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Why Students Cheat—and What to Do About It

A teacher seeks answers from researchers and psychologists. 

“Why did you cheat in high school?” I posed the question to a dozen former students.

“I wanted good grades and I didn’t want to work,” said Sonya, who graduates from college in June. [The students’ names in this article have been changed to protect their privacy.]

My current students were less candid than Sonya. To excuse her plagiarized Cannery Row essay, Erin, a ninth-grader with straight As, complained vaguely and unconvincingly of overwhelming stress. When he was caught copying a review of the documentary Hypernormalism , Jeremy, a senior, stood by his “hard work” and said my accusation hurt his feelings.

Cases like the much-publicized ( and enduring ) 2012 cheating scandal at high-achieving Stuyvesant High School in New York City confirm that academic dishonesty is rampant and touches even the most prestigious of schools. The data confirms this as well. A 2012 Josephson Institute’s Center for Youth Ethics report revealed that more than half of high school students admitted to cheating on a test, while 74 percent reported copying their friends’ homework. And a survey of 70,000 high school students across the United States between 2002 and 2015 found that 58 percent had plagiarized papers, while 95 percent admitted to cheating in some capacity.

So why do students cheat—and how do we stop them?

According to researchers and psychologists, the real reasons vary just as much as my students’ explanations. But educators can still learn to identify motivations for student cheating and think critically about solutions to keep even the most audacious cheaters in their classrooms from doing it again.

Rationalizing It


First, know that students realize cheating is wrong—they simply see themselves as moral in spite of it.

“They cheat just enough to maintain a self-concept as honest people. They make their behavior an exception to a general rule,” said Dr. David Rettinger , professor at the University of Mary Washington and executive director of the Center for Honor, Leadership, and Service, a campus organization dedicated to integrity.

According to Rettinger and other researchers, students who cheat can still see themselves as principled people by rationalizing cheating for reasons they see as legitimate.

Some do it when they don’t see the value of work they’re assigned, such as drill-and-kill homework assignments, or when they perceive an overemphasis on teaching content linked to high-stakes tests.

“There was no critical thinking, and teachers seemed pressured to squish it into their curriculum,” said Javier, a former student and recent liberal arts college graduate. “They questioned you on material that was never covered in class, and if you failed the test, it was progressively harder to pass the next time around.”

But students also rationalize cheating on assignments they see as having value.

High-achieving students who feel pressured to attain perfection (and Ivy League acceptances) may turn to cheating as a way to find an edge on the competition or to keep a single bad test score from sabotaging months of hard work. At Stuyvesant, for example, students and teachers identified the cutthroat environment as a factor in the rampant dishonesty that plagued the school.

And research has found that students who receive praise for being smart—as opposed to praise for effort and progress—are more inclined to exaggerate their performance and to cheat on assignments , likely because they are carrying the burden of lofty expectations.

A Developmental Stage

When it comes to risk management, adolescent students are bullish. Research has found that teenagers are biologically predisposed to be more tolerant of unknown outcomes and less bothered by stated risks than their older peers.

“In high school, they’re risk takers developmentally, and can’t see the consequences of immediate actions,” Rettinger says. “Even delayed consequences are remote to them.”

While cheating may not be a thrill ride, students already inclined to rebel against curfews and dabble in illicit substances have a certain comfort level with being reckless. They’re willing to gamble when they think they can keep up the ruse—and more inclined to believe they can get away with it.

Cheating also appears to be almost contagious among young people—and may even serve as a kind of social adhesive, at least in environments where it is widely accepted.  A study of military academy students from 1959 to 2002 revealed that students in communities where cheating is tolerated easily cave in to peer pressure, finding it harder not to cheat out of fear of losing social status if they don’t.

Michael, a former student, explained that while he didn’t need to help classmates cheat, he felt “unable to say no.” Once he started, he couldn’t stop.

Technology Facilitates and Normalizes It

With smartphones and Alexa at their fingertips, today’s students have easy access to quick answers and content they can reproduce for exams and papers.  Studies show that technology has made cheating in school easier, more convenient, and harder to catch than ever before.

To Liz Ruff, an English teacher at Garfield High School in Los Angeles, students’ use of social media can erode their understanding of authenticity and intellectual property. Because students are used to reposting images, repurposing memes, and watching parody videos, they “see ownership as nebulous,” she said.

As a result, while they may want to avoid penalties for plagiarism, they may not see it as wrong or even know that they’re doing it.

This confirms what Donald McCabe, a Rutgers University Business School professor,  reported in his 2012 book ; he found that more than 60 percent of surveyed students who had cheated considered digital plagiarism to be “trivial”—effectively, students believed it was not actually cheating at all.

Strategies for Reducing Cheating

Even moral students need help acting morally, said  Dr. Jason M. Stephens , who researches academic motivation and moral development in adolescents at the University of Auckland’s School of Learning, Development, and Professional Practice. According to Stephens, teachers are uniquely positioned to infuse students with a sense of responsibility and help them overcome the rationalizations that enable them to think cheating is OK.

1. Turn down the pressure cooker. Students are less likely to cheat on work in which they feel invested. A multiple-choice assessment tempts would-be cheaters, while a unique, multiphase writing project measuring competencies can make cheating much harder and less enticing. Repetitive homework assignments are also a culprit, according to research , so teachers should look at creating take-home assignments that encourage students to think critically and expand on class discussions. Teachers could also give students one free pass on a homework assignment each quarter, for example, or let them drop their lowest score on an assignment.

2. Be thoughtful about your language.   Research indicates that using the language of fixed mindsets , like praising children for being smart as opposed to praising them for effort and progress , is both demotivating and increases cheating. When delivering feedback, researchers suggest using phrases focused on effort like, “You made really great progress on this paper” or “This is excellent work, but there are still a few areas where you can grow.”

3. Create student honor councils. Give students the opportunity to enforce honor codes or write their own classroom/school bylaws through honor councils so they can develop a full understanding of how cheating affects themselves and others. At Fredericksburg Academy, high school students elect two Honor Council members per grade. These students teach the Honor Code to fifth graders, who, in turn, explain it to younger elementary school students to help establish a student-driven culture of integrity. Students also write a pledge of authenticity on every assignment. And if there is an honor code transgression, the council gathers to discuss possible consequences. 

4. Use metacognition. Research shows that metacognition, a process sometimes described as “ thinking about thinking ,” can help students process their motivations, goals, and actions. With my ninth graders, I use a centuries-old resource to discuss moral quandaries: the play Macbeth . Before they meet the infamous Thane of Glamis, they role-play as medical school applicants, soccer players, and politicians, deciding if they’d cheat, injure, or lie to achieve goals. I push students to consider the steps they take to get the outcomes they desire. Why do we tend to act in the ways we do? What will we do to get what we want? And how will doing those things change who we are? Every tragedy is about us, I say, not just, as in Macbeth’s case, about a man who succumbs to “vaulting ambition.”

5. Bring honesty right into the curriculum. Teachers can weave a discussion of ethical behavior into curriculum. Ruff and many other teachers have been inspired to teach media literacy to help students understand digital plagiarism and navigate the widespread availability of secondary sources online, using guidance from organizations like Common Sense Media .

There are complicated psychological dynamics at play when students cheat, according to experts and researchers. While enforcing rules and consequences is important, knowing what’s really motivating students to cheat can help you foster integrity in the classroom instead of just penalizing the cheating.

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Why Students Cheat on Homework and How to Prevent It

One of the most frustrating aspects of teaching in today’s world is the cheating epidemic. There’s nothing more irritating than getting halfway through grading a large stack of papers only to realize some students cheated on the assignment. There’s really not much point in teachers grading work that has a high likelihood of having been copied or otherwise unethically completed. So. What is a teacher to do? We need to be able to assess students. Why do students cheat on homework, and how can we address it?

Like most new teachers, I learned the hard way over the course of many years of teaching that it is possible to reduce cheating on homework, if not completely prevent it. Here are six suggestions to keep your students honest and to keep yourself sane.

ASSIGN LESS HOMEWORK

One of the reasons students cheat on homework is because they are overwhelmed. I remember vividly what it felt like to be a high school student in honors classes with multiple extracurricular activities on my plate. Other teens have after school jobs to help support their families, and some don’t have a home environment that is conducive to studying.

While cheating is  never excusable under any circumstances, it does help to walk a mile in our students’ shoes. If they are consistently making the decision to cheat, it might be time to reduce the amount of homework we are assigning.

I used to give homework every night – especially to my advanced students. I wanted to push them. Instead, I stressed them out. They wanted so badly to be in the Top 10 at graduation that they would do whatever they needed to do in order to complete their assignments on time – even if that meant cheating.

When assigning homework, consider the at-home support, maturity, and outside-of-school commitments involved. Think about the kind of school and home balance you would want for your own children. Go with that.

PROVIDE CLASS TIME

Allowing students time in class to get started on their assignments seems to curb cheating to some extent. When students have class time, they are able to knock out part of the assignment, which leaves less to fret over later. Additionally, it gives them an opportunity to ask questions.

When students are confused while completing assignments at home, they often seek “help” from a friend instead of going in early the next morning to request guidance from the teacher. Often, completing a portion of a homework assignment in class gives students the confidence that they can do it successfully on their own. Plus, it provides the social aspect of learning that many students crave. Instead of fighting cheating outside of class , we can allow students to work in pairs or small groups  in class to learn from each other.

Plus, to prevent students from wanting to cheat on homework, we can extend the time we allow them to complete it. Maybe students would work better if they have multiple nights to choose among options on a choice board. Home schedules can be busy, so building in some flexibility to the timeline can help reduce pressure to finish work in a hurry.

GIVE MEANINGFUL WORK

If you find students cheat on homework, they probably lack the vision for how the work is beneficial. It’s important to consider the meaningfulness and valuable of the assignment from students’ perspectives. They need to see how it is relevant to them.

In my class, I’ve learned to assign work that cannot be copied. I’ve never had luck assigning worksheets as homework because even though worksheets have value, it’s generally not obvious to teenagers. It’s nearly impossible to catch cheating on worksheets that have “right or wrong” answers. That’s not to say I don’t use worksheets. I do! But. I use them as in-class station, competition, and practice activities, not homework.

So what are examples of more effective and meaningful types of homework to assign?

• Ask students to complete a reading assignment and respond in writing .
• Have students watch a video clip and answer an oral entrance question.
• Require that students contribute to an online discussion post.
• Assign them a reflection on the day’s lesson in the form of a short project, like a one-pager or a mind map.

As you can see, these options require unique, valuable responses, thereby reducing the opportunity for students to cheat on them. The more open-ended an assignment is, the more invested students need to be to complete it well.

DIFFERENTIATE

Part of giving meaningful work involves accounting for readiness levels. Whenever we can tier assignments or build in choice, the better. A huge cause of cheating is when work is either too easy (and students are bored) or too hard (and they are frustrated). Getting to know our students as learners can help us to provide meaningful differentiation options. Plus, we can ask them!

This is what you need to be able to demonstrate the ability to do. How would you like to show me you can do it?

REDUCE THE POINT VALUE

If you’re sincerely concerned about students cheating on assignments, consider reducing the point value. Reflect on your grading system.

Are homework grades carrying so much weight that students feel the need to cheat in order to maintain an A? In a standards-based system, will the assignment be a key determining factor in whether or not students are proficient with a skill?

Each teacher has to do what works for him or her. In my classroom, homework is worth the least amount out of any category. If I assign something for which I plan on giving completion credit, the point value is even less than it typically would be. Projects, essays, and formal assessments count for much more.

CREATE AN ETHICAL CULTURE

To some extent, this part is out of educators’ hands. Much of the ethical and moral training a student receives comes from home. Still, we can do our best to create a classroom culture in which we continually talk about integrity, responsibility, honor, and the benefits of working hard. What are some specific ways can we do this?

Building Community and Honestly

• Talk to students about what it means to cheat on homework. Explain to them that there are different kinds. Many students are unaware, for instance, that the “divide and conquer (you do the first half, I’ll do the second half, and then we will trade answers)” is cheating.
• As a class, develop expectations and consequences for students who decide to take short cuts.
• Decorate your room with motivational quotes that relate to honesty and doing the right thing.
• Discuss how making a poor decision doesn’t make you a bad person. It is an opportunity to grow.
• Share with students that you care about them and their futures. The assignments you give them are intended to prepare them for success.
• Offer them many different ways to seek help from you if and when they are confused.
• Provide revision opportunities for homework assignments.
• Explain that you partner with their parents and that guardians will be notified if cheating occurs.
• Explore hypothetical situations.  What if you have a late night? Let’s pretend you don’t get home until after orchestra and Lego practices. You have three hours of homework to do. You know you can call your friend, Bob, who always has his homework done. How do you handle this situation?

EDUCATE ABOUT PLAGIARISM

Many students don’t realize that plagiarism applies to more than just essays. At the beginning of the school year, teachers have an energized group of students, fresh off of summer break. I’ve always found it’s easiest to motivate my students at this time. I capitalize on this opportunity by beginning with a plagiarism mini unit .

While much of the information we discuss is about writing, I always make sure my students know that homework can be plagiarized. Speeches can be plagiarized. Videos can be plagiarized. Anything can be plagiarized, and the repercussions for stealing someone else’s ideas (even in the form of a simple worksheet) are never worth the time saved by doing so.

In an ideal world, no one would cheat. However, teaching and learning in the 21st century is much different than it was fifty years ago. Cheating? It’s increased. Maybe because of the digital age… the differences in morals and values of our culture…  people are busier. Maybe because students don’t see how the school work they are completing relates to their lives.

No matter what the root cause, teachers need to be proactive. We need to know why students feel compelled to cheat on homework and what we can do to help them make learning for beneficial. Personally, I don’t advocate for completely eliminating homework with older students. To me, it has the potential to teach students many lessons both related to school and life. Still, the “right” answer to this issue will be different for each teacher, depending on her community, students, and culture.

STRATEGIES FOR ADDRESSING CHALLENGING BEHAVIORS IN SECONDARY

You are so right about communicating the purpose of the assignment and giving students time in class to do homework. I also use an article of the week on plagiarism. I give students points for the learning – not the doing. It makes all the difference. I tell my students why they need to learn how to do “—” for high school or college or even in life experiences. Since, they get an A or F for the effort, my students are more motivated to give it a try. No effort and they sit in my class to work with me on the assignment. Showing me the effort to learn it — asking me questions about the assignment, getting help from a peer or me, helping a peer are all ways to get full credit for the homework- even if it’s not complete. I also choose one thing from each assignment for the test which is a motivator for learning the material – not just “doing it.” Also, no one is permitted to earn a D or F on a test. Any student earning an F or D on a test is then required to do a project over the weekend or at lunch or after school with me. All of this reinforces the idea – learning is what is the goal. Giving students options to show their learning is also important. Cheating is greatly reduced when the goal is to learn and not simply earn the grade.

Thanks for sharing your unique approaches, Sandra! Learning is definitely the goal, and getting students to own their learning is key.

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The Real Roots of Student Cheating

Let's address the mixed messages we are sending to young people..

Updated September 28, 2023 | Reviewed by Ray Parker

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• Cheating is rampant, yet young people consistently affirm honesty and the belief that cheating is wrong.
• This discrepancy arises, in part, from the tension students perceive between honesty and the terms of success.
• In an integrated environment, achievement and the real world are not seen as at odds with honesty.

The release of ChatGPT has high school and college teachers wringing their hands. A Columbia University undergraduate rubbed it in our face last May with an opinion piece in the Chronicle of Higher Education titled I’m a Student. You Have No Idea How Much We’re Using ChatGPT.

He goes on to detail how students use the program to “do the lion’s share of the thinking,” while passing off the work as their own. Catching the deception , he insists, is impossible.

As if students needed more ways to cheat. Every survey of students, whether high school or college, has found that cheating is “rampant,” “epidemic,” “commonplace, and practically expected,” to use a few of the terms with which researchers have described the scope of academic dishonesty.

In a 2010 study by the Josephson Institute, for example, 59 percent of the 43,000 high school students admitted to cheating on a test in the past year. According to a 2012 white paper, Cheat or Be Cheated? prepared by Challenge Success, 80 percent admitted to copying another student’s homework. The other studies summarized in the paper found self-reports of past-year cheating by high school students in the 70 percent to 80 percent range and higher.

At colleges, the situation is only marginally better. Studies consistently put the level of self-reported cheating among undergraduates between 50 percent and 70 percent depending in part on what behaviors are included. 1

The sad fact is that cheating is widespread.

