comprehensive research homework

Is Homework Good for Kids? Here’s What the Research Says

A s kids return to school, debate is heating up once again over how they should spend their time after they leave the classroom for the day.

The no-homework policy of a second-grade teacher in Texas went viral last week , earning praise from parents across the country who lament the heavy workload often assigned to young students. Brandy Young told parents she would not formally assign any homework this year, asking students instead to eat dinner with their families, play outside and go to bed early.

But the question of how much work children should be doing outside of school remains controversial, and plenty of parents take issue with no-homework policies, worried their kids are losing a potential academic advantage. Here’s what you need to know:

For decades, the homework standard has been a “10-minute rule,” which recommends a daily maximum of 10 minutes of homework per grade level. Second graders, for example, should do about 20 minutes of homework each night. High school seniors should complete about two hours of homework each night. The National PTA and the National Education Association both support that guideline.

But some schools have begun to give their youngest students a break. A Massachusetts elementary school has announced a no-homework pilot program for the coming school year, lengthening the school day by two hours to provide more in-class instruction. “We really want kids to go home at 4 o’clock, tired. We want their brain to be tired,” Kelly Elementary School Principal Jackie Glasheen said in an interview with a local TV station . “We want them to enjoy their families. We want them to go to soccer practice or football practice, and we want them to go to bed. And that’s it.”

A New York City public elementary school implemented a similar policy last year, eliminating traditional homework assignments in favor of family time. The change was quickly met with outrage from some parents, though it earned support from other education leaders.

New solutions and approaches to homework differ by community, and these local debates are complicated by the fact that even education experts disagree about what’s best for kids.

The research

The most comprehensive research on homework to date comes from a 2006 meta-analysis by Duke University psychology professor Harris Cooper, who found evidence of a positive correlation between homework and student achievement, meaning students who did homework performed better in school. The correlation was stronger for older students—in seventh through 12th grade—than for those in younger grades, for whom there was a weak relationship between homework and performance.

Cooper’s analysis focused on how homework impacts academic achievement—test scores, for example. His report noted that homework is also thought to improve study habits, attitudes toward school, self-discipline, inquisitiveness and independent problem solving skills. On the other hand, some studies he examined showed that homework can cause physical and emotional fatigue, fuel negative attitudes about learning and limit leisure time for children. At the end of his analysis, Cooper recommended further study of such potential effects of homework.

Despite the weak correlation between homework and performance for young children, Cooper argues that a small amount of homework is useful for all students. Second-graders should not be doing two hours of homework each night, he said, but they also shouldn’t be doing no homework.

Not all education experts agree entirely with Cooper’s assessment.

Cathy Vatterott, an education professor at the University of Missouri-St. Louis, supports the “10-minute rule” as a maximum, but she thinks there is not sufficient proof that homework is helpful for students in elementary school.

“Correlation is not causation,” she said. “Does homework cause achievement, or do high achievers do more homework?”

Vatterott, the author of Rethinking Homework: Best Practices That Support Diverse Needs , thinks there should be more emphasis on improving the quality of homework tasks, and she supports efforts to eliminate homework for younger kids.

“I have no concerns about students not starting homework until fourth grade or fifth grade,” she said, noting that while the debate over homework will undoubtedly continue, she has noticed a trend toward limiting, if not eliminating, homework in elementary school.

The issue has been debated for decades. A TIME cover in 1999 read: “Too much homework! How it’s hurting our kids, and what parents should do about it.” The accompanying story noted that the launch of Sputnik in 1957 led to a push for better math and science education in the U.S. The ensuing pressure to be competitive on a global scale, plus the increasingly demanding college admissions process, fueled the practice of assigning homework.

“The complaints are cyclical, and we’re in the part of the cycle now where the concern is for too much,” Cooper said. “You can go back to the 1970s, when you’ll find there were concerns that there was too little, when we were concerned about our global competitiveness.”

Cooper acknowledged that some students really are bringing home too much homework, and their parents are right to be concerned.

“A good way to think about homework is the way you think about medications or dietary supplements,” he said. “If you take too little, they’ll have no effect. If you take too much, they can kill you. If you take the right amount, you’ll get better.”

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Write to Katie Reilly at [email protected]

A Commentary on the Science and Practice of Homework in Cognitive Behavioral Therapy

• Published: 18 March 2021
• Volume 45 , pages 303–309, ( 2021 )

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• Keith S. Dobson   ORCID: orcid.org/0000-0001-9542-0822 1  

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This article discusses the concept of homework in cognitive-behavioral therapy (CBT), and reviews the articles in this special issue of Cognitive Therapy and Research. The article underscores the pivotal role of between- session activities and demonstrates that this role has been recognized for many years.

This article reviews the articles from this special issue and uses them as a springboard to review other research and then discuss potential directions for future theory and research.

The initial research in this area focused on documenting the magnitude of the relationship between homework adherence and outcome in CBT. Research has now advanced to incorporate studies that examine issues such as homework competence, the complicated relationship between homework and other aspects of CBT such as the therapeutic relationship, and the use of technology to try to enhance homework completion and clinical outcomes.

Conclusions

The use of homework assignments in CBT remains a critical issue for development. Limitations of the literature are addressed, and directions for future research in the field are provided.

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Dobson, K.S. A Commentary on the Science and Practice of Homework in Cognitive Behavioral Therapy. Cogn Ther Res 45 , 303–309 (2021). https://doi.org/10.1007/s10608-021-10217-5

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Quantity and Quality of Homework Compliance: A Meta-Analysis of Relations With Outcome in Cognitive Behavior Therapy

Affiliations.

