impact of homework on students' learning

Does Homework Really Help Students Learn?

A conversation with a Wheelock researcher, a BU student, and a fourth-grade teacher

“Quality homework is engaging and relevant to kids’ lives,” says Wheelock’s Janine Bempechat. “It gives them autonomy and engages them in the community and with their families. In some subjects, like math, worksheets can be very helpful. It has to do with the value of practicing over and over.” Photo by iStock/Glenn Cook Photography

Do your homework.

If only it were that simple.

Educators have debated the merits of homework since the late 19th century. In recent years, amid concerns of some parents and teachers that children are being stressed out by too much homework, things have only gotten more fraught.

“Homework is complicated,” says developmental psychologist Janine Bempechat, a Wheelock College of Education & Human Development clinical professor. The author of the essay “ The Case for (Quality) Homework—Why It Improves Learning and How Parents Can Help ” in the winter 2019 issue of Education Next , Bempechat has studied how the debate about homework is influencing teacher preparation, parent and student beliefs about learning, and school policies.

She worries especially about socioeconomically disadvantaged students from low-performing schools who, according to research by Bempechat and others, get little or no homework.

BU Today  sat down with Bempechat and Erin Bruce (Wheelock’17,’18), a new fourth-grade teacher at a suburban Boston school, and future teacher freshman Emma Ardizzone (Wheelock) to talk about what quality homework looks like, how it can help children learn, and how schools can equip teachers to design it, evaluate it, and facilitate parents’ role in it.

BU Today: Parents and educators who are against homework in elementary school say there is no research definitively linking it to academic performance for kids in the early grades. You’ve said that they’re missing the point.

Bempechat : I think teachers assign homework in elementary school as a way to help kids develop skills they’ll need when they’re older—to begin to instill a sense of responsibility and to learn planning and organizational skills. That’s what I think is the greatest value of homework—in cultivating beliefs about learning and skills associated with academic success. If we greatly reduce or eliminate homework in elementary school, we deprive kids and parents of opportunities to instill these important learning habits and skills.

We do know that beginning in late middle school, and continuing through high school, there is a strong and positive correlation between homework completion and academic success.

That’s what I think is the greatest value of homework—in cultivating beliefs about learning and skills associated with academic success.

You talk about the importance of quality homework. What is that?

Quality homework is engaging and relevant to kids’ lives. It gives them autonomy and engages them in the community and with their families. In some subjects, like math, worksheets can be very helpful. It has to do with the value of practicing over and over.

What are your concerns about homework and low-income children?

The argument that some people make—that homework “punishes the poor” because lower-income parents may not be as well-equipped as affluent parents to help their children with homework—is very troubling to me. There are no parents who don’t care about their children’s learning. Parents don’t actually have to help with homework completion in order for kids to do well. They can help in other ways—by helping children organize a study space, providing snacks, being there as a support, helping children work in groups with siblings or friends.

Isn’t the discussion about getting rid of homework happening mostly in affluent communities?

Yes, and the stories we hear of kids being stressed out from too much homework—four or five hours of homework a night—are real. That’s problematic for physical and mental health and overall well-being. But the research shows that higher-income students get a lot more homework than lower-income kids.

Teachers may not have as high expectations for lower-income children. Schools should bear responsibility for providing supports for kids to be able to get their homework done—after-school clubs, community support, peer group support. It does kids a disservice when our expectations are lower for them.

The conversation around homework is to some extent a social class and social justice issue. If we eliminate homework for all children because affluent children have too much, we’re really doing a disservice to low-income children. They need the challenge, and every student can rise to the challenge with enough supports in place.

What did you learn by studying how education schools are preparing future teachers to handle homework?

My colleague, Margarita Jimenez-Silva, at the University of California, Davis, School of Education, and I interviewed faculty members at education schools, as well as supervising teachers, to find out how students are being prepared. And it seemed that they weren’t. There didn’t seem to be any readings on the research, or conversations on what high-quality homework is and how to design it.

Erin, what kind of training did you get in handling homework?

Bruce : I had phenomenal professors at Wheelock, but homework just didn’t come up. I did lots of student teaching. I’ve been in classrooms where the teachers didn’t assign any homework, and I’ve been in rooms where they assigned hours of homework a night. But I never even considered homework as something that was my decision. I just thought it was something I’d pull out of a book and it’d be done.

I started giving homework on the first night of school this year. My first assignment was to go home and draw a picture of the room where you do your homework. I want to know if it’s at a table and if there are chairs around it and if mom’s cooking dinner while you’re doing homework.

The second night I asked them to talk to a grown-up about how are you going to be able to get your homework done during the week. The kids really enjoyed it. There’s a running joke that I’m teaching life skills.

Friday nights, I read all my kids’ responses to me on their homework from the week and it’s wonderful. They pour their hearts out. It’s like we’re having a conversation on my couch Friday night.

It matters to know that the teacher cares about you and that what you think matters to the teacher. Homework is a vehicle to connect home and school…for parents to know teachers are welcoming to them and their families.

Bempechat : I can’t imagine that most new teachers would have the intuition Erin had in designing homework the way she did.

Ardizzone : Conversations with kids about homework, feeling you’re being listened to—that’s such a big part of wanting to do homework….I grew up in Westchester County. It was a pretty demanding school district. My junior year English teacher—I loved her—she would give us feedback, have meetings with all of us. She’d say, “If you have any questions, if you have anything you want to talk about, you can talk to me, here are my office hours.” It felt like she actually cared.

Bempechat : It matters to know that the teacher cares about you and that what you think matters to the teacher. Homework is a vehicle to connect home and school…for parents to know teachers are welcoming to them and their families.

Ardizzone : But can’t it lead to parents being overbearing and too involved in their children’s lives as students?

Bempechat : There’s good help and there’s bad help. The bad help is what you’re describing—when parents hover inappropriately, when they micromanage, when they see their children confused and struggling and tell them what to do.

Good help is when parents recognize there’s a struggle going on and instead ask informative questions: “Where do you think you went wrong?” They give hints, or pointers, rather than saying, “You missed this,” or “You didn’t read that.”

Bruce : I hope something comes of this. I hope BU or Wheelock can think of some way to make this a more pressing issue. As a first-year teacher, it was not something I even thought about on the first day of school—until a kid raised his hand and said, “Do we have homework?” It would have been wonderful if I’d had a plan from day one.

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There are 81 comments on Does Homework Really Help Students Learn?

Insightful! The values about homework in elementary schools are well aligned with my intuition as a parent.

when i finish my work i do my homework and i sometimes forget what to do because i did not get enough sleep

same omg it does not help me it is stressful and if I have it in more than one class I hate it.

Same I think my parent wants to help me but, she doesn’t care if I get bad grades so I just try my best and my grades are great.

I think that last question about Good help from parents is not know to all parents, we do as our parents did or how we best think it can be done, so maybe coaching parents or giving them resources on how to help with homework would be very beneficial for the parent on how to help and for the teacher to have consistency and improve homework results, and of course for the child. I do see how homework helps reaffirm the knowledge obtained in the classroom, I also have the ability to see progress and it is a time I share with my kids

The answer to the headline question is a no-brainer – a more pressing problem is why there is a difference in how students from different cultures succeed. Perfect example is the student population at BU – why is there a majority population of Asian students and only about 3% black students at BU? In fact at some universities there are law suits by Asians to stop discrimination and quotas against admitting Asian students because the real truth is that as a group they are demonstrating better qualifications for admittance, while at the same time there are quotas and reduced requirements for black students to boost their portion of the student population because as a group they do more poorly in meeting admissions standards – and it is not about the Benjamins. The real problem is that in our PC society no one has the gazuntas to explore this issue as it may reveal that all people are not created equal after all. Or is it just environmental cultural differences??????

I get you have a concern about the issue but that is not even what the point of this article is about. If you have an issue please take this to the site we have and only post your opinion about the actual topic

This is not at all what the article is talking about.

This literally has nothing to do with the article brought up. You should really take your opinions somewhere else before you speak about something that doesn’t make sense.

we have the same name

so they have the same name what of it?

lol you tell her

totally agree

What does that have to do with homework, that is not what the article talks about AT ALL.

Yes, I think homework plays an important role in the development of student life. Through homework, students have to face challenges on a daily basis and they try to solve them quickly.I am an intense online tutor at 24x7homeworkhelp and I give homework to my students at that level in which they handle it easily.

More than two-thirds of students said they used alcohol and drugs, primarily marijuana, to cope with stress.

You know what’s funny? I got this assignment to write an argument for homework about homework and this article was really helpful and understandable, and I also agree with this article’s point of view.

I also got the same task as you! I was looking for some good resources and I found this! I really found this article useful and easy to understand, just like you! ^^

i think that homework is the best thing that a child can have on the school because it help them with their thinking and memory.

I am a child myself and i think homework is a terrific pass time because i can’t play video games during the week. It also helps me set goals.

Homework is not harmful ,but it will if there is too much

I feel like, from a minors point of view that we shouldn’t get homework. Not only is the homework stressful, but it takes us away from relaxing and being social. For example, me and my friends was supposed to hang at the mall last week but we had to postpone it since we all had some sort of work to do. Our minds shouldn’t be focused on finishing an assignment that in realty, doesn’t matter. I completely understand that we should have homework. I have to write a paper on the unimportance of homework so thanks.

homework isn’t that bad

Are you a student? if not then i don’t really think you know how much and how severe todays homework really is

i am a student and i do not enjoy homework because i practice my sport 4 out of the five days we have school for 4 hours and that’s not even counting the commute time or the fact i still have to shower and eat dinner when i get home. its draining!

i totally agree with you. these people are such boomers

why just why

they do make a really good point, i think that there should be a limit though. hours and hours of homework can be really stressful, and the extra work isn’t making a difference to our learning, but i do believe homework should be optional and extra credit. that would make it for students to not have the leaning stress of a assignment and if you have a low grade you you can catch up.

Studies show that homework improves student achievement in terms of improved grades, test results, and the likelihood to attend college. Research published in the High School Journal indicates that students who spent between 31 and 90 minutes each day on homework “scored about 40 points higher on the SAT-Mathematics subtest than their peers, who reported spending no time on homework each day, on average.” On both standardized tests and grades, students in classes that were assigned homework outperformed 69% of students who didn’t have homework. A majority of studies on homework’s impact – 64% in one meta-study and 72% in another – showed that take home assignments were effective at improving academic achievement. Research by the Institute for the Study of Labor (IZA) concluded that increased homework led to better GPAs and higher probability of college attendance for high school boys. In fact, boys who attended college did more than three hours of additional homework per week in high school.

So how are your measuring student achievement? That’s the real question. The argument that doing homework is simply a tool for teaching responsibility isn’t enough for me. We can teach responsibility in a number of ways. Also the poor argument that parents don’t need to help with homework, and that students can do it on their own, is wishful thinking at best. It completely ignores neurodiverse students. Students in poverty aren’t magically going to find a space to do homework, a friend’s or siblings to help them do it, and snacks to eat. I feel like the author of this piece has never set foot in a classroom of students.

THIS. This article is pathetic coming from a university. So intellectually dishonest, refusing to address the havoc of capitalism and poverty plays on academic success in life. How can they in one sentence use poor kids in an argument and never once address that poor children have access to damn near 0 of the resources affluent kids have? Draw me a picture and let’s talk about feelings lmao what a joke is that gonna put food in their belly so they can have the calories to burn in order to use their brain to study? What about quiet their 7 other siblings that they share a single bedroom with for hours? Is it gonna force the single mom to magically be at home and at work at the same time to cook food while you study and be there to throw an encouraging word?

Also the “parents don’t need to be a parent and be able to guide their kid at all academically they just need to exist in the next room” is wild. Its one thing if a parent straight up is not equipped but to say kids can just figured it out is…. wow coming from an educator What’s next the teacher doesn’t need to teach cause the kid can just follow the packet and figure it out?

Well then get a tutor right? Oh wait you are poor only affluent kids can afford a tutor for their hours of homework a day were they on average have none of the worries a poor child does. Does this address that poor children are more likely to also suffer abuse and mental illness? Like mentioned what about kids that can’t learn or comprehend the forced standardized way? Just let em fail? These children regularly are not in “special education”(some of those are a joke in their own and full of neglect and abuse) programs cause most aren’t even acknowledged as having disabilities or disorders.

But yes all and all those pesky poor kids just aren’t being worked hard enough lol pretty sure poor children’s existence just in childhood is more work, stress, and responsibility alone than an affluent child’s entire life cycle. Love they never once talked about the quality of education in the classroom being so bad between the poor and affluent it can qualify as segregation, just basically blamed poor people for being lazy, good job capitalism for failing us once again!

why the hell?

you should feel bad for saying this, this article can be helpful for people who has to write a essay about it

This is more of a political rant than it is about homework

I know a teacher who has told his students their homework is to find something they are interested in, pursue it and then come share what they learn. The student responses are quite compelling. One girl taught herself German so she could talk to her grandfather. One boy did a research project on Nelson Mandela because the teacher had mentioned him in class. Another boy, a both on the autism spectrum, fixed his family’s computer. The list goes on. This is fourth grade. I think students are highly motivated to learn, when we step aside and encourage them.

The whole point of homework is to give the students a chance to use the material that they have been presented with in class. If they never have the opportunity to use that information, and discover that it is actually useful, it will be in one ear and out the other. As a science teacher, it is critical that the students are challenged to use the material they have been presented with, which gives them the opportunity to actually think about it rather than regurgitate “facts”. Well designed homework forces the student to think conceptually, as opposed to regurgitation, which is never a pretty sight

Wonderful discussion. and yes, homework helps in learning and building skills in students.

not true it just causes kids to stress

Homework can be both beneficial and unuseful, if you will. There are students who are gifted in all subjects in school and ones with disabilities. Why should the students who are gifted get the lucky break, whereas the people who have disabilities suffer? The people who were born with this “gift” go through school with ease whereas people with disabilities struggle with the work given to them. I speak from experience because I am one of those students: the ones with disabilities. Homework doesn’t benefit “us”, it only tears us down and put us in an abyss of confusion and stress and hopelessness because we can’t learn as fast as others. Or we can’t handle the amount of work given whereas the gifted students go through it with ease. It just brings us down and makes us feel lost; because no mater what, it feels like we are destined to fail. It feels like we weren’t “cut out” for success.

homework does help

here is the thing though, if a child is shoved in the face with a whole ton of homework that isn’t really even considered homework it is assignments, it’s not helpful. the teacher should make homework more of a fun learning experience rather than something that is dreaded

This article was wonderful, I am going to ask my teachers about extra, or at all giving homework.

I agree. Especially when you have homework before an exam. Which is distasteful as you’ll need that time to study. It doesn’t make any sense, nor does us doing homework really matters as It’s just facts thrown at us.

Homework is too severe and is just too much for students, schools need to decrease the amount of homework. When teachers assign homework they forget that the students have other classes that give them the same amount of homework each day. Students need to work on social skills and life skills.

I disagree.

Beyond achievement, proponents of homework argue that it can have many other beneficial effects. They claim it can help students develop good study habits so they are ready to grow as their cognitive capacities mature. It can help students recognize that learning can occur at home as well as at school. Homework can foster independent learning and responsible character traits. And it can give parents an opportunity to see what’s going on at school and let them express positive attitudes toward achievement.

Homework is helpful because homework helps us by teaching us how to learn a specific topic.

As a student myself, I can say that I have almost never gotten the full 9 hours of recommended sleep time, because of homework. (Now I’m writing an essay on it in the middle of the night D=)

I am a 10 year old kid doing a report about “Is homework good or bad” for homework before i was going to do homework is bad but the sources from this site changed my mind!