Commitment to Honesty

Yet, when asked, most young people affirm the moral value of honesty and the belief that cheating is wrong. For example, in a survey of more than 3,000 teens conducted by my colleagues at the University of Virginia, the great majority (83 percent) indicated that to become “honest—someone who doesn’t lie or cheat,” was very important, if not essential to them.

On a long list of traits and qualities, they ranked honesty just below “hard-working” and “reliable and dependent,” and far ahead of traits like being “ambitious,” “a leader ,” and “popular.” When asked directly about cheating, only 6 percent thought it was rarely or never wrong.

Other studies find similar commitments, as do experimental studies by psychologists. In experiments, researchers manipulate the salience of moral beliefs concerning cheating by, for example, inserting moral reminders into the test situation to gauge their effect. Although students often regard some forms of cheating, such as doing homework together when they are expected to do it alone, as trivial, the studies find that young people view cheating in general, along with specific forms of dishonesty, such as copying off another person’s test, as wrong.

They find that young people strongly care to think of themselves as honest and temper their cheating behavior accordingly. 2

The Discrepancy Between Belief and Behavior

Bottom line: Kids whose ideal is to be honest and who know cheating is wrong also routinely cheat in school.

What accounts for this discrepancy? In the psychological and educational literature, researchers typically focus on personal and situational factors that work to override students’ commitment to do the right thing.

These factors include the force of different motives to cheat, such as the desire to avoid failure, and the self-serving rationalizations that students use to excuse their behavior, like minimizing responsibility—“everyone is doing it”—or dismissing their actions because “no one is hurt.”

While these explanations have obvious merit—we all know the gap between our ideals and our actions—I want to suggest another possibility: Perhaps the inconsistency also reflects the mixed messages to which young people (all of us, in fact) are constantly subjected.

Mixed Messages

Consider the story that young people hear about success. What student hasn’t been told doing well includes such things as getting good grades, going to a good college, living up to their potential, aiming high, and letting go of “limiting beliefs” that stand in their way? Schools, not to mention parents, media, and employers, all, in various ways, communicate these expectations and portray them as integral to the good in life.

They tell young people that these are the standards they should meet, the yardsticks by which they should measure themselves.

In my interviews and discussions with young people, it is clear they have absorbed these powerful messages and feel held to answer, to themselves and others, for how they are measuring up. Falling short, as they understand and feel it, is highly distressful.

At the same time, they are regularly exposed to the idea that success involves a trade-off with honesty and that cheating behavior, though regrettable, is “real life.” These words are from a student on a survey administered at an elite high school. “People,” he continued, “who are rich and successful lie and cheat every day.”

In this thinking, he is far from alone. In a 2012 Josephson Institute survey of 23,000 high school students, 57 percent agreed that “in the real world, successful people do what they have to do to win, even if others consider it cheating.” 3

Putting these together, another high school student told a researcher: “Grades are everything. You have to realize it’s the only possible way to get into a good college and you resort to any means necessary.”

In a 2021 survey of college students by College Pulse, the single biggest reason given for cheating, endorsed by 72 percent of the respondents, was “pressure to do well.”

What we see here are two goods—educational success and honesty—pitted against each other. When the two collide, the call to be successful is likely to be the far more immediate and tangible imperative.

A young person’s very future appears to hang in the balance. And, when asked in surveys , youths often perceive both their parents’ and teachers’ priorities to be more focused on getting “good grades in my classes,” than on character qualities, such as being a “caring community member.”

In noting the mixed messages, my point is not to offer another excuse for bad behavior. But some of the messages just don’t mix, placing young people in a difficult bind. Answering the expectations placed on them can be at odds with being an honest person. In the trade-off, cheating takes on a certain logic.

The proposed remedies to academic dishonesty typically focus on parents and schools. One commonly recommended strategy is to do more to promote student integrity. That seems obvious. Yet, as we saw, students already believe in honesty and the wrongness of (most) cheating. It’s not clear how more teaching on that point would make much of a difference.

Integrity, though, has another meaning, in addition to the personal qualities of being honest and of strong moral principles. Integrity is also the “quality or state of being whole or undivided.” In this second sense, we can speak of social life itself as having integrity.

It is “whole or undivided” when the different contexts of everyday life are integrated in such a way that norms, values, and expectations are fairly consistent and tend to reinforce each other—and when messages about what it means to be a good, accomplished person are not mixed but harmonious.

While social integrity rooted in ethical principles does not guarantee personal integrity, it is not hard to see how that foundation would make a major difference. Rather than confronting students with trade-offs that incentivize “any means necessary,” they would receive positive, consistent reinforcement to speak and act truthfully.

Talk of personal integrity is all for the good. But as pervasive cheating suggests, more is needed. We must also work to shape an integrated environment in which achievement and the “real world” are not set in opposition to honesty.

1. Liora Pedhazur Schmelkin, et al. “A Multidimensional Scaling of College Students’ Perceptions of Academic Dishonesty.” The Journal of Higher Education 79 (2008): 587–607.

2. See, for example, the studies in Christian B. Miller, Character and Moral Psychology. New York: Oxford University Press, 2014, Ch. 3.

3. Josephson Institute. The 2012 Report Card on the Ethics of American Youth (Installment 1: Honesty and Integrity). Josephson Institute of Ethics, 2012.

Joseph E. Davis is Research Professor of Sociology and Director of the Picturing the Human Colloquy of the Institute for Advanced Studies in Culture at the University of Virginia.

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Academic dishonesty when doing homework: How digital technologies are put to bad use in secondary schools

Juliette c. désiron.

Institute of Education, University of Zürich, Zürich, Switzerland

Dominik Petko

Associated data.

The data that support the findings of this study are openly available in SISS base at 10.23662/FORS-DS-1285-1, reference number 1285.

The growth in digital technologies in recent decades has offered many opportunities to support students’ learning and homework completion. However, it has also contributed to expanding the field of possibilities concerning homework avoidance. Although studies have investigated the factors of academic dishonesty, the focus has often been on college students and formal assessments. The present study aimed to determine what predicts homework avoidance using digital resources and whether engaging in these practices is another predictor of test performance. To address these questions, we analyzed data from the Program for International Student Assessment 2018 survey, which contained additional questionnaires addressing this issue, for the Swiss students. The results showed that about half of the students engaged in one kind or another of digitally-supported practices for homework avoidance at least once or twice a week. Students who were more likely to use digital resources to engage in dishonest practices were males who did not put much effort into their homework and were enrolled in non-higher education-oriented school programs. Further, we found that digitally-supported homework avoidance was a significant negative predictor of test performance when considering information and communication technology predictors. Thus, the present study not only expands the knowledge regarding the predictors of academic dishonesty with digital resources, but also confirms the negative impact of such practices on learning.

Introduction

Academic dishonesty is a widespread and perpetual issue for teachers made even more easier to perpetrate with the rise of digital technologies (Blau & Eshet-Alkalai, 2017 ; Ma et al., 2008 ). Definitions vary but overall an academically dishonest practices correspond to learners engaging in unauthorized practice such as cheating and plagiarism. Differences in engaging in those two types of practices mainly resides in students’ perception that plagiarism is worse than cheating (Evering & Moorman, 2012 ; McCabe, 2005 ). Plagiarism is usually defined as the unethical act of copying part or all of someone else’s work, with or without editing it, while cheating is more about sharing practices (Krou et al., 2021 ). As a result, most students do report cheating in an exam or for homework (Ma et al., 2008 ). To note, other research follow a different distinction for those practices and consider that plagiarism is a specific – and common – type of cheating (Waltzer & Dahl, 2022 ). Digital technologies have contributed to opening possibilities of homework avoidance and technology-related distraction (Ma et al., 2008 ; Xu, 2015 ).

The question of whether the use of digital resources hinders or enhances homework has often been investigated in large-scale studies, such as the Program for International Student Assessment (PISA), the Trends in International Mathematics and Science Study (TIMSS), and the Progress in International Reading Literacy Study (PIRLS). While most of the early large-scale studies showed positive overall correlations between the use of digital technologies for learning at home and test scores in language, mathematics, and science (e.g., OECD, 2015 ; Petko et al., 2017 ; Skryabin et al., 2015 ), there have been more recent studies reporting negative associations as well (Agasisti et al., 2020 ; Odell et al., 2020 ). One reason for these inconclusive findings is certainly the complex interplay of related factors, which include diverse ways of measuring homework, gender, socioeconomic status, personality traits, learning goals, academic abilities, learning strategies, motivation, and effort, as well as support from teachers and parents. Despite this complexity, it needs to be acknowledged that doing homework digitally does not automatically lead to productive learning activities, and it might even be associated with counter-productive practices such as digital distraction or academic dishonesty. Digitally enhanced academic dishonesty has mostly been investigated regarding formal assessment-related examinations (Evering & Moorman, 2012 ; Ma et al., 2008 ); however, it might be equally important to investigate its effects regarding learning-related assignments such as homework. Although a large body of research exists on digital academic dishonesty regarding assignments in higher education, relatively few studies have investigated this topic on K12 homework. To investigate this issue, we integrated questionnaire items on homework engagement and digital homework avoidance in a national add-on to PISA 2018 in Switzerland. Data from the Swiss sample can serve as a case study for further research with a wider cultural background. This study provides an overview of the descriptive results and tries to identify predictors of the use of digital technology for academic dishonesty when completing homework.

Prevalence and factors of digital academic dishonesty in schools

According to Pavela’s ( 1997 ) framework, four different types of academic dishonesty can be distinguished: cheating by using unauthorized materials, plagiarism by copying the work of others, fabrication of invented evidence, and facilitation by helping others in their attempts at academic dishonesty. Academic dishonesty can happen in assessment situations, as well as in learning situations. In formal assessments, academic dishonesty usually serves the purpose of passing a test or getting a better grade despite lacking the proper abilities or knowledge. In learning-related situations such as homework, where assignments are mandatory, cheating practices equally qualify as academic dishonesty. For perpetrators, these practices can be seen as shortcuts in which the willingness to invest the proper time and effort into learning is missing (Chow, 2021; Waltzer & Dahl,  2022 ). The interviews by Waltzer & Dahl ( 2022 ) reveal that students do perceive cheating as being wrong but this does not prevent them from engaging in at least one type of dishonest practice. While academic dishonesty is not a new phenomenon, it has been changing together with the development of new digital technologies (Anderman & Koenka, 2017 ; Ercegovac & Richardson, 2004 ). With the rapid growth in technologies, new forms of homework avoidance, such as copying and plagiarism, are developing (Evering & Moorman, 2012 ; Ma et al., 2008 ) summarized the findings of the 2006 U.S. surveys of the Josephson Institute of Ethics with the conclusion that the internet has led to a deterioration of ethics among students. In 2006, one-third of high school students had copied an internet document in the past 12 months, and 60% had cheated on a test. In 2012, these numbers were updated to 32% and 51%, respectively (Josephson Institute of Ethics, 2012 ). Further, 75% reported having copied another’s homework. Surprisingly, only a few studies have provided more recent evidence on the prevalence of academic dishonesty in middle and high schools. The results from colleges and universities are hardly comparable, and until now, this topic has not been addressed in international large-scale studies on schooling and school performance.

Despite the lack of representative studies, research has identified many factors in smaller and non-representative samples that might explain why some students engage in dishonest practices and others do not. These include male gender (Whitley et al., 1999 ), the “dark triad” of personality traits in contrast to conscientiousness and agreeableness (e.g., Cuadrado et al., 2021 ; Giluk & Postlethwaite, 2015 ), extrinsic motivation and performance/avoidance goals in contrast to intrinsic motivation and mastery goals (e.g., Anderman & Koenka,  2017 ; Krou et al., 2021 ), self-efficacy and achievement scores (e.g., Nora & Zhang,  2010 ; Yaniv et al., 2017 ), unethical attitudes, and low fear of being caught (e.g., Cheng et al., 2021 ; Kam et al., 2018 ), influenced by the moral norms of peers and the conditions of the educational context (e.g., Isakov & Tripathy,  2017 ; Kapoor & Kaufman, 2021 ). Similar factors have been reported regarding research on the causes of plagiarism (Husain et al., 2017 ; Moss et al., 2018 ). Further, the systematic review from Chiang et al. ( 2022 ) focused on factors of academic dishonesty in online learning environments. The analyses, based on the six-components behavior engineering, showed that the most prominent factors were environmental (effect of incentives) and individual (effect of motivation). Despite these intensive research efforts, there is still no overarching model that can comprehensively explain the interplay of these factors.

Effects of homework engagement and digital dishonesty on school performance

In meta-analyses of schools, small but significant positive effects of homework have been found regarding learning and achievement (e.g., Baş et al., 2017 ; Chen & Chen, 2014 ; Fan et al., 2017 ). In their review, Fan et al. ( 2017 ) found lower effect sizes for studies focusing on the time or frequency of homework than for studies investigating homework completion, homework grades, or homework effort. In large surveys, such as PISA, homework measurement by estimating after-school working hours has been customary practice. However, this measure could hide some other variables, such as whether teachers even give homework, whether there are school or state policies regarding homework, where the homework is done, whether it is done alone, etc. (e.g., Fernández-Alonso et al., 2015 , 2017 ). Trautwein ( 2007 ) and Trautwein et al. ( 2009 ) repeatedly showed that homework effort rather than the frequency or the time spent on homework can be considered a better predictor for academic achievement Effort and engagement can be seen as closely interrelated. Martin et al. ( 2017 ) defined engagement as the expressed behavior corresponding to students’ motivation. This has been more recently expanded by the notion of the quality of homework completion (Rosário et al., 2018 ; Xu et al., 2021 ). Therefore, it is a plausible assumption that academic dishonesty when doing homework is closely related to low homework effort and a low quality of homework completion, which in turn affects academic achievement. However, almost no studies exist on the effects of homework avoidance or academic dishonesty on academic achievement. Studies investigating the relationship between academic dishonesty and academic achievement typically use academic achievement as a predictor of academic dishonesty, not the other way around (e.g., Cuadrado et al., 2019 ; McCabe et al., 2001 ). The results of these studies show that low-performing students tend to engage in dishonest practices more often. However, high-performing students also seem to be prone to cheating in highly competitive situations (Yaniv et al., 2017 ).

Present study and hypotheses

The present study serves three combined purposes.

First, based on the additional questionnaires integrated into the Program for International Student Assessment 2018 (PISA 2018) data collection in Switzerland, we provide descriptive figures on the frequency of homework effort and the various forms of digitally-supported homework avoidance practices.

Second, the data were used to identify possible factors that explain higher levels of digitally-supported homework avoidance practices. Based on our review of the literature presented in Section 1.1 , we hypothesized (Hypothesis 1 – H1) that these factors include homework effort, age, gender, socio-economic status, and study program.

Finally, we tested whether digitally-supported homework avoidance practices were a significant predictor of test score performance. We expected (Hypothesis 2 – H2) that technology-related factors influencing test scores include not only those reported by Petko et al. ( 2017 ) but also self-reported engagement in digital dishonesty practices. .

Participants

Our analyses were based on data collected for PISA 2018 in Switzerland, made available in June 2021 (Erzinger et al., 2021 ). The target sample of PISA was 15-year-old students, with a two-phase sampling: schools and then students (Erzinger et al., 2019 , p.7–8, OECD, 2019a ). A total of 228 schools were selected for Switzerland, with an original sample of 5822 students. Based on the PISA 2018 technical report (OECD, 2019a ), only participants with a minimum of three valid responses to each scale used in the statistical analyses were included (see Section 2.2 ). A final sample of 4771 responses (48% female) was used for statistical analyses. The mean age was 15 years and 9 months ( SD  = 3 months). As Switzerland is a multilingual country, 60% of the respondents completed the questionnaires in German, 23% in French, and 17% in Italian.

Digital dishonesty in homework scale

This six-item digital dishonesty for homework scale assesses the use of digital technology for homework avoidance and copying (IC801 C01 to C06), is intended to work as a single overall scale for digital homework dishonesty practice constructed to include items corresponding to two types of dishonest practices from Pavela ( 1997 ), namely cheating and plagiarism (see Table  1 ). Three items target individual digital practices to avoid homework, which can be referred to as plagiarism (items 1, 2 and 5). Two focus more on social digital practices, for which students are cheating together with peers (items 4 and 6). One item target cheating as peer authorized plagiarism. Response options are based on questions on the productive use of digital technologies for homework in the common PISA survey (IC010), with an additional distinction for the lowest frequency option (6-point Likert scale). The scale was not tested prior to its integration into the PISA questionnaire, as it was newly developed for the purposes of this study.

