essay on cheating in online exams

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A systematic review of research on cheating in online exams from 2010 to 2021

Fakhroddin noorbehbahani.

Faculty of Computer Engineering, University of Isfahan, Azadi square, 8174673441 Isfahan, Iran

Azadeh Mohammadi

Mohammad aminazadeh.

In recent years, online learning has received more attention than ever before. One of the most challenging aspects of online education is the students' assessment since academic integrity could be violated due to various cheating behaviors in online examinations. Although a considerable number of literature reviews exist about online learning, there is no such review study to provide comprehensive insight into cheating motivations, cheating types, cheating detection, and cheating prevention in the online setting. The current study is a review of 58 publications about online cheating, published from January 2010 to February 2021. We present the categorization of the research and show topic trends in the field of online exam cheating. The study can be a valuable reference for educators and researchers working in the field of online learning to obtain a comprehensive view of cheating mitigation, detection, and prevention.

Introduction

Today, distance education has been transformed into online settings, and the COVID-19 pandemic has raised online learning significantly across the world. The COVID-19 enforced the closing of traditional learning all over the world, resulting in 1.5 billion students and 63 million educators shifting from face-to-face learning to online learning. This situation has revealed the strengths and weaknesses of the digital transformation of education (Valverde-Berrocoso et al., 2020 ).

In (Martin et al., 2020 ), it has been shown that the online learning publications are continuously being increased from 2009 to 2018, and one of the leading research themes is course assessment. Course assessment is very challenging in online learning due to the lack of direct control over students and educators.

For an educational institution, assessment integrity is essential because it affects institutional reputation. It is necessary to employ traditional cheating detection besides prevention methods and new digital monitoring and validation techniques to support assessment integrity in online exams (Fluck, 2019 ).

The study (Watson & Sottile, 2010 ) has reported that students are remarkably more likely to get answers from others during online exams or quizzes compared to live (face-to-face) ones. Therefore, preserving the integrity of online exams is more challenging. There are some strategies to mitigate online exam cheating, such as getting offline (face-to-face) proctored exam, developing cheat-resistant questions (e.g., using subjective measures instead of objective measures), and lessening the exam score percentage contributing to the overall course grade.

Traditional cheating methods include, hiding notes in a pencil case, behind ruler, or clothes, writing on arms/hands, leaving the room, etc. (Curran et al., 2011 ). Technological advances and online learning have enhanced education, however, they also have facilitated cheating in courses (Turner & Uludag, 2013 ). For instance, an examinee could use a mobile phone to text someone to get the answer. Although this would be difficult in the exam hall, some examinees could text without looking at the mobile phone. Applying scientific calculators, Mp3 players calculator, and wireless equipment such as an earphone and a microphone are other tools that facilitate cheating in offline exams (Curran et al., 2011 ).

Although cheating motivations in online and offline exams are not significantly different (Turner & Uludag, 2013 ), detecting and mitigating online cheating could be more intricate. This is because, in addition to traditional cheating methods that also could be exploited in online exam cheating, there exist various technologies and tools that could be applied for cheating in online exams more easily. For example, using remote desktop and share screen, searching for solutions on Internet, using social networks, etc.

Cheating in an online setting is more convenient than a traditional offline exam. Accordingly, detecting and preventing online cheating is critical for online assessment. Therefore, this issue is one of the biggest challenges that MOOC (Massive Open Online Courses) summative assessment faces.

Recent researches imply that a critical issue in online education is academic dishonesty and cheating. Today, paid services exist that impersonate students in online courses to ensure their identity. In recent years, proctoring technologies such as identity authentication, keystroke recognition, and webcam proctoring will be extended to secure online exams (Xiong & Suen, 2018 ). Apart from direct proctoring, there are some techniques such as controlling the browser, limiting exam time, randomizing questions and choices, etc. However, it seems cheating in online courses is pretty common (Dendir & Maxwell, 2020 ).

Although one of the most critical challenges in online learning is to mitigate and handle cheating, there is no comprehensive literature review and classification in this field. Hence, in this paper, we present a systematic mapping review of researches in online examination cheating. The research questions are as follows:

• RQ1: What are the publication trends in online cheating?
• RQ2: What are the main reasons for online cheating?
• RQ3: What are the cheating types in online exams?
• RQ4: How can online cheating be detected?
• RQ5: How can online exam cheating be prevented?