Homeowkr is god for stusenrs

I agree with hunter because homework can be so stressful especially with this whole covid thing no one has time for homework and every one just wants to get back to there normal lives it is especially stressful when you go on a 2 week vaca 3 weeks into the new school year and and then less then a week after you come back from the vaca you are out for over a month because of covid and you have no way to get the assignment done and turned in

As great as homework is said to be in the is article, I feel like the viewpoint of the students was left out. Every where I go on the internet researching about this topic it almost always has interviews from teachers, professors, and the like. However isn’t that a little biased? Of course teachers are going to be for homework, they’re not the ones that have to stay up past midnight completing the homework from not just one class, but all of them. I just feel like this site is one-sided and you should include what the students of today think of spending four hours every night completing 6-8 classes worth of work.

Are we talking about homework or practice? Those are two very different things and can result in different outcomes.

Homework is a graded assignment. I do not know of research showing the benefits of graded assignments going home.

Practice; however, can be extremely beneficial, especially if there is some sort of feedback (not a grade but feedback). That feedback can come from the teacher, another student or even an automated grading program.

As a former band director, I assigned daily practice. I never once thought it would be appropriate for me to require the students to turn in a recording of their practice for me to grade. Instead, I had in-class assignments/assessments that were graded and directly related to the practice assigned.

I would really like to read articles on “homework” that truly distinguish between the two.

oof i feel bad good luck!

thank you guys for the artical because I have to finish an assingment. yes i did cite it but just thanks

thx for the article guys.

Homework is good

I think homework is helpful AND harmful. Sometimes u can’t get sleep bc of homework but it helps u practice for school too so idk.

I agree with this Article. And does anyone know when this was published. I would like to know.

It was published FEb 19, 2019.

Studies have shown that homework improved student achievement in terms of improved grades, test results, and the likelihood to attend college.

i think homework can help kids but at the same time not help kids

This article is so out of touch with majority of homes it would be laughable if it wasn’t so incredibly sad.

There is no value to homework all it does is add stress to already stressed homes. Parents or adults magically having the time or energy to shepherd kids through homework is dome sort of 1950’s fantasy.

What lala land do these teachers live in?

Homework gives noting to the kid

Homework is Bad

homework is bad.

why do kids even have homework?

Comments are closed.

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More than two hours of homework may be counterproductive, research suggests.

A Stanford education researcher found that too much homework can negatively affect kids, especially their lives away from school, where family, friends and activities matter.   "Our findings on the effects of homework challenge the traditional assumption that homework is inherently good," wrote Denise Pope , a senior lecturer at the Stanford Graduate School of Education and a co-author of a study published in the Journal of Experimental Education .   The researchers used survey data to examine perceptions about homework, student well-being and behavioral engagement in a sample of 4,317 students from 10 high-performing high schools in upper-middle-class California communities. Along with the survey data, Pope and her colleagues used open-ended answers to explore the students' views on homework.   Median household income exceeded $90,000 in these communities, and 93 percent of the students went on to college, either two-year or four-year.   Students in these schools average about 3.1 hours of homework each night.   "The findings address how current homework practices in privileged, high-performing schools sustain students' advantage in competitive climates yet hinder learning, full engagement and well-being," Pope wrote.   Pope and her colleagues found that too much homework can diminish its effectiveness and even be counterproductive. They cite prior research indicating that homework benefits plateau at about two hours per night, and that 90 minutes to two and a half hours is optimal for high school.   Their study found that too much homework is associated with:   • Greater stress : 56 percent of the students considered homework a primary source of stress, according to the survey data. Forty-three percent viewed tests as a primary stressor, while 33 percent put the pressure to get good grades in that category. Less than 1 percent of the students said homework was not a stressor.   • Reductions in health : In their open-ended answers, many students said their homework load led to sleep deprivation and other health problems. The researchers asked students whether they experienced health issues such as headaches, exhaustion, sleep deprivation, weight loss and stomach problems.   • Less time for friends, family and extracurricular pursuits : Both the survey data and student responses indicate that spending too much time on homework meant that students were "not meeting their developmental needs or cultivating other critical life skills," according to the researchers. Students were more likely to drop activities, not see friends or family, and not pursue hobbies they enjoy.   A balancing act   The results offer empirical evidence that many students struggle to find balance between homework, extracurricular activities and social time, the researchers said. Many students felt forced or obligated to choose homework over developing other talents or skills.   Also, there was no relationship between the time spent on homework and how much the student enjoyed it. The research quoted students as saying they often do homework they see as "pointless" or "mindless" in order to keep their grades up.   "This kind of busy work, by its very nature, discourages learning and instead promotes doing homework simply to get points," said Pope, who is also a co-founder of Challenge Success , a nonprofit organization affiliated with the GSE that conducts research and works with schools and parents to improve students' educational experiences..   Pope said the research calls into question the value of assigning large amounts of homework in high-performing schools. Homework should not be simply assigned as a routine practice, she said.   "Rather, any homework assigned should have a purpose and benefit, and it should be designed to cultivate learning and development," wrote Pope.   High-performing paradox   In places where students attend high-performing schools, too much homework can reduce their time to foster skills in the area of personal responsibility, the researchers concluded. "Young people are spending more time alone," they wrote, "which means less time for family and fewer opportunities to engage in their communities."   Student perspectives   The researchers say that while their open-ended or "self-reporting" methodology to gauge student concerns about homework may have limitations – some might regard it as an opportunity for "typical adolescent complaining" – it was important to learn firsthand what the students believe.   The paper was co-authored by Mollie Galloway from Lewis and Clark College and Jerusha Conner from Villanova University.

Clifton B. Parker is a writer at the Stanford News Service .

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Study shows students in ‘active learning’ classrooms learn more than they think

For decades, there has been evidence that classroom techniques designed to get students to participate in the learning process produces better educational outcomes at virtually all levels.

And a new Harvard study suggests it may be important to let students know it.

The study , published Sept. 4 in the Proceedings of the National Academy of Sciences, shows that, though students felt as if they learned more through traditional lectures, they actually learned more when taking part in classrooms that employed so-called active-learning strategies.

Lead author Louis Deslauriers , the director of science teaching and learning and senior physics preceptor, knew that students would learn more from active learning. He published a key study in Science in 2011 that showed just that. But many students and faculty remained hesitant to switch to it.

“Often, students seemed genuinely to prefer smooth-as-silk traditional lectures,” Deslauriers said. “We wanted to take them at their word. Perhaps they actually felt like they learned more from lectures than they did from active learning.”

In addition to Deslauriers, the study is authored by director of sciences education and physics lecturer Logan McCarty , senior preceptor in applied physics Kelly Miller, preceptor in physics Greg Kestin , and Kristina Callaghan, now a physics lecturer at the University of California, Merced.

The question of whether students’ perceptions of their learning matches with how well they’re actually learning is particularly important, Deslauriers said, because while students eventually see the value of active learning, initially it can feel frustrating.

“Deep learning is hard work. The effort involved in active learning can be misinterpreted as a sign of poor learning,” he said. “On the other hand, a superstar lecturer can explain things in such a way as to make students feel like they are learning more than they actually are.”

To understand that dichotomy, Deslauriers and his co-authors designed an experiment that would expose students in an introductory physics class to both traditional lectures and active learning.

For the first 11 weeks of the 15-week class, students were taught using standard methods by an experienced instructor. In the 12th week, half the class was randomly assigned to a classroom that used active learning, while the other half attended highly polished lectures. In a subsequent class, the two groups were reversed. Notably, both groups used identical class content and only active engagement with the material was toggled on and off.

Following each class, students were surveyed on how much they agreed or disagreed with statements such as “I feel like I learned a lot from this lecture” and “I wish all my physics courses were taught this way.” Students were also tested on how much they learned in the class with 12 multiple-choice questions.

When the results were tallied, the authors found that students felt as if they learned more from the lectures, but in fact scored higher on tests following the active learning sessions. “Actual learning and feeling of learning were strongly anticorrelated,” Deslauriers said, “as shown through the robust statistical analysis by co-author Kelly Miller, who is an expert in educational statistics and active learning.”

Those results, the study authors are quick to point out, shouldn’t be interpreted as suggesting students dislike active learning. In fact, many studies have shown students quickly warm to the idea, once they begin to see the results. “In all the courses at Harvard that we’ve transformed to active learning,” Deslauriers said, “the overall course evaluations went up.”

Co-author Kestin, who in addition to being a physicist is a video producer with PBS’ NOVA, said, “It can be tempting to engage the class simply by folding lectures into a compelling ‘story,’ especially when that’s what students seem to like. I show my students the data from this study on the first day of class to help them appreciate the importance of their own involvement in active learning.”

McCarty, who oversees curricular efforts across the sciences, hopes this study will encourage more of his colleagues to embrace active learning.

“We want to make sure that other instructors are thinking hard about the way they’re teaching,” he said. “In our classes, we start each topic by asking students to gather in small groups to solve some problems. While they work, we walk around the room to observe them and answer questions. Then we come together and give a short lecture targeted specifically at the misconceptions and struggles we saw during the problem-solving activity. So far we’ve transformed over a dozen classes to use this kind of active-learning approach. It’s extremely efficient — we can cover just as much material as we would using lectures.”

A pioneer in work on active learning, Balkanski Professor of Physics and Applied Physics Eric Mazur hailed the study as debunking long-held beliefs about how students learn.

“This work unambiguously debunks the illusion of learning from lectures,” he said. “It also explains why instructors and students cling to the belief that listening to lectures constitutes learning. I recommend every lecturer reads this article.”

Dean of Science Christopher Stubbs , Samuel C. Moncher Professor of Physics and of Astronomy, was an early convert. “When I first switched to teaching using active learning, some students resisted that change. This research confirms that faculty should persist and encourage active learning. Active engagement in every classroom, led by our incredible science faculty, should be the hallmark of residential undergraduate education at Harvard.”

Ultimately, Deslauriers said, the study shows that it’s important to ensure that neither instructors nor students are fooled into thinking that lectures are the best learning option. “Students might give fabulous evaluations to an amazing lecturer based on this feeling of learning, even though their actual learning isn’t optimal,” he said. “This could help to explain why study after study shows that student evaluations seem to be completely uncorrelated with actual learning.”

This research was supported with funding from the Harvard FAS Division of Science.

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Is Homework Necessary? Education Inequity and Its Impact on Students

The Problem with Homework: It Highlights Inequalities

How much homework is too much homework, when does homework actually help, negative effects of homework for students, how teachers can help.

Schools are getting rid of homework from Essex, Mass., to Los Angeles, Calif. Although the no-homework trend may sound alarming, especially to parents dreaming of their child’s acceptance to Harvard, Stanford or Yale, there is mounting evidence that eliminating homework in grade school may actually have great benefits , especially with regard to educational equity.

In fact, while the push to eliminate homework may come as a surprise to many adults, the debate is not new . Parents and educators have been talking about this subject for the last century, so that the educational pendulum continues to swing back and forth between the need for homework and the need to eliminate homework.

One of the most pressing talking points around homework is how it disproportionately affects students from less affluent families. The American Psychological Association (APA) explained:

“Kids from wealthier homes are more likely to have resources such as computers, internet connections, dedicated areas to do schoolwork and parents who tend to be more educated and more available to help them with tricky assignments. Kids from disadvantaged homes are more likely to work at afterschool jobs, or to be home without supervision in the evenings while their parents work multiple jobs.”

[RELATED] How to Advance Your Career: A Guide for Educators >> 

While students growing up in more affluent areas are likely playing sports, participating in other recreational activities after school, or receiving additional tutoring, children in disadvantaged areas are more likely headed to work after school, taking care of siblings while their parents work or dealing with an unstable home life. Adding homework into the mix is one more thing to deal with — and if the student is struggling, the task of completing homework can be too much to consider at the end of an already long school day.

While all students may groan at the mention of homework, it may be more than just a nuisance for poor and disadvantaged children, instead becoming another burden to carry and contend with.

Beyond the logistical issues, homework can negatively impact physical health and stress — and once again this may be a more significant problem among economically disadvantaged youth who typically already have a higher stress level than peers from more financially stable families .

Yet, today, it is not just the disadvantaged who suffer from the stressors that homework inflicts. A 2014 CNN article, “Is Homework Making Your Child Sick?” , covered the issue of extreme pressure placed on children of the affluent. The article looked at the results of a study surveying more than 4,300 students from 10 high-performing public and private high schools in upper-middle-class California communities.

“Their findings were troubling: Research showed that excessive homework is associated with high stress levels, physical health problems and lack of balance in children’s lives; 56% of the students in the study cited homework as a primary stressor in their lives,” according to the CNN story. “That children growing up in poverty are at-risk for a number of ailments is both intuitive and well-supported by research. More difficult to believe is the growing consensus that children on the other end of the spectrum, children raised in affluence, may also be at risk.”

When it comes to health and stress it is clear that excessive homework, for children at both ends of the spectrum, can be damaging. Which begs the question, how much homework is too much?

The National Education Association and the National Parent Teacher Association recommend that students spend 10 minutes per grade level per night on homework . That means that first graders should spend 10 minutes on homework, second graders 20 minutes and so on. But a study published by The American Journal of Family Therapy found that students are getting much more than that.

While 10 minutes per day doesn’t sound like much, that quickly adds up to an hour per night by sixth grade. The National Center for Education Statistics found that high school students get an average of 6.8 hours of homework per week, a figure that is much too high according to the Organization for Economic Cooperation and Development (OECD). It is also to be noted that this figure does not take into consideration the needs of underprivileged student populations.

In a study conducted by the OECD it was found that “after around four hours of homework per week, the additional time invested in homework has a negligible impact on performance .” That means that by asking our children to put in an hour or more per day of dedicated homework time, we are not only not helping them, but — according to the aforementioned studies — we are hurting them, both physically and emotionally.

What’s more is that homework is, as the name implies, to be completed at home, after a full day of learning that is typically six to seven hours long with breaks and lunch included. However, a study by the APA on how people develop expertise found that elite musicians, scientists and athletes do their most productive work for about only four hours per day. Similarly, companies like Tower Paddle Boards are experimenting with a five-hour workday, under the assumption that people are not able to be truly productive for much longer than that. CEO Stephan Aarstol told CNBC that he believes most Americans only get about two to three hours of work done in an eight-hour day.

In the scope of world history, homework is a fairly new construct in the U.S. Students of all ages have been receiving work to complete at home for centuries, but it was educational reformer Horace Mann who first brought the concept to America from Prussia. 

Since then, homework’s popularity has ebbed and flowed in the court of public opinion. In the 1930s, it was considered child labor (as, ironically, it compromised children’s ability to do chores at home). Then, in the 1950s, implementing mandatory homework was hailed as a way to ensure America’s youth were always one step ahead of Soviet children during the Cold War. Homework was formally mandated as a tool for boosting educational quality in 1986 by the U.S. Department of Education, and has remained in common practice ever since.  

School work assigned and completed outside of school hours is not without its benefits. Numerous studies have shown that regular homework has a hand in improving student performance and connecting students to their learning. When reviewing these studies, take them with a grain of salt; there are strong arguments for both sides, and only you will know which solution is best for your students or school. 

Homework improves student achievement.

• Source: The High School Journal, “ When is Homework Worth the Time?: Evaluating the Association between Homework and Achievement in High School Science and Math ,” 2012. 
• Source: IZA.org, “ Does High School Homework Increase Academic Achievement? ,” 2014. **Note: Study sample comprised only high school boys. 

Homework helps reinforce classroom learning.

• Source: “ Debunk This: People Remember 10 Percent of What They Read ,” 2015.

Homework helps students develop good study habits and life skills.

• Sources: The Repository @ St. Cloud State, “ Types of Homework and Their Effect on Student Achievement ,” 2017; Journal of Advanced Academics, “ Developing Self-Regulation Skills: The Important Role of Homework ,” 2011.
• Source: Journal of Advanced Academics, “ Developing Self-Regulation Skills: The Important Role of Homework ,” 2011.