• 1 Monash University. Electronic address: [email protected].
• 2 University College London.
• 3 Monash University.
• 4 Australian National University.
• 5 Boston University.
• PMID: 27816086
• DOI: 10.1016/j.beth.2016.05.002

Homework assignments have been shown to produce both causal and correlational effects in prior meta-analytic reviews of cognitive behavior therapy (CBT), but this research area has been characterized by a focus on the amount of compliance (i.e., quantity), and little is known about the role of skill acquisition (i.e., quality). A landmark study by Neimeyer and Feixas (1990) showed stronger homework-outcome relations when quality was assessed, but previous reviews have not considered whether the same pattern is evident across studies. Seventeen studies of CBT (N = 2,312 clients) published following calls for research on homework quality were included in the current meta-analysis. In the present review, homework compliance relations were demonstrated when outcome was assessed at posttreatment (quality Hedges' g = 0.78, 95% Confidence Interval [CI] = 0.03 to 1.53, k = 3, n = 417; quantity g = 0.79, 95% CI = 0.57 to 1.02, k = 15, n = 1537) and at follow-up (quality g = 1.07, 95% CI = 0.06 to 2.08, k = 3, n = 417; quantity g = 0.51, 95% CI = 0.28 to 0.74, k = 7, n = 1291). All effect sizes were different from 0, ps < .05. Differences that were obtained in homework-outcome relations among sources of compliance data (client, therapist, objective) were tentative due to overlapping CIs, but suggest a potential moderating effect. If confirmed by further research, the present findings would suggest that trial methods capable of assessing both quantity and quality have been an important omission in research on homework-outcome relations in CBT.

Keywords: cognitive therapy; cognitive-behavior therapy; homework compliance; meta-analysis.

Copyright © 2016. Published by Elsevier Ltd.

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Students' Achievement and Homework Assignment Strategies

Rubén fernández-alonso.

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

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

Marcos Álvarez-Díaz

Javier suárez-Álvarez.

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

José Muñiz

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.

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.

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.

Table ​ Table1 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.

Descriptive statistics and Pearson correlation matrix between the variables .

Table ​ Table2 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.

Distribution of the variance in the null model .

Table ​ Table3 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.

Multilevel models for prediction of achievement in four subjects .

β, Standardized weight; SE, Standard Error; SECI, Socioeconomic and cultural index; AC, Autonomous Communities .

The results indicated that the adjustment variables behaved satisfactorily, with enough control to analyze the net effects of the homework variables. This was backed up by two results, firstly, the two variables with highest standardized coefficients were those related to educational history: academic expectations at the time of the test, and being in the school year corresponding to age. Motivation demonstrated a smaller effect but one which was significant in all cases. Secondly, the adjustment variables explained the majority of the variance in the results. The percentages of total explained variance in Table ​ Table2 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 ​ Figure1 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.

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

Figure ​ Figure2 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.

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 ​ Figure2 2 shows how the differences in performance would vary within a school between the more and lesser able students according to amount of homework assigned. In type 1 schools, with 1 h of homework per day, the difference in achievement between quick and slow students would be around 5% of a standard deviation, while in schools assigning 2 h per day the difference would be 12%. On the other hand, the slow student in a type 2 school would score 6 points more than the quick student in a type 1 school. However, to achieve this, the slow student in a type 2 school would need to spend five times as much time on homework in a week (20.4 weekly hours rather than 4.1). It seems like a lot of work for such a small gain.

Discussion and conclusions

The data in this study reaffirm the multilevel nature of homework (Trautwein and Köller, 2003 ) and support this study's first hypothesis: the amount of homework (mean daily minutes the student spends on homework) is positively associated with academic results, whereas the time students spent on homework considered individually is negatively associated with academic results. These findings are in line with previous research, which indicate that school-level variables, such as amount of homework assigned, have more explanatory power than individual variables such as time spent (De Jong et al., 2000 ; Dettmers et al., 2010 ; Scheerens et al., 2013 ; Fernández-Alonso et al., 2015 ). In this case it was found that for each additional hour of homework assigned by a school, a gain of 25% of a standard deviation is expected in all subjects except mathematics, where the gain is around 15%. On the basis of this evidence, common sense would dictate the conclusion that frequent and abundant homework assignment may be one way to improve school efficiency.

However, as noted previously, the relationship between homework and achievement is paradoxical- appearances are deceptive and first conclusions are not always confirmed. Analysis demonstrates another two complementary pieces of data which, read together, raise questions about the previous conclusion. In the first place, time spent on homework at the individual level was found to have a negative effect on achievement, which confirms the findings of other multilevel-approach research (Trautwein, 2007 ; Trautwein et al., 2009b ; Chang et al., 2014 ; Fernández-Alonso et al., 2016 ). Furthermore, it was found that an increase in assigned homework volume is associated with an increase in the differences in time students need to complete it. Taken together, the conclusion is that, schools with more homework tend to exhibit more variation in student achievement. These results seem to confirm our second hypothesis, as a positive covariation was found between the amount of homework in a school (the mean homework time by school) and the increase in differences within the school, both in student homework time and in the academic results themselves. The data seem to be in line with those who argue that homework is a source of inequity because it affects those less academically-advantaged students and students with greater limitations in their home environments (Kohn, 2006 ; Rømming, 2011 ; OECD, 2013b ).

This new data has clear implications for educational action and school homework policies, especially in compulsory education. If quality compulsory education is that which offers the best results for the largest number (Barber and Mourshed, 2007 ; Mourshed et al., 2010 ), then assigning an excessive volume of homework at those school levels could accentuate differences, affecting students who are slower, have more gaps in their knowledge, or are less privileged, and can make them feel overwhelmed by the amount of homework assigned to them (Martinez, 2011 ; OECD, 2014b ; Suárez et al., 2016 ). The data show that in a school with 60 min of assigned homework, a quick student will need just 4 h a week to finish their homework, whereas a slow student will spend 10 h a week, 2.5 times longer, with the additional aggravation of scoring one twentieth of a standard deviation below their quicker classmates. And in a school assigning 120 min of homework per day, a quick student will need 7.5 h per week whereas a slow student will have to triple this time (20 h per week) to achieve a result one eighth worse, that is, more time for a relatively worse result.