Frequencies of averaged digital dishonesty in homework (weighted data)

Homework engagement scale

The scale, originally developed by Trautwein et al. (Trautwein, 2007 ; Trautwein et al., 2006 ), measures homework engagement (IC800 C01 to C06) and can be subdivided into two sub-scales: homework compliance and homework effort. The reliability of the scale was tested and established in different variants, both in Germany (Trautwein et al., 2006 ; Trautwein & Köller, 2003 ) and in Switzerland (Schnyder et al., 2008 ; Schynder Godel, 2015 ). In the adaptation used in the PISA 2018 survey, four items were positively poled (items 1, 2, 4, and 6), and two items were negatively poled (items 3 and 5) and presented with a 4-point Likert scale ranging from “Does not apply at all” to “Applies absolutely.” This adaptation showed acceptable reliability in previous studies in Switzerland (α = 0.73 and α = 0.78). The present study focused on homework effort, and thus only data from the corresponding sub-scale was analyzed (items 2 [I always try to do all of my homework], 4 [When it comes to homework, I do my best], and 6 [On the whole, I think I do my homework more conscientiously than my classmates]).

Demographics

Previous studies showed that demographic characteristics, such as age, gender, and socioeconomic status, could impact learning outcomes (Jacobs et al., 2002 ) and intention to use digital tools for learning (Tarhini et al., 2014 ). Gender is a dummy variable (ST004), with 1 for female and 2 for male. Socioeconomic status was analyzed based on the PISA 2018 index of economic, social, and cultural status (ESCS). It is computed from three other indices (OECD, 2019b , Annex A1): parents’ highest level of education (PARED), parents’ highest occupational status (HISEI), and home possessions (HOMEPOS). The final ESCS score is transformed so that 0 corresponds to an average OECD student. More details can be found in Annex A1 from PISA 2018 Results Volume 3 (OECD, 2019b ).

Study program

Although large-scale studies on schools have accounted for the differences between schools, the study program can also be a factor that directly affects digital homework dishonesty practices. In Switzerland, 15-year-old students from the PISA sampling pool can be part of at least six main study programs, which greatly differ in terms of learning content. In this study, study programs distinguished both level and type of study: lower secondary education (gymnasial – n  = 798, basic requirements – n  = 897, advanced requirements – n  = 1235), vocational education (classic – n  = 571, with baccalaureate – n  = 275), and university entrance preparation ( n  = 745). An “other” category was also included ( n  = 250). This 6-level ordinal variable was dummy coded based on the available CNTSCHID variable.

Technologies and schools

The PISA 2015 ICT (Information and Communication Technology) familiarity questionnaire included most of the technology-related variables tested by Petko et al. ( 2017 ): ENTUSE (frequency of computer use at home for entertainment purposes), HOMESCH (frequency of computer use for school-related purposes at home), and USESCH (frequency of computer use at school). However, the measure of student’s attitudes toward ICT in the 2015 survey was different from that of the 2012 dataset. Based on previous studies (Arpacı et al., 2021 ; Kunina-Habenicht & Goldhammer, 2020 ), we thus included INICT (Student’s ICT interest), COMPICT (Students’ perceived ICT competence), AUTICT (Students’ perceived autonomy related to ICT use), and SOIACICT (Students’ ICT as a topic in social interaction) instead of the variable ICTATTPOS of the 2012 survey.

Test scores

The PISA science, mathematics, and reading test scores were used as dependent variables to test our second hypothesis. Following Aparicio et al. ( 2021 ), the mean scores from plausible values were computed for each test score and used in the test score analysis.

Data analyses

Our hypotheses aim to assess the factors explaining student digital homework dishonesty practices (H1) and test score performance (H2). At the student level, we used multilevel regression analyses to decompose the variance and estimate associations. As we used data for Switzerland, in which differences between school systems exist at the level of provinces (within and between), we also considered differences across schools (based on the variable CNTSCHID).

Data were downloaded from the main PISA repository, and additional data for Switzerland were available on forscenter.ch (Erzinger et al., 2021 ). Analyses were computed with Jamovi (v.1.8 for Microsoft Windows) statistics and R packages (GAMLj, lavaan).

Additional scales for Switzerland

Digital dishonesty in homework practices.

The digital homework dishonesty scale (6 items), computed with the six items IC801, was found to be of very good reliability overall (α = 0.91, ω = 0.91). After checking for reliability, a mean score was computed for the overall scale. The confirmatory factor analysis for the one-dimensional model reached an adequate fit, with three modifications using residual covariances between single items χ 2 (6) = 220, p  < 0.001, TLI = 0.969, CFI = 0.988, RMSEA (Root Mean Square Error of Approximation) = 0.086, SRMR = 0.016).

On the one hand, the practice that was the least reported was copying something from the internet and presenting it as their own (51% never did). On the other hand, students were more likely to partially copy content from the internet and modify it to present as their own (47% did it at least once a month). Copying answers shared by friends was rather common, with 62% of the students reporting that they engaged in such practices at least once a month.

When all surveyed practices were taken together, 7.6% of the students reported that they had never engaged in digitally dishonest practices for homework, while 30.6% reported cheating once or twice a week, 12.1% almost every day, and 6.9% every day (Table  1 ).

Homework effort

The overall homework engagement scale consisted of six items (IC800), and it was found to be acceptably reliable (α = 0.76, ω = 0.79). Items 3 and 5 were reversed for this analysis. The homework compliance sub-scale had a low reliability (α = 0.58, ω = 0.64), whereas the homework effort sub-scale had an acceptable reliability (α = 0.78, ω = 0.79). Based on our rationale, the following statistical analyses used only the homework effort sub-scale. Furthermore, this focus is justified by the fact that the homework compliance scale might be statistically confounded with the digital dishonesty in homework scale.

Descriptive weighted statistics per item (Table  2 ) showed that while most students (80%) tried to complete all of their homework, only half of the students reported doing those diligently (53.3%). Most students also reported that they believed they put more effort into their homework than their peers (77.7%). The overall mean score of the composite scale was 2.81 ( SD  = 0.69).

Frequencies of averaged homework engagement (weighted data)

Multilevel regression analysis: Predictors of digital dishonesty in homework (H1)

Mixed multilevel modeling was used to analyze predictors of digital homework avoidance while considering the effect of school (random component). Based on our first hypothesis, we compared several models by progressively including the following fixed effects: homework effort and personal traits (age, gender) (Model 2), then socio-economic status (Model 3), and finally, study program (Model 4). The results are presented in Table  3 . Except for the digital homework dishonesty and homework efforts scales, all other scales were based upon the scores computed according to the PISA technical report (OECD, 2019a ).

Multilevel models explaining variations in students’ self-reported homework avoidance with digital resources

Note : * p < 0.05, ** p < 0.01, *** p < 0.001

We first compared variance components. Variance was decomposed into student and school levels. Model 1 provides estimates of the variance component without any covariates. The intraclass coefficient (ICC) indicated that about 6.6% of the total variance was associated with schools. The parameter (b  = 2.56, SE b  = 0.025 ) falls within the 95% confidence interval. Further, CI is above 0 and thus we can reject the null hypothesis. Comparing the empty model to models with covariates, we found that Models 2, 3 and 4 showed an increase in total explained variance to 10%. Variance explained by the covariates was about 3% in Models 2 and 3, and about 4% in Model 4. Interestingly, in our models, student socio-economic status, measured by the PISA index, never accounted for variance in digitally-supported dishonest practices to complete homework.

Further, model comparison based on AIC indicates that Model 4, including homework effort, personal traits, socio-economic status, and study program, was the better fit for the data. In Model 4 (Table  3 ; Fig.  1 ), we observed that homework effort and gender were negatively associated with digital dishonesty. Male students who invested less effort in their homework were more prone to engage in digital dishonesty. The study program was positively but weakly associated with digital dishonesty. Students in programs that target higher education were less likely to engage in digital dishonesty when completing homework.

Summary of the two-steps Model 4 (estimates - β, with standard errors and significance levels, *** p < 0.001)

Multilevel regression analysis: Cheating and test scores (H2)

Our first hypothesis aimed to provide insights into characteristics of students reporting that they regularly use digital resources dishonestly when completing homework. Our second hypothesis focused on whether digitally-supported homework avoidance practices was linked to results of test scores. Mixed multilevel modeling was used to analyze predictors of test scores while considering the effect of school (random component). Based on the study by Petko et al. ( 2017 ), we compared several models by progressively including the following fixed effects ICT use (three measures) (Model 2), then attitude toward ICT (four measures) (Model 3), and finally, digital dishonesty in homework (single measure) (Model 4). The results are presented in Table  4 for science, Table  5 for mathematics, and Table  6 for reading.

Multilevel models explaining variations in student test scores in science (standardized coefficients and model parameters)

Multilevel models explaining variations in student test scores in mathematics (standardized coefficients and model parameters)

Multilevel models explaining variations in student test scores in reading (standardized coefficients and model parameters)

Variance components were decomposed into student and school level. ICC for Model 1 indicated that 37.9% of the variance component without covariates was associated with schools.

Taking Model 1 as a reference, we observed an increase in total explained variance to 40.5% with factors related to ICT use (Model 2), to 40.8% with factors related to attitude toward ICT (Model 3), and to 41.1% with the single digital dishonesty factor. It is interesting to note that we obtained different results from those reported by Petko et al. ( 2017 ). In their study, they found significant effects on the explained variances of ENTUSE, USESCH, and ICTATTPOS but not of HOMESCH for Switzerland. In the present study (Model 3), HOMESCH and USESCH were significant predictors but not ENTUSE, and for attitude toward ICT, all but INTICT were significant predictors of the variance. However, factors corresponding to ICT use were negatively associated with test performance, as in the study by Petko et al. ( 2017 ). Similarly, all components of attitude toward ICT positively affected science test scores, except for students’ ICT as a topic in social interaction.

Based on the AIC values, Model 4, including ICT use, attitude toward ICT, and digital dishonesty, was the better fit for the data. The parameter ( b  = 498.00, SE b  = 3.550) shows that our sample falls within the 95% confidence interval and that we can reject the null hypothesis. In this model, all factors except the use of ICT outside of school for leisure were significant predictors of explained variance in science test scores. These results are consistent with those reported by Petko et al. ( 2017 ), in which more frequent use of ICT negatively affected science test scores, with an overall positive effect of positive attitude toward ICT. Further, we observed that homework avoidance with digital resources strongly negatively affected performance, with lower performance associated with students reporting a higher frequency of engagement in digital dishonesty practices.

For mathematics test scores, results from Models 2 and 3 showed a similar pattern than those for science, and Model 4 also explained the highest variance (41.2%). The results from Model 4 contrast with those found by Petko et al. ( 2017 ), as in this study, HOMESCH was the only significant variable of ICT use. Regarding attitudes toward ICT, only two measures (COMPICT and AUTICT) were significant positive factors in Model 4. As for science test scores, digital dishonesty practices were a significantly strong negative predictor. Students who reported cheating more frequently were more likely to perform poorly on mathematics tests.

The analyses of PISA test scores for reading in Model 2 was similar to that of science and mathematics, with ENTUSE being a non-significant predictor when we included only measures of ICT use as predictors. In Model 3, contrary to the science and mathematics test scores models, in which INICT was non-significant, all measures of attitude toward ICT were positively significant predictors. Nevertheless, as for science and mathematics, Model 4, which included digital dishonesty, explained the greater variance in reading test scores (42.2%). We observed that for reading, all predictors were significant in Model 4, with an overall negative effect of ICT use, a positive effect of attitude toward ICT—except for SOIAICT, and a negative effect of digital dishonesty on test scores. Interestingly, the detrimental effect of using digital resources to engage in dishonest homework completion was the strongest in reading test scores.

In this study, we were able to provide descriptive statistics on the prevalence of digital dishonesty among secondary students in the Swiss sample of PISA 2018. Students from this country were selected because they received additional questions targeting both homework effort and the frequency with which they engaged in digital dishonesty when doing homework. Descriptive statistics indicated that fairly high numbers of students engage in dishonest homework practices, with 49.6% reporting digital dishonesty at least once or twice a week. The most frequently reported practice was copying answers from friends, which was undertaken at least once a month by more than two-thirds of respondents. Interestingly, the most infamous form of digital dishonesty, that is plagiarism by copy-pasting something from the internet (Evering & Moorman, 2012 ), was admitted to by close to half of the students (49%). These results for homework avoidance are close to those obtained by previous research on digital academic plagiarism (e.g., McCabe et al., 2001 ).

We then investigated what makes a cheater, based on students’ demographics and effort put in doing their homework (H1), before looking at digital dishonesty as an additional ICT predictor of PISA test scores (mathematics, reading, and science) (H2).

The goal of our first research hypothesis was to determine student-related factors that may predict digital homework avoidance practices. Here, we focused on factors linked to students’ personal characteristics and study programs. Our multilevel model explained about 10% of the variance overall. Our analysis of which students are more likely to digital resources to avoid homework revealed an increased probability for male students who did not put much effort into doing their homework and who were studying in a program that was not oriented toward higher education. Thus, our findings tend to support results from previous research that stresses the importance of gender and motivational factors for academic dishonesty (e.g., Anderman & Koenka,  2017 ; Krou et al., 2021 ). Yet, as our model only explained little variance and more research is needed to provide an accurate representation of the factors that lead to digital dishonesty. Future research could include more aspects that are linked to learning, such as peer-related or teaching-related factors. Possibly, how closely homework is embedded in the teaching and learning culture may play a key role in digital dishonesty. Additional factors might be linked to the overall availability and use of digital tools. For example, the report combining factors from the PISA 2018 school and student questionnaires showed that the higher the computer–student ratio, the lower students scored in the general tests (OECD, 2020b ). A positive association with reading disappeared when socio-economic background was considered. This is even more interesting when considering previous research indicating that while internet access is not a source of divide among youths, the quality of use is still different based on gender or socioeconomic status (Livingstone & Helsper, 2007 ). Thus, investigating the usage-related “digital divide” as a potential source of digital dishonesty is an interesting avenue for future research (Dolan, 2016 ).

Our second hypothesis considered that digital dishonesty in homework completion can be regarded as an additional ICT-related trait and thus could be included in models targeting the influence of traditional ICT on PISA test scores, such as Petko et al. ( 2017 ) study. Overall, our results on the influence of ICT use and attitudes toward ICT on test scores are in line with those reported by Petko et al. ( 2017 ). Digital dishonesty was found to negatively influence test scores, with a higher frequency of cheating leading to lower performance in all major PISA test domains, and particularly so for reading. For each subject, the combined models explained about 40% of the total variance.

Conclusions and recommendations

Our results have several practical implications. First, the amount of cheating on homework observed calls for new strategies for raising homework engagement, as this was found to be a clear predictor of digital dishonesty. This can be achieved by better explaining the goals and benefits of homework, the adverse effects of cheating on homework, and by providing adequate feedback on homework that was done properly. Second, teachers might consider new forms of homework that are less prone to cheating, such as doing homework in non-digital formats that are less easy to copy digitally or in proctored digital formats that allow for the monitoring of the process of homework completion, or by using plagiarism software to check homework. Sometimes, it might even be possible to give homework and explicitly encourage strategies that might be considered cheating, for example, by working together or using internet sources. As collaboration is one of the 21st century skills that students are expected to develop (Bray et al., 2020 ), this can be used to turn cheating into positive practice. There is already research showing the beneficial impact of computer-supported collaborative learning (e.g., Janssen et al., 2012 ). Zhang et al. ( 2011 ) compared three homework assignment (creation of a homepage) conditions: individually, in groups with specific instructions, and in groups with general instructions. Their results showed that computer supported collaborative homework led to better performance than individual settings, only when the instructions were general. Thus, promoting digital collaborative homework could support the development of students’ digital and collaborative skills.

Further, digital dishonesty in homework needs to be considered different from cheating in assessments. In research on assessment-related dishonesty, cheating is perceived as a reprehensible practice because grades obtained are a misrepresentation of student knowledge, and cheating “implies that efficient cheaters are good students, since they get good grades” (Bouville, 2010 , p. 69). However, regarding homework, this view is too restrictive. Indeed, not all homework is graded, and we cannot know for sure whether students answered this questionnaire while considering homework as a whole or only graded homework (assessments). Our study did not include questions about whether students displayed the same attitudes and practices toward assessments (graded) and practice exercises (non-graded), nor did it include questions on how assessments and homework were related. By cheating on ungraded practice exercises, students will primarily hamper their own learning process. Future research could investigate in more depth the kinds of homework students cheat on and why.