Homework allows parents to be involved with their children’s learning.

• Parents can see what their children are learning and working on in school every day. 
• Parents can participate in their children’s learning by guiding them through homework assignments and reinforcing positive study and research habits.
• Homework observation and participation can help parents understand their children’s academic strengths and weaknesses, and even identify possible learning difficulties.
• Source: Phys.org, “ Sociologist Upends Notions about Parental Help with Homework ,” 2018.

While some amount of homework may help students connect to their learning and enhance their in-class performance, too much homework can have damaging effects. 

Students with too much homework have elevated stress levels. 

• Source: USA Today, “ Is It Time to Get Rid of Homework? Mental Health Experts Weigh In ,” 2021.
• Source: Stanford University, “ Stanford Research Shows Pitfalls of Homework ,” 2014.

Students with too much homework may be tempted to cheat. 

• Source: The Chronicle of Higher Education, “ High-Tech Cheating Abounds, and Professors Bear Some Blame ,” 2010.
• Source: The American Journal of Family Therapy, “ Homework and Family Stress: With Consideration of Parents’ Self Confidence, Educational Level, and Cultural Background ,” 2015.

Homework highlights digital inequity. 

• Sources: NEAToday.org, “ The Homework Gap: The ‘Cruelest Part of the Digital Divide’ ,” 2016; CNET.com, “ The Digital Divide Has Left Millions of School Kids Behind ,” 2021.
• Source: Investopedia, “ Digital Divide ,” 2022; International Journal of Education and Social Science, “ Getting the Homework Done: Social Class and Parents’ Relationship to Homework ,” 2015.
• Source: World Economic Forum, “ COVID-19 exposed the digital divide. Here’s how we can close it ,” 2021.

Homework does not help younger students.

• Source: Review of Educational Research, “ Does Homework Improve Academic Achievement? A Synthesis of Researcher, 1987-2003 ,” 2006.

To help students find the right balance and succeed, teachers and educators must start the homework conversation, both internally at their school and with parents. But in order to successfully advocate on behalf of students, teachers must be well educated on the subject, fully understanding the research and the outcomes that can be achieved by eliminating or reducing the homework burden. There is a plethora of research and writing on the subject for those interested in self-study.

For teachers looking for a more in-depth approach or for educators with a keen interest in educational equity, formal education may be the best route. If this latter option sounds appealing, there are now many reputable schools offering online master of education degree programs to help educators balance the demands of work and family life while furthering their education in the quest to help others.

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What we know about online learning and the homework gap amid the pandemic

America’s K-12 students are returning to classrooms this fall after 18 months of virtual learning at home during the COVID-19 pandemic. Some students who lacked the home internet connectivity needed to finish schoolwork during this time – an experience often called the “ homework gap ” – may continue to feel the effects this school year.

Here is what Pew Research Center surveys found about the students most likely to be affected by the homework gap and their experiences learning from home.

Children across the United States are returning to physical classrooms this fall after 18 months at home, raising questions about how digital disparities at home will affect the existing homework gap between certain groups of students.

Methodology for each Pew Research Center poll can be found at the links in the post.

With the exception of the 2018 survey, everyone who took part in the surveys is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the  ATP’s methodology .

The 2018 data on U.S. teens comes from a Center poll of 743 U.S. teens ages 13 to 17 conducted March 7 to April 10, 2018, using the NORC AmeriSpeak panel. AmeriSpeak is a nationally representative, probability-based panel of the U.S. household population. Randomly selected U.S. households are sampled with a known, nonzero probability of selection from the NORC National Frame, and then contacted by U.S. mail, telephone or face-to-face interviewers. Read more details about the NORC AmeriSpeak panel methodology .

Around nine-in-ten U.S. parents with K-12 children at home (93%) said their children have had some online instruction since the coronavirus outbreak began in February 2020, and 30% of these parents said it has been very or somewhat difficult for them to help their children use technology or the internet as an educational tool, according to an April 2021 Pew Research Center survey .

Gaps existed for certain groups of parents. For example, parents with lower and middle incomes (36% and 29%, respectively) were more likely to report that this was very or somewhat difficult, compared with just 18% of parents with higher incomes.

This challenge was also prevalent for parents in certain types of communities – 39% of rural residents and 33% of urban residents said they have had at least some difficulty, compared with 23% of suburban residents.

Around a third of parents with children whose schools were closed during the pandemic (34%) said that their child encountered at least one technology-related obstacle to completing their schoolwork during that time. In the April 2021 survey, the Center asked parents of K-12 children whose schools had closed at some point about whether their children had faced three technology-related obstacles. Around a quarter of parents (27%) said their children had to do schoolwork on a cellphone, 16% said their child was unable to complete schoolwork because of a lack of computer access at home, and another 14% said their child had to use public Wi-Fi to finish schoolwork because there was no reliable connection at home.

Parents with lower incomes whose children’s schools closed amid COVID-19 were more likely to say their children faced technology-related obstacles while learning from home. Nearly half of these parents (46%) said their child faced at least one of the three obstacles to learning asked about in the survey, compared with 31% of parents with midrange incomes and 18% of parents with higher incomes.

Of the three obstacles asked about in the survey, parents with lower incomes were most likely to say that their child had to do their schoolwork on a cellphone (37%). About a quarter said their child was unable to complete their schoolwork because they did not have computer access at home (25%), or that they had to use public Wi-Fi because they did not have a reliable internet connection at home (23%).

A Center survey conducted in April 2020 found that, at that time, 59% of parents with lower incomes who had children engaged in remote learning said their children would likely face at least one of the obstacles asked about in the 2021 survey.

A year into the outbreak, an increasing share of U.S. adults said that K-12 schools have a responsibility to provide all students with laptop or tablet computers in order to help them complete their schoolwork at home during the pandemic. About half of all adults (49%) said this in the spring 2021 survey, up 12 percentage points from a year earlier. An additional 37% of adults said that schools should provide these resources only to students whose families cannot afford them, and just 13% said schools do not have this responsibility.

While larger shares of both political parties in April 2021 said K-12 schools have a responsibility to provide computers to all students in order to help them complete schoolwork at home, there was a 15-point change among Republicans: 43% of Republicans and those who lean to the Republican Party said K-12 schools have this responsibility, compared with 28% last April. In the 2021 survey, 22% of Republicans also said schools do not have this responsibility at all, compared with 6% of Democrats and Democratic leaners.

Even before the pandemic, Black teens and those living in lower-income households were more likely than other groups to report trouble completing homework assignments because they did not have reliable technology access. Nearly one-in-five teens ages 13 to 17 (17%) said they are often or sometimes unable to complete homework assignments because they do not have reliable access to a computer or internet connection, a 2018 Center survey of U.S. teens found.

One-quarter of Black teens said they were at least sometimes unable to complete their homework due to a lack of digital access, including 13% who said this happened to them often. Just 4% of White teens and 6% of Hispanic teens said this often happened to them. (There were not enough Asian respondents in the survey sample to be broken out into a separate analysis.)

A wide gap also existed by income level: 24% of teens whose annual family income was less than $30,000 said the lack of a dependable computer or internet connection often or sometimes prohibited them from finishing their homework, but that share dropped to 9% among teens who lived in households earning $75,000 or more a year.

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Katherine Schaeffer is a research analyst at Pew Research Center .

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Effects of homework creativity on academic achievement and creativity disposition: Evidence from comparisons with homework time and completion based on two independent Chinese samples

Huiyong fan.

1 College of Educational Science, Bohai University, Jinzhou, China

2 Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian, China

Jianzhong Xu

3 Department of Counseling, Educational Psychology, and Foundations, College of Education, Mississippi State University, MS, United States

Shengli Guo

Associated data.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

During the past several decades, the previous studies have been focusing on the related theoretical issues and measuring tool of homework behaviors (mainly including homework time, completion, and homework creativity). However, the effects of these homework behaviors on general creativity remain unknown. Employing a number of questionnaires, this study investigated two samples from middle schools of Mainland China. The results showed that (1) the eight-item version of Homework Creativity Behaviors Scale had acceptable validity and reliability; (2) compared with homework completion and homework time, homework creativity explained less variety of academic achievement (3.7% for homework creativity; 5.4% for completion and time); (3) homework creativity explained more variance of general creativity than that of homework completion and homework time accounted (7.0% for homework creativity; 1.3% for completion and time); and (4) homework creativity was negatively associated with grade level. Contrary to the popular beliefs, homework completion and homework creativity have positive effects on the students’ general creativity. Several issues that need further studies were also discussed.

Introduction

Homework is an important part of the learning and instruction process. Each week, students around the world spend 3–14 hours on homework, with an average of 5 hours a week ( Dettmers et al., 2009 ; OECD, 2014 ). The results of the previous studies and meta-analysis showed that the homework time is correlated significantly with students’ gains on the academic tests ( Cooper et al., 2012 ; Fan et al., 2017 ; Fernández-Alonso et al., 2019 ).

Homework is a multi-faceted process which has many attributes – each attribute can be identified, defined, and measured independently ( Guo and Fan, 2018 ). Some attributes, such as homework time ( Núñez et al., 2013 ; Kalenkoski and Pabilonia, 2017 ), homework frequency ( Fernández-Alonso et al., 2015 ), homework completion ( Rosário et al., 2015 ), homework effort ( Trautwein and Lüdtke, 2007 ; Fernández-Alonso et al., 2015 ), homework purpose ( Trautwein and Lüdtke, 2009 ; Xu, 2010 , 2021 ), homework performance and problems ( Power et al., 2007 ), homework management behavior ( Xu, 2008 ), homework expectation ( Xu, 2017 ), and self-regulation of homework behavior ( Yang and Tu, 2020 ), have been well recorded in the literature, and operationally defined and measured.

Recently, a research community has noticed the “creativity” in homework (in short form, “homework creativity”) who have raised some speculations about its effects on students’ academic achievement and general creativity disposition ( Kaiipob, 1951 ; Beghetto and Kaufman, 2007 ; Kaufman and Beghetto, 2009 ; Guo, 2018 ; Guo and Fan, 2018 ; Chang, 2019 ). However, the scientific measurement of homework creativity has not been examined systematically. The relationship between homework creativity, academic achievement, and general creativity disposition, as well as the grade difference in homework creativity, are still in the state of conjectures consequently.

As a scientific probe to homework creativity, this study included three main sections. In the “Literature Review” section, the conceptualization and relevant measurement of homework creativity were summarized; the relationship between homework behaviors and academic achievements, general creativity, and the grade difference in homework behaviors and general creativity were also evaluated. These four main results related to the four research questions were also presented in the body of this article. They are reliability and validity of homework creativity behavior scale (HCBS), the relationships between the scores of HCBS and those of general creativity and academic achievement, and the grade effects of scores of HCBS. In the “Discussion” section, the scientific contributions and interpretations of the findings of this study were elaborated.

Homework creativity

Conceptual background of homework creativity.

As an attribute of homework process, homework creativity refers to the novelty and uniqueness of homework ( Guo and Fan, 2018 ). Specifically, the ways relating to homework creativity with extant theoretical literature are presented below.

First, creativity is a natural part of homework process which serves as a sub-process of learning. Guilford (1950) is the first psychologist who linked creativity with learning, pointing out that the acquisition of creativity is a typical quality of human learning, and that a complete learning theory must take creativity into account.

Second, according to the Four-C Model of Creativity (e.g., Kaufman and Beghetto, 2009 ), the homework creativity can be divided mainly into the category of “Transformative Learning” (Mini-C creativity), which is different from the “Everyday Innovation” (Little-C creativity), “Professional Expertise” (Pro-C creativity), or “Eminent Accomplishments” (Big-C creativity, Beghetto and Kaufman, 2007 ; Kaufman and Beghetto, 2009 ; Kozbelt et al., 2011 ).

The Mini-C is defined as a type of intrapersonal creativity which has personal meaning, not solid contribution or breakthrough in a field ( Beghetto and Kaufman, 2007 , p. 76, Table 1 ). The most important point which distinguishes Mini-C from other types of creativity is the level of novelty of product. The Mini-C creativity involves the personal insight or interpretation which is new to a particular individual, but may be ordinary to others. The Little-C creativity refers to any small, but solid innovation in daily life. The Pro-C creativity is represented in the form of professional contribution which is still not a breakthrough. The Big-C creativity generates a real breakthrough appears in some field which is considered as something new to all human beings. The other difference is related with the subjects of sub-types of creativity. The Mini-C creativity mainly happens in all kinds of students. The Little-C creativity can be widely found in normal people. The Pro-C creativity’s masters are those who are proficient in some field. The Big-C creativity is related frequently with those giants who has made eminent contribution to human being.

Basic information of samples 1 and 2 included.

The Mini-C creativity frequently happens in learning process. When the contribution of the Mini-C creativity grows big enough, it can move into the category of the Little-C creativity, or the Big-C creativity. Most homework creativity is of Mini-C creativity, and of which a small part may grow as the Little-C and Big-C creativities. For example, when students independently find a unique solution to a problem in homework which has scientific meaning, a Little-C or Big-C occurs.

Third, the education researchers have observed homework creativity for many years and been manipulating them in educational practice. Kaiipob (1951) described that homework is a semi-guide learning process in which homework such as composition, report, public speech, difficult and complex exercises, experiments, and making tools and models consumes a lot of time and accelerate the development of students’ creativity disposition (p. 153).

In the recent years, creativity has become a curriculum or instruction goal in many countries (the case of United Kingdom, see Smith and Smith, 2010 ; Chinese case, see Pang and Plucker, 2012 ). Homework is the most important way that accomplish this goal. Considering Chinese in primary and secondary schools in China as an example, the curriculum standards have clearly required homework to cultivate students’ creative spirit, creative thinking, and ability to imagination since the year 2000. The results of Qian’s (2006) investigation revealed that the percent of these creative homework items in each unit fluctuates between 29 and 45%.

Previous instruments of homework behaviors

Those existent instruments measuring homework behavior can be divided into the following two categories: The single-indicator instruments and the multi-dimension instruments ( Guo and Fan, 2018 ). The single-indicator instruments employ only one item to measure homework attributes, such as homework time (e.g., Trautwein and Lüdtke, 2007 ), homework frequency (e.g., De Jong et al., 2000 ), homework completion (e.g., Xu et al., 2019 ), and effort (e.g., Liu et al., 2013 ).

The typical multi-dimension instruments include Homework Process Inventory ( Cooper et al., 1998 ), Homework Purpose Scale ( Xu, 2010 ), Homework Performance Questionnaire ( Pendergast et al., 2014 ), Homework Management Scale (HMS; Xu and Corno, 2003 ), Homework Evaluating Scale ( Fernández-Alonso et al., 2015 ), Homework Problem Checklist ( Anesko et al., 1987 ), Science Homework Scale ( Tas et al., 2016 ), Homework Expectancy Value Scale ( Yang and Xu, 2017 ), and Online Homework Distraction Scale ( Xu et al., 2020 ).

Although the previous tools measured some dimensions of homework ( Guo and Fan, 2018 ), there is hardly any tool that can be employed to gauge the homework creativity. Guo and Fan (2018) extracted several attributes (i.e., time, completion, quality, purpose, effort, creativity, sociality, liking) represented in the existent instruments of homework behaviors, and put forth a multi-faceted model of homework behaviors which intuitionally predicts the existence of homework creativity.

Under the guideline of the multi-faceted model ( Guo and Fan, 2018 ), Guo (2018) developed a multi-dimensional homework behavior instrument, which detected the homework creativity as a dimension in the homework behavior of middle school students. A typical item of homework creativity in Guo (2018) is “The way I do my homework is different from others.” The subscale homework creativity reported by Guo (2018) needs to be improved because it has a small number of items with lower reliability.