The paper is structured as follows. In Section 2 , the research method is described, including study selection criteria, databases and search strategy, and study selection. Section 3 presents review results and provides the answers to research questions. Sections 4 and 5 discuss the results and conclude the paper, respectively.

The current study is a literature review about cheating in online exams. A literature review identifies, selects, and synthesizes primary research studies in order to provide a picture of the topic under investigation. According to (Page et al., 2021 ), a record is the title or abstract (or both) of a report indexed in a database or website, and a report is a document (in paper or electronic format) supplying information about a particular study. It could be a journal article, preprint, conference abstract, study register entry, clinical study report, dissertation, unpublished manuscript, government report, or any other document providing relevant information. The current literature search has been performed based on the well-established PRISMA principles (Page et al., 2021 ).

Inclusion and exclusion criteria

The main criteria for the articles considered in the current review are as follows.

Inclusion criteria:

• Researches should be written in English.
• Records should be retrieved utilizing the designed search query.
• Studies should be published between January 2010 and February 2021.
• In cases where several papers reported the same study, only the most recent ones were included (i.e., theses and papers extracted from theses, extended version of papers published in journals).

Exclusion criteria:

• Papers merely related to methods applicable to traditional cheating types, detection, and prevention are eliminated.
• Studies not related to research questions are ignored.
• Articles only related to cyber-attacks to online exam systems are excluded.
• Low-quality researches are discarded (i.e., studies published by non-reputable publishers without peer review, too short review time, and so on, studies with poor theoretical background, experimental evaluation, or structure).

Databases and search strategy

We applied a wide range of databases as our primary source, including Google Scholar, Web of Science, and Scopus. We also added the publications which had cited the extracted records. Records were searched using the following search terms for the title, keywords, and abstract sections.

(Cheat OR e-Cheating OR Fraud OR Dishonesty OR Anti-cheating OR Cheat-resistant OR Abnormal behavior OR Misconduct OR Integrity OR Plagiarism) AND

(Electronic OR Online OR Digital OR Virtual OR Cyber OR Academic) AND

(Exam OR e-Exam OR Course OR e-Course OR Assessment OR e-Assessment OR Test OR e-Test OR Environment OR e-Environment) AND

(Prevent OR Detect OR Mitigate OR Reduce OR Minimize OR Monitor OR Proctor OR Reason OR Motivation OR Type OR Deter OR Control).

Study selection

The search result included 289 records, 26 of which were duplicated, and so they were deleted. From 263 screened records, 54 records were excluded by examining either the title or the abstract. In the next step, 12 reports were eliminated because they were not retrieved because were not accessible. Furthermore, after full-text eligibility checking, 144 reports have been excluded according to the inclusion and exclusion criteria as mentioned earlier. ‌

This resulted in 53 reports that along with 5 other reports (obtained from citation searching and assessed for eligibility), were finally selected for literature review about online cheating. The flow of information through different phases of the review is presented in the PRISMA flow diagram depicted in Fig. ​ Fig.1 1 .

The PRISMA flow diagram

After selecting 58 studies, three domain experts were asked to assign a Credibility Score (CS) to each study. After evaluation of each study, experts agreed on a credibility score ranging from 0 to 5 based on the following criteria: publisher credibility, number of citations per year, theoretical and experimental quality, and organization and structure. CS statistics are as follows: mean = 3.81, SD =0.79, min = 2.5, max =5.

A summary of online cheating research papers and their study themes is presented in Table ​ Table1. 1 . (Appendix ​ (Appendix1 1 .)

Online cheating studies

Several findings emerged as a result of the research synthesis of the selected fifty-eight records on online cheating. The selected studies were categorized into four main topics, namely Cheating reasons, Cheating types, Cheating detection, and Cheating prevention, as shown in Fig. ​ Fig.2. 2 . All subsequent classifications reported in this paper have been provided by the authors. The studies under every four main topics are investigated by three experts, and a list of items is extracted for each category. Notably, some studies were corresponded to multiple main topics. Next, several brainstorming sessions have been conducted to classify each main topic further. To extract the classifications, the XMind tool has been employed, which is a professional and popular mind mapping software.

Online cheating research classification

In the following sub-sections, the detailed analysis of the review results is described according to the five research questions we defined to drive the research.