Finally, the question of how to foster engaging homework with digital tools becomes even more important in pandemic situations. Numerous studies following the switch to home schooling at the beginning of the 2020 COVID-19 pandemic have investigated the difficulties for parents in supporting their children (Bol, 2020 ; Parczewska, 2021 ); however, the question of digital homework has not been specifically addressed. It is unknown whether the increase in digital schooling paired with discrepancies in access to digital tools has led to an increase in digital dishonesty practices. Data from the PISA 2018 student questionnaires (OECD, 2020a ) indicated that about 90% of students have a computer for schoolwork (OECD average), but the availability per student remains unknown. Digital homework can be perceived as yet another factor of social differences (see for example Auxier & Anderson,  2020 ; Thorn & Vincent-Lancrin, 2022 ).

Limitations and directions

The limitations of the study include the format of the data collected, with the accuracy of self-reports to mirror actual practices restricted, as these measures are particularly likely to trigger response bias, such as social desirability. More objective data on digital dishonesty in homework-related purposes could, for example, be obtained by analyzing students’ homework with plagiarism software. Further, additional measures that provide a more complete landscape of contributing factors are necessary. For example, in considering digital homework as an alternative to traditional homework, parents’ involvement in homework and their attitudes toward ICT are factors that have not been considered in this study (Amzalag, 2021 ). Although our results are in line with studies on academic digital dishonesty, their scope is limited to the Swiss context. Moreover, our analyses focused on secondary students. Results might be different with a sample of younger students. As an example, Kiss and Teller ( 2022 ) measured primary students cheating practices and found that individual characteristics were not a stable predictor of cheating between age groups. Further, our models included school as a random component, yet other group variables, such as class and peer groups, may well affect digital homework avoidance strategies.

The findings of this study suggest that academic dishonesty when doing homework needs to be addressed in schools. One way, as suggested by Chow et al. ( 2021 ) and Djokovic et al. ( 2022 ), is to build on students’ practices to explain which need to be considered cheating. This recommendation for institutions to take preventive actions and explicit to students the punishment faced in case of digital academic behavior was also raised by Chiang et al. ( 2022 ). Another is that teachers may consider developing homework formats that discourage cheating and shortcuts (e.g., creating multimedia documents instead of text-based documents, using platforms where answers cannot be copied and pasted, or using advanced forms of online proctoring). It may also be possible to change homework formats toward more open formats, where today’s cheating practices are allowed when they are made transparent (open-book homework, collaborative homework). Further, experiences from the COVID-19 pandemic have stressed the importance of understanding the factors related to the successful integration of digital homework and the need to minimize the digital “homework gap” (Auxier & Anderson, 2020 ; Donnelly & Patrinos, 2021 ). Given that homework engagement is a core predictor of academic dishonesty, students should receive meaningful homework in preparation for upcoming lessons or for practicing what was learned in past lessons. Raising student’s awareness of the meaning and significance of homework might be an important piece of the puzzle to honesty in learning.

List of abbreviations related to PISA datasets

Juliette C. Désiron: Formal analysis, Writing (Original, Review and Editing), Dominik Petko: Conceptualization, Writing (Original, Review and Editing), Supervision.

Open access funding provided by University of Zurich

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Motivation is a key factor in whether students cheat

Assistant Professor of Education, Texas State University

Research Analyst, Teachers College, Columbia University

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Ever since the COVID-19 pandemic caused many U.S. colleges to shift to remote learning in the spring of 2020, student cheating has been a concern for instructors and students alike.

To detect student cheating, considerable resources have been devoted to using technology to monitor students online . This online surveillance has increased students’ anxiety and distress . For instance, some students have indicated the monitoring technology required them to stay at their desks or risk being labeled as cheaters.

Although relying on electronic eyes may partially curb cheating, there’s another factor in the reasons students cheat that often gets overlooked – student motivation.

As a team of researchers in educational psychology and higher education , we became interested in how students’ motivation to learn, or what drives them to want to succeed in class, affects how much they cheated in their schoolwork.

To shine light on why students cheat, we conducted an analysis of 79 research studies and published our findings in the journal Educational Psychology Review. We determined that a variety of motivational factors, ranging from a desire for good grades to a student’s academic confidence, come into play when explaining why students cheat. With these factors in mind, we see a number of things that both students and instructors can do to harness the power of motivation as a way to combat cheating, whether in virtual or in-person classrooms. Here are five takeaways:

1. Avoid emphasizing grades

Although obtaining straight A’s is quite appealing, the more students are focused solely on earning high grades, the more likely they are to cheat. When the grade itself becomes the goal, cheating can serve as a way to achieve this goal.

Students’ desire to learn can diminish when instructors overly emphasize high test scores, beating the curve, and student rankings. Graded assessments have a role to play, but so does acquisition of skills and actually learning the content, not only doing what it takes to get good grades.

2. Focus on expertise and mastery

Striving to increase one’s knowledge and improve skills in a course was associated with less cheating. This suggests that the more students are motivated to gain expertise, the less likely they are to cheat. Instructors can teach with a focus on mastery, such as providing additional opportunities for students to redo assignments or exams. This reinforces the goal of personal growth and improvement.

3. Combat boredom with relevance

Compared with students motivated by either gaining rewards or expertise, there might be a group of students who are simply not motivated at all, or experiencing what researchers call amotivation. Nothing in their environment or within themselves motivates them to learn. For these students, cheating is quite common and seen as a viable pathway to complete coursework successfully rather than putting forth their own effort. However, when students find relevance in what they’re learning, they are less likely to cheat.

When students see connections between their coursework and other courses, fields of study or their future careers, it can stimulate them to see how valuable the subject might be. Instructors can be intentional in providing rationales for why learning a particular topic might be useful and connecting students’ interest to the course content.

4. Encourage ownership of learning

When students struggle, they sometimes blame circumstances beyond their control, such as believing their instructor to have unrealistic standards. Our findings show that when students believe they are responsible for their own learning, they are less likely to cheat.

Encouraging students to take ownership over their learning and put in the required effort can decrease academic dishonesty. Also, providing meaningful choices can help students feel they are in charge of their own learning journey, rather than being told what to do.

5. Build confidence

Our study found that when students believed they could succeed in their coursework, cheating decreased. When students do not believe they will be successful, a teaching approach called scaffolding is key. Essentially, the scaffolding approach involves assigning tasks that match the students’ ability level and gradually increase in difficulty. This progression slowly builds students’ confidence to take on new challenges. And when students feel confident to learn, they are willing to put in more effort in school.

An inexpensive solution

With these tips in mind, we expect cheating might pose less of a threat during the pandemic and beyond. Focusing on student motivation is a much less controversial and inexpensive solution to curtail any tendencies students may have to cheat their way through school.

Are these motivational strategies the cure-all to cheating? Not necessarily. But they are worth considering – along with other strategies – to fight against academic dishonesty.

[ Research into coronavirus and other news from science Subscribe to The Conversation’s new science newsletter .]

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Academic dishonesty when doing homework: How digital technologies are put to bad use in secondary schools

• Open access
• Published: 23 July 2022
• Volume 28 , pages 1251–1271, ( 2023 )

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• Juliette C. Désiron   ORCID: orcid.org/0000-0002-3074-9018 1 &
• Dominik Petko   ORCID: orcid.org/0000-0003-1569-1302 1  

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The growth in digital technologies in recent decades has offered many opportunities to support students’ learning and homework completion. However, it has also contributed to expanding the field of possibilities concerning homework avoidance. Although studies have investigated the factors of academic dishonesty, the focus has often been on college students and formal assessments. The present study aimed to determine what predicts homework avoidance using digital resources and whether engaging in these practices is another predictor of test performance. To address these questions, we analyzed data from the Program for International Student Assessment 2018 survey, which contained additional questionnaires addressing this issue, for the Swiss students. The results showed that about half of the students engaged in one kind or another of digitally-supported practices for homework avoidance at least once or twice a week. Students who were more likely to use digital resources to engage in dishonest practices were males who did not put much effort into their homework and were enrolled in non-higher education-oriented school programs. Further, we found that digitally-supported homework avoidance was a significant negative predictor of test performance when considering information and communication technology predictors. Thus, the present study not only expands the knowledge regarding the predictors of academic dishonesty with digital resources, but also confirms the negative impact of such practices on learning.

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1 Introduction

Academic dishonesty is a widespread and perpetual issue for teachers made even more easier to perpetrate with the rise of digital technologies (Blau & Eshet-Alkalai, 2017 ; Ma et al., 2008 ). Definitions vary but overall an academically dishonest practices correspond to learners engaging in unauthorized practice such as cheating and plagiarism. Differences in engaging in those two types of practices mainly resides in students’ perception that plagiarism is worse than cheating (Evering & Moorman, 2012 ; McCabe, 2005 ). Plagiarism is usually defined as the unethical act of copying part or all of someone else’s work, with or without editing it, while cheating is more about sharing practices (Krou et al., 2021 ). As a result, most students do report cheating in an exam or for homework (Ma et al., 2008 ). To note, other research follow a different distinction for those practices and consider that plagiarism is a specific – and common – type of cheating (Waltzer & Dahl, 2022 ). Digital technologies have contributed to opening possibilities of homework avoidance and technology-related distraction (Ma et al., 2008 ; Xu, 2015 ).

The question of whether the use of digital resources hinders or enhances homework has often been investigated in large-scale studies, such as the Program for International Student Assessment (PISA), the Trends in International Mathematics and Science Study (TIMSS), and the Progress in International Reading Literacy Study (PIRLS). While most of the early large-scale studies showed positive overall correlations between the use of digital technologies for learning at home and test scores in language, mathematics, and science (e.g., OECD, 2015 ; Petko et al., 2017 ; Skryabin et al., 2015 ), there have been more recent studies reporting negative associations as well (Agasisti et al., 2020 ; Odell et al., 2020 ). One reason for these inconclusive findings is certainly the complex interplay of related factors, which include diverse ways of measuring homework, gender, socioeconomic status, personality traits, learning goals, academic abilities, learning strategies, motivation, and effort, as well as support from teachers and parents. Despite this complexity, it needs to be acknowledged that doing homework digitally does not automatically lead to productive learning activities, and it might even be associated with counter-productive practices such as digital distraction or academic dishonesty. Digitally enhanced academic dishonesty has mostly been investigated regarding formal assessment-related examinations (Evering & Moorman, 2012 ; Ma et al., 2008 ); however, it might be equally important to investigate its effects regarding learning-related assignments such as homework. Although a large body of research exists on digital academic dishonesty regarding assignments in higher education, relatively few studies have investigated this topic on K12 homework. To investigate this issue, we integrated questionnaire items on homework engagement and digital homework avoidance in a national add-on to PISA 2018 in Switzerland. Data from the Swiss sample can serve as a case study for further research with a wider cultural background. This study provides an overview of the descriptive results and tries to identify predictors of the use of digital technology for academic dishonesty when completing homework.

1.1 Prevalence and factors of digital academic dishonesty in schools

According to Pavela’s ( 1997 ) framework, four different types of academic dishonesty can be distinguished: cheating by using unauthorized materials, plagiarism by copying the work of others, fabrication of invented evidence, and facilitation by helping others in their attempts at academic dishonesty. Academic dishonesty can happen in assessment situations, as well as in learning situations. In formal assessments, academic dishonesty usually serves the purpose of passing a test or getting a better grade despite lacking the proper abilities or knowledge. In learning-related situations such as homework, where assignments are mandatory, cheating practices equally qualify as academic dishonesty. For perpetrators, these practices can be seen as shortcuts in which the willingness to invest the proper time and effort into learning is missing (Chow, 2021; Waltzer & Dahl,  2022 ). The interviews by Waltzer & Dahl ( 2022 ) reveal that students do perceive cheating as being wrong but this does not prevent them from engaging in at least one type of dishonest practice. While academic dishonesty is not a new phenomenon, it has been changing together with the development of new digital technologies (Anderman & Koenka, 2017 ; Ercegovac & Richardson, 2004 ). With the rapid growth in technologies, new forms of homework avoidance, such as copying and plagiarism, are developing (Evering & Moorman, 2012 ; Ma et al., 2008 ) summarized the findings of the 2006 U.S. surveys of the Josephson Institute of Ethics with the conclusion that the internet has led to a deterioration of ethics among students. In 2006, one-third of high school students had copied an internet document in the past 12 months, and 60% had cheated on a test. In 2012, these numbers were updated to 32% and 51%, respectively (Josephson Institute of Ethics, 2012 ). Further, 75% reported having copied another’s homework. Surprisingly, only a few studies have provided more recent evidence on the prevalence of academic dishonesty in middle and high schools. The results from colleges and universities are hardly comparable, and until now, this topic has not been addressed in international large-scale studies on schooling and school performance.

Despite the lack of representative studies, research has identified many factors in smaller and non-representative samples that might explain why some students engage in dishonest practices and others do not. These include male gender (Whitley et al., 1999 ), the “dark triad” of personality traits in contrast to conscientiousness and agreeableness (e.g., Cuadrado et al., 2021 ; Giluk & Postlethwaite, 2015 ), extrinsic motivation and performance/avoidance goals in contrast to intrinsic motivation and mastery goals (e.g., Anderman & Koenka,  2017 ; Krou et al., 2021 ), self-efficacy and achievement scores (e.g., Nora & Zhang,  2010 ; Yaniv et al., 2017 ), unethical attitudes, and low fear of being caught (e.g., Cheng et al., 2021 ; Kam et al., 2018 ), influenced by the moral norms of peers and the conditions of the educational context (e.g., Isakov & Tripathy,  2017 ; Kapoor & Kaufman, 2021 ). Similar factors have been reported regarding research on the causes of plagiarism (Husain et al., 2017 ; Moss et al., 2018 ). Further, the systematic review from Chiang et al. ( 2022 ) focused on factors of academic dishonesty in online learning environments. The analyses, based on the six-components behavior engineering, showed that the most prominent factors were environmental (effect of incentives) and individual (effect of motivation). Despite these intensive research efforts, there is still no overarching model that can comprehensively explain the interplay of these factors.

1.2 Effects of homework engagement and digital dishonesty on school performance

In meta-analyses of schools, small but significant positive effects of homework have been found regarding learning and achievement (e.g., Baş et al., 2017 ; Chen & Chen, 2014 ; Fan et al., 2017 ). In their review, Fan et al. ( 2017 ) found lower effect sizes for studies focusing on the time or frequency of homework than for studies investigating homework completion, homework grades, or homework effort. In large surveys, such as PISA, homework measurement by estimating after-school working hours has been customary practice. However, this measure could hide some other variables, such as whether teachers even give homework, whether there are school or state policies regarding homework, where the homework is done, whether it is done alone, etc. (e.g., Fernández-Alonso et al., 2015 , 2017 ). Trautwein ( 2007 ) and Trautwein et al. ( 2009 ) repeatedly showed that homework effort rather than the frequency or the time spent on homework can be considered a better predictor for academic achievement Effort and engagement can be seen as closely interrelated. Martin et al. ( 2017 ) defined engagement as the expressed behavior corresponding to students’ motivation. This has been more recently expanded by the notion of the quality of homework completion (Rosário et al., 2018 ; Xu et al., 2021 ). Therefore, it is a plausible assumption that academic dishonesty when doing homework is closely related to low homework effort and a low quality of homework completion, which in turn affects academic achievement. However, almost no studies exist on the effects of homework avoidance or academic dishonesty on academic achievement. Studies investigating the relationship between academic dishonesty and academic achievement typically use academic achievement as a predictor of academic dishonesty, not the other way around (e.g., Cuadrado et al., 2019 ; McCabe et al., 2001 ). The results of these studies show that low-performing students tend to engage in dishonest practices more often. However, high-performing students also seem to be prone to cheating in highly competitive situations (Yaniv et al., 2017 ).

1.3 Present study and hypotheses

The present study serves three combined purposes.

First, based on the additional questionnaires integrated into the Program for International Student Assessment 2018 (PISA 2018) data collection in Switzerland, we provide descriptive figures on the frequency of homework effort and the various forms of digitally-supported homework avoidance practices.

Second, the data were used to identify possible factors that explain higher levels of digitally-supported homework avoidance practices. Based on our review of the literature presented in Section 1.1 , we hypothesized (Hypothesis 1 – H1) that these factors include homework effort, age, gender, socio-economic status, and study program.