Following Guo’s (2018) work, Chang (2019) conducted a new investigation focusing on homework creativity behavior. Using an open-ended questionnaire, a total of 30 students from primary, middle, and high schools were invited to answer this question, that is, “What characteristics can be considered as creative in the process of completing the homework?” Here, “creativity” refers to novelty, uniqueness, and high quality. A group of 23 specific behaviors were reported, among which the top 10 are as follows: Learning by analogy, open minded, one question with multiple solutions, unique solution, summarizing the cause of errors, constructing a personal understanding, analyzing knowledge points clearly, classifying homework contents, making more applications, having rich imagination, and a neat handwriting (see Chang, 2019 , Table 4 , p. 14). Based on these results of open-ended questionnaire, Chang (2019) invented a nine-item scale (see Table 1 and Supplementary Table S3 for details) called as the HCBS which has a good reliability coefficient (α = 0.87).

Regression analyses of homework creative behavior on academic achievement and general creativity.

AA, academic achievement; WCAPt, total score of WCAP; TWk, time spent on homework in week days; TWw, time spent on homework in weekend; HCp, homework completion; HCb, homework creativity behavior.

Previous studies on the relationship between homework behaviors and academic achievement

In the literature, homework behaviors is one cluster of variables typically including homework time, homework completion, effort, purpose, frequency, etc. Academic achievement is an outcome of homework which is operationally measured using the scores on the standardized tests, or non-standardized tests (including final examinations, or teachers’ grades, or estimations by participants themselves, those forms were used widely in the literature, see Fan et al., 2017 ). Academic achievement may be affected by a lot of factors inherited in the process of learning (see Hattie, 2009 for an overview of its correlates). The relationship between homework behaviors and academic achievement is one of the most important questions in homework field, because it is related to the effectiveness of homework ( Cooper et al., 2006 , 2012 ; Fan et al., 2017 ).

Most of the previous studies focused on the relationship between homework time and academic achievement. Cooper et al. (2006) synthesized the primary studies published from 1989 to 2003, and found that the correlation between homework time of America students and their academic achievement was about 0.15. Fan et al. (2017) reviewed those individual studies published before June 2015, and reported that the averaged correlation between homework time of international students and their science, technology, engineering, and mathematics (STEM) academic achievement was about 0.20. Fernández-Alonso et al. (2017) investigated a representative sample of Spanish students (more than 26,000), and the results of multi-level analysis indicated that the correlation between homework time and academic achievement was negative at student level, but positive at school level ( r = 0.16). Fernández-Alonso et al. (2019) took a survey on a big sample from 16 countries from Latin America, and reported that the relationship between homework time and academic achievement was very weak. Valle et al. (2019) analyzed the homework time, time management, and achievement of 968 Spain students finding that homework time management was positively related to academic achievement. Taken all these together, we will find that the homework has some small significant correlations with academic achievement, the average r = 0.15.

The correlation between homework completion and academic achievement has also been investigated for decades. Based on a review of 11 primary studies, Fan et al. (2017) reported a high correlation of 0.59 between them. Rosário et al. (2015) investigated 638 students, and demonstrated a correlation of 0.22 between amount of homework completed and math test scores. Xu et al. (2019) took a survey using a sample of 1,450 Chinese eighth graders, and found that the correlations between homework completion and the gains in math test scores ranged from 0.25 to 0.28. Dolean and Lervag (2022) employed the Randomized Controlled Trial design, and demonstrated that amount of homework completed has immediate effect on writing competency in which the effect of moderate amount of homework can last for 4 months. Integrating the aforementioned results, we can find that the averaged correlation between homework completion and academic achievement was higher than that between homework time with academic achievement.

Homework effort was also found to be correlated with academic achievement. Fan et al. (2017) reviewed four primary studies and returned that a medium correlation ( r = 0.31) between homework effort and academic achievement. Two recent investigations showed that this relationship is positively and reciprocally related ( r = 0.41–0.42) ( Xu, 2020 ; Xu et al., 2021 ).

The effect of homework purpose was also correlated with the academic achievement. Fan et al. (2017) summarized four existent primary studies and reported an averaged correlation of 0.11 between them. Later, Rosário et al. (2015) found a similar correlation coefficient of these two variables on a sample of 638 students. Xu’s (2018) investigation revealed that the correlation between purpose and academic achievement was about 0.40. Sun et al. (2021) investigated a larger sample ( N = 1,365), and found that the subscales of homework purpose had different correlation patterns with academic achievement (academic purpose is 0.40, self-regulatory purpose is 0.20, and approval-seeking purpose is 0.10).

Considering the case of homework creativity, there is only one study preliminarily investigated its relationship with academic achievement. Guo (2018) investigated a sample of 1,808 middle school students, and reported a significant correlation between homework creativity and academic achievement ( r = 0.34, p < 0.05).

Previous studies on the relationship between homework behaviors and general creativity

General creativity refers to the psychological attributes which can generate novel and valuable products ( Kaufman and Glăveanu, 2019 ; Sternberg and Karami, 2022 ). These psychological attributes typically included attitude (e.g., willing to take appropriate risk), motivations (e.g., intrinsic motivation, curiosity), abilities (e.g., divergent thinking), and personality (e.g., independence) ( Kaufman and Glăveanu, 2019 ; Long et al., 2022 ). These attributes can be assessed independently, or in the form of grouping ( Plucker et al., 2019 ; Sternberg, 2019 ). For instance, the divergent thinking was measured independently ( Kaufman et al., 2008 ). Also, the willing to take appropriate risk was measured in tools contain other variables ( Williams, 1979 ). There are many studies examined the relationship between learning and general creativity in the past several decades indicating that the correlation between them was around 0.22 (e.g., Gajda et al., 2017 ; Karwowski et al., 2020 ).

Regarding the relationship between homework behaviors and general creativity, there are few studies which presented some contradictory viewpoints. Kaiipob (1951) posited that homework could accelerate development of students’ general creativity disposition, because the tasks in homework provide opportunities to exercise creativity. Cooper et al. (2012) argued that homework can diminish creativity. Furthermore, Zheng (2013) insisted that homework will reduce curiosity and the ability to challenging – the two core components of creativity. The preliminary results of Chang (2019) indicated that the score of HCBS is significantly correlated with scores of a test of general creativity, Williams’ creativity packet ( r = 0.25–0.33, p < 0.05).

Previous studies on the relationship between homework behaviors and homework creativity

In Guo and Fan’s (2018) theoretical work, homework creativity was combined from two independent words, homework and creativity, which was defined as a new attribute of homework process and was considered as a new member of homework behaviors. Up till now, there are two works providing preliminary probe to the relationship between homework behaviors and homework creativity. Guo (2018) investigated a sample of 1808 middle school students, and found that homework creativity was correlated significantly with liking ( r = 0.33), correctness ( r = 0.47), completion ( r = 0.57), and purpose ( r = 0.53). Based on another sample of Chinese students (elementary school students, N = 300; middle school students, N = 518; high school students, N = 386), Chang (2019) showed that the score of homework creativity was correlated significantly with homework time ( r = 0.11), completion ( r = 0.39), correctness ( r = 0.63), effort ( r = 0.73), social interaction ( r = 0.35), quality ( r = 0.69), interpersonal relation purpose ( r = 0.17), and purpose of personal development ( r = 0.41).

Previous studies on grade differences of homework behaviors and general creativity

Grade differences of homework behaviors.

As a useful indicator, homework time was recorded frequently (e.g., Cooper et al., 2006 ; Fan et al., 2017 ). A recent meta-analysis included 172 primary studies (total N = 144,416) published from 2003 to 2019, and demonstrated that time Chinese K-12 students spent on homework increased significantly along with increasing of grades ( Zhai and Fan, 2021 , October).

Regarding homework managing time, some studies reported the grade difference was insignificant. Xu (2006) surveyed 426 middle school students and found that there was no difference between middle school students and high school students. Xu and Corno (2003) reported that urban junior school students ( N = 86) had no grade difference in homework Managing time. Yang and Tu (2020) surveyed 305 Chinese students in grades 7–9, and found that in managing time behavior, the grade differences were insignificant. The rest studies showed that the grade effect is significant. A survey by Xu et al. (2014) based on 1799 Chinese students in grades 10 and 11 showed that the higher level the grade, the lower level of time management.

Grade differences of general creativity

The findings from the previous studies suggested that the scores of general creativity deceases as the grade increases except for some dimensions. Kim (2011) reviewed the Torrance Tests of Creative thinking (TTCT) scores change using five datasets from 1974 to 2008, and reported that three dimensions of creative thinking (i.e., “Fluency,” “Originality,” and “Elaboration”) significantly decreased along with grades increase, while the rest dimension (i.e., “Abstractness of titles”) significantly increased when grades increase. Nie and Zheng (2005) investigated a sample of 3,729 participants from grades 3–12 using the Williams’ Creativity Assessment Packet (WCAP), and reported that the creativity scores decreased from grades 9–12. Said-Metwaly et al. (2021) synthesized 41 primary studies published in the past 60 years, and concluded that the ability of divergent thinking had a whole increase tendency from grades 1 to 12 with a decrease tendency from grades 8 to 11 at the same time.

The purpose and questions of this study

What we have known about homework creativity hitherto is nothing except for its notation and a preliminary version of measurement. To get deeper understanding of homework creativity, this study made an endeavor to examine its relationships with relevant variables based on a confirmation of the reliability and validity of HCBS. Specifically, there are four interrelated research questions, as the following paragraphs (and their corresponding hypotheses) described.

(i) What is the reliability and validity of the HCBS?

Because the earlier version of the HCBS showed a good Cronbach α coefficient of 0.87, and a set of well-fitting indices ( Chang, 2019 ), this study expected that the reliability and validity will also behave well in the current conditions as before. Then, we present the first set of hypotheses as follows:

H1a: The reliability coefficient will equal or greater than 0.80.

H1b: The one-factor model will also fit the current data well; and all indices will reach or over the criteria as the expertise suggested.

(ii) What degree is the score of the HCBS related with academic achievement?

As suggested by the review section, the correlations between homework behaviors and academic achievement ranged from 0.15 and 0.59 (e.g., Fan et al., 2017 ), then we expected that the relationship between homework creativity and academic achievement will fall into this range, because homework creativity is a member of homework behaviors.

The results of the previous studies also demonstrated that the correlation between general creativity and academic achievement changed in a range of 0.19–0.24 with a mean of 0.19 ( Gajda et al., 2017 ). Because it can be treated as a sub-category of general creativity, we predicted that homework creativity will have a similar behavior under the current condition.

Taken aforementioned information together, Hypothesis H2 is presented as follows:

H2: There will be a significant correlation between homework creativity and academic achievement which might fall into the interval of 0.15–0.59.

(iii) What degree is the relationship between HCBS and general creativity?

As discussed in the previous section, there are no inconsistent findings about the relationship between the score of HCBS and general creativity. Some studies postulated that these two variables be positive correlated (e.g., Kaiipob, 1951 ; Chang, 2019 ); other studies argued that this relationship be negative (e.g., Cooper et al., 2012 ; Zheng, 2013 ). Because homework creativity is a sub-category of general creativity, we expected that this relationship would be positive and its value might be equal or less than 0.33. Based on those reasoning, we presented our third hypothesis as follows:

H3: The correlation between homework creativity and general creativity would be equal or less than 0.33.

(iv) What effect does grade have on the HCBS score?

Concerning the grade effect of homework behaviors, the previous findings were contradictory ( Xu et al., 2014 ; Zhai and Fan, 2021 , October). However, the general creativity decreased as the level of grade increases from grade 8 to grade 11 ( Kim, 2011 ; Said-Metwaly et al., 2021 ). Taken these previous findings and the fact that repetitive exercises increase when grades go up ( Zheng, 2013 ), we were inclined to expect that the level of homework creativity is negative correlated with the level of grade. Thus, we presented our fourth hypothesis as follows:

H4: The score of HCBS might decrease as the level of grades goes up.

Materials and methods

Participants.

To get more robust result, this study investigated two convenient samples from six public schools in a medium-sized city in China. Among them, two schools were of high schools (including a key school and a non-key school), and the rest four schools were middle schools (one is key school, and the rest is non-key school). All these schools included here did not have free lunch system and written homework policy. Considering the students were mainly prepared for entrance examination of higher stage, the grades 9 and 12 were excluded in this survey. Consequently, students of grades 7, 8, 10, and 11 were included in our survey. After getting permission of the education bureau of the city investigated, the headmasters administrated the questions in October 2018 (sample 1) and November 2019 (sample 2).

A total of 850 questionnaires were released and the valid number of questionnaires returned is 639 with a valid return rate of 75.18%. Therefore, there were 639 valid participants in sample 1. Among them, there were 273 boys and 366 girls (57.2%); 149 participants from grade 7 (23.31%), 118 from grade 8 (18.47%), 183 from grade 10 (28.64%), and 189 from grade 11 (29.58%); the average age was 15.25 years, with a standard deviation (SD) of 1.73 years. See Table 1 for the information about each grade.

Those participants included received homework assignments every day (see Table 1 for the distribution of homework frequency). During the working days, the averaged homework time was 128.29 minutes with SD = 6.65 minutes. In the weekend, the average homework time was 3.75 hours, with SD = 0.22 hours. The percentage distribution here is similar with that of a national representative sample ( Sun et al., 2020 ), because the values of Chi-squared (χ 2 ) were 7.46 (father) and 8.46 (mother), all p -values were above 0.12 (see Supplementary Table S1 for details).

Another package of 850 questionnaires were released. The valid number of questionnaires returned is 710 with a valid return rate of 83.53%. Among them, there were 366 girls (51.50%); 171 participants from grade 7 (24.23%), 211 from grade 8 (26.06%), 190 from the grade 10 (22.96%), and 216 from grade 11 (26.76%); the average age was 15.06 years, with SD = 1.47 years.

Those participants included received homework assignments almost each day (see Table 1 for details for the distribution of homework frequency). During the working days, the averaged homework time was 123.02 minutes with SD = 6.13 minutes. In weekend, the average homework time was 3.47 hours, with SD = 0.21 hours.

The percentage distribution here is insignificantly different from that of a national representative sample ( Sun et al., 2020 ), because the values of χ 2 were 5.20 (father) and 6.05 (mother), p -values were above 0.30 (see Supplementary Table S1 for details).

Instruments

The homework creativity behavior scale.

The HCBS contains nine items representing students’ creativity behaviors in the process of completing homework (for example, “I do my homework in an innovative way”) ( Chang, 2019 , see Supplementary Table S3 for details). The HCBS employs a 5-point rating scale, where 1 means “completely disagree” and 5 means “completely agree.” The higher the score, the stronger the homework creative behavior students have. The reliability and validity of the HCBS can be found in Section “Reliability and validity of the homework creativity behavior scale” (see Table 2 and Figures 1 , ​ ,2 2 for details).

Results of item discrimination analysis and exploratory factor analysis.

**p < 0.01, two side-tailed. The same for below.

a Correlations for sample 1; b Correlations for sample 2. c Seventh item should be removed away according to the results of CFA (see section “Reliability and validity of the HCBS” for details).

Parallel analysis scree plots of the HCBS data.

The standardized solution for HCBS eight-item model. hcb, homework creativity behavior; it 1∼9, item1 ∼6, 8∼9.

Homework management scale

The HMS contains 22 items describing specific behaviors related to self-management in homework (for example, “I will choose a quiet place to do my homework” or “Tell myself to calm down when encountering difficulties”) ( Xu and Corno, 2003 ; Xu, 2008 ). The HMS employs a 5-point Likert scale, ranging from 1 (completely disagree) to 5 (completely agree). All items can be divided into five dimensions, i.e., arranging environment, managing time, focusing attention, monitoring motivation, and monitoring and controlling emotion. Among them, the monitoring and controlling emotion dimension adopts a method of reverse scoring.

Except for the internal consistency of arranging environment in sample 1, which is 0.63, the internal consistency coefficients of the five dimensions based two samples in this study are all greater than 0.7, ranging from 0.70 to 0.79. The Cronbach’s coefficients of the overall HMS-based two samples are 0.88 and 0.87, respectively. The ω coefficients of the dimensions of HMS ranged from 0.64 to 0.80. The ω coefficients of the HMS total scores were 0.88 and 0.87 for samples 1 and 2, respectively. Those reliability coefficients were acceptable for research purpose ( Clark and Watson, 1995 ; Peterson and Kim, 2013 ).

Williams’ creativity assessment packet

The WCAP including a total of 40 items is a revised version to measure general disposition of creativity (for example, “I like to ask some questions out of other’s expectation” or “I like to imagine something novel, even if it looks useless”) ( Williams, 1979 ; Wang and Lin, 1986 ; Liu et al., 2016 ). The WCAP uses a 3-point Likert scales, in which 1 = disagree, 2 = uncertain, and 3 = agree. The higher WCAP score, the higher is the general creativity level. All items of WCAP can be scattered into four dimensions: adventure, curiosity, imagination, and challenge ( Williams, 1979 ; Wang and Lin, 1986 ; Liu et al., 2016 ). In this study, the Cronbach’s α coefficients of adventure, curiosity, imagination, challenge, and total scale are 0.62, 0.71, 0.78, 0.64, and 0.90, respectively. The ω coefficients were in sequence 0.61, 0.70, 0.77, 0.63, and 0.90 for adventure, curiosity, imagination, challenge, and the total score of WCAP. The correlations between the four dimensions of WCAP are between 0.47 and 0.65. The patterns of reliability coefficients and correlations between dimensions are similar to those results reported by the previous studies ( Williams, 1979 ; Wang and Lin, 1986 ; Liu et al., 2016 ) which stand acceptable reliability and validity ( Clark and Watson, 1995 ; Peterson and Kim, 2013 ).

Homework indicators

Homework time.

The participants were asked to report the time spent on homework in the past week. This technique has been employed widely in many international survey programs, such as PISA from OECD (e.g., Trautwein and Lüdtke, 2007 ). The items are as follows: (1) “Every day, from Monday to Friday, in last week, how many minutes you spent on homework?” The options are as follows: (A) 0–30 min; (B) 31–60 min (C) 61–90 min (D) 91–120 min; (E) 121–180 min; (F) 181 min or more. (2) “In last weekend, how many hours you spent on homework?” The options are as follows: (A) 0–1 h; (B) 1.1–3 h; (C) 3.1–5 h; (D) 5.1–7 h; (E) 7.1 h or more.

Homework completion

The homework completion is a useful indicator demonstrated in the previous studies ( Welch et al., 1986 ; Austin, 1988 ; Swank, 1999 ; Pelletier, 2005 ; Wilson, 2010 ), and had large correlation with achievement, as a meta-analytic results suggested ( Fan et al., 2017 ). In the survey of this study, the participants were also asked to estimate a percent of the completion of homework in the past week and fill in the given blank space. It includes three items which are as follows: “What is the percentage of Chinese/Maths/English homework assignment you completed in the last week?” “Please estimate and write a number from 0 to 100 in the blank space.”

Academic achievement

To record the academic achievement, an item required participants to make a choice based on their real scores of tests, not estimate their tests scores. The item is, “In the last examination, what is the rank of your score in your grade?” (A) The first 2%; (B) The first 3–13%; (C) The first 14–50%; (D) The first 51–84%; (E) The last 16%. The options here correspond to the percentage in the normal distribution, it is convenient to compute a Z -score for each student.

The method employed here is effective to retrieve participants’ test scores. First, the self-report method is more effective than other method under the condition of anonymous investigation. To our knowledge, participants do not have the will to provide their real information in the real name format. Second, this method transforms test scores from different sources into the same space of norm distribution which benefits the comparisons. Third, the validity of this method has been supported by empirical data. Using another sample ( N = 234), we got the academic achievement they reported and real test scores their teacher recorded. The correlation between ranks self-reported and the real scores from Chinese test were r = 0.81, p < 0.001; and the correlation coefficient for mathematics was also large, i.e., r = 0.79, p < 0.001.

Data collection procedure

There are three phases in data collection. The first one is the design stage. At this stage, the corresponding author of this study designed the study content, prepared the survey tools, and got the ethical approve of this project authorized from research ethic committee of school the corresponding author belongs to.

The second stage is to releasing questionnaire prepared. The questionnaire was distributed and retrieved by the head master of those classes involved. Neither the teachers nor the students knew the purpose of this research. During this stage, students can stop answering at any time, or simply withdraw from the survey. None of the teachers and students in this study received payment.

The third stage is the data entry stage. At this stage, the corresponding author of this study recruited five volunteers majored in psychology and education, and explained to them the coding rules, missing value processing methods, identification of invalid questionnaires, and illustrated how to deal with these issues. The volunteers used the same data template for data entry. The corresponding author of this study controlled the data entry quality by selective check randomly.

Data analysis strategies

R packages employed.

The “psych” package in R environment ( R Core Team, 2019 ) was employed to do descriptive statistics, correlation analysis, mean difference comparisons, exploratory factor analysis (EFA), reliability Analysis ( Revelle, 2022 ); and the “lavaan” package was used in confirmatory factor analysis (CFA) and measurement invariance test ( Rosseel, 2012 ); and the “semPlot” package was employed to draw the picture of CFA’s outputs ( Epskamp et al., 2022 ).

Analysis strategies of exploratory factor analysis and reliability

Sample 1 was used for item analysis, EFA, reliability analysis. In EFA, factors were extracted using maximum likelihood, and the promax method served as the rotation method. The number of factors were determined according to the combination of the results from screen plot, and the rule of Eigenvalues exceeding 1.0, and parallel analysis ( Luo et al., 2019 ).

The Cronbach’s α and MacDonald’s ω test were employed to test the reliability of the scale. The rigorous criteria that α ≥ 0.70 ( Nunnally and Bernstein, 1994 ) and ω ≥ 0.7 ( Green and Yang, 2015 ) were taken as acceptable level of the reliability of HCBS.

Analysis strategies of confirmatory factor analysis

As suggested by Hu and Bentler (1999) , two absolute goodness-of-fit indices, namely, the root mean square error of approximation (RMSEA) and the standardized root mean square residual (SRMR), and two relative goodness-of-fit indices, namely, comparative fit index (CFI) and Tucker–Lewis Index (TLI) were recruited as fitting indicators. The absolute goodness-of-fit indices are less than 0.08, and the relative goodness-of-fit indices greater than 0.90 are considered as a good fit. The CFA was conducted using the second sample.

Strategies for measurement invariance

Measurement invariance testing included four models, they are Configural invariance (Model 1), which is to test whether the composition of latent variables between different groups is the same; Weak invariance (Factor loading invariance, Model 2), which is to test whether the factor loading is equal among the groups; Intercept invariance (Model 3), that is, whether the intercepts of the observed variables are equal; Strict equivalent (Residual Variance invariance, Model 4), that is, to test whether the error variances between different groups are equal ( Chen, 2007 ; Putnick and Bornstein, 2016 ).

Since the χ 2 test will be affected easily by the sample size, even small differences will result in significant differences as the sample size will increase. Therefore, this study used the changes of model fitting index CFI, RMSEA, and SRMR (ΔCFI, ΔRMSEA, and ΔSRMR) to evaluate the invariance of the measurement. When ΔCFI ≤ 0.010, ΔRMSEA ≤ 0.015, and ΔSRMR ≤ 0.030 (for metric invariance) or 0.015 (for scalar or residual invariance), the invariance model is considered acceptable ( Cheung and Rensvold, 2002 ; Chen, 2007 ; Putnick and Bornstein, 2016 ).

Strategies of controlling common methods biases

The strategy of controlling common methods biases is mainly hided in the directions. Each part of the printed questionnaire had a sub-direction which invites participants answer the printed questions honestly. The answer formats between any two neighboring parts were different from each other which requested participants change their mind in time. For example, on some part, the answering continuum varied from “1 = totally disagreed” to “5 = total agreed,” while the answering continuum on the neighboring part is the from “5 = totally disagreed” to “1 = total agreed.” Additionally, according to the suggestion of the previous studies, the one factor CFA model and the bi-factor model can be used to detect the common methods biases (e.g., Podsakoff et al., 2012 ).

Detection of common method biases

The fitting results of the one-common-factor model using CFA technique were as follows: χ 2 = 15,073, df = 3320, p < 0.001; χ 2 / df = 4.54, CFI = 0.323, TLI = 0.306, RMSEA = 0.071, 90% CI: 0.070–0.072, and SRMR = 0.101. The results of the bi-factor model under CFA framework were presented as follows: χ 2 = 2,225.826, df = 117, p < 0.001; χ 2 / df = 19.024, CFI = 0.650, TLI = 0.543, RMSEA = 0.159, 90% CI: 0.154–0.164, and SRMR = 0.127. These poor indices of the two models suggested that the one-common-factor model failed to fit the data well and that the biases of common method be ignored ( Podsakoff et al., 2012 ).

Reliability and validity of the homework creativity behavior scale

Item analysis.

Based on the sample 1, the correlation coefficients between the items of the HCBS were between 0.34 and 0.64, p -values were below 0.01. The correlations between the items and the total score of HCBS vary from 0.54 to 0.75 ( p -values are below 0.01). On the condition of sample 2, the correlations between the items fluctuate between 0.31 and 0.58, the correlation coefficients between the items and the total score of the HCBS change from 0.63 to 0.75 ( p -values were below 0.01). All correlation coefficients between items and total score are larger than those between items and reached the criterion suggested ( Ferketich, 1991 ; see Table 2 for details).

Results of exploratory factor analysis

The EFA results (based on sample 1) showed that the KMO was 0.89, and the χ 2 of Bartlett’s test = 1,666.07, p < 0.01. The rules combining eigenvalue larger than 1 and the results of parallel analysis (see Figure 1 for details) suggested that one factor should be extracted. The eigenvalue of the factor extracted was 3.63. The average variance extracted was 0.40. This factor accounts 40% variance with factor loadings fluctuating from 0.40 to 0.76 (see Table 2 ).

Results of confirmatory factor analysis

In the CFA situation (based on sample 2) the fitting indices of the nine-item model of the HCBS are acceptable marginally, they are χ 2 = 266.141; df = 27; χ 2 / df = 9.857; CFI = 0.904; TLI = 0.872; RMSEA = 0.112; 90% CI: 0.100–0.124; SRMR = 0.053.

The modification indices of item 7 were too big (MI value = 74.339, p < 0.01), so it is necessary to consider to delete item 7. Considering its content of “I designed a neat, clean and clear homework format by myself,” item 7 is an indicator of strictness which is weakly linked with creativity. Therefore, the item 7 should be deleted.

After removing item 7, the fitting results were, χ 2 = 106.111; df = 20; χ 2 / df = 5.306; CFI = 0.957; TLI = 0.939; RMSEA = 0.078; 90% CI: 0.064–0.093; SRMR = 0.038). The changes of the fitting indices of the two nested models (eight-item vs. nine-item models) are presented as follows: Δχ 2 = 160.03, Δ df = 7, χ 2 (α = 0.01, df = 7) = 18.48, p < 0.05. After deleting item 7, both CFI and TLI indices increased to above 0.93, and RMSEAs decreased below 0.08 which suggested that the factor model on which eight items loaded fitted the data well. The average variance extracted was 0.50 which is adequate according to the criteria suggested by Fornell and Larcker (1981) . The standardized solution for the eight-item model of the HCBS was shown in Figure 2 .

Correlations between the homework creativity behavior scale and similar concepts

The results showed that the score of the HCBS was significantly correlated with the total score and four dimensions of WCAP and their correlation coefficients ranged from 0.20 to 0.29, p -values were below 0.01. Similarly, the correlations between the score of the HCBS and the scores of arranging environment, managing time, motivation management, and controlling emotion, and total score of the HMS ranged from 0.08 to 0.22, p -values were 0.01; at the meanwhile, the correlation between the score of HCBS and the distraction dimension of the HMS was r = –0.14, p -values were 0.01. The HCBS score was also significantly related to homework completion ( r = 0.18, p < 0.01), but insignificantly related to homework time (see Table 3 for details).

Correlation matrix between variables included and the corresponding descriptive statistics.

About correlation between variables, the results of sample 1 and sample 2 were presented in the lower, upper triangle, respectively.

a In analyses, grades 7, 8, 10, and 11 were valued 1, 2, 3, and 4, respectively.

b TWk, the time spent on homework in the weekend; TWw, the time spent on homework from Monday to Friday; HCp, homework completion; HMSt, total score of homework management scale; AE, arrange environment; MT, manage time; MM, monitor motivation; CE, control emotion; FA, focus attention; WCAPt, WCAP total score; AD, adventure; CU, curiosity; IM, imagination; CH, challenging; HCb, homework creativity behavior; AA, academic achievement.

c Since sample 1 did not answer the WCAP, so the corresponding cells in the lower triangle are blank. *p < 0.05, two side-tailed, the same for below.

d Since there is only one item from variable 1 to 4, the α and ω coefficients cannot be computed.

Correlations between the homework creativity behavior scale and distinct concepts

The correlation analysis results demonstrated that both the correlation coefficients between the score of HCBS and the time spent on homework in week days, and time spent on in weekend days were insignificant ( r -values = 0.02, p -values were above 0.05), which indicated a non-overlap between two distinct constructs of homework creativity and time spent on homework.

Reliability analyses

The results revealed that both the Cronbach’s α coefficients of sample 1 and sample 2 were 0.86, which were greater than a 0.70 criteria the previous studies suggest ( Nunnally and Bernstein, 1994 ; Green and Yang, 2015 ).

Effect of homework creativity on academic achievement

The results (see Table 4 ) of hierarchical regression analyses demonstrated that (1) gender and grade explained 0.8% variation of the score of academic achievement. This number means closing to zero because the regression equation failed to pass the significance test; (2) homework time and completion explained 5.4% variation of academic achievement; considering the β coefficients of the time spent on homework is insignificant, this contribution should be attributed to homework completion totally, and (3) the score of the HCBS explained 3.7% variation of the academic achievement independently.

Effect of homework creativity on general creativity

The results showed the following (see Table 4 for details):

(1) Gender and grade explained 1.3% variation of the total score of general creativity (i.e., the total score of WACP); homework time and completion explained 1.3% variation of the total score of general creativity disposition; and the score of the HCBS independently explained 7.0% variation of the total score of general creativity.

(2) Gender and grade explained 1.7% variation of the adventure score, and homework time and completion explained 1.6% variation of the adventure score, and the score of the HCBS independently explained 6.4% variation of the adventure score.

(3) Gender and grade explained 2.4% variation of the curiosity score, and homework time and completion explained 1.1% variation of the curiosity score, and the score of the HCBS independently explained 5.1% variation of the curiosity score.

(4) Gender and grade explained 0.3% variation of the imagination score, homework time completion explained 0.3% variation of the imagination score. The real values of the two “0.3%” are zeros because both the regression equations and coefficients failed to pass the significance tests. Then the score of the HCBS independently explained 4.4% variation of the imagination score.

(5) Gender and grade explained 0.3% variation of the score of the challenge dimension, homework time and completion explained 2.3% variation of the challenge score, and the score of the HCBS independently explained 4.9% variation of the challenge score.

Grade differences of the homework creativity behavior scale

Test of measurement invariance.

The results of measurement invariance test across four grades indicated the following:

(1) The fitting states of the four models (Configural invariance, Factor loading invariance, Intercept invariance, and Residual variance invariance) were marginally acceptable, because values of CFIs (ranged from 0.89 to 0.93), TLIs (varied from 0.91 to 0.93), RMSEAs (fluctuated from 0.084 to 0.095), and SRMRs (changed from 0.043 to 0.074) located the cutoff intervals suggested by methodologists ( Cheung and Rensvold, 2002 ; Chen, 2007 ; Putnick and Bornstein, 2016 ; see Table 5 for fitting indices, and refer to Supplementary Table S2 for the estimation of parameters).

Fitting results of invariance tests across grades.

(2) When setting factor loadings equal across four grades (i.e., grades 7, 8, 10, and 11), the ΔCFA was –0.006, ΔRMSEA was –0.007, and ΔSRMR was 0.016 which indicated that it passed the test of factor loading invariance. After adding the limit of intercepts equal across four groups, the ΔCFA was –0.008, ΔRMSEA was –0.004, and the ΔSRMR was 0.005 which supported that it passed the test of intercept invariance. At the last step, the error variances were also added as equal, the ΔCFA was –0.027, ΔRMSEA was 0.005, and the ΔSRMR was 0.019 which failed to pass the test of residual variance invariance (see Table 5 for changes of fitting indices). Taking into these fitting indices into account, the subsequent comparisons between the means of factors can be conducted because the residuals are not part of the latent factor ( Cheung and Rensvold, 2002 ; Chen, 2007 ; Putnick and Bornstein, 2016 ).

Grade differences in homework creativity and general creativity

The results of ANOVA showed that there were significant differences in the HCBS among the four grades [ F (3,1345) = 27.49, p < 0.001, η 2 = 0.058, see Table 6 for details]. Further post-test tests returned that the scores of middle school students were significantly higher than those of high school students (Cohen’s d values ranged from 0.46 to 0.54; the averaged Cohen’s d = 0.494), and no significant difference occurs between grades 7 and 8, or between grades 10 and 11. See Figure 3 for details.

Grade differences in HCBS.

***p < 0.001.

The mean differences of the HCBS between the groups of grades.

To address the gap in the previous research on homework creativity, this study examined the psychometric proprieties of the HCBS and its relationship with academic achievement and general creativity. The main findings were (1) Hypotheses H1a and H1b were supported that the reliability and validity of the HCBS were acceptable; (2) Hypothesis H2 was supported that the correlation between the score of the HCBS and academic achievement was significant ( r -values = 0.23–0.26 for two samples); (3) Hypothesis H3 received support that the correlation between the scores of HCBS and WCAP was significant ( r -values = 0.20–0.29 for two samples); and (4) the H4 was supported from the current data that the score of high school students’ was lower than that of the middle school students’ (Cohen’s d = 0.49).

The positive correlations among homework creativity, homework completion, and general creativity

The first key finding should be noted is that the positive correlations with between pairs of homework creativity, homework completion, and general creativity. This result is inconsistent with prediction of an argument that homework diminishes creativity ( Cooper et al., 2012 ; Zheng, 2013 ). Specifically, the correlation between homework completion and curiosity was insignificant ( r = 0.08, p > 0.05) which did not support the argument that homework hurts curiosity of creativity ( Zheng, 2013 ). The possible reason may be homework can provide opportunities to foster some components of creativity by independently finding and developing new ways of understanding what students have learned in class, as Kaiipob (1951) argued. It may be the homework creativity that served as the way to practice the components of general creativity. In fact, the content of items of the HCBS are highly related with creative thinking (refer to Table 2 for details).

Possible reasons of the grade effect of the score of the homework creativity behavior scale

The second key finding should be noted is that the score of the HCBS decreased as the level of grades increased from 7 to 11. This is consistent with the basic trend recorded in the previous meta-analyses ( Kim, 2011 ; Said-Metwaly et al., 2021 ). There are three possible explanations leading to this grade effect. The first one is the repetitive exercises in homework. As Zheng (2013) observed, to get higher scores in the highly competitive entrance examination of high school and college, those Chinese students chose to practice a lot of repetitive exercises. The results of some behavior experiments suggested that repetitive activity could reduce the diverse thinking of subjects’ (e.g., Main et al., 2020 ). Furthermore, the repetitive exercises would lead to fast habituation (can be observed by skin conductance records) which hurts the creative thinking of participants ( Martindale et al., 1996 ). The second explanation is that the stress level in Chinese high schools is higher than in middle school because of the college entrance examination. The previous studies (e.g., Beversdorf, 2018 ) indicated that the high level of stress will trigger the increase activity of the noradrenergic system and the hypothalamic–pituitary–adrenal (HPA) axis which could debase the individual’s performance of creativity. Another likely explanation is the degree of the certainty of the college entrance examination. The level of certainty highly increases (success or failure) when time comes closer to the deadline of the entrance examination. The increase of degree of certainty will lead to the decrease of activity of the brain areas related to curiosity (e.g., Jepma et al., 2012 ).

The theoretical implications

From the theoretical perspective, there are two points deserving to be emphasized. First, the findings of this study extended the previous work ( Beghetto and Kaufman, 2007 ; Kaufman and Beghetto, 2009 ). This study revealed that homework creativity had two typical characteristics, including the personal meaning of students (as represented by the content of items of the HCBS) and the small size of “creativity” and limited in the scope of exercises (small correlations with general creativity). These characteristics are in line with what Mini-C described by the previous studies ( Beghetto and Kaufman, 2007 ; Kaufman and Beghetto, 2009 ). Second, this study deepened our understanding of the relationship between learning (homework is a part of learning) and creativity which has been discussed more than half a century. One of the main viewpoints is learning and creativity share some fundamental similarities, but no one explained what is the content of these “fundamental similarities” (e.g., Gajda et al., 2017 ). This study identified one similarity between learning and creativity in the context of homework, that is homework creativity. Homework creativity has the characteristics of homework and creativity at the same time which served as an inner factor in which homework promote creativity.

The practical implications

The findings in this study also have several potential practical implications. First, homework creativity should be a valuable goal of learning, because homework creativity may make contributions to academic achievement and general creativity simultaneously. They accounted for a total of 10.7% variance of academic achievement and general creativity which are the main goals of learning. Therefore, it is valuable to imbed homework creativity as a goal of learning, especially in the Chinese society ( Zheng, 2013 ).

Second, the items of the HCBS can be used as a vehicle to help students how to develop about homework creativity. Some studies indicated that the creative performance of students will improve just only under the simple requirement of “to be creative please” ( Niu and Sternberg, 2003 ). Similarly, some simple requirements, like “to do your homework in an innovative way,” “don’t stick to what you learned in class,” “to use a simpler method to do your homework,” “to use your imagination when you do homework,” “to design new problems on the basis what learnt,” “to find your own unique insights into your homework,” and “to find multiple solutions to the problem,” which rewritten from the items of the HCBS, can be used in the process of directing homework of students. In fact, these directions are typical behaviors of creative teaching (e.g., Soh, 2000 ); therefore, they are highly possible to be effective.

Third, the HCBS can be used to measure the degree of homework creativity in ordinary teaching or experimental situations. As demonstrated in the previous sections, the reliability and validity of the HCBS were good enough to play such a role. Based on this tool, the educators can collect the data of homework creativity, and make scientific decisions to improve the performance of people’s teaching or learning.

Strengths, limitations, and issues for further investigation

The main contribution is that this study accumulated some empirical knowledge about the relationship among homework creativity, homework completion, academic achievement, and general creativity, as well as the psychometric quality of the HCBS. However, the findings of this study should be treated with cautions because of the following limitations. First, our study did not collect the test–retest reliability of the HCBS. This makes it difficult for us to judge the HCBS’s stability over time. Second, the academic achievement data in our study were recorded by self-reported methods, and the objectivity may be more accurate. Third, the lower reliability coefficients existed in two dimensions employed, i.e., the arrange environment of the HMS (the α coefficient was 0.63), and the adventure of the WCAP (the α coefficient was 0.61). Fourth, the samples included here was not representative enough if we plan to generalize the finding to the population of middle and high school students in main land of China.

In addition to those questions listed as laminations, there are a number of issues deserve further examinations. (1) Can these findings from this study be generalized into other samples, especially into those from other cultures? For instances, can the reliability and validity of the HCBS be supported by the data from other samples? Or can the grade effect of the score of the HCBS be observed in other societies? Or can the correlation pattern among homework creativity, homework completion, and academic achievement be reproduced in other samples? (2) What is the role of homework creativity in the development of general creativity? Through longitudinal study, we can systematically observe the effect of homework creativity on individual’s general creativity, including creative skills, knowledge, and motivation. The micro-generating method ( Kupers et al., 2018 ) may be used to reveal how the homework creativity occurs in the learning process. (3) What factors affect homework creativity? Specifically, what effects do the individual factors (e.g., gender) and environmental factors (such as teaching styles of teachers) play in the development of homework creativity? (4) What training programs can be designed to improve homework creativity? What should these programs content? How about their effect on the development of homework creativity? What should the teachers do, if they want to promote creativity in their work situation? All those questions call for further explorations.

Homework is a complex thing which might have many aspects. Among them, homework creativity was the latest one being named ( Guo and Fan, 2018 ). Based on the testing of its reliability and validity, this study explored the relationships between homework creativity and academic achievement and general creativity, and its variation among different grade levels. The main findings of this study were (1) the eight-item version of the HCBS has good validity and reliability which can be employed in the further studies; (2) homework creativity had positive correlations with academic achievement and general creativity; (3) compared with homework completion, homework creativity made greater contribution to general creativity, but less to academic achievement; and (4) the score of homework creativity of high school students was lower than that of middle school students. Given that this is the first investigation, to our knowledge, that has systematically tapped into homework creativity, there is a critical need to pursue this line of investigation further.

Data availability statement

Ethics statement.

The studies involving human participants were reviewed and approved by the research ethic committee, School of Educational Science, Bohai University. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.

Author contributions

HF designed the research, collected the data, and interpreted the results. YM and SG analyzed the data and wrote the manuscript. HF, JX, and YM revised the manuscript. YC and HF prepared the HCBS. All authors read and approved the final manuscript.

Acknowledgments

We thank Dr. Liwei Zhang for his supports in collecting data, and Lu Qiao, Dounan Lu, Xiao Zhang for their helps in the process of inputting data.

This work was supported by the LiaoNing Revitalization Talents Program (grant no. XLYC2007134) and the Funding for Teaching Leader of Bohai University.

Conflict of Interest

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

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2022.923882/full#supplementary-material

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• Open access
• Published: 16 May 2024

How peer relationships affect academic achievement among junior high school students: The chain mediating roles of learning motivation and learning engagement

• Yanhong Shao 1 ,
• Shumin Kang 2 ,
• Quan Lu 3 ,
• Chao Zhang 2 &
• Ruoxi Li 4  

BMC Psychology volume  12 , Article number:  278 ( 2024 ) Cite this article

Metrics details

Despite the recognition of the impact of peer relationships, learning motivation, and learning engagement on academic achievement, there is still a gap in understanding the specific mechanisms through which peer relationships impact academic achievement via learning motivation and learning engagement.

This study aims to investigate how peer relationships affect junior high school students’ academic achievement through the chain mediating roles of learning motivation and learning engagement, employing the self-system model of motivational development as the theoretical framework. In January 2024, 717 participants were selected from two middle schools in eastern China (mean age = 13.49 years, SD = 0.5). The data analysis in this study was performed using the structural equation model (SEM) in AMOS 24.0 and SPSS 24.0.

The results showed that peer relationships were directly and significantly related to junior high school students’ academic achievement, and that peer relationships were indirectly and positively related to junior high school students’ academic achievement via learning motivation and learning engagement respectively. The results also revealed a significant indirect and positive relationship between peer relationships and junior high school students’ academic achievement, mediated by the sequential mediating roles of learning motivation and learning engagement. Moreover, the path “peer relationship→learning motivation→academic achievement” has the strongest indirect effect.

For junior high school students to achieve academic success, the appropriate interventions should be implemented to improve peer relationships, learning motivation, and learning engagement.

Peer Review reports

Introduction

Academic achievement is a multifaceted construct that can be defined in broad and narrow aspects. Marsh and McCallum defined it broadly as the extent to which students achieve the objectives or goals of their educational institution or program [ 1 ]. In contrast, Hattie defined it narrowly as the progress that students make in their academic studies, demonstrated through their performance on tests, exams, and other assessments [ 2 ]. Many researchers have adopted the narrow definition, focusing on test scores in specific subjects [ 3 , 4 , 5 ]. In China, academic achievement is often measured by test scores in Chinese, Math, and English [ 6 , 7 ]. Therefore, academic achievement in this study refers to students’ test scores in these subjects. Academic achievement holds substantial importance not only for students’ future prospects but also serves as a critical indicator for evaluating the effectiveness of national educational systems [ 8 ].

Peer relationships have been recognized as influential factors in adolescents’ academic achievement [ 9 ]. Peer relationships refer to the social interactions and connections that individuals establish with their peers, including interpersonal relationship, social emotion, communication interaction [ 10 ]. They can have a profound impact on students’ academic outcomes, as peers can serve as sources of both positive and negative influence. Positive peer relationships have been associated with higher levels of academic achievement, while negative peer relationships can hinder students’ academic progress [ 11 ].

Learning motivation and learning engagement are two psychological constructs that have been extensively studied in relation to academic achievement [ 12 ]. Learning motivation encompasses the internal drive and inclination to participate in learning activities, which can be classified into two main categories: intrinsic motivation and extrinsic motivation [ 13 ]. Intrinsic motivation stems from personal interest, curiosity, and the inherent satisfaction derived from the learning process itself, while extrinsic motivation is driven by external factors such as rewards, grades, or social recognition [ 14 ]. Learning engagement encompasses the active involvement, effort, and persistence that individuals exhibit during the learning process, categorized into three components: vigor, dedication, and absorption [ 15 ]. Vigor is often used to describe an individual’s level of enthusiasm, engagement, and persistence in their studies. Dedication refers to an individual’s commitment and devotion to their academic pursuits. Absorption refers to an individual’s deep focus and concentration on what is studied [ 16 ]. Both learning motivation and learning engagement have been found to exhibit a positive correlation with academic achievement. For example, Wentzel suggested that learning motivation plays a positive role in academic achievement [ 17 ]. Similarly, Li et al. observed a noteworthy positive association between academic motivation and mathematics achievement among junior high school students [ 18 ]. Liem and Martin posited that school engagement has a positive impact on academic performance [ 19 ]. The findings highlight the importance of considering both learning motivation and learning engagement in understanding academic achievement.

Despite scholars proposing the influence of these factors on academic achievement, the specific mechanisms through which peer relationships influence academic achievement via learning motivation and learning engagement remain underexplored. To address this research gap, the primary objective of the current study is to investigate the interactive effects of peer relationships, learning motivation, and learning engagement on academic achievement, thereby providing a holistic comprehension of the interplay between these factors. Furthermore, this study endeavors to examine the chain mediating roles of learning motivation and learning engagement in the association between peer relationships and academic achievement among junior high school students. By examining these mediating pathways, this study seeks to elucidate the underlying mechanisms by which peer relationships impact academic outcomes. This study differs from those in investigating the chain mediating roles of learning motivation and learning engagement in the association between peer relationships and academic achievement within a unified conceptual framework, contributing to a deeper understanding of the factors that shape students’ academic success.

The self-system model of motivational development (SSMMD) serves as a conceptual framework for this study. Proposed by Connell and Wellborn [ 20 ] and supported by Skinner et al. [ 21 ], the SSMMD is rooted in the self-determination theory [ 22 ] and emphasizes the importance of individuals’ intrinsic motivation and psychological needs for autonomy, competence, and relatedness [ 23 ]. The SSMMD comprises four interconnected components: social context, self-system, action, and developmental results. The social context, consisting of peers, teachers, and parents, shapes an individual’s self-system. It is within this social context that an individual’s self-beliefs, motivation, and engagement in activities are developed. The self-system, as a relatively stable personal resource, is influenced by long-term interactions with the surrounding context and can effectively predict the level of involvement in activities. This level of involvement, in turn, directly influences various aspects of an individual’s development, including behavior and academic performance [ 24 ]. The SSMMD presents a linear developmental pathway, where the social context influences the self-system, which then influences actions and subsequently developmental outcomes. In this study, we utilize the SSMMD framework to explore the relationship between peer relationships, learning motivation, learning engagement, and academic achievement. The relationship between the four variables and SSMMD can be elaborated as follows: Peer relationships, as a component of the social context, shapes an individual’s self-beliefs, which significantly influences their learning motivation. Students who possess higher levels of learning motivation are more likely to get active engagement in learning activities (as a component of the action), and impact their academic achievement positively (as a developmental outcome) [ 25 ]. Based on this model, this study hypothesizes that peer relationships (as a social context factor) may influence adolescents’ learning motivation (as a self-system factor), which in turn affects their learning engagement (as individual action), ultimately resulting in a positive impact on academic achievement (as developmental outcomes). This theoretical model in the study is visually represented in Fig.  1 .

The proposed theoretical model

Peer relationships and academic achievement

Previous research has consistently demonstrated the positive influence of peer relationships on academic achievement [ 26 ]. Several studies have examined the positive impact of peer relationships on overall academic achievement. For instance, Wentzel noted that peers’ support in homework was positively related to academic achievement [ 17 ]. Jacobson and Burdsal found that positive peer influence in middle schools predicted higher academic achievement [ 27 ]. In a longitudinal study, Gallardo et al. (2016) demonstrated the positive influence of peer relationships on mid-adolescents’ academic achievement [ 11 ]. Additionally, research has investigated the positive effects of peer relationships on academic achievement in specific subjects. For example, Li et al. reported a significantly positive effect of peer relationships on the mathematics achievement of junior high school students [ 18 ]. Li et al. (2020) identified a significantly positive connection between peer relationships and science literacy among 596 ethnic minority junior school students in China [ 28 ]. Moreover, previous studies have suggested that the positive impact of peer relationships on academic achievement increases with grade level [ 29 ] and that same-gender peer relationships are particularly important in predicting academic achievement [ 19 ]. Overall, these findings emphasize the critical role of positive peer relationships in academic achievement, highlighting that adolescents who cultivate supportive relationships with their peers are more inclined to achieve success in their academic pursuits. On the basis of this, the following hypothesis is proposed.

H1: Peer relationships are positively correlated with academic achievement.

Learning motivation as a mediator

Peer relationships have been demonstrated to have a significant influence on learning motivation [ 11 ]. Positive peer relationships can enhance students’ motivation in learning by providing support, encouragement, and a sense of belonging. For example, Li et al. have indicated that positive peer relationships could encourage students to strive towards predetermined learning goals [ 30 ]. Similarly, Kuo et al. have shown that regular peer interaction could increase students’ motivation and interest in learning [ 31 ]. Wentzel et al. conducted a questionnaire survey involving 240 participants, and found that adolescents who receive positive support from their peers are more prone to exhibit higher levels of motivation [ 32 ]. In a study by Huangfu et al. it was observed that peer support in the context of chemistry education had a significant positive impact on students’ continuing motivation in chemistry [ 33 ]. Conversely, negative peer relationships can lead to decreased motivation. For instance, Juvonen and Graham found that students who experienced bullying, as a form of negative peer relationship, reported lower levels of motivation to engage in academic tasks [ 34 ]. Similarly, Wentzel et al. revealed that peer rejection, as another form of negative peer relationship, was associated with lower levels of intrinsic motivation in students [ 35 ]. These finding underscore the crucial role of peer relationships in influencing students’ motivation in specific academic domains.

Furthermore, learning motivation has been found to have a positive correlation with academic achievement [ 36 ]. Students who possess high levels of motivation to learn tend to excel in classroom activities, put forth great effort to complete their learning assignments, and achieve their academic achievement [ 37 ]. Researchers have demonstrated that learning motivation, as a potential mechanism is associated with perceived academic achievement [ 38 ]. Moreover, intrinsic motivation has been found to have a positive correlation with students’ grades, while extrinsic motivation shows a negative association with academic outcomes [ 39 ]. In addition, researchers have shown that learning motivation exerts both direct and indirect influences on students’ academic achievement through learning activities [ 40 ]. Peer interactions have also been emphasized as influential factors in adolescent learning motivation and subsequent learning outcomes [ 41 ]. Li et al. highlighted the mediating role of learning motivation in the relationship between peer relationships and mathematics achievement [ 18 ]. Although the study focused on Zhuang ethnic minority students in China and limited the academic achievement to mathematics, it provides valuable insights and direction for the mediation hypothesis in this research. Based on these findings, the following assumptions are proposed:

H2: Peer relationships are positively correlated with learning motivation.

H3: Learning motivation is positively correlated with academic achievement.

H4: Learning motivation mediates the association between peer relationships and junior high school students’ academic achievement.

Learning engagement as a mediator

Research has consistently shown that peer relationships have an impact on students’ learning engagement [ 42 ]. For instance, Kiefer et al. have proposed that peer support may help middle school students improve their learning engagement [ 43 ]. Besides, Research has demonstrated that both academic and emotional support from peers can enhance students’ learning engagement [ 44 ]. Lee et al. have claimed that peer interaction can help students sustain their engagement in e-learning [ 45 ]. In addition, Yuan and Kim have suggested that peer appraisal in peer interactions can affect teenagers’ cognitive and emotional involvement [ 46 ].

Learning engagement is considered to be an important factor that affects students’ academic achievement [ 12 ]. High levels of learning engagement allow students to devote more time to learning activities and ultimately achieve better academic outcomes [ 47 ]. Liem and Martin found that active participation and investment in learning activities positively predict academic success [ 19 ]. Wang et al. further supported this by demonstrating that higher levels of classroom engagement are associated with better academic performance [ 4 ]. Additionally, Saqr et al. highlighted the longitudinal effects of engagement, showing that sustained high levels of engagement lead to improved academic outcomes over time [ 48 ]. Taken together, these recent studies underscore the critical role of student engagement in fostering academic achievement.

Learning motivation has been demonstrated to have a significant impact on students’ engagement in learning activities [ 49 ]. When students are motivated to learn, they are more likely to set ambitious goals and actively participate in their learning activities [ 50 ]. Research has consistently found a positive relationship between learning motivation and engagement [ 25 , 41 ]. For instance, a study by Froiland and Worrell explored the role of motivation in student engagement and found that intrinsic motivation, which stems from personal interest and enjoyment, was positively associated with higher levels of engagement [ 51 ]. Similarly, a study by Huang and Yang highlighted the importance of learning motivation, where students feel a sense of desire and enjoyment in their learning, in promoting engagement [ 52 ]. The self-system model of motivational development suggests that social contexts, including interactions with peers, can impact students’ self-systems, such as their motivation and self-efficacy in learning. When students’ self-systems, including learning motivation, are strengthened, they are more likely to engage in learning activities, leading to improved academic outcomes, such as academic achievement. Therefore, based on the aforementioned research, it is postulated that peer relationships can promote academic achievement by enhancing students’ motivation and engagement in learning activities. Hypotheses were derived from the aforesaid analysis:

H5: Peer relationships are positively correlated with learning engagement.

H6: Learning motivation is positively correlated with learning engagement.

H7: Learning engagement is positively correlated with academic achievement.

H8: Learning engagement mediates the association between peer relationships and junior high school students’ academic achievement.

H9: Learning motivation and learning engagement play a chain mediating role in the association between peer relationships and junior high school students’ academic achievement.

Materials and methods

Sampling and data collection.

Prior to conducting the survey, ethical approval and support were obtained from the Ethics Committee of Qufu Normal University. To ensure the privacy and confidentiality of the students, several measures were implemented. Firstly, the personal identification information of the students was anonymized, with the utilization of student ID numbers instead of real names on the questionnaire. Secondly, explicit assurances were provided to the participants that designated members of the research team would have access to and process the collected data. Lastly, strict adherence to legal regulations and ethical guidelines was maintained throughout the entire research process.

The sample size for the study was determined based on the guidelines of Structural Equation Modeling (SEM), which recommend a sample size of at least ten times the total number of observed variables [ 53 ]. Consistent with this recommendation, a sample of 717 participants, aged 13–14 years old, was drawn from two middle schools in Jiangsu province, Eastern China, in January 2024. The two schools selected for this study, in that they exhibit diversity in terms of student backgrounds, academic performance, and socio-economic status, reflecting the overall characteristics of students in the region. The participants were randomly chosen from Grades 7 and 8.

Data collection consisted of two distinct steps. Firstly, paper questionnaires were distributed with an explanation of the study. Students were encouraged to participate in the study voluntarily and express their ideas freely. Those who did not provide informed consent or failed to complete the questionnaire were excluded from the analysis. Totally, 717 valid questionnaires were collected, with a response rate of 89.6%. Secondly, the students’ academic achievement was also collected as part of the study. Specifically, the study collected scores from the final exams in the subjects of Chinese, math, and English as a measure of participants’ academic achievement, and matched the students’ grades with their IDs. To ensure comparability and facilitate analysis across different subjects, the overall scores, ranging from 0 to 120 were standardized. These standardized scores were then utilized as the observational variables of academic achievement.

Research instruments

Peer relationship scale.

Peer relationships were measured by the Peer Relationship Scale developed by Wei [ 10 ]. This scale comprises 20 items, categorized into three dimensions: interpersonal relationship (e.g., “My classmates all enjoy being with me.”), social emotions (e.g., “When I am with my classmates, I feel very happy.”), communication interaction (e.g., “If I see my classmates feeling upset or crying, I will go comfort them.”). The 5-point Likert scale was used, with scores ranging from 1 to 5 indicating “strongly disagree” to “strongly agree”, with higher scores indicating higher peer relationships. The scale has good reliability and validity, which has been validated by recent research [ 54 ].

Learning motivation scale

Learning motivation was measured by the Learning Motivation Scale, developed by Amabile et al. [ 55 ], and later revised by Chi et al. [ 56 ]. This scale comprises 30 items, including two subscales for intrinsic motivation (e.g., “I enjoy independently thinking to solve difficult problems.”) and extrinsic motivation (e.g., “I care a lot about how others react to my opinions.”). The scale uses a 4-point rating, with scores ranging from 1 to 4, representing “strongly disagree” to “strongly agree”. Studies have demonstrated good reliability and validity of this scale among Chinese adolescents [ 49 ].

Learning engagement scale

Learning engagement was assessed by the scale revised by Fang et al. [ 57 ] based on the Utrecht Work Engagement Scale-Student (UWES-S) [ 58 ]. This scale comprises 17 items, including three dimensions: vigor (e.g., “I feel energized when studying.”), dedication (e.g., “When I study, I feel time flying.”), and absorption (e.g., “I take pride in my learning.”). The scale uses a 7-point rating, with scores ranging from 1 to 7, representing “Never” to “Always”. The scale demonstrated good reliability, which has been validated by An et al. [ 49 ]

• Academic achievement

Based on previous research [ 4 , 5 , 6 , 7 ], this study employed the final exam scores in Chinese, Mathematics, and English for grades 7 and 8 during the first semester as measures of academic achievement. A significant correlation was observed among the scores of these three subjects. Subsequently, the scores for each subject were standardized, and the average of these standardized scores was calculated as the overall indicator of academic achievement.

Statistical analysis

Data analysis was conducted using Amos 24.0 and SPSS 24.0. Initially, the Harman single-factor test was performed to explore the possibility of common method bias. Subsequently, descriptive analysis was carried out to provide an accurate portrayal of the sample’s characteristics. Then, a structural equation modeling (SEM) analysis was conducted to test both the measurement and structural models. The measurement model was assessed through confirmatory factor analysis, while the structural model was evaluated by analyzing goodness-of-fit indices and path coefficients. Lastly, the significance of mediating effects was determined using the bootstrapping approach.

Common method variance

To mitigate potential bias inherent in self-reported data obtained from junior high school students, the Harman single-factor test was conducted using SPSS 24.0 [ 59 ]. According to the test result, 11 factors exhibited characteristic roots exceeding 1, with the first factor accounting for 31.029% of the total variance, which fell below the critical threshold of 40% [ 60 ]. These findings suggest that no significant common method variance was present, indicating that the study’s reliability and validity were not substantially impacted.

Sample characteristics

The sample was composed of 717 participants selected from two middle schools in eastern China. The average age of participants was 13.49 years (SD = 0.5, range = 13–14 years). As indicated in Table  1 , the sample was gender-balanced, with males accounting for 50.1% and females accounting for 49.9%. The distribution of students across different grades was as follows: 53.7% in Grade Seven and 46.3% in Grade Eight. The majority of students resided in towns. Regarding the educational level of the participants’ fathers, 48.8% had completed junior high school or below, 36.8% had attended senior high school or vocational school, 8.9% had attended college, and 5.4% had attended university. Similarly, for the participants’ mothers, 51.9% had completed junior high school or below, 33.8% had attended senior high school or vocational school, 9.2% had graduated from colleges, and 5.2% had attended university.

Measurement model

The conventional approach to assessing a measurement model involves examining its reliability and validity [ 53 ]. In this study, the skewness of the 4 variables ranged from − 1.867 to 1.111, and the kurtosis ranged from − 0.351 to 3.512, which conforms to the normal distribution standards proposed by Hair et al. [ 61 ], providing a basis for the subsequent analysis. Reliability is commonly evaluated using Cronbach’s alpha, with coefficients from 0.80 to 0.89 considered acceptable. Convergent validity is evaluated through standardized factor loadings, composite reliability (CR), and average variance extracted (AVE), where values exceeding 0.5 are deemed acceptable [ 62 ]. Discriminant validity is assessed by comparing the square root value of AVE with the correlation coefficient value between constructs. It is generally expected that the square root value of AVE will exceed the correlation coefficient value [ 63 ].

Table  2 presents the results of the reliability and convergent validity analysis. The measurement model demonstrated acceptable reliability, as indicated by Cronbach’s alpha coefficients ranging from 0.839 to 0.961. Additionally, the standardized factor loadings ranged from 0.762 to 0.922, while the composite reliability (CR) and average variance extracted (AVE) values ranged from 0.835 to 0.937 and from 0.678 to 0.832, respectively, indicating acceptable convergent validity. Table  3 shows that the square root values of AVE for each construct were larger than the correlation coefficient values between the other constructs, indicating acceptable discriminant validity.

Structural model

The structural model was evaluated using the goodness-of-fit indices and path coefficients. Jackson et al. have suggested that a structural model fits the data when the goodness-of-fit index is between 1 and 3 for x 2 / df, greater than 0.9 for GFI, AGFI, NFI, TLI, and CFI, less than 0.08 for SMSEA [ 64 ]. Table  4 displays the following fit indices: X 2 / df = 1.142 (X 2  = 2663.1543, df = 2331), GFI = 0.946, AGFI = 0.942, CFI = 0.993, TII = 0.993, NFI = 0.946. All the values met the recommended thresholds, indicating a good fit for the structural model. Additionally, sensitivity analysis indicated that the effect size was 0.49, meeting the threshold proposed by Cohen [ 65 ] for a strong statistical test with a sample size of 717.

Hypothesis test

As depicted in Table  5 , the results revealed a significant and positive association between peer relationships and academic achievement (β =  0.178 , P  < 0.001), providing support for H1. A significant and positive correlation was observed between peer relationships and learning motivation (β =  0.534 , P  < 0.001 ), conforming H2. Learning motivation was found to have a significant and positive impact on academic achievement (β =  0.181, P  <  0.001 ), thus supporting H3. Peer relationships exhibited a significant and positive influence on learning engagement (β =  0.183 , P  < 0.001 ), providing support for H5. Learning motivation had a significant and positive effect on learning engagement (β =  0.224 , P  < 0.001 ), thus H6 was supported. Learning engagement demonstrated a significant and positive impact on academic achievement (β =  0.217 , P  < 0.001 ), providing support for hypothesis H7. Overall, the empirical data supported the expected directions of H1, H2, H3, H5, H6, and H7, indicating the significance of these relationships.

Analyses of the mediating effect of peer relationship on academic achievement

In this study, Structural Equation Modeling (SEM) was employed as the statistical technique to examine the mediating effect of learning motivation and learning engagement. SEM is considered more appropriate for examining mediation [ 66 ]. To determine the confidence intervals for the mediation effects in SEM, the bootstrap method was utilized [ 67 ]. Specifically, a mediating effect is considered statistically significant when the 95% bias-corrected confidence intervals (95% bias-corrected CI)does not include 0, and t exceeds 1.96 [ 68 ]. For data analysis, Amos 24.0 software was utilized. In this analysis, academic accomplishment was considered as the dependent variable, while peer relationship was treated as the independent variable. Additionally, learning motivation and learning engagement were regarded as mediating variables. To enhance the reliability of our results, a bootstrap resample size of 5000 was utilized, and the bias-corrected confidence interval level was set at 95%.

The results indicated in Table  6 demonstrate the statistical significance of the total effect and direct effect of peer relationships on academic achievement. The total effect of peer relationships on academic achievement was 2.510 (t = 6.213, 95% bias-corrected CI [1.745, 3.309], P  < 0.01), while the direct effect was 1.313 (t = 3.712, 95% bias-corrected CI [0.487, 2.178], P  < 0.01). Furthermore, the analysis revealed significant indirect effects in three pathways. The pathway of peer relationships→learning motivation→learning engagement→academic achievement had an indirect effect of 0.191 (t = 2.653, 95% bias-corrected CI [0.076, 0.365], P  < 0.01). The pathway of peer relationships→learning motivation→learning engagement had an indirect effect of 0.713 (t = 2.493,95% bias-corrected CI [0.193, 1.326], P  < 0.01). Lastly, the pathway of peer relationships→learning engagement→academic achievement had an indirect effect of 0.293 (t = 2.307, 95% bias-corrected CI [0.081, 0.585], P  < 0.01). These results indicate that the three mediating effects were all statistically significant, providing support for H4, H8, and H9.

In addition, the indirect effect percentage of learning motivation and learning engagement as partial mediators were examined. As indicated in Table  6 , among the three significant indirect mediators, the indirect effect of learning motivation accounts for 59.5% of the total indirect effect, while the indirect effect of learning engagement accounts for 24.5% of the total indirect effect. Besides, the indirect effect of earning motivation and learning engagement accounts for 16% of the total indirect effect. The pathway “peer relationships → learning motivation → academic achievement” exhibited the strongest effect. The specific pathways of peer relationship acting on academic achievement through learning motivation and learning engagement are detailed in Fig.  2 .

The path diagram, *** p  <  0.001

This study aimed to examine the interactive effects of peer relationships, learning motivation, learning engagement, and academic achievement among junior high school students. Additionally, the study sought to investigate the potential mediating roles of learning motivation and learning engagement in the association between peer relationships and academic achievement within this specific context. The study tentatively demonstrated the applicability of SSMMD in explaining the factors influencing academic achievement in junior high school settings. The findings of the study are presented below.

The results of the study revealed a direct and positive association between peer relationships and academic achievement among junior high school students. This finding not only confirms the research result of Jacobson and Burdsal [ 27 ], and that of Gallardo et al. [ 11 ], showing a positive correlation between peer relationships and academic achievement among middle school students but also reflects the idea presented by Escalante et al. [ 69 ] that academic achievement is affected by school climate, of which peer relationships are the dominant factor. This finding can be attributed to the notion that junior high school students in China who have stronger peer relationships within their school environment may receive greater support in their learning endeavors. This increased support may help alleviate learning-related stress, bolster their confidence levels, and enhance their self-esteem, thereby contributing to improved academic performance [ 26 ]. Additionally, it is noteworthy that peer influence exerts a substantial impact on shaping students’ academic behavior. For instance, students may observe their peers’ self-regulated behavior and diligence and be inclined to imitate them, thereby adopting similar study habits and strategies [ 70 ]. This study further demonstrates that peer relationships are a predictive factor of academic achievement.

The results of the study indicated that learning motivation partially mediated the association between peer relationships and academic achievement among Chinese middle school students. The finding builds upon previous research conducted by Wentzel [ 17 ], as it further elucidates the mediating role of learning motivation as a mediator between peer relationships and academic achievement among junior high school students. This finding can be explained by the increased reliance on peers for support and guidance, particularly after transitioning to junior high school. In Chinese culture, where collective values and social harmony are emphasized, peer relationships serve as a crucial source of support and guidance for students [ 71 ]. This heightened interaction with peers positively influences their learning attitude and personal values [ 72 ]. Consequently, this positive influence on learning attitudes and personal values contributes to the enhancement of learning motivation, ultimately leading to improved academic achievements among junior high school students. Additionally, the study’s results indicated the most substantial mediating role of learning motivation, supporting the notion that motivation is a more critical contributor to academic achievement [ 25 ]. This finding provides further evidence of the significant role of learning motivation in mediating the correlation between peer relationships and junior high school students’ academic achievement.

The results of the study demonstrated that learning engagement also partially mediated the association between peer relationships and academic achievement among junior high school students. This suggests that a high level of learning engagement can help elucidate why junior high school students who foster positive relationships with their peers tend to exhibit improved academic performance. When students have positive peer relationships, their increased learning engagement is reflected in their active participation in class, eagerness to complete assignments, and proactive pursuit of additional learning opportunities, ultimately leading to enhanced academic achievement [ 19 ]. This finding aligns with prior research [ 73 , 74 ], which postulates that learning engagement is a pivotal factor linking peer relationships and junior high school students’ academic achievement. The connections that teenagers forge with their contemporaries will facilitate increased participation in the educational process, which in turn will lead to enhanced academic performance [ 75 ]. The finding provided more evidence that learning engagement plays a significant role in the link between peer relationships and academic achievement.

The study further revealed that learning motivation and learning engagement played a chain mediation role in the association between peer relationships and academic achievement, which is one of the most astonishing conclusions drawn from the investigation. This result aligns with the self-system model of motivational development [ 20 ], which suggests that positive interactions and support from peers contribute to the development of individuals’ learning motivation. This motivation, in turn, influences their level of learning engagement, leading to improved academic achievement. Furthermore, the study revealed that junior high school students’ learning motivation contributed less to their level of learning engagement (β = 0.244, P  < 0.001) than their peer relationships (β = 0.183, P  < 0.001). This suggests that junior high school students’ primary source of learning engagement was learning motivation, because motivation plays a crucial role in driving their interest, effort, and persistence in academic tasks [ 49 ].

The theoretical and practical implications

This study holds significant theoretical implications. Firstly, it un derscores the complex interplay between peer relationships, learning motivation, learning engagement, and academic achievement. This expands our understanding of the underlying mechanisms that link these variables together. Secondly, it provides empirical support for the self-system model of motivational development, which suggests that peer relationships have an indirect influence on academic achievement through the mediating roles of learning motivation and learning engagement. This highlights the significance of social factors in shaping students’ motivation and engagement in the learning process.

This study carries practical implications for educators. Firstly, fostering positive peer relationships should be prioritized in educational settings. Teachers should implement strategies to promote a supportive and external classroom environment, such as peer mentoring programs or cooperative learning activities. Besides, teachers should create an inclusive and internal classroom environment that values diversity and promotes respect, empathy, and cooperation. By enhancing positive interactions among students, the motivation and engagement of individuals can be positively influenced, leading to improved academic achievement. Secondly, interventions targeting learning motivation and learning engagement should be implemented. Regarding learning motivation, teachers should encourage students to participate in problem-solving activities that connect learning to students’ lives and experiences, and motivate students to embrace challenges and solve problems [ 76 ]. Furthermore, teachers should provide timely and constructive feedback that helps students monitor their learning progress and adjust their strategies accordingly to foster students’ sense of intrinsic motivation. Additionally, teachers should understand the pressures students face in the learning process and provide appropriate support and strategies, such as offering flexible deadlines and providing alternative assignments. To enhance learning engagement, teachers should strive to gain a deeper understanding of teenagers’ needs and employ tactics and skills that strengthen their commitment to learning through meaningful classroom activities. Additionally, emotional support should be provided to help prevent learning fatigue and promote a positive attitude toward the learning process.

This study contributes to the literature in two ways. Firstly, it investigates the complex relationships among peer relationships, learning motivation, learning engagement, and academic achievement utilizing the self-system model of motivational development, which may provide insights for future research in other countries. Secondly, it explores the mediating mechanism between peer relationships and junior high school students’ academic achievement through examining the roles of learning motivation and learning engagement. The novel perspective can enrich our understanding of the link between peer relationships and academic achievement among junior high school students.

Limitations and future research directions

There are some limitations that should be acknowledged. Firstly, the study was carried out in a cross-sectional manner, making it difficult to establish a causal relationship between variables. Therefore, future longitudinal research is needed to investigate the association between peer relationships and academic achievement more conclusively. Secondly, this study was conducted within the context of China’s test-oriented learning environment, which may limit the generalizability of the findings to other educational settings. To enhance the external validity of the study, future research should be conducted in different countries. Thirdly, the study did not account for potential confounding factors such as academic pressure and self-evaluation, which may also influence academic achievement. Future research should consider these factors within a comprehensive theoretical framework. Finally, apart from academic achievement, all other variables were self-reported by participants, which may introduce potential bias. Future studies could benefit from incorporating observational data from parents, teachers, and classmates to provide a more objective perspective.

Data availability

The datasets generated and/or analysed during the current study are not publicly available due to ethical issues but are available from the corresponding author on reasonable request.

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Shao, Y., Kang, S., Lu, Q. et al. How peer relationships affect academic achievement among junior high school students: The chain mediating roles of learning motivation and learning engagement. BMC Psychol 12 , 278 (2024). https://doi.org/10.1186/s40359-024-01780-z

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New advances in technology are upending education, from the recent debut of new artificial intelligence (AI) chatbots like ChatGPT to the growing accessibility of virtual-reality tools that expand the boundaries of the classroom. For educators, at the heart of it all is the hope that every learner gets an equal chance to develop the skills they need to succeed. But that promise is not without its pitfalls.

“Technology is a game-changer for education – it offers the prospect of universal access to high-quality learning experiences, and it creates fundamentally new ways of teaching,” said Dan Schwartz, dean of Stanford Graduate School of Education (GSE), who is also a professor of educational technology at the GSE and faculty director of the Stanford Accelerator for Learning . “But there are a lot of ways we teach that aren’t great, and a big fear with AI in particular is that we just get more efficient at teaching badly. This is a moment to pay attention, to do things differently.”

For K-12 schools, this year also marks the end of the Elementary and Secondary School Emergency Relief (ESSER) funding program, which has provided pandemic recovery funds that many districts used to invest in educational software and systems. With these funds running out in September 2024, schools are trying to determine their best use of technology as they face the prospect of diminishing resources.

Here, Schwartz and other Stanford education scholars weigh in on some of the technology trends taking center stage in the classroom this year.

AI in the classroom

In 2023, the big story in technology and education was generative AI, following the introduction of ChatGPT and other chatbots that produce text seemingly written by a human in response to a question or prompt. Educators immediately worried that students would use the chatbot to cheat by trying to pass its writing off as their own. As schools move to adopt policies around students’ use of the tool, many are also beginning to explore potential opportunities – for example, to generate reading assignments or coach students during the writing process.

AI can also help automate tasks like grading and lesson planning, freeing teachers to do the human work that drew them into the profession in the first place, said Victor Lee, an associate professor at the GSE and faculty lead for the AI + Education initiative at the Stanford Accelerator for Learning. “I’m heartened to see some movement toward creating AI tools that make teachers’ lives better – not to replace them, but to give them the time to do the work that only teachers are able to do,” he said. “I hope to see more on that front.”

He also emphasized the need to teach students now to begin questioning and critiquing the development and use of AI. “AI is not going away,” said Lee, who is also director of CRAFT (Classroom-Ready Resources about AI for Teaching), which provides free resources to help teach AI literacy to high school students across subject areas. “We need to teach students how to understand and think critically about this technology.”

Immersive environments

The use of immersive technologies like augmented reality, virtual reality, and mixed reality is also expected to surge in the classroom, especially as new high-profile devices integrating these realities hit the marketplace in 2024.

The educational possibilities now go beyond putting on a headset and experiencing life in a distant location. With new technologies, students can create their own local interactive 360-degree scenarios, using just a cell phone or inexpensive camera and simple online tools.

“This is an area that’s really going to explode over the next couple of years,” said Kristen Pilner Blair, director of research for the Digital Learning initiative at the Stanford Accelerator for Learning, which runs a program exploring the use of virtual field trips to promote learning. “Students can learn about the effects of climate change, say, by virtually experiencing the impact on a particular environment. But they can also become creators, documenting and sharing immersive media that shows the effects where they live.”

Integrating AI into virtual simulations could also soon take the experience to another level, Schwartz said. “If your VR experience brings me to a redwood tree, you could have a window pop up that allows me to ask questions about the tree, and AI can deliver the answers.”

Gamification

Another trend expected to intensify this year is the gamification of learning activities, often featuring dynamic videos with interactive elements to engage and hold students’ attention.

“Gamification is a good motivator, because one key aspect is reward, which is very powerful,” said Schwartz. The downside? Rewards are specific to the activity at hand, which may not extend to learning more generally. “If I get rewarded for doing math in a space-age video game, it doesn’t mean I’m going to be motivated to do math anywhere else.”

Gamification sometimes tries to make “chocolate-covered broccoli,” Schwartz said, by adding art and rewards to make speeded response tasks involving single-answer, factual questions more fun. He hopes to see more creative play patterns that give students points for rethinking an approach or adapting their strategy, rather than only rewarding them for quickly producing a correct response.

Data-gathering and analysis

The growing use of technology in schools is producing massive amounts of data on students’ activities in the classroom and online. “We’re now able to capture moment-to-moment data, every keystroke a kid makes,” said Schwartz – data that can reveal areas of struggle and different learning opportunities, from solving a math problem to approaching a writing assignment.

But outside of research settings, he said, that type of granular data – now owned by tech companies – is more likely used to refine the design of the software than to provide teachers with actionable information.

The promise of personalized learning is being able to generate content aligned with students’ interests and skill levels, and making lessons more accessible for multilingual learners and students with disabilities. Realizing that promise requires that educators can make sense of the data that’s being collected, said Schwartz – and while advances in AI are making it easier to identify patterns and findings, the data also needs to be in a system and form educators can access and analyze for decision-making. Developing a usable infrastructure for that data, Schwartz said, is an important next step.

With the accumulation of student data comes privacy concerns: How is the data being collected? Are there regulations or guidelines around its use in decision-making? What steps are being taken to prevent unauthorized access? In 2023 K-12 schools experienced a rise in cyberattacks, underscoring the need to implement strong systems to safeguard student data.

Technology is “requiring people to check their assumptions about education,” said Schwartz, noting that AI in particular is very efficient at replicating biases and automating the way things have been done in the past, including poor models of instruction. “But it’s also opening up new possibilities for students producing material, and for being able to identify children who are not average so we can customize toward them. It’s an opportunity to think of entirely new ways of teaching – this is the path I hope to see.”