Publication trends

In Fig. ​ Fig.3, 3 , the number of publications per year is displayed (in this study, the final publication date is applied). In 2017, the greatest number of studies corresponding to the conducted review have been published. As shown in Fig. ​ Fig.4, 4 , the dominant publication type is journal papers with 53% of the total publications. In terms of the average citations of the selected studies regarding their classes, the maximum average citations belong to the journal papers with an average citation of 19.65 (see Fig. ​ Fig.5 5 ).

Number of publications per year

Distribution of publication per types

Average citation per publication type

There are 747 works cite the selected studies related to the review. As displayed in Fig. ​ Fig.6, 6 , the greatest and lowest shares of the total citations pertain to the journal articles and the theses, respectively. The number of publications per research theme is shown in Fig. ​ Fig.7. 7 . The cheating prevention and detection themes are the most prevalent research themes in online cheating. In the following four subsections, the studies under each of the four research themes are described and classified thoroughly.

Distribution of publications according to citations

Number of publications per research theme

Cheating reasons

The primary reason for cheating is that examinees feel the rewards outweigh the risks (Lancaster & Clarke, 2017 ). There exists a wide variety of reasons why candidates decide to commit cheating, still, they could be categorized into four general reasons, namely Teacher-related, Institutional, Internal, and Environmental reasons. The complete classification of the cheating reasons is displayed in Fig. ​ Fig.8, 8 , which is described in the following sections.

Teacher-related reasons

All the reasons related to the teacher or the course instructor are put into this category. Maeda ( 2019 ), has observed that the student’s relationship with the teacher has crucial influences on academic integrity. Teachers’ unethical behaviors, such as favoring those who have bribed over those who have not, or favoring the students who participated in private tutoring sessions, motivate the oppressed students to cheat. The author also found that teachers’ low interest in students’ depth of learning, which also results in a poor pedagogical style, could be an important reason that motivates students to participate in any kind of unethical behavior (Maeda, 2019 ).

Course difficulty could motivate the examinees to cheat. Some students blamed their teachers for complicated and complex course materials. In some specific cases, this reason could be a consequence of students’ lack of perseverance. They find cheating as a way to relieve these difficulties (Amigud & Lancaster, 2019 ).

As a result of distributed learning with online courses and examinations, Moten et al. ( 2013 ), have expressed that students feel isolated in an online environment. They often become frustrated when they do not get the help they immediately need, for instance, the night before an exam. This situation is closely dependent on the presence time of the teacher in online communication environments.

Some teachers restrain from punishing the cheaters appropriately due to ethical issues. This could be due to the sympathy of some teachers with cheaters. After listening to the cheater’s excuses and justifications, the teacher might give them a second chance. Sometimes, teachers are worried about the consequences of punishments and the corresponding pressures that cheaters experience, hence they don’t punish the cheater or the punishment is too mellow.

This increases the students’ courage to cheat during online exams due to decreased risk of being punished after being caught and implies that cheating penalties are insignificant over the long run (Topîrceanu, 2017 ).

Exam design is one of the most important contributing factors that motivates examinees to cheat in the exam. Weakly designed exams such as similar multiple-questions for every examinee or easy accessibility of solutions over the web, can make it easy to cheat. On the other hand, questions being too complex and irrelevant to course materials, forces students to commit cheating during online exams (Srikanth & Asmatulu, 2014 ).

Institutional reasons

In (Maeda, 2019 ), it is observed that the rules and policies of the institution are directly related to the number of unethical behaviors occurrences. It is found that institutions with stricter regulations and better commitment to strengthening academic integrity, face much less cheating behavior between their students. Institutional policies not only create an anti-cheating atmosphere, but also makes dishonest academic behaviors challenging to take place. Also, Backman ( 2019 ) emphasizes that if it becomes easy for students to cheat, they will cheat.

Impulsiveness is a crucial reason why students try to cheat during online examinations. They feel isolated and disconnected, so they may imagine they won’t get caught or the instructor does not care if they commit academic dishonesty. Unethical behaviors have a direct relationship with the student’s impulsiveness (Moten et al., 2013 ).

Moreover, in an isolated environment, due to the lack of face-to-face communications with teachers, students have much less respect for their teachers that leads to increasing misbehaviors. That is why teachers should personalize the online environment for students by calling their names or listening to their voices, so that online classes become more engaging and interactive for students (Moten et al., 2013 ).