Finally, we tested whether digitally-supported homework avoidance practices were a significant predictor of test score performance. We expected (Hypothesis 2 – H2) that technology-related factors influencing test scores include not only those reported by Petko et al. ( 2017 ) but also self-reported engagement in digital dishonesty practices. .

2.1 Participants

Our analyses were based on data collected for PISA 2018 in Switzerland, made available in June 2021 (Erzinger et al., 2021 ). The target sample of PISA was 15-year-old students, with a two-phase sampling: schools and then students (Erzinger et al., 2019 , p.7–8, OECD, 2019a ). A total of 228 schools were selected for Switzerland, with an original sample of 5822 students. Based on the PISA 2018 technical report (OECD, 2019a ), only participants with a minimum of three valid responses to each scale used in the statistical analyses were included (see Section 2.2 ). A final sample of 4771 responses (48% female) was used for statistical analyses. The mean age was 15 years and 9 months ( SD  = 3 months). As Switzerland is a multilingual country, 60% of the respondents completed the questionnaires in German, 23% in French, and 17% in Italian.

2.2 Measures

2.2.1 digital dishonesty in homework scale.

This six-item digital dishonesty for homework scale assesses the use of digital technology for homework avoidance and copying (IC801 C01 to C06), is intended to work as a single overall scale for digital homework dishonesty practice constructed to include items corresponding to two types of dishonest practices from Pavela ( 1997 ), namely cheating and plagiarism (see Table  1 ). Three items target individual digital practices to avoid homework, which can be referred to as plagiarism (items 1, 2 and 5). Two focus more on social digital practices, for which students are cheating together with peers (items 4 and 6). One item target cheating as peer authorized plagiarism. Response options are based on questions on the productive use of digital technologies for homework in the common PISA survey (IC010), with an additional distinction for the lowest frequency option (6-point Likert scale). The scale was not tested prior to its integration into the PISA questionnaire, as it was newly developed for the purposes of this study.

2.2.2 Homework engagement scale

The scale, originally developed by Trautwein et al. (Trautwein, 2007 ; Trautwein et al., 2006 ), measures homework engagement (IC800 C01 to C06) and can be subdivided into two sub-scales: homework compliance and homework effort. The reliability of the scale was tested and established in different variants, both in Germany (Trautwein et al., 2006 ; Trautwein & Köller, 2003 ) and in Switzerland (Schnyder et al., 2008 ; Schynder Godel, 2015 ). In the adaptation used in the PISA 2018 survey, four items were positively poled (items 1, 2, 4, and 6), and two items were negatively poled (items 3 and 5) and presented with a 4-point Likert scale ranging from “Does not apply at all” to “Applies absolutely.” This adaptation showed acceptable reliability in previous studies in Switzerland (α = 0.73 and α = 0.78). The present study focused on homework effort, and thus only data from the corresponding sub-scale was analyzed (items 2 [I always try to do all of my homework], 4 [When it comes to homework, I do my best], and 6 [On the whole, I think I do my homework more conscientiously than my classmates]).

2.2.3 Demographics

Previous studies showed that demographic characteristics, such as age, gender, and socioeconomic status, could impact learning outcomes (Jacobs et al., 2002 ) and intention to use digital tools for learning (Tarhini et al., 2014 ). Gender is a dummy variable (ST004), with 1 for female and 2 for male. Socioeconomic status was analyzed based on the PISA 2018 index of economic, social, and cultural status (ESCS). It is computed from three other indices (OECD, 2019b , Annex A1): parents’ highest level of education (PARED), parents’ highest occupational status (HISEI), and home possessions (HOMEPOS). The final ESCS score is transformed so that 0 corresponds to an average OECD student. More details can be found in Annex A1 from PISA 2018 Results Volume 3 (OECD, 2019b ).

2.2.4 Study program

Although large-scale studies on schools have accounted for the differences between schools, the study program can also be a factor that directly affects digital homework dishonesty practices. In Switzerland, 15-year-old students from the PISA sampling pool can be part of at least six main study programs, which greatly differ in terms of learning content. In this study, study programs distinguished both level and type of study: lower secondary education (gymnasial – n  = 798, basic requirements – n  = 897, advanced requirements – n  = 1235), vocational education (classic – n  = 571, with baccalaureate – n  = 275), and university entrance preparation ( n  = 745). An “other” category was also included ( n  = 250). This 6-level ordinal variable was dummy coded based on the available CNTSCHID variable.

2.2.5 Technologies and schools

The PISA 2015 ICT (Information and Communication Technology) familiarity questionnaire included most of the technology-related variables tested by Petko et al. ( 2017 ): ENTUSE (frequency of computer use at home for entertainment purposes), HOMESCH (frequency of computer use for school-related purposes at home), and USESCH (frequency of computer use at school). However, the measure of student’s attitudes toward ICT in the 2015 survey was different from that of the 2012 dataset. Based on previous studies (Arpacı et al., 2021 ; Kunina-Habenicht & Goldhammer, 2020 ), we thus included INICT (Student’s ICT interest), COMPICT (Students’ perceived ICT competence), AUTICT (Students’ perceived autonomy related to ICT use), and SOIACICT (Students’ ICT as a topic in social interaction) instead of the variable ICTATTPOS of the 2012 survey.

2.2.6 Test scores

The PISA science, mathematics, and reading test scores were used as dependent variables to test our second hypothesis. Following Aparicio et al. ( 2021 ), the mean scores from plausible values were computed for each test score and used in the test score analysis.

2.3 Data analyses

Our hypotheses aim to assess the factors explaining student digital homework dishonesty practices (H1) and test score performance (H2). At the student level, we used multilevel regression analyses to decompose the variance and estimate associations. As we used data for Switzerland, in which differences between school systems exist at the level of provinces (within and between), we also considered differences across schools (based on the variable CNTSCHID).

Data were downloaded from the main PISA repository, and additional data for Switzerland were available on forscenter.ch (Erzinger et al., 2021 ). Analyses were computed with Jamovi (v.1.8 for Microsoft Windows) statistics and R packages (GAMLj, lavaan).

3.1 Additional scales for Switzerland

3.1.1 digital dishonesty in homework practices.

The digital homework dishonesty scale (6 items), computed with the six items IC801, was found to be of very good reliability overall (α = 0.91, ω = 0.91). After checking for reliability, a mean score was computed for the overall scale. The confirmatory factor analysis for the one-dimensional model reached an adequate fit, with three modifications using residual covariances between single items χ 2 (6) = 220, p  < 0.001, TLI = 0.969, CFI = 0.988, RMSEA (Root Mean Square Error of Approximation) = 0.086, SRMR = 0.016).

On the one hand, the practice that was the least reported was copying something from the internet and presenting it as their own (51% never did). On the other hand, students were more likely to partially copy content from the internet and modify it to present as their own (47% did it at least once a month). Copying answers shared by friends was rather common, with 62% of the students reporting that they engaged in such practices at least once a month.

When all surveyed practices were taken together, 7.6% of the students reported that they had never engaged in digitally dishonest practices for homework, while 30.6% reported cheating once or twice a week, 12.1% almost every day, and 6.9% every day (Table  1 ).

3.1.2 Homework effort

The overall homework engagement scale consisted of six items (IC800), and it was found to be acceptably reliable (α = 0.76, ω = 0.79). Items 3 and 5 were reversed for this analysis. The homework compliance sub-scale had a low reliability (α = 0.58, ω = 0.64), whereas the homework effort sub-scale had an acceptable reliability (α = 0.78, ω = 0.79). Based on our rationale, the following statistical analyses used only the homework effort sub-scale. Furthermore, this focus is justified by the fact that the homework compliance scale might be statistically confounded with the digital dishonesty in homework scale.

Descriptive weighted statistics per item (Table  2 ) showed that while most students (80%) tried to complete all of their homework, only half of the students reported doing those diligently (53.3%). Most students also reported that they believed they put more effort into their homework than their peers (77.7%). The overall mean score of the composite scale was 2.81 ( SD  = 0.69).

3.2 Multilevel regression analysis: Predictors of digital dishonesty in homework (H1)

Mixed multilevel modeling was used to analyze predictors of digital homework avoidance while considering the effect of school (random component). Based on our first hypothesis, we compared several models by progressively including the following fixed effects: homework effort and personal traits (age, gender) (Model 2), then socio-economic status (Model 3), and finally, study program (Model 4). The results are presented in Table  3 . Except for the digital homework dishonesty and homework efforts scales, all other scales were based upon the scores computed according to the PISA technical report (OECD, 2019a ).

We first compared variance components. Variance was decomposed into student and school levels. Model 1 provides estimates of the variance component without any covariates. The intraclass coefficient (ICC) indicated that about 6.6% of the total variance was associated with schools. The parameter (b  = 2.56, SE b  = 0.025 ) falls within the 95% confidence interval. Further, CI is above 0 and thus we can reject the null hypothesis. Comparing the empty model to models with covariates, we found that Models 2, 3 and 4 showed an increase in total explained variance to 10%. Variance explained by the covariates was about 3% in Models 2 and 3, and about 4% in Model 4. Interestingly, in our models, student socio-economic status, measured by the PISA index, never accounted for variance in digitally-supported dishonest practices to complete homework.

Summary of the two-steps Model 4 (estimates - β, with standard errors and significance levels, *** p < 0.001)

Further, model comparison based on AIC indicates that Model 4, including homework effort, personal traits, socio-economic status, and study program, was the better fit for the data. In Model 4 (Table  3 ; Fig.  1 ), we observed that homework effort and gender were negatively associated with digital dishonesty. Male students who invested less effort in their homework were more prone to engage in digital dishonesty. The study program was positively but weakly associated with digital dishonesty. Students in programs that target higher education were less likely to engage in digital dishonesty when completing homework.

3.3 Multilevel regression analysis: Cheating and test scores (H2)

Our first hypothesis aimed to provide insights into characteristics of students reporting that they regularly use digital resources dishonestly when completing homework. Our second hypothesis focused on whether digitally-supported homework avoidance practices was linked to results of test scores. Mixed multilevel modeling was used to analyze predictors of test scores while considering the effect of school (random component). Based on the study by Petko et al. ( 2017 ), we compared several models by progressively including the following fixed effects ICT use (three measures) (Model 2), then attitude toward ICT (four measures) (Model 3), and finally, digital dishonesty in homework (single measure) (Model 4). The results are presented in Table  4 for science, Table  5 for mathematics, and Table  6 for reading.

Variance components were decomposed into student and school level. ICC for Model 1 indicated that 37.9% of the variance component without covariates was associated with schools.

Taking Model 1 as a reference, we observed an increase in total explained variance to 40.5% with factors related to ICT use (Model 2), to 40.8% with factors related to attitude toward ICT (Model 3), and to 41.1% with the single digital dishonesty factor. It is interesting to note that we obtained different results from those reported by Petko et al. ( 2017 ). In their study, they found significant effects on the explained variances of ENTUSE, USESCH, and ICTATTPOS but not of HOMESCH for Switzerland. In the present study (Model 3), HOMESCH and USESCH were significant predictors but not ENTUSE, and for attitude toward ICT, all but INTICT were significant predictors of the variance. However, factors corresponding to ICT use were negatively associated with test performance, as in the study by Petko et al. ( 2017 ). Similarly, all components of attitude toward ICT positively affected science test scores, except for students’ ICT as a topic in social interaction.

Based on the AIC values, Model 4, including ICT use, attitude toward ICT, and digital dishonesty, was the better fit for the data. The parameter ( b  = 498.00, SE b  = 3.550) shows that our sample falls within the 95% confidence interval and that we can reject the null hypothesis. In this model, all factors except the use of ICT outside of school for leisure were significant predictors of explained variance in science test scores. These results are consistent with those reported by Petko et al. ( 2017 ), in which more frequent use of ICT negatively affected science test scores, with an overall positive effect of positive attitude toward ICT. Further, we observed that homework avoidance with digital resources strongly negatively affected performance, with lower performance associated with students reporting a higher frequency of engagement in digital dishonesty practices.

For mathematics test scores, results from Models 2 and 3 showed a similar pattern than those for science, and Model 4 also explained the highest variance (41.2%). The results from Model 4 contrast with those found by Petko et al. ( 2017 ), as in this study, HOMESCH was the only significant variable of ICT use. Regarding attitudes toward ICT, only two measures (COMPICT and AUTICT) were significant positive factors in Model 4. As for science test scores, digital dishonesty practices were a significantly strong negative predictor. Students who reported cheating more frequently were more likely to perform poorly on mathematics tests.

The analyses of PISA test scores for reading in Model 2 was similar to that of science and mathematics, with ENTUSE being a non-significant predictor when we included only measures of ICT use as predictors. In Model 3, contrary to the science and mathematics test scores models, in which INICT was non-significant, all measures of attitude toward ICT were positively significant predictors. Nevertheless, as for science and mathematics, Model 4, which included digital dishonesty, explained the greater variance in reading test scores (42.2%). We observed that for reading, all predictors were significant in Model 4, with an overall negative effect of ICT use, a positive effect of attitude toward ICT—except for SOIAICT, and a negative effect of digital dishonesty on test scores. Interestingly, the detrimental effect of using digital resources to engage in dishonest homework completion was the strongest in reading test scores.

4 Discussion

In this study, we were able to provide descriptive statistics on the prevalence of digital dishonesty among secondary students in the Swiss sample of PISA 2018. Students from this country were selected because they received additional questions targeting both homework effort and the frequency with which they engaged in digital dishonesty when doing homework. Descriptive statistics indicated that fairly high numbers of students engage in dishonest homework practices, with 49.6% reporting digital dishonesty at least once or twice a week. The most frequently reported practice was copying answers from friends, which was undertaken at least once a month by more than two-thirds of respondents. Interestingly, the most infamous form of digital dishonesty, that is plagiarism by copy-pasting something from the internet (Evering & Moorman, 2012 ), was admitted to by close to half of the students (49%). These results for homework avoidance are close to those obtained by previous research on digital academic plagiarism (e.g., McCabe et al., 2001 ).

We then investigated what makes a cheater, based on students’ demographics and effort put in doing their homework (H1), before looking at digital dishonesty as an additional ICT predictor of PISA test scores (mathematics, reading, and science) (H2).

The goal of our first research hypothesis was to determine student-related factors that may predict digital homework avoidance practices. Here, we focused on factors linked to students’ personal characteristics and study programs. Our multilevel model explained about 10% of the variance overall. Our analysis of which students are more likely to digital resources to avoid homework revealed an increased probability for male students who did not put much effort into doing their homework and who were studying in a program that was not oriented toward higher education. Thus, our findings tend to support results from previous research that stresses the importance of gender and motivational factors for academic dishonesty (e.g., Anderman & Koenka,  2017 ; Krou et al., 2021 ). Yet, as our model only explained little variance and more research is needed to provide an accurate representation of the factors that lead to digital dishonesty. Future research could include more aspects that are linked to learning, such as peer-related or teaching-related factors. Possibly, how closely homework is embedded in the teaching and learning culture may play a key role in digital dishonesty. Additional factors might be linked to the overall availability and use of digital tools. For example, the report combining factors from the PISA 2018 school and student questionnaires showed that the higher the computer–student ratio, the lower students scored in the general tests (OECD, 2020b ). A positive association with reading disappeared when socio-economic background was considered. This is even more interesting when considering previous research indicating that while internet access is not a source of divide among youths, the quality of use is still different based on gender or socioeconomic status (Livingstone & Helsper, 2007 ). Thus, investigating the usage-related “digital divide” as a potential source of digital dishonesty is an interesting avenue for future research (Dolan, 2016 ).

Our second hypothesis considered that digital dishonesty in homework completion can be regarded as an additional ICT-related trait and thus could be included in models targeting the influence of traditional ICT on PISA test scores, such as Petko et al. ( 2017 ) study. Overall, our results on the influence of ICT use and attitudes toward ICT on test scores are in line with those reported by Petko et al. ( 2017 ). Digital dishonesty was found to negatively influence test scores, with a higher frequency of cheating leading to lower performance in all major PISA test domains, and particularly so for reading. For each subject, the combined models explained about 40% of the total variance.