It might be argued that the differences are not very large, as between 1 and 2 h of assigned homework, the level of inequality increases 7% on a standardized scale. But this percentage increase has been estimated after statistically, or artificially, accounting for sociological and psychological student factors and other variables at school and region level. The adjustment variables influence both achievement and time spent on homework, so it is likely that in a real classroom situation the differences estimated here might be even larger. This is especially important in comprehensive education systems, like the Spanish (Eurydice, 2015 ), in which the classroom groups are extremely heterogeneous, with a variety of students in the same class in terms of ability, interest, and motivation, in which the aforementioned variables may operate more strongly.

The results of this research must be interpreted bearing in mind a number of limitations. The most significant limitation in the research design is the lack of a measure of previous achievement, whether an ad hoc test (Murillo and Martínez-Garrido, 2013 ) or school grades (Núñez et al., 2014 ), which would allow adjustment of the data. In an attempt to alleviate this, our research has placed special emphasis on the construction of variables which would work to exclude academic history from the model. The use of the repetition of school year variable was unavoidable because Spain has one of the highest levels of repetition in the European Union (Eurydice, 2011 ) and repeating students achieve worse academic results (Ministerio de Educación, 2011 ). Similarly, the expectation and motivation variables were included in the group of adjustment factors assuming that in this research they could be considered background variables. In this way, once the background factors are discounted, the homework variables explain 2% of the total variance, which is similar to estimations from other multilevel studies (De Jong et al., 2000 ; Trautwein, 2007 ; Dettmers et al., 2009 ; Fernández-Alonso et al., 2016 ). On the other hand, the statistical models used to analyze the data are correlational, and as such, one can only speak of an association between variables and not of directionality or causality in the analysis. As Trautwein and Lüdtke ( 2009 ) noted, the word “effect” must be understood as “predictive effect.” In other words, it is possible to say that the amount of homework is connected to performance; however, it is not possible to say in which direction the association runs. Another aspect to be borne in mind is that the homework time measures are generic -not segregated by subject- when it its understood that time spent and homework behavior are not consistent across all subjects (Trautwein et al., 2006 ; Trautwein and Lüdtke, 2007 ). Nonetheless, when the dependent variable is academic results it has been found that the relationship between homework time and achievement is relatively stable across all subjects (Lubbers et al., 2010 ; Chang et al., 2014 ) which leads us to believe that the results given here would have changed very little even if the homework-related variables had been separated by subject.

Future lines of research should be aimed toward the creation of comprehensive models which incorporate a holistic vision of homework. It must be recognized that not all of the time spent on homework by a student is time well spent (Valle et al., 2015 ). In addition, research has demonstrated the importance of other variables related to student behavior such as rate of completion, the homework environment, organization, and task management, autonomy, parenting styles, effort, and the use of study techniques (Zimmerman and Kitsantas, 2005 ; Xu, 2008 , 2013 ; Kitsantas and Zimmerman, 2009 ; Kitsantas et al., 2011 ; Ramdass and Zimmerman, 2011 ; Bembenutty and White, 2013 ; Xu and Wu, 2013 ; Xu et al., 2014 ; Rosário et al., 2015a ; Osorio and González-Cámara, 2016 ; Valle et al., 2016 ), as well as the role of expectation, value given to the task, and personality traits (Lubbers et al., 2010 ; Goetz et al., 2012 ; Pedrosa et al., 2016 ). Along the same lines, research has also indicated other important variables related to teacher homework policies, such as reasons for assignment, control and feedback, assignment characteristics, and the adaptation of tasks to the students' level of learning (Trautwein et al., 2009a ; Dettmers et al., 2010 ; Patall et al., 2010 ; Buijs and Admiraal, 2013 ; Murillo and Martínez-Garrido, 2013 ; Rosário et al., 2015b ). All of these should be considered in a comprehensive model of homework.

In short, the data seem to indicate that in year 8 of compulsory education, 60–70 min of homework a day is a recommendation that, slightly more optimistically than Cooper's ( 2001 ) “10 min rule,” gives a reasonable gain for the whole school, without exaggerating differences or harming students with greater learning difficulties or who work more slowly, and is in line with other available evidence (Fernández-Alonso et al., 2015 ). These results have significant implications when it comes to setting educational policy in schools, sending a clear message to head teachers, teachers and those responsible for education. The results of this research show that assigning large volumes of homework increases inequality between students in pursuit of minimal gains in achievement for those who least need it. Therefore, in terms of school efficiency, and with the aim of improving equity in schools it is recommended that educational policies be established which optimize all students' achievement.

Ethics statement

This study was carried out in accordance with the recommendations of the University of Oviedo with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the University of Oviedo.

Author contributions

RF and JM have designed the research; RF and JS have analyzed the data; MA and JM have interpreted the data; RF, MA, and JS have drafted the paper; JM has revised it critically; all authors have provided final approval of the version to be published and have ensured the accuracy and integrity of the work.

This research was funded by the Ministerio de Economía y Competitividad del Gobierno de España. References: PSI2014-56114-P, BES2012-053488. We would like to express our utmost gratitude to the Ministerio de Educación Cultura y Deporte del Gobierno de España and to the Consejería de Educación y Cultura del Gobierno del Principado de Asturias, without whose collaboration this research would not have been possible.

Conflict of interest statement

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

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• DOI: 10.1007/s10608-021-10247-z
• Corpus ID: 235725342

A Comprehensive Model of Homework in Cognitive Behavior Therapy

• N. Kazantzis , Allen R. Miller
• Published in Cognitive Therapy and… 3 July 2021

20 Citations

Between-session homework in clinical training and practice: a transtheoretical perspective.

• Highly Influenced

Homework as a driver of change in psychotherapy.

Therapist competence, homework engagement, and client characteristics in cbt for youth depression: a study of mediation and moderation in a community-based trial, approaches to tailoring between-session mental health therapy activities, predictors of homework engagement in group cbt for social anxiety: client beliefs about homework, its consequences, group cohesion, and working alliance, cognitive behavioral therapy: strategies for enhancing treatment engagement., intersession experiences and internalized representations of psychotherapy: a scoping review., predictors of engagement wth between-session work in cognitive behavioural therapy (cbt) - based interventions, the relationships among working alliance, group cohesion and homework engagement in group cognitive behaviour therapy for social anxiety disorder, predictors of engagement with between-session work in cognitive behavioural therapy (cbt)-based interventions: a mixed-methods systematic review and "best fit" framework synthesis., 111 references, understanding and enhancing the effects of homework in cognitive‐behavioral therapy, integrating between-session interventions (homework) in therapy: the importance of the therapeutic relationship and cognitive case conceptualization., homework adherence in cognitive-behavioral therapy for adolescent depression, how to supervise the use of homework in cognitive behavior therapy: the role of trainee therapist beliefs, compliance with homework assignments in cognitive-behavioral psychotherapy for depression: relation to outcome and methods of enhancement, group cognitive behavioural therapy for depression outcomes predicted by willingness to engage in homework, compliance with homework, and cognitive restructuring skill acquisition, therapist behaviors as predictors of immediate homework engagement in cognitive therapy for depression, attitudes and use of homework assignments in therapy: a survey of german psychotherapists, the relationship between therapist competence and homework compliance in maintenance cognitive therapy for recurrent depression: secondary analysis of a randomized trial., mediators and moderators of homework–outcome relations in cbt for depression: a study of engagement, therapist skill, and client factors, related papers.

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Case Summary: Eckert, Richard L

Richard L. Eckert, Ph.D., University of Maryland, Baltimore : Based on the report of an investigation conducted by the University of Maryland, Baltimore (UMB) and additional analysis conducted by the Office of Research Integrity (ORI) in its oversight review, ORI found that Richard L. Eckert, Ph.D., (Respondent), who was Professor, Chair of the Department of Biochemistry and Molecular Biology, and Deputy Director of the University of Maryland and Stewart Greenebaum Comprehensive Cancer Center, UMB, engaged in research misconduct in research supported by U.S. Public Health Service (PHS) funds, specifically National Cancer Institute (NCI), National Institutes of Health (NIH), grants R01 CA211909, R01 CA184027, R01 CA131074, R01 CA131064, R01 CA092201, R01 CA109196, P30 CA134274, and P30 CA043703, National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), NIH, grants R21 AR065266, R01 AR046494, R01 AR053851, R01 AR060388, P30 AR039750, R01 AR041456, R01 AR049713, and R01 AR045357, National Eye Institute (NEI), NIH, grants P30 EY011373 and T32 EY007157, and National Institute of General Medical Sciences (NIGMS), NIH, grant R01 GM043751. The questioned research was included in two (2) grant applications submitted for PHS funds, specifically R01 CA233450-01 and R01 CA233450-01A1 submitted to NCI, NIH.

ORI found that Respondent engaged in research misconduct by intentionally, knowingly, or recklessly falsifying and/or fabricating data in the following thirteen (13) published papers and two (2) PHS grant applications:

• Inhibition of YAP function overcomes BRAF inhibitor resistance in melanoma cancer stem cells. Oncotarget . 2017 Nov 22; 8(66):110257-110272. doi: 10.18632/oncotarget.22628 (hereafter referred to as “ Oncotarget 2017”).    
• The Bmi-1 helix-turn and ring finger domains are required for Bmi-1 antagonism of (-) epigallocatechin-3-gallate suppression of skin cancer cell survival. Cell Signal . 2015 Jul;27(7):1336-44. doi: 10.1016/j.cellsig.2015.03.021 (hereafter referred to as “ Cell Signal 2015”). Erratum in: Cell Signal . 2021 Jun;82:109952. doi: 10.1016/j.cellsig.2021.109952.  
• P38δ regulates p53 to control p21Cip1 expression in human epidermal keratinocytes. J Biol Chem . 2014 Apr 18; 289(16):11443-11453. doi: 10.1074/jbc.M113.543165 (hereafter referred to as “ J Biol Chem . 2014”).  
• Methylosome protein 50 and PKCδ/p38δ protein signaling control keratinocyte proliferation via opposing effects on p21Cip1 gene expression. J Biol Chem . 2015 May 22;290(21):13521-30. doi: 10.1074/jbc.M115.642868 (hereafter referred to as “ J Biol Chem . 2015”).  
• Transamidase site-targeted agents alter the conformation of the transglutaminase cancer stem cell survival protein to reduce GTP binding activity and cancer stem cell survival. Oncogene . 2017 May 25;36(21):2981-2990. doi: 10.1038/onc.2016.452 (hereafter referred to as “ Oncogene 2017”). Erratum in: Oncogene . 2021 Apr;40(13):2479-2481. doi: 10.1038/s41388-021-01709-5.  
• Suppression of AP1 transcription factor function in keratinocyte suppresses differentiation. PLoS One . 2012;7(5):e36941. doi: 10.1371/journal.pone.0036941 (hereafter referred to as “ PLoS One 2012”). Retraction in: PLoS One . 2021 Feb  11;16(2):e0247222. doi: 10.1371/journal.pone.0247222.  
• Suppressing AP1 factor signaling in the suprabasal epidermis produces a keratoderma phenotype. J Invest Dermatol . 2015 Jan;135(1):170-180. doi: 10.1038/jid.2014.310 (hereafter referred to as “ J Invest Dermatol . 2015”). Erratum in: J Invest Dermatol. 2021 Jul; 141(7):1862. doi: 10.1016/j.jid.2021.05.008.  
• Protein kinase C (PKC) delta suppresses keratinocyte proliferation by increasing p21(Cip1) level by a KLF4 transcription factor-dependent mechanism. J Biol Chem . 2011 Aug 19; 286(33):28772-28782. doi: 10.1074/jbc.M110.205245 (hereafter referred to as “ J Biol Chem . 2011”).  
• The Bmi-1 polycomb protein antagonizes the (-)-epigallocatechin-3-gallate-dependent suppression of skin cancer cell survival. Carcinogene sis. 2010 Mar;31(3):496-503. doi: 10.1093/carcin/bgp314 (hereafter referred to as “ Carcinogenesis 2010”).  
• PKC-delta and –eta, MEKK-1, MEK-6, MEK-3, and p38-delta are essential mediators of the response of normal human epidermal keratinocytes to differentiating agents. J Invest Dermatol . 2010 Aug;130(8):2017-30. doi: 10.1038/jid.2010.108 (hereafter referred to as “ J Invest Dermatol . 2010”).  
• Sulforaphane suppresses PRMT5/MEP50 function in epidermal squamous cell carcinoma leading to reduced tumor formation. Carcinogenesis . 2017 Aug 1;38(8):827-836. doi: 10.1093/carcin/bgx044 (hereafter referred to as “ Carcinogenesis 2017”). Erratum in: Carcinogenesis . 2023 Oct 20;44(7):626-627. doi: 10.1093/carcin/bgad044.  
• Localization of the TIG3 transglutaminase interaction domain and demonstration that the amino-terminal region is required for TIG3 function as a keratinocyte differentiation regulator. J Invest Dermatol . 2008 Mar;128(3):517-29. doi: 10.1038/sj.jid.5701035 (hereafter referred to as “ J Invest Dermatol. 2008”).  
• Transglutaminase interaction with α6/β4-integrin stimulates YAP1-Dependent ∆Np63α stabilization and leads to enhanced cancer stem cell survival and tumor formation. Cancer Res . 2016 Dec 15;76(24):7265-7276. doi: 10.1158/0008-5472.CAN-16-2032 (hereafter referred to as “ Cancer Res . 2016”).  
• R01 CA233450-01, “Sulforaphane suppression of PRMT5 epigenetics to reduce cancer stem cell survival,” submitted to NCI, NIH, on 01/26/2018, administratively withdrawn by NCI on 07/01/2020  
• R01 CA233450-01A1, “Sulforaphane suppression of PRMT5 epigenetics to reduce cancer stem cell survival,” submitted to NCI, NIH, on 10/30/2018, administratively withdrawn by NCI on 03/01/2021  

Specifically,   ORI found that Respondent intentionally, knowingly, or recklessly falsified and/or fabricated Western blot image data and microscopy image data by:  