Dobrovska ( 2017 ), expressed that the poor quality of the institution’s online learning system discourages students from learning the course materials, and makes it difficult for them to learn, hence, they are more motivated to cheat.

Academic aptitude is one of the most important and underrated reasons leading students to commit misbehaviors. It means educational institutions don’t discriminate between students and ignore their unique abilities, skills, and different levels of preparedness for a specific task. This makes unprepared students feel frustrated about that particular task or course, which leads them to seek help from more talented and prepared students in that specific context (Amigud & Lancaster, 2019 ).

Internal reasons

Another category of cheating reasons is internal motivators. The motivators over which the candidate has complete control, including intrinsic factors, personality and psychological characteristics, lie in this category. The internal reasons are divided into three subcategories as follows.

Student’s academic performance

One significant internal factor is the student’s academic performance. There are several reasons that could result in poor academic performance as follows: lack of learning and skills to find resources, students unwillingness to follow recommended practices, inability to seek appropriate help, procrastination, poor time management (Dobrovska, 2017 ), and lack of confidence in their ability to learn course materials (Norris, 2019 ).

Low intrinsic interest in the course materials

Low intrinsic interest in the course is another reason mentioned in (Dobrovska, 2017 ), which could be caused by a lack of sufficient interest in course materials and subjects or the mindset that these materials and knowledge are unnecessary and unimportant for future life (Norris, 2019 ).

Personal characteristics

There is a strong relationship between students’ moral attitudes toward cheating and their level of participation in academic misbehaviors (Maeda, 2019 ). Therefore, conscientious belief is considered as an internal reason stopping students from unethical behaviors. However, it has been shown that religious beliefs do not necessarily lower cheating behaviors (Srikanth & Asmatulu, 2014 ).

Other reasons included in studies are student’s laziness for sufficient home preparation before the exam (Dobrovska, 2017 ), competition with others and the desire to get ahead (Amigud & Lancaster, 2019 ), desire to help other peers (Moten et al., 2013 ) and the student’s thrill of taking risk (Hylton et al., 2016 ).

Environmental reasons

The reasons mentioned in this section highly depend on the atmosphere and type of environment a student is in, either during the online exam or beforehand in social media or communication with people. We put these reasons in four major categories: Peers’ behavior, Parents’ attitudes, Personal issues and, Social factors.

Peers’ behavior

Peers could influence individuals in a manner that their cheating motivations are increased. In an academic environment, however, it is primarily because of the competing objectives, such as the desire to get ahead in scores. This depends on the amount of competition in the academic environment (Amigud & Lancaster, 2019 ).

Experimental research among Cambodian students, has figured out that being among a group of cheaters, psychologically drives the students to repeat their peers’ actions and commit cheating. In addition, there is high pressure on those who do not collaborate with peers, or reject participating in their group work. It is found that they are blamed for being odd and unkind (Maeda, 2019 ).

According to (Srikanth & Asmatulu, 2014 ), being in an environment where peers’ cheating remains undetected, gives this kind of feeling to non-cheaters that they are setting back in scores and are unfairly disadvantaged compared to those cheaters.

Parents’ attitude

Parents’ acceptance of cheating behaviors, massively affects the student’s mindset toward these behaviors. As expressed in (Maeda, 2019 ), parents’ behaviors toward their child’s cheating, vary from complete unacceptance to active involvement and support. Another reason related to parents’ attitudes is putting their children under pressure to achieve good or higher than average grades (Backman, 2019 ).

Personal issues

Personal issues could be mental and physical health problems (Amigud & Lancaster, 2019 ), problems within the family (e.g., parents arguing, separation and divorce, etc.), and fear of failure in exams and its further consequences like financial and time setbacks (Hylton et al., 2016 ).

Societal factors

Poor economic conditions and the development level of a country are examples of societal factors affecting students’ motivation to cheat and achieve academic success (Maeda, 2019 ).

Countries with various cultures, social expectancies, and people’s attitudes have different behaviors regarding academic performance. In some countries, academic performance and grades are known to be crucial for success in life, whereas, in other countries, academic performance is relatively low valued. This range of different expectations from students leads to various social beliefs and behaviors toward cheating (Maeda, 2019 ). In research presented in (Holden et al., 2020 ), it is shown that a primary reason could be the existence of a cheating culture. