maurice Elias sees this as a common mistake: Individual teachers create homework policies that in aggregate can overwhelm students. He suggests that teachers work together to develop a school-wide homework policy and make it a key topic of back-to-school night and the first parent-teacher conferences of the school year.

Parents Play a Key Role

Homework can be a powerful tool to help parents become more involved in their child's learning (Walker et al., 2004). It can provide insights into a child's strengths and interests, and can also encourage conversations about a child's life at school. If a parent has positive attitudes toward homework, their children are more likely to share those same values, promoting academic success.

But it's also possible for parents to be overbearing, putting too much emphasis on test scores or grades, which can be disruptive for children (Madjar, Shklar, & Moshe, 2015). Parents should avoid being overly intrusive or controlling-students report feeling less motivated to learn when they don't have enough space and autonomy to do their homework (Orkin, May, & Wolf, 2017; Patall, Cooper, & Robinson, 2008; Silinskas & Kikas, 2017). So while homework can encourage parents to be more involved with their kids, it's important to not make it a source of conflict.

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Decades of research show that homework has some benefits, especially for students in middle and high school-but there are risks to assigning too much.

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The students thoughts and actions about how they are doing on a certain topic. Giving homework that makes the student go back through problems there were struggling with and eventually will conquer will increase the self efficacy in students. Thus, the student will have more confidence and feelings toward the current assessment in which will eliminate test anxiety because they feel better about their math ability.

Many students throughout their schooling have failed a math class because they have low self efficacy because they have not done so well on older material.

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Going over old problems that require basic knowledge will build the skill and make the student understand it more such that when they get to more difficult problems they can focus on the new techniques rather than struggle over the old techniques.

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After all the research has been looked at and discussed, what should educators do?

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There is a fine line between giving too much homework which will lead to other negative factors and giving homework that will reinforce and build skills.

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Being able to have a better time on task would make a student be more efficient with their time and be able to retain information better. If the student can only spend 30 mins on homework instead of 2 hours then the student would have a more positive view on work and school.

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