• In Figure 3F of Oncotarget 2017, the bands in rows 4 and 7 of the A375-PLX-R right-side panel, representing expression of TAZ- P (row 4) and ERK1/2 (row 7), are falsified and/or fabricated by using unrelated bands from a source image representing different proteins in an unrelated experiment    
• In Figure 2B of J Biol Chem . 2014, the bands in row 2 in the top panel, representing MEK3 expression in normal human keratinocytes (KERn) infected with Ad5-EV, Ad5-MEK3, and Ad5-PKCδ (from left to right), are falsified and/or fabricated by compiling unrelated bands from a source image representing p44 expression in an unrelated experiment    
• In Figure 2B of J Biol Chem . 2014, the bands in row 3 in the top panel, representing p38δ expression in KERn infected with Ad5-EV, Ad5-MEK3, and Ad5-PKCδ (from left to right), are falsified and/or fabricated by compiling unrelated bands from a source image representing β-actin expression in an unrelated experiment    
• In Figure 1B of J Biol Chem . 2015, the bands in rows 1-3 in the upper panel, representing expression of MEP50 (row 1), FLAG (row 2), and β-actin (row 3), are falsified and/or fabricated by compiling different bands from source images representing expression of different proteins in unrelated experiments    
• In Figure 7C of J Biol Chem . 2011, the bands in row 2 in the right panel, representing p21 Cip1 expression under treatments of Control-siRNA or hKLF4-siRNA, are falsified and/or fabricated by using unrelated bands from a source image representing p21 expression in cells treated with Ad5-EV or Ad5-PKCd    
• In Figure 1B of PLoS One 2012, the bands in row 1, representing TAM67-FLAG expression, are falsified and/or fabricated by using unrelated bands from a source image representing CyclinA expression    
• In Figure 2C of PLoS One 2012, the bands in rows 3 and 4, representing negative expression of junB (row 3) and junD (row 4), are falsified and/or fabricated by using blank areas that were far from the target molecular weight in a source image
•   using 3 bands from a source image representing β-actin expression in an unrelated experiment for bands 1-3
•   duplicating band 3 to create band 4
•   In Figure 1B of Carcinogenesis 2010, the bands in rows 1, 2, and 5 in the left panel, representing expression of Ezh2 (row 1), H3 K27-3M (row 2), and β-actin (row 5) in two different cell types treated with 60 µM EGCG, are falsified and/or fabricated by using unrelated bands from a source image representing expression of the same proteins under an unrelated experiment
•   the bands 1-4 in the upper panel of Figure 2A, representing Ezh2 expression treated with 0, 10, 20, and 40 µM EGCG are used from the bands representing the same protein but treated with different doses of EGCG in the source image
•   the bands 1 and 5 in the upper panel of Figure 2A, representing Ezh2 expression, are reused and relabeled in the bands in Figure 1B, row 3 in the right panel to represent Suz12 expression
•   In Figure 4A of Carcinogenesis 2010, the bands in rows 6 and 7, representing expression of cyclin E (row 6) and cyclin A (row 7) in cells treated with 60 µm EGCG plus other reagents, are falsified and/or fabricated by reusing and relabeling the bands from a source image representing cyclin E expression in cells treated with 150 µm EGCG plus other reagents
•   bands 1 and 5 (including the empty lanes) in the COX4 panel, representing expression of COX4 treated with EV (band 1) and TIG3 1-134 (band 5), are falsified and/or fabricated by reusing a band labeled as TGI C377 sample 3 from the primary data
•   band 8 (including the empty lanes) in the Cytochrome c panel, representing expression of Cytochrome c treated with TIG3 124-164, is falsified and/or fabricated by using an unrelated band from unknown source
• In Figure 2 of Cell Signal 2015, two control samples in the bottom panel, representing cells in tAd5-FLAG-hBmi∆RF condition (left) and tAd5-FLAG-hBmi-1∆HT condition (right), are reused from different fields of a same source image    
• In J Biol Chem . 2014, Figure 2B, bands 2 and 3 in row 1 of 3 rd panel, representing ATF2- P expression, and Figure 6C, bands 1 and 2 in row 2 of the 3 rd panel, representing p38α expression, are identical    
• In J Biol Chem . 2014, Figure 2C, bands 1 and 3 in row 3 of the upper panel, representing MEK3 expression, and Figure 6C, bands 1 and 2 in row 2 of the top panel, representing p38α expression, are identical    
• In Figure 3C of Oncogene 2017, band 9, representing TG2 expression treated with total CP4d, is falsified and/or fabricated by reusing and relabeling band 3, representing TG2 expression treated with NC9 (total) in the same figure    
• In Carcinogenesis 2010, Figure 3C, the bands in row 2, representing β-actin expression, and Figure 4C, the bands in row 3, representing procaspase 9 expression, are identical    
• In Figure 7b of J Invest Dermatol . 2010, the bands in the upper panel, representing expression of MEKK1 and its β-Actin control, are falsified and/or fabricated by reusing and relabeling the bands in the middle panel, representing expression of MEK6 and its β-Actin control in the same figure    
• In Figure 1D of Carcinogenesis 2017, Figure 5B of R01 CA233450-01 and Figure 3B of R01 CA233450-01A1, the bands in rows 3 in both the upper and bottom panels, representing H4 expression, are falsified and/or fabricated by reusing and relabeling the same source images that are used for the bands in row 2 in Figure 3J of Carcinogenesis 2017, representing PRMT5 expression    
• In Figure 1c of J Invest Dermatol . 2008, the background area between molecular weight 20-45 in the TIG3 (41-164) lanes of the right panel is falsified and/or fabricated by reusing and relabeling the background area of TIG3 WT group with flipping    
• In Figure 1c of J Invest Dermatol . 2008, the bands in lanes 7-8 of the left panel, representing expression of TIG3 monomer under TIG3 (100-164) condition, are falsified a nd/or fabricated by reusing and relabeling the bands in lanes 9-10 of the left panel, representing expression of TIG3 monomer under TIG3 (41-164) condition    
• In Cancer Res . 2016, bands 2-3 in the bottom row in Figure 3C, representing β-actin expression treated with Integrin α6-siRNA (band 2) and Integrin β4-siRNA (band 3), and bands 1-2 in the bottom row in Figure 3D, representing β-actin expression treated with Control-siRNA (band 1) and FAK-siRNA (band 2), are identical    
• manipulating the data to exclude the band from a source image to falsely show a favorable result in Figure 2C of PLoS One 2012 by erasing the band in the left lane of the top row to falsely represent a lack of TAM67-FLAG expression

Respondent entered into a Voluntary Exclusion Agreement (Agreement) and voluntarily agreed to the following:  

• Respondent will exclude himself voluntarily for a period of eight (8) years beginning on August 1, 2024 (the “Exclusion Period”) from any contracting or subcontracting with any agency of the United States Government and from eligibility for or involvement in nonprocurement or procurement transactions referred to as “covered transactions” in 2 C.F.R. Parts 180 and 376 (collectively the “Debarment Regulations”).  
• During the Exclusion Period, Respondent will not apply for, permit his name to be used on an application for, receive, or be supported by funds of the United States Government and its agencies made available through contracts, subcontracts, or covered transactions.  
• During the Exclusion Period, Respondent will exclude himself voluntarily from serving in any advisory or consultant capacity to PHS including, but not limited to, service on any PHS advisory committee, board, and/or peer review committee.  
• Respondent will request that the following papers be corrected or retracted:   
• Oncotarget  2017 Nov 22;8(66):110257-110272. doi: 10.18632/oncotarget.22628.
• J Biol Chem . 2014 Apr 18;289(16):11443-11453. doi: 10.1074/jbc.M113.543165.
• J Biol Chem.  2015 May 22;290(21):13521-30. doi: 10.1074/jbc.M115.642868.
• J Biol Chem.  2011 Aug 19;286(33):28772-28782. doi: 10.1074/jbc.M110.205245.
• Carcinogenesis 2010 Mar;31(3):496-503. doi: 10.1093/carcin/bgp314.
• J Invest Dermatol . 2008 Mar; 128(3):517-29. doi: l 0.1038/sj.jid.5701035.
• J Invest Dermatol . 2010 Aug;130(8):2017-30. doi: 10.1038/jid.2010.108.
• Cancer Res . 2016 Dec 15;76(24):7265-7276. doi: 10.1158/0008-5472.CAN-16-2032.

Respondent will copy ORI and the Research Integrity Officer at UMB on the correspondence with the journals.

A Federal Register notice (FRN) has been submitted to the Federal Register for this case. When the FRN is published in the Federal Register ,  the link will be provided here.

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MIT Launches the First Ever Comprehensive Database of A.I. Risks

While similar databases exist, none are nearly as comprehensive..

Curiosity around A.I. is on the rise, and numbers reflect that. The most recent Census data shows the use of A.I. for business purposes rose to 5.4 percent from 3.7 percent in a five-month stretch this year. But so far, the risk associated with the emerging technology is a looming beast that has remained opaque to everyone, from average ChatGPT users to high-level decision-makers. A special project by MIT might finally change that. This week, the FutureTech Research Project at the Massachusetts Institute of Technology’s Computer Science & Artificial Intelligence Laboratory (CSAIL) released the AI Risk Repository , a comprehensive and searchable database that outlines more than 700 risks associated with A.I.—whether caused by humans or machines.

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“We want to understand how organizations respond to the risks of artificial intelligence,” Peter Slattery , a visiting researcher at FutureTech and the project’s research lead, told Observer. However , in trying to find a complete framework of risks, the group fell short. That’s when Slattery realized, “This seems like a bigger contribution and maybe more important than we realized.”

Through a systematic review backed by a machine learning process called active learning, FutureTech sifted through more than 17,000 records and engaged relevant experts to identify more than 700 risks, which researchers further organized into various categories to make the repository easier to use.

What’s in the database? 

The risks are grouped into seven domains: discrimination and toxicity; privacy and security; misinformation; malicious actors and misuse; human-computer interaction; socioeconomic and environmental harms; and A.I. system safety, failures and limitations. More specifically, the risks fall into 23 subdomains, some of which are exposure to toxic content, system security vulnerability, false or misleading information, weapon development, loss of human agency, decline in employment and lack of transparency. The FutureTech project is supported by grants from Open Philanthropy, the National Science Foundation, Accenture, IBM and MIT.

FutureTech said the database’s content could change over time (and the group welcomes feedback, even including a form on its site). However, Neil Thompson , director of FutureTech and a research scientist at MIT Initiative on the Digital Economy, told the Observer, “I think we’re hopeful that it will have a shelf life that lasts us a little while.”

While similar databases exist, none are nearly as comprehensive. FutureTech analyzed existing frameworks, including those from Robust Intelligence ,  MITRE and AVID , which only managed to include, on average, 34 percent of the risks it identified. Thompson said the lightning-fast growth of A.I. has contributed to people having a fragmented view of the technology. “Establishing this gives us a much more unified view,” he said. 

The database is accessible to everyone but most useful to policymakers, risk evaluators, academics and industry professionals. FutureTech Leaders intend to grow the repository and enhance its applicability over time. In fact, the repository is en route to quantifying A.I. risks or assessing the riskiness of a particular tool or model, which Thompson said is the goal in the coming months. 

• SEE ALSO : What Melinda French Gates’s Philanthropy Could Look Like Post-Gates Foundation

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New research gives unprecedented view of colorectal cancer genetic makeup

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In a recent study published in Nature , a group of researchers provided a comprehensive genomic characterization of colorectal carcinoma (CRC), a type of cancer that starts in the colon or rectum, through whole-genome sequencing (WGS) of 2,023 samples, identifying novel driver genes, molecular subgroups, and potential clinical implications.

Background 

CRC is the third most common cancer globally. Previous CRC sequencing efforts were limited in scope, focusing on a few hundred cases and primarily using whole-exome or gene panel sequencing, leaving the full range of genomic alterations and clinical associations unclear. Further research is needed to explore the functional significance of newly identified driver mutations and to develop targeted therapies for diverse CRC subgroups.

About the study  

Sample collection for the present study followed a detailed protocol, beginning with ethics approval granted by the Health Research Authority (HRA) Committee East of England–Cambridge South research ethics committee. The 100,000 Genomes Project (100kGP) cancer program, a high-throughput tumor sequencing initiative for National Health Service (NHS) cancer patients, facilitated the collection of samples across thirteen Genomic Medicine Centres (GMCs) established by the NHS and 100kGP.

Specialist nurses and other staff identified patients scheduled for CRC resections, and all participants provided written informed consent. Blood samples were taken, and tumor samples were evaluated in histopathology cut-ups, with associated clinicopathological data retrieved from health records.

Frozen tumor sub-samples underwent assessment for purity and other histological characteristics after which blood and tumor samples passing quality control were sent to regional genetics laboratories for Deoxyribonucleic Acid (DNA) extraction. Extracted DNA was transferred to the 100kGP central national biorepository, where Illumina performed WGS of paired tumor-constitutional DNA.

Processed Binary Alignment/Map (BAM) files were then transferred to Genomics England for quality checks, additional processing, and data storage. All sequencing and clinicopathological data were subsequently transferred to the Colorectal Cancer Domain (GECIP) for further quality control and data analysis, ensuring the integrity and thoroughness of the genomic data utilized in this study.

Study results 

CRCs were classified into three established subtypes such as DNA polymerase ε proofreading-deficient (POL), microsatellite instability-positive (MSI) (mismatch repair deficient), and microsatellite-stable (MSS). Among the 2,023 samples analyzed, 18 were POL, 364 were MSI, and 1,641 were MSS, with nearly all metastasis samples falling into the MSS category. While MSI and POL cancers exhibited near-diploid genomes, MSS cancers displayed highly variable ploidy.

The mutational signature activities of single-base substitutions (SBS), doublet-base substitutions (DBS), and small insertions–deletions (indels) were generally consistent with existing research, though some novel patterns emerged. Notably, SBS93, a signature associated with oesophageal and gastric cancers, was found in approximately 40% of MSS primary tumors but was almost absent in MSI cases.

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Driver gene identification was conducted separately for MSI, MSS primary, POL, and MSS metastasis CRCs, leading to the discovery of 193 putative CRC driver genes. Among these, 89 were identified in MSS primary, 49 in POL, 96 in MSI, and 39 in MSS metastasis groups. A total of 57 drivers were found across multiple subtypes, while the remaining 136 were subtype-specific. Several newly identified candidate driver genes were previously unreported in cancer, including those involved in Ribonucleic Acid (RNA) regulation and transcriptional control.

The study also highlighted new roles for minor Rat Sarcoma (RAS) ( a family of related proteins involved in transmitting signals within cells) and Mitogen-Activated Protein (MAP) kinase pathway genes, which appear to function as modifiers of major RAS drivers rather than as substitutes.

MSS tumors typically harbored four pathogenic driver mutations, compared to 23 in primary MSI and 30 in POL tumors. The study identified 30 shared driver genes between MSS and MSI cancers, emphasizing common pathways like Wingless-related Integration Site (WNT), RAS–Rapidly Accelerated Fibrosarcoma (RAF) (a family of serine/threonine-specific protein kinases)–Mitogen-Activated Protein Kinase (MEK)– Extracellular Signal-Regulated Kinase (ERK), Phosphoinositide 3-Kinase (PI3K), and Transforming Growth Factor Beta (TGFβ)– Bone Morphogenetic Protein (BMP). However, distinct functional differences were observed between MSS and MSI tumors, particularly in immune escape mechanisms and the tolerance for multiple or non-canonical changes in driver pathways.

The identification of driver mutations remains a challenge, especially in hypermutated cancers and low-quality samples. This study replicated only 7% of nearly 1,000 previously reported CRC drivers. Structural variants (SVs) and copy number alterations (CNAs) were also analyzed, revealing nine SV signatures across the cohort, including previously unreported unbalanced inversions and translocations.

The study found 45 non-fragile SV hotspots in MSS primary tumors and three in MSI tumors, identifying several candidate driver changes and recurrent SV hotspots. Moreover, extrachromosomal DNA (ecDNA) was more prevalent in MSS primary tumors, with a modest role in oncogene amplification compared to other cancer types.

Conclusions

To summarize, this study provides a comprehensive analysis of the genomic landscape of CRC, identifying numerous novel driver mutations, SVs, and CNAs and highlighting the distinct molecular characteristics of MSI, MSS, and POL subtypes. The findings offer valuable insights into the complex biology of CRC and potential avenues for targeted therapies.

• Cornish, A.J., Gruber, A.J., Kinnersley, B. et al. The genomic landscape of 2,023 colorectal cancers. Nature (2024). DOI: https://doi.org/10.1038/s41586-024-07747-9   https://www.nature.com/articles/s41586-024-07747-9

Posted in: Medical Science News | Medical Research News | Disease/Infection News

Tags: Blood , Bone , Bone Morphogenetic Protein , Cancer , Carcinoma , Colon , Colorectal , Colorectal Cancer , DNA , DNA Polymerase , Fibrosarcoma , Gene , Genes , Genetic , Genetics , Genome , Genomic , Genomics , Growth Factor , Histopathology , Illumina , Kinase , Medicine , Metastasis , Oncogene , Polymerase , Protein , Quality Control , Research , Ribonucleic Acid , RNA , Sarcoma , Serine , Threonine , Tumor

Vijay Kumar Malesu

Vijay holds a Ph.D. in Biotechnology and possesses a deep passion for microbiology. His academic journey has allowed him to delve deeper into understanding the intricate world of microorganisms. Through his research and studies, he has gained expertise in various aspects of microbiology, which includes microbial genetics, microbial physiology, and microbial ecology. Vijay has six years of scientific research experience at renowned research institutes such as the Indian Council for Agricultural Research and KIIT University. He has worked on diverse projects in microbiology, biopolymers, and drug delivery. His contributions to these areas have provided him with a comprehensive understanding of the subject matter and the ability to tackle complex research challenges.    

Please use one of the following formats to cite this article in your essay, paper or report:

Kumar Malesu, Vijay. (2024, August 15). New research gives unprecedented view of colorectal cancer genetic makeup. News-Medical. Retrieved on August 16, 2024 from https://www.news-medical.net/news/20240815/New-research-gives-unprecedented-view-of-colorectal-cancer-genetic-makeup.aspx.

Kumar Malesu, Vijay. "New research gives unprecedented view of colorectal cancer genetic makeup". News-Medical . 16 August 2024. <https://www.news-medical.net/news/20240815/New-research-gives-unprecedented-view-of-colorectal-cancer-genetic-makeup.aspx>.

Kumar Malesu, Vijay. "New research gives unprecedented view of colorectal cancer genetic makeup". News-Medical. https://www.news-medical.net/news/20240815/New-research-gives-unprecedented-view-of-colorectal-cancer-genetic-makeup.aspx. (accessed August 16, 2024).

Kumar Malesu, Vijay. 2024. New research gives unprecedented view of colorectal cancer genetic makeup . News-Medical, viewed 16 August 2024, https://www.news-medical.net/news/20240815/New-research-gives-unprecedented-view-of-colorectal-cancer-genetic-makeup.aspx.

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