4.1 Conclusions and recommendations

Our results have several practical implications. First, the amount of cheating on homework observed calls for new strategies for raising homework engagement, as this was found to be a clear predictor of digital dishonesty. This can be achieved by better explaining the goals and benefits of homework, the adverse effects of cheating on homework, and by providing adequate feedback on homework that was done properly. Second, teachers might consider new forms of homework that are less prone to cheating, such as doing homework in non-digital formats that are less easy to copy digitally or in proctored digital formats that allow for the monitoring of the process of homework completion, or by using plagiarism software to check homework. Sometimes, it might even be possible to give homework and explicitly encourage strategies that might be considered cheating, for example, by working together or using internet sources. As collaboration is one of the 21st century skills that students are expected to develop (Bray et al., 2020 ), this can be used to turn cheating into positive practice. There is already research showing the beneficial impact of computer-supported collaborative learning (e.g., Janssen et al., 2012 ). Zhang et al. ( 2011 ) compared three homework assignment (creation of a homepage) conditions: individually, in groups with specific instructions, and in groups with general instructions. Their results showed that computer supported collaborative homework led to better performance than individual settings, only when the instructions were general. Thus, promoting digital collaborative homework could support the development of students’ digital and collaborative skills.

Further, digital dishonesty in homework needs to be considered different from cheating in assessments. In research on assessment-related dishonesty, cheating is perceived as a reprehensible practice because grades obtained are a misrepresentation of student knowledge, and cheating “implies that efficient cheaters are good students, since they get good grades” (Bouville, 2010 , p. 69). However, regarding homework, this view is too restrictive. Indeed, not all homework is graded, and we cannot know for sure whether students answered this questionnaire while considering homework as a whole or only graded homework (assessments). Our study did not include questions about whether students displayed the same attitudes and practices toward assessments (graded) and practice exercises (non-graded), nor did it include questions on how assessments and homework were related. By cheating on ungraded practice exercises, students will primarily hamper their own learning process. Future research could investigate in more depth the kinds of homework students cheat on and why.

Finally, the question of how to foster engaging homework with digital tools becomes even more important in pandemic situations. Numerous studies following the switch to home schooling at the beginning of the 2020 COVID-19 pandemic have investigated the difficulties for parents in supporting their children (Bol, 2020 ; Parczewska, 2021 ); however, the question of digital homework has not been specifically addressed. It is unknown whether the increase in digital schooling paired with discrepancies in access to digital tools has led to an increase in digital dishonesty practices. Data from the PISA 2018 student questionnaires (OECD, 2020a ) indicated that about 90% of students have a computer for schoolwork (OECD average), but the availability per student remains unknown. Digital homework can be perceived as yet another factor of social differences (see for example Auxier & Anderson,  2020 ; Thorn & Vincent-Lancrin, 2022 ).

4.2 Limitations and directions

The limitations of the study include the format of the data collected, with the accuracy of self-reports to mirror actual practices restricted, as these measures are particularly likely to trigger response bias, such as social desirability. More objective data on digital dishonesty in homework-related purposes could, for example, be obtained by analyzing students’ homework with plagiarism software. Further, additional measures that provide a more complete landscape of contributing factors are necessary. For example, in considering digital homework as an alternative to traditional homework, parents’ involvement in homework and their attitudes toward ICT are factors that have not been considered in this study (Amzalag, 2021 ). Although our results are in line with studies on academic digital dishonesty, their scope is limited to the Swiss context. Moreover, our analyses focused on secondary students. Results might be different with a sample of younger students. As an example, Kiss and Teller ( 2022 ) measured primary students cheating practices and found that individual characteristics were not a stable predictor of cheating between age groups. Further, our models included school as a random component, yet other group variables, such as class and peer groups, may well affect digital homework avoidance strategies.

The findings of this study suggest that academic dishonesty when doing homework needs to be addressed in schools. One way, as suggested by Chow et al. ( 2021 ) and Djokovic et al. ( 2022 ), is to build on students’ practices to explain which need to be considered cheating. This recommendation for institutions to take preventive actions and explicit to students the punishment faced in case of digital academic behavior was also raised by Chiang et al. ( 2022 ). Another is that teachers may consider developing homework formats that discourage cheating and shortcuts (e.g., creating multimedia documents instead of text-based documents, using platforms where answers cannot be copied and pasted, or using advanced forms of online proctoring). It may also be possible to change homework formats toward more open formats, where today’s cheating practices are allowed when they are made transparent (open-book homework, collaborative homework). Further, experiences from the COVID-19 pandemic have stressed the importance of understanding the factors related to the successful integration of digital homework and the need to minimize the digital “homework gap” (Auxier & Anderson, 2020 ; Donnelly & Patrinos, 2021 ). Given that homework engagement is a core predictor of academic dishonesty, students should receive meaningful homework in preparation for upcoming lessons or for practicing what was learned in past lessons. Raising student’s awareness of the meaning and significance of homework might be an important piece of the puzzle to honesty in learning.

Data availability

The data that support the findings of this study are openly available in SISS base at https://doi.org/10.23662/FORS-DS-1285-1 , reference number 1285.

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List of abbreviations related to PISA datasets

students’ perceived autonomy related to ICT use

students’ perceived ICT competence

frequency of computer use at home for entertainment purposes

index of economic, social, and cultural status (computed from PARED, HISEI and HOMEPOS)

parents’ highest occupational status

home possessions

frequency of computer use for school-related purposes at home

digital cheating for homework items for Switzerland

homework engagement items for Switzerland

positive attitude towards ICT as a learning tool

student’s ICT interest

parents’ highest level of education

students’ ICT as a topic in social interaction

frequency of computer use at school

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Désiron, J.C., Petko, D. Academic dishonesty when doing homework: How digital technologies are put to bad use in secondary schools. Educ Inf Technol 28 , 1251–1271 (2023). https://doi.org/10.1007/s10639-022-11225-y

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Homework – Top 3 Pros and Cons

Pro/Con Arguments | Discussion Questions | Take Action | Sources | More Debates

From dioramas to book reports, from algebraic word problems to research projects, whether students should be given homework, as well as the type and amount of homework, has been debated for over a century. [ 1 ]

While we are unsure who invented homework, we do know that the word “homework” dates back to ancient Rome. Pliny the Younger asked his followers to practice their speeches at home. Memorization exercises as homework continued through the Middle Ages and Enlightenment by monks and other scholars. [ 45 ]

In the 19th century, German students of the Volksschulen or “People’s Schools” were given assignments to complete outside of the school day. This concept of homework quickly spread across Europe and was brought to the United States by Horace Mann , who encountered the idea in Prussia. [ 45 ]

In the early 1900s, progressive education theorists, championed by the magazine Ladies’ Home Journal , decried homework’s negative impact on children’s physical and mental health, leading California to ban homework for students under 15 from 1901 until 1917. In the 1930s, homework was portrayed as child labor, which was newly illegal, but the prevailing argument was that kids needed time to do household chores. [ 1 ] [ 2 ] [ 45 ] [ 46 ]

Public opinion swayed again in favor of homework in the 1950s due to concerns about keeping up with the Soviet Union’s technological advances during the Cold War . And, in 1986, the US government included homework as an educational quality boosting tool. [ 3 ] [ 45 ]

A 2014 study found kindergarteners to fifth graders averaged 2.9 hours of homework per week, sixth to eighth graders 3.2 hours per teacher, and ninth to twelfth graders 3.5 hours per teacher. A 2014-2019 study found that teens spent about an hour a day on homework. [ 4 ] [ 44 ]

Beginning in 2020, the COVID-19 pandemic complicated the very idea of homework as students were schooling remotely and many were doing all school work from home. Washington Post journalist Valerie Strauss asked, “Does homework work when kids are learning all day at home?” While students were mostly back in school buildings in fall 2021, the question remains of how effective homework is as an educational tool. [ 47 ]

Is Homework Beneficial?

Pro 1 Homework improves student achievement. Studies have shown that homework improved student achievement in terms of improved grades, test results, and the likelihood to attend college. Research published in the High School Journal indicated 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.” [ 6 ] Students in classes that were assigned homework outperformed 69% of students who didn’t have homework on both standardized tests and grades. 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. [ 7 ] [ 8 ] 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. [ 10 ] Read More

Pro 2 Homework helps to reinforce classroom learning, while developing good study habits and life skills. Students typically retain only 50% of the information teachers provide in class, and they need to apply that information in order to truly learn it. Abby Freireich and Brian Platzer, co-founders of Teachers Who Tutor NYC, explained, “at-home assignments help students learn the material taught in class. Students require independent practice to internalize new concepts… [And] these assignments can provide valuable data for teachers about how well students understand the curriculum.” [ 11 ] [ 49 ] Elementary school students who were taught “strategies to organize and complete homework,” such as prioritizing homework activities, collecting study materials, note-taking, and following directions, showed increased grades and more positive comments on report cards. [ 17 ] Research by the City University of New York noted that “students who engage in self-regulatory processes while completing homework,” such as goal-setting, time management, and remaining focused, “are generally more motivated and are higher achievers than those who do not use these processes.” [ 18 ] Homework also helps students develop key skills that they’ll use throughout their lives: accountability, autonomy, discipline, time management, self-direction, critical thinking, and independent problem-solving. Freireich and Platzer noted that “homework helps students acquire the skills needed to plan, organize, and complete their work.” [ 12 ] [ 13 ] [ 14 ] [ 15 ] [ 49 ] Read More

Pro 3 Homework allows parents to be involved with children’s learning. Thanks to take-home assignments, parents are able to track what their children are learning at school as well as their academic strengths and weaknesses. [ 12 ] Data from a nationwide sample of elementary school students show that parental involvement in homework can improve class performance, especially among economically disadvantaged African-American and Hispanic students. [ 20 ] Research from Johns Hopkins University found that an interactive homework process known as TIPS (Teachers Involve Parents in Schoolwork) improves student achievement: “Students in the TIPS group earned significantly higher report card grades after 18 weeks (1 TIPS assignment per week) than did non-TIPS students.” [ 21 ] Homework can also help clue parents in to the existence of any learning disabilities their children may have, allowing them to get help and adjust learning strategies as needed. Duke University Professor Harris Cooper noted, “Two parents once told me they refused to believe their child had a learning disability until homework revealed it to them.” [ 12 ] Read More

Con 1 Too much homework can be harmful. A poll of California high school students found that 59% thought they had too much homework. 82% of respondents said that they were “often or always stressed by schoolwork.” High-achieving high school students said too much homework leads to sleep deprivation and other health problems such as headaches, exhaustion, weight loss, and stomach problems. [ 24 ] [ 28 ] [ 29 ] Alfie Kohn, an education and parenting expert, said, “Kids should have a chance to just be kids… it’s absurd to insist that children must be engaged in constructive activities right up until their heads hit the pillow.” [ 27 ] Emmy Kang, a mental health counselor, explained, “More than half of students say that homework is their primary source of stress, and we know what stress can do on our bodies.” [ 48 ] Excessive homework can also lead to cheating: 90% of middle school students and 67% of high school students admit to copying someone else’s homework, and 43% of college students engaged in “unauthorized collaboration” on out-of-class assignments. Even parents take shortcuts on homework: 43% of those surveyed admitted to having completed a child’s assignment for them. [ 30 ] [ 31 ] [ 32 ] Read More

Con 2 Homework exacerbates the digital divide or homework gap. Kiara Taylor, financial expert, defined the digital divide as “the gap between demographics and regions that have access to modern information and communications technology and those that don’t. Though the term now encompasses the technical and financial ability to utilize available technology—along with access (or a lack of access) to the Internet—the gap it refers to is constantly shifting with the development of technology.” For students, this is often called the homework gap. [ 50 ] [ 51 ] 30% (about 15 to 16 million) public school students either did not have an adequate internet connection or an appropriate device, or both, for distance learning. Completing homework for these students is more complicated (having to find a safe place with an internet connection, or borrowing a laptop, for example) or impossible. [ 51 ] A Hispanic Heritage Foundation study found that 96.5% of students across the country needed to use the internet for homework, and nearly half reported they were sometimes unable to complete their homework due to lack of access to the internet or a computer, which often resulted in lower grades. [ 37 ] [ 38 ] One study concluded that homework increases social inequality because it “potentially serves as a mechanism to further advantage those students who already experience some privilege in the school system while further disadvantaging those who may already be in a marginalized position.” [ 39 ] Read More

Con 3 Homework does not help younger students, and may not help high school students. We’ve known for a while that homework does not help elementary students. A 2006 study found that “homework had no association with achievement gains” when measured by standardized tests results or grades. [ 7 ] Fourth grade students who did no homework got roughly the same score on the National Assessment of Educational Progress (NAEP) math exam as those who did 30 minutes of homework a night. Students who did 45 minutes or more of homework a night actually did worse. [ 41 ] Temple University professor Kathryn Hirsh-Pasek said that homework is not the most effective tool for young learners to apply new information: “They’re learning way more important skills when they’re not doing their homework.” [ 42 ] In fact, homework may not be helpful at the high school level either. Alfie Kohn, author of The Homework Myth, stated, “I interviewed high school teachers who completely stopped giving homework and there was no downside, it was all upside.” He explains, “just because the same kids who get more homework do a little better on tests, doesn’t mean the homework made that happen.” [ 52 ] Read More

Discussion Questions

1. Is homework beneficial? Consider the study data, your personal experience, and other types of information. Explain your answer(s).

2. If homework were banned, what other educational strategies would help students learn classroom material? Explain your answer(s).

3. How has homework been helpful to you personally? How has homework been unhelpful to you personally? Make carefully considered lists for both sides.

Take Action

1. Examine an argument in favor of quality homework assignments from Janine Bempechat.

2. Explore Oxford Learning’s infographic on the effects of homework on students.

3. Consider Joseph Lathan’s argument that homework promotes inequality .

4. Consider how you felt about the issue before reading this article. After reading the pros and cons on this topic, has your thinking changed? If so, how? List two to three ways. If your thoughts have not changed, list two to three ways your better understanding of the “other side of the issue” now helps you better argue your position.

5. Push for the position and policies you support by writing US national senators and representatives .

More School Debate Topics

Should K-12 Students Dissect Animals in Science Classrooms? – Proponents say dissecting real animals is a better learning experience. Opponents say the practice is bad for the environment.

Should Students Have to Wear School Uniforms? – Proponents say uniforms may increase student safety. Opponents say uniforms restrict expression.

Should Corporal Punishment Be Used in K-12 Schools? – Proponents say corporal punishment is an appropriate discipline. Opponents say it inflicts long-lasting physical and mental harm on students.

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What is the real reason students turn to cheating?

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Issues of student cheating have been increasing over the years as more and more ‘help with homework’ sites have been accessible to students across the UK. A 2018 study by Swansea University found that as many as one in seven graduates had used contract cheating services to complete assignments . The switch to remote learning brought about by the COVID-19 pandemic only exacerbated these issues, and in 2020 the number of students outsourcing their coursework rose rapidly with usage figures for one of the most popular ‘help with homework’ websites increasing by 196%. In early 2021 there were contract cheating websites operating in the UK. 

In October 2021 The Department for Education announced that it would introduce an amendment to the Skills and Post-16 Education Bill, that would make it a criminal offence to provide, arrange or advertise contract cheating services or ‘essay mills’. Australia, New Zealand, Ireland and some US states have taken similar steps. However, a recent study from the journal Assessment and Evaluation in Higher Education revealed that students will still engage with third party homework services even when they believe they are breaking the law. These findings bring into question how effective legislation will be against contract cheating, and what can be done to prevent students seeking out alternative opportunities to outsource their work.

Why do students turn to contract cheating services? Students’ skills in academic writing, such as reports, essays and other written formal documents are becoming an increasing source of anxiety for them. In a Pearson HE learner survey from June 2020, 77% said they had struggled with their first assignments , partly owing to a lack of confidence in their academic skills as they step up to a university standard of working.  

Therefore, instead of viewing students who outsource their coursework as cheats who are undermining the value of academic performance, should we instead question why they are lacking confidence and turn to cheating in the first place – and what role do institutions play in this? 

Download Pearson’s latest report to take an in-depth look at the issue of academic writing, understand why students turn to ‘contract cheating’, and see how universities can take action to nurture their writing abilities with legitimate support and feedback, so they learn, gain confidence, and improve their own work.

How can you re-engage students with reading?

Providing teaching support for large cohorts, how can you solve the maths problem in non-specialist subjects.

Ever since the COVID-19 pandemic caused many U.S. colleges to shift to remote learning in the spring of 2020, student cheating has been a concern for instructors and students alike.

To detect student cheating, considerable resources have been devoted to using technology to monitor students online . This online surveillance has increased students’ anxiety and distress . For instance, some students have indicated the monitoring technology required them to stay at their desks or risk being labeled as cheaters.

Although relying on electronic eyes may partially curb cheating, there’s another factor in the reasons students cheat that often gets overlooked – student motivation.

As a team of researchers in educational psychology and higher education , we became interested in how students’ motivation to learn, or what drives them to want to succeed in class, affects how much they cheated in their schoolwork.

To shine light on why students cheat, we conducted an analysis of 79 research studies and published our findings in the journal Educational Psychology Review. We determined that a variety of motivational factors, ranging from a desire for good grades to a student’s academic confidence, come into play when explaining why students cheat. With these factors in mind, we see a number of things that both students and instructors can do to harness the power of motivation as a way to combat cheating, whether in virtual or in-person classrooms. Here are five takeaways:

1. Avoid emphasizing grades

Although obtaining straight A’s is quite appealing, the more students are focused solely on earning high grades, the more likely they are to cheat. When the grade itself becomes the goal, cheating can serve as a way to achieve this goal.

Students’ desire to learn can diminish when instructors overly emphasize high test scores, beating the curve, and student rankings. Graded assessments have a role to play, but so does acquisition of skills and actually learning the content, not only doing what it takes to get good grades.

2. Focus on expertise and mastery

Striving to increase one’s knowledge and improve skills in a course was associated with less cheating. This suggests that the more students are motivated to gain expertise, the less likely they are to cheat. Instructors can teach with a focus on mastery, such as providing additional opportunities for students to redo assignments or exams. This reinforces the goal of personal growth and improvement.

3. Combat boredom with relevance

Compared with students motivated by either gaining rewards or expertise, there might be a group of students who are simply not motivated at all, or experiencing what researchers call amotivation. Nothing in their environment or within themselves motivates them to learn. For these students, cheating is quite common and seen as a viable pathway to complete coursework successfully rather than putting forth their own effort. However, when students find relevance in what they’re learning, they are less likely to cheat.

When students see connections between their coursework and other courses, fields of study or their future careers, it can stimulate them to see how valuable the subject might be. Instructors can be intentional in providing rationales for why learning a particular topic might be useful and connecting students’ interest to the course content.

4. Encourage ownership of learning

When students struggle, they sometimes blame circumstances beyond their control, such as believing their instructor to have unrealistic standards. Our findings show that when students believe they are responsible for their own learning, they are less likely to cheat.

Encouraging students to take ownership over their learning and put in the required effort can decrease academic dishonesty. Also, providing meaningful choices can help students feel they are in charge of their own learning journey, rather than being told what to do.

5. Build confidence

Our study found that when students believed they could succeed in their coursework, cheating decreased. When students do not believe they will be successful, a teaching approach called scaffolding is key. Essentially, the scaffolding approach involves assigning tasks that match the students’ ability level and gradually increase in difficulty. This progression slowly builds students’ confidence to take on new challenges. And when students feel confident to learn, they are willing to put in more effort in school.

An inexpensive solution

With these tips in mind, we expect cheating might pose less of a threat during the pandemic and beyond. Focusing on student motivation is a much less controversial and inexpensive solution to curtail any tendencies students may have to cheat their way through school.

Are these motivational strategies the cure-all to cheating? Not necessarily. But they are worth considering – along with other strategies – to fight against academic dishonesty.

About The Conversation

The Conversation ( https://theconversation.com/us ) is an independent, nonprofit publisher of commentary and analysis, authored by academics and edited by journalists for the general public. The Conversation publishes short articles (800-1000 words) by academics on timely topics related to their research.

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Common Reasons Students Cheat

Poor Time Management

The most common reason students cite for committing academic dishonesty is that they ran out of time. The good news is that this is almost always avoidable. Good time management skills are a must for success in college (as well as in life). Visit the Undergraduate Academic Advisement website  for tips on how to manage your time in college.

Stress/Overload

Another common reason students engage in dishonest behavior has to do with overload: too many homework assignments, work issues, relationship problems, COVID-19. Before you resort to behaving in an academically dishonest way, we encourage you to reach out to your professor, your TA, your academic advisor or even  UB’s counseling services .

Wanting to Help Friends

While this sounds like a good reason to do something, it in no way helps a person to be assisted in academic dishonesty. Your friends are responsible for learning what is expected of them and providing evidence of that learning to their instructor. Your unauthorized assistance falls under the “ aiding in academic dishonesty ” violation and makes both you and your friend guilty.

Fear of Failure

Students report that they resort to academic dishonesty when they feel that they won’t be able to successfully perform the task (e.g., write the computer code, compose the paper, do well on the test). Fear of failure prompts students to get unauthorized help, but the repercussions of cheating far outweigh the repercussions of failing. First, when you are caught cheating, you may fail anyway. Second, you tarnish your reputation as a trustworthy student. And third, you are establishing habits that will hurt you in the long run. When your employer or graduate program expects you to have certain knowledge based on your coursework and you don’t have that knowledge, you diminish the value of a UB education for you and your fellow alumni.

"Everyone Does it" Phenomenon

Sometimes it can feel like everyone around us is dishonest or taking shortcuts. We hear about integrity scandals on the news and in our social media feeds. Plus, sometimes we witness students cheating and seeming to get away with it. This feeling that “everyone does it” is often reported by students as a reason that they decided to be academically dishonest. The important thing to remember is that you have one reputation and you need to protect it. Once identified as someone who lacks integrity, you are no longer given the benefit of the doubt in any situation. Additionally, research shows that once you cheat, it’s easier to do it the next time and the next, paving the path for you to become genuinely dishonest in your academic pursuits.

Temptation Due to Unmonitored Environments or Weak Assignment Design

When students take assessments without anyone monitoring them, they may be tempted to access unauthorized resources because they feel like no one will know. Especially during the COVID-19 pandemic, students have been tempted to peek at online answer sites, Google a test question, or even converse with friends during a test. Because our environments may have changed does not mean that our expectations have. If you wouldn’t cheat in a classroom, don’t be tempted to cheat at home. Your personal integrity is also at stake.

Different Understanding of Academic Integrity Policies

Standards and norms for academically acceptable behavior can vary. No matter where you’re from, whether the West Coast or the far East, the standards for academic integrity at UB must be followed to further the goals of a premier research institution. Become familiar with our policies that govern academically honest behavior.

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How Do I Stop Students From Copying Each Other’s Homework Assignments?

Five steps that worked for me.

My students, like students everywhere, are smart and funny and creative and wonderful in so many ways. Also like students everywhere, they constantly seem to be looking for shortcuts on their homework. One of the bus drivers told me last year that the kids openly ask her to turn the interior lights on so they can finish copying homework before they get to school! Sigh. At least they’re motivated enough to copy, right?

This year, I made it a major goal to stop students from cheating. I put this five-step process in place, and it really cut down on the homework copying in my classroom. Here it is. 

Step 1: Check the quality of your assignments.

First of all, it’s worth taking a close look at the kind of homework you assign. If you do a lot of worksheets, you might find those work better for in-class activities. Instead, try focusing homework on in-depth writing assignments and individual written responses.

If you’re a math teacher, having kids respond in writing about how they solved a problem always works, as does having them write their own problems or exemplars for what they’ve been learning. Anything that requires student-generated content is automatically going to be harder to copy.

Step 2: Check the quantity.

Of course, this creates a lot more grading than worksheets, which led me to reflect on the amount of homework I assigned. At first, I found myself overwhelmed. I had to wonder if this was how my students felt when they looked at a night’s homework load. If there had been someone whose grading I could have copied, I probably would have done it!

The result? I assigned a lot less homework as the year went on. Put your homework to this test: If it’s not worth your time to grade carefully, it’s not worth the students’ time to do it.

Step 3: Explain the changes.

Once you’ve started assigning less homework, you’ll want to make your reasons explicit to your students. “I’m assigning less homework because I don’t want to waste your time. That means that anything I do assign is really important, and it’s important for you to actually do it on your own.” This speech went a long way with many of my students, but I had another trick up my sleeve.

Step 4: Allow time to learn and make mistakes.

You might also want to try a few get-out-of-jail-free cards when it comes to homework. My middle schoolers are still in the process of learning how to budget their time and stay organized, and sometimes they make mistakes. I gave each kid three one-day extensions that they could use over the course of the year to avoid a penalty for late homework.

There were certain assignments on which these could not be used, like rough drafts we needed to edit or group projects. It lowered the general stress level and set a culture of respect and accountability that encouraged my kids to plan ahead. For the naysayers who say, “The real world won’t give them extensions,” I would respectfully offer my disagreement. What? You’ve never posted your grades after the deadline?

Step 5: Bring the pain.

Although this cut down on copying substantially, kids will always test your limits. That’s when you move on to the final step. It works like this: Read every word of every assignment. Make sure you grade an entire class at once so you’ll know if a phrase or a creatively spelled word seems familiar, and then hunt back through 35 other papers until you find the one it’s copied from. It is important that you identify when students cheat and that your justice is swift and merciless.

I had an escalating system of consequences for cheating. First time, you split the grade. If the assignment gets a 90, each person gets a 45. Second time, each person gets a zero and a lunch detention. Third time, it’s a phone call home in addition to a zero and an after-school detention. Not a single kid made it to the third offense. They have to believe that you’re documenting this and you’ll follow through. Let them see you putting their names in your file so they know you know what offense they’re on. It is a logistical pain, but it’s effective.

So did my kids ace the standardized test because they had done their homework all year? Not to brag, but their writing scores were pretty high. And I don’t think they missed out on many valuable educational experiences when I stopped assigning worksheets. After all, they’d have just copied them anyway!

How do you stop students from cheating? Come and share  in our WeAreTeachers HELPLINE group  on Facebook. 

Plus, check out  how to give meaningful homework, even when it’s not graded ..

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EdTech Goes Undercover: An Insider’s View of What Students Post on Contract Cheating Sites

Amelia Pang is a journalist and an editor at EdTech: Focus on Higher Education. Her work has appeared in the New Republic, Mother Jones, and  The New York Times Sunday Review, among other publications.

Editor’s Note: This is part 1 of a 2-part investigation. Part 2 covers how IT departments can detect and prevent contract cheating in higher education.

“Please complete my assignment,” a student posts on a microtutoring website that universities say  facilitates contract cheating . The assignment is on the history of public health. APA format. Three sources. At least 750 words. In less than 15 minutes,  EdTech  sees a university ghostwriter accepting the assignment for $20.

There are hundreds of “homework help” websites that have seen an  exponential increase in customers  since the start of the pandemic. The services offered on sites like these typically run the gamut of legitimate tutoring to selling exam documents and answers. Some flat out offer to take an entire online course or exam for students.

The shadow industry of contract cheating falls into a legal gray area. When students and tutors make an account on a homework help site, they must sign a terms-of-service agreement and honor code that forbids academic cheating. But an undercover  EdTech  investigation found this agreement appears to be rarely enforced.

“I have definitely seen an increase in customers since the pandemic began,” Alex, an academic ghostwriter who currently works for a homework help site, tells  EdTech.  “Specifically, there has been an increase in the number of students posting that they want full online classes done for them. Most of the time, students have no problem finding a contractor.”

What Is Contract Cheating, and How Does It Work?

To avoid legal liability, some homework help sites are using automation tools to edit the language of posts. Whenever students submit a post, the first line always says something like “I need help understanding the assignment,” or “Help me learn.”

But  EdTech  saw this as mostly a cursory statement. Many students will also directly say, “Please complete my assignment.” Some even go so far as to request that the “tutor” be available at a certain date and time to take an online exam for them.

“I would say that 30 percent of the requests are for ‘help’ versus completing assignments,”  a tutor for one of these sites told BRIGHT Magazine in 2016.  “It is largely a place for students to cheat.”

When  EdTech  created a tutor account at a homework help site earlier this year, we found that not much has changed since the BRIGHT Magazine article came out five years ago.

An insider's view of what students post on contract cheating sites.

Although students are blatantly asking for “tutors” to complete assignments and exams for them,  EdTech  saw academic ghostwriters making bids and accepting the work — often within minutes.

Students Hire Academic Ghostwriters to Take Online Courses for Them

Former and current academic ghostwriters also say that taking an entire online course for students is a common practice in the industry — a practice that has existed since the inception of online education. “That was always standard operating procedure,” says Dave Tomar, a former academic ghostwriter who started his decade-long career in contract cheating in 2000. He is currently the managing editor of  Academic Influence , where he  shares his insights  on how educators can counter the surge of contract cheating during the pandemic.

“When I started doing this, I would frequently get these full online modules at the beginning of a rolling semester," Tomar says. “I got the full syllabus, and everything that I was expected to do over the next couple of months. Now, with countless students forced into remote learning, you have a whole new customer pool that is growing.”

As for how much students are willing to pay, the contractors charge “anywhere from $300 to $700 for a full class depending on the student, the subject and the difficulty,” says Alex, who currently works for a homework help site.

INSIDER EXCLUSIVE:   Read Part 2 – What can universities do about contract cheating?

Fake Tutors Entice Unknowing Students to Engage in Contract Cheating

Academic cheating sites also strongly encourage students to sell their coursework— an act that may be illegal in 17 states.

“Distributing any post-secondary assignment for a profit with reasonable knowledge that it will be submitted by another person for academic credit is a crime in many US states,” Citron Research, an investment research firm that investigates overvalued fraudulent companies, stated in  a report.

It’s a big problem for many institutions. According to Douglas Harrison, vice president and dean of the school of cybersecurity and information technology at the  University of Maryland Global Campus , some of these contract cheating websites are “facilitating massive transfers of institutional proprietary material into their file-sharing systems.”

Harrison says many students may not even realize they are cheating when they download a university’s copyrighted classroom assessment materials because these websites reframe downloading answers to tests as a form of studying or tutoring. “They reframe file-sharing as educational, even though these are behaviors that conventional norms of academic integrity would consider misconduct,” he says.

Dave Tomar former academic ghostwriter.

To make matters worse, these websites have mastered sophisticated techniques to lure unsuspecting students. Several of these prominent homework tutoring sites will offer to give students a discount if they let their academic ghostwriter have access to the online course. This often results in the contract cheater stealing other students’ personal information.

“So the contract cheater then reaches out to other students and says, ‘I’m a tutor in your course. And I’ve helped another student in your class with their assignments. Would you like a little help?’” Harrison says, describing how the contract cheater pitches cheating “services” to other students.

This can be especially confusing for students, who may not know how to tell the difference between a contract cheater and a legitimate tutor who is affiliated with the university.

“Most of the students who we find in academic misconduct settings after inappropriately using materials on these sites, they did not set out to be malicious cheaters. Now that doesn’t mean we don’t hold them accountable, but we have to hold them accountable in proportion to the root cause of the situation,” Harrison says.

Who Is Using Academic Ghostwriters?

According to the ghostwriters who are contracted to help students cheat, their customers are usually underserved students who need access to remedial courses, and nontraditional students who struggle to balance coursework with full-time employment.

“I would argue that what is facilitating the surge of contract cheating is the fact that students are increasingly desperate and lacking support,” says Tomar.

During Tomar’s time as an academic ghostwriter, he caught glimpses into their personal circumstances. “Some would tell you they are a parent working full time. And they just can’t deal with this challenge right now. Some say, ‘I’ve invested X number of dollars into this education, and I cannot afford to fail this class. But I don’t know how to do this assignment.’”

Alex mentions that many are also English language learners. “As I noted, some students are asking for whole classes to be done, and a lot of those are English or writing-intensive courses,” he says. “That does not mean that they are ESL, but [my sense is] most of them are.”

To fundamentally address the cheating pandemic, universities and colleges may need to invest in more resources for vulnerable student populations.

“It begins with figuring out who’s struggling, why they’re struggling and what we can do to help them before they end up as contract cheating customers,” Tomar says.

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Does A.I. Really Encourage Cheating in Schools?

For my columns during the back-to-school season, I thought it would be useful to go over the state of public education in America. This series will be similar to the one I wrote on parenting a few months back in that it will be wide-ranging in subject, so please bear with me.

This past spring, Turnitin, a company that makes anti-cheating tools to detect the use of A.I. in student papers, released its findings based on more than two hundred million samples reviewed by its software. Three per cent of papers had been more or less entirely written by A.I. and roughly ten per cent exhibited some traces of A.I. It’s never a great idea to rely on data that a for-profit company releases about its own product, but these numbers do not suggest some epidemic of cheating. Other research has shown that there hasn’t been a significant increase in student plagiarism since the unveiling and mass popularization of large language models such as ChatGPT. Students seem to cheat a lot, generally—up to seventy per cent of students reported at least one instance of cheating in the past month—but they cheated at the same rates before the advent of A.I.

What has increased is the number of teachers and adults who seem convinced that all the kids are cheating. A study by the Center for Democracy and Technology found that “a majority of teachers still report that generative AI has made them more distrustful of whether their students’ work is actually theirs.” Such suspicions have been paired with real questions about the efficacy of A.I.-detection tools, including one concerning finding that showed A.I. detectors were more likely to flag the writing of non-native English speakers. This uncertainty, along with the failure of many school districts to implement a clear and comprehensive A.I. policy, has led to another layer of debate among educators about how to handle instances of alleged cheating. A set of guidelines on the use of Turnitin, which was recently released by the Center for Teaching Excellence at the University of Kansas, warned teachers against making “quick judgments” based on the company’s software and recommended that educators instead “take a few more steps to gather information,” including comparing previous examples of the student’s work, offering second chances, and talking to the student. (Earlier this month, the Wall Street Journal reported that OpenAI, the company that developed ChatGPT, had built its own detection tool, which was much more accurate than its competitors’ software, but had held off releasing it, because admitting that students did indeed use ChatGPT to cheat might be bad for business.)

Educational data is notoriously unreliable. There’s a whole lot of it—kids take tests every day and have nearly every part of their educational journeys tracked from the age of five—but, if you dig into many education studies, you’ll find a whole lot of noise and almost no signal. When trying to parse what, for example, a small increase in statewide reading scores might mean about the efficacy of a given program, the best one can do is look at the data, try to eyeball some larger trend, and then present it somewhat halfheartedly. Here’s what I believe is happening in schools with ChatGPT: teachers are probably a little overly suspicious of students, in part because they have been given tools to catch cheaters. Those panoptic tools have likely scared some students straight, but cheats are going to cheat. When I was in high school, graphing calculators were blamed for student cheating. Ten years later, the ubiquity of cell phones in classrooms stirred up visions of kids across the country texting one another test answers whenever a teacher’s back was turned. Wikipedia also had its moment as the destroyer of research and knowledge in schools; today, it’s clear that Wikipedia has been a net good for society and probably more accurate and less biased than the Encyclopædia Britannicas it replaced.

The situation reminds me of the problem with sports-gambling apps . Gambling, like plagiarism, isn’t new. If you stick a hundred people who have never placed a bet in their lives in a casino, a small number of them will come back the next day, and the next, and the next. The rest will either never bet again or gamble only occasionally and in a responsible manner. Cheating in school strikes me as a similar phenomenon—maybe it’s true that most kids engage in a little bit of unethical schoolwork, but some portion of kids never will and many more likely do so only in the most trivial (or trying) situations. Technology does change the experience; it can encourage edge cases to start tossing dice at a craps table or asking ChatGPT to write a paper. But, for the most part, it’s not why adults gamble on sports or why kids cheat at school. And just as Wikipedia didn’t ruin the written word—and likely deepened the research of many student papers by simplifying the introductory task of getting to know a subject—the five-paragraph essay will survive large language models.

The rush to solve A.I. cheating and the myriad educational tools that have been developed and sold to schools across the country raise a tertiary, and far more interesting, question than whether or not the written word will survive. When we think about students’ work, where do we draw the line between what has sprung out of their developing mind and what has not?

In STEM subjects, the lines are a little clearer. If a student just looks over a neighbor’s shoulder and writes down the same answer, most people agree that’s cheating. But if a student is trying to prove that he understands how to solve a complicated math problem that involves some multiplication, does the use of a calculator mean that the student is cheating? He is not being tested on whether he knows how to multiply or not, so why waste time and potentially introduce careless errors? I do not think that having ChatGPT write a paper is the same thing as using a calculator for more menial and elementary tasks within a larger math problem, but it’s worth asking why we feel differently about the automation of research and the written word. Even in the fine arts, patrons and appreciators have long accepted that the artist doesn’t need to actually perform each brushstroke, construct every sculpture, or build every bit of a large installation. Small armies of uncredited assistants have their hands all over the works of Andy Warhol, Damien Hirst, and Jeff Koons, which has kicked up periodic controversies, but not enough to end the practice. Would we think less of these artists if a machine just did all of the assistants’ work?

These questions are abstract and ridiculous, but they also reflect the arbitrary way in which we think about what constitutes cheating and what does not. Outside of blatant acts of plagiarism, the line between cheating and not cheating in the humanities seems to rely on the amount of time it takes to complete a task. For example, if a student visited a library archive to research what happened in the week after D Day, spooled some microfiche into an ancient machine, and dutifully jotted down notes, we would likely think more highly of that effort than if the student found the same article in a Google search, and certainly more so than if he paraphrased some Wikipedia editor’s reading of that article.

Under this logic, school isn’t about creating new scholarship or answering questions correctly—it’s about teaching proper work habits. A young person who takes the time to go into a library is more likely to develop the types of work habits that will allow him to find accompanying bits of information that might be useful in creating a novel, an algorithm, or a convincing argument. Setting aside the obvious offense of dishonesty, the problem with cheating isn’t so much that the student skips over the process of explaining what they learned—it’s that they deprive themselves of the time-consuming labor of actually reading the book, typing out the sentences, and thinking through the prompt.

One of the fundamental crises that the Internet brought to classrooms was the sense that, because references to facts and history no longer needed to be stored in your brain, nothing really needed to be learned anymore. Search engines, Wikipedia, and ChatGPT all demanded the same explanation: If we have these tools, what’s the point of these lessons? Schools tend to change slowly, even if education trends come and go. This is a good thing and mostly owes to the fact that good teachers tend to have long careers. But, since the days when I was a teacher, in the mid-two-thousands, I’ve noticed a subtle shift in the way people think about what kids should learn in the humanities. The idea of memorization, for the most part, has gone away; children are no longer forced to rattle off the date of the First Defenestration of Prague (1419) or commit the same lists of vocabulary words to memory. At the same time, most of the political fights that people get into over schools these days hinge on curriculum choices, which have always struck me as both silly and wildly beside the point. It’s actually pretty hard to shake a child’s beliefs with a stray book or lesson. But I sometimes wonder if the doctrinaire push in today’s schools, the intense fights over how to teach history or math, the censorious book bans in some states, come from a collective fear that the knowledge-retention part of school might now be outdated. Since it’s hard to justify why kids should learn dates and vocabulary words and the like, we have subtly shifted the purpose of school to teaching them what to believe and how to go through life as a good person. This is an admirable goal but will usually end in bitter conflict over which values matter.

Opinions in education, as a rule, move very quickly and oftentimes in a reactionary way. But the actual implementation of any consensus can take decades to complete. This inefficiency can be harmful—it’s taken far too long to remove phones from schools, for example—but it also allows for little panics like the current one around large-language-model cheating. I do not think A.I. encourages cheating in some revolutionary way, and I imagine any rise in plagiarism might have more to do with the extraordinary pressure of college admissions and the overly competitive atmosphere in many high schools. Until that changes, some population of kids will convert any new app into a cheating tool, educational technology will sell blockers, and the cycle will just repeat itself. It doesn’t have to be this way. The A.I.-cheating panic gives us a chance to reëmphasize the work-habit part of schooling and to walk away from claims that the books that children read are somehow dangerous or that only one version of history can be taught. This, it should be said, is not so different from the way that thousands of teachers across the country already think about their jobs, but the work part of school has become far more gauche than it used to be, with schools across the country eliminating homework and focussing more on developing a student’s love of a subject or the implied politics of a curriculum. A little revanchism, such as in-class essays written with a paper and pencil in elementary and middle school, might go a long way. The lesson is almost always the actual doing of the lesson, not the facts that are learned. ♦

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September 28, 2020 Teaching & Learning

How to avoid online cheating & encourage learning instead.

Students tempted to find easy answers while distance learning

By Sherry Posnick-Goodwin

Joline Martinez suspected many of her students were cheating after her school closed last spring and she transitioned to distance learning. They showed their work on equations and came up with the correct answers, but something was definitely off, says the Yosemite High School math teacher.

Joline Martinez

“My students were solving problems with ridiculous fractions,” says Martinez, a member of Yosemite Unified Teachers Association. “They were using steps they had never been taught. It was a huge issue. I suspected they were cheating. I was losing sleep over this.”

Martinez was so frustrated, she posted about it on CTA’s “Teaching, Learning and Life During COVID-19” Facebook page, and found she was not alone. Numerous CTA members responded to her post, saying they also suspected students were cheating while working from home.

One of them, Maggie Strode, was troubled that students who were struggling when attending school on campus were suddenly turning in perfect papers during distance learning.

“Students were combining several steps into one while solving equations, and always moved the variable to the left side of the equation,” says Strode, a math teacher at South Hills High School and member of the Covina Unified Education Association. “It’s something I do not have my students do, because when they are doing the equations on their own, it leads to errors.” During online office hours she asked them to solve similar problems, and they didn’t have a clue.

Both teachers figured out their students were using Photomath, an app that utilizes a cellphone’s camera to recognize mathematical equations and display a step-by-step solution onscreen — which may differ from how students were taught.

“It’s frustrating,” says Strode. “I was creating videos showing students how to do the work, but they weren’t watching them. Instead, they used this app. It’s much easier to keep an eye on students when you have them in your classroom. When they work from home, it is much more challenging.”

“I gave them the opportunity to resubmit. Students were going through a lot, and I wanted to demonstrate compassion.” — Karin Prasad, Liberty Education Association

Students are more tempted with distance learning

When schools closed abruptly last March due to COVID-19, older students knew that their grades couldn’t be lowered, only raised. Nonetheless, many cheated while working from home, even those with passing grades, say teachers.

Educators admit they were so overwhelmed with transitioning to distance learning that it was difficult to police students who were intent on beating the system. Students can Google answers instantly on their phones during exams and watch videos about how to cheat on YouTube. (Some colleges are having students install a second camera on their devices and clearing their workspace, so that instructors can see students’ hands during exam time.)

Karin Prasad

Distance learning has created more temptations for students, observes Karin Prasad, an English teacher at Heritage High School in Brentwood. She uses turnitin.com , an online program that compares her students’ work with other student essays in the system and also published work. After schools closed due to the pandemic, two essays were red-flagged in what’s called a “similarity report.”

Normally she would have given both students a zero on the assignment. But Prasad gave them some leeway because of the state of the world.

“Being in a pandemic is weird and scary,” says the Liberty Education Association member. “So instead of giving them a zero, which I would have done in a normal school year, I gave them the opportunity to resubmit. Students were going through a lot, and I wanted to demonstrate compassion.”

Martinez also didn’t make a big fuss the way she would have under normal circumstances. “I didn’t really push the issue. I didn’t want to have to contact all of the parents; I have 200 students in my classes. It was definitely an uphill battle.”

This year will be different, vows Martinez, whose district will begin the year online. Students will be held accountable for work done from home, and the no-cheating rule will be strictly enforced.

“I give timed quizzes, where they only have a short time for each question — and no time to look it up.” — Pedro Quintanilla, Imperial Valley Teachers Association

• How teachers can put the kibosh on cheating

“If you can Google the answer to a question, it’s not worth asking,” says Katie Hollman, a seventh grade math teacher at Walter Stiern Middle School in Bakersfield. “Students immediately jump on Google to hunt for answers in class by opening a second tab on their computer, so you can just imagine what happens at home on cellphones.”

Hollman, a member of the Bakersfield Elementary Teachers Association, asks students to explain their work on Flipgrid videos they create. She also has students create their own real-world math word problems, and then solve them. It might involve visiting a restaurant and explaining the bill, deciding how much they want to tip, adding the tax, and figuring out percentages, for example. Or going to various grocery stores and comparing the unit rates of various items for sale to discern which is a better bargain. Because students are mostly at home, the research for menu and grocery store items happens online, of course.

Pedro Quintanilla

Imperial High School teacher Pedro Quintanilla can tell if students are cheating on exams while solving math problems with paper and pencil, by looking at handwriting when assignments are submitted online. If the work seems too perfect, without pressure points in some spots and nothing crossed out or erased, he becomes suspicious.

“If you don’t see any struggle, that is a big sign,” says Quintanilla, an Imperial Valley Teachers Association member.

“One of the ways I assess knowledge of major concepts is by giving a timed quiz, and have them submit their answers to each question, one at a time, almost immediately. Also, I include a Quizzizz activity [a fast-paced, interactive game] where they need to perform the skills learned in a lesson. In addition, no pun intended, I have them submit their notes for a lesson. And I give timed quizzes, where they only have a short time for each question — and no time to look it up.”

Suzie Priebe

Suzie Priebe, a history teacher at Amelia Earhart Middle School, asks students to write about things they are knowledgeable about on the first day of class so she can hear their “voice” and get a “flavor” of how they write. She compares their tone to essay questions later, to determine authenticity.

She also asks them interpretive questions on history, such as “What do you think is the most important thing about the Bill of Rights and why?”

“In history, it’s not as important to memorize, because you look up things on Google, such as when the Declaration of Independence was signed. But knowing why it was signed and being able to explain that is just better.”

Other ideas to prevent cheating online:

• Mix it up , with tests having a variety of multiple-choice, true/false and open-ended questions. It’s more difficult for students to share answers when they must explain concepts.
• Have every student start the exam at the same time and set a time limit. The key is having enough time for students who know the information to respond, but not enough time for students who don’t know the material to search online for answers.
• Only show one question at a time , so students can’t be searching ahead on Google.
• Change test question sequence , so that all students do not have the same question at one time, to avoid screen sharing.
• Give students different versions of the same test to thwart screen sharing.
• Give students their scores all at the same time , so that students who finish early don’t confirm answers for those still working.
• Increase points for class participation .
• Talk about integrity , and have students sign an “academic integrity” agreement.

“I want my students to be successful. If they rely on shortcuts and cheat, they won’t survive in the real world.” —Maggie Strode, Covina Unified Education Association

Encourage students to be honest

Talking to students about integrity, trust and doing the right thing also prevents cheating.

Maggie Strode

“I let my students know that once you are labeled a cheat, it’s very hard to regain trust,” says Strode. “I tell students I’d rather they not turn in an assignment than turn in work they didn’t do. They don’t realize that they sometimes put more time and effort into cheating than it would take to just do the assignment. I love my students. I want them to be successful — not only in my classroom, but in life. If they rely on shortcuts and cheat, they won’t survive in the real world. No one will make allowances for them there.”

Hollman discusses cheating in her weekly “Life Lessons with Hollman” sessions, urging students to resist the temptation and instead ask for help.

Katie Hollman

“I want to help them understand the material so we can fix the problem. I make time for tutoring during online office hours. And I explain that if they cheat in college, they won’t just get a zero on an assignment — they will get kicked out of school.”

She also explains that it’s in their own best interest: If enough students cheat, the teacher assumes the class has mastered the material, and makes the curriculum even more challenging.

Quintanilla talks to his students about the importance of digital citizenship and the value of the honor system in his classes.

“With distance learning, you have to establish a good relationship with students, and then, when you emphasize honesty, you have more buy-in from them.”

“I would rather see the child attempt something, fail, and ask for help, rather than not try.”

Distance Learning: Parents Doing Children’s Work?

Even in normal times, second grade teacher Nailah Legohn has seen the lines blur between parental support and parents doing the homework, so their children don’t fall behind. But with distance learning, parents and sometimes older siblings are doing schoolwork of children more frequently.

Nailah Legohn

“Sometimes it’s hard to know who is really doing the work,” says Legohn, a teacher at Ridgemoor Elementary School in Sun City. “The little ones need a lot of parent support. And they may be saying, ‘I don’t get it.’ If they whine and cry enough, the parent may give in and provide the answer because they want the child to get credit — or they want their child to go outside and play. Parents are under so much pressure. Many of them are also working at home while trying to help their children.”

Parents think they are helping, but they are not, says Legohn, a member of the Menifee Teachers Association. “I tell them, ‘Please don’t do the work for them.’ I explain that they are not setting up their child for success. If kids know that someone else is going to provide the answer, they will expect that to happen when they go back into the regular classroom. And that’s not how it’s going to be. When schools reopen, students are going to have to do the work themselves. If they aren’t used to it, it will be much more of a struggle.”

Legohn asks her students to circle problems that are difficult for them, and then she helps students understand the material by offering extra help during virtual office hours. They can also message her on Google Classroom to ask questions.

“I want my students to love learning and understand how to learn,” says Legohn. “I am pushing for them to have a growth mindset and the ability to ask questions. I would rather see the child attempt something, fail, and ask for help, rather than not try. Parents are role models, and the best way they can help is teaching their children to take responsibility for their own learning.”

#online learning

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May 1, 2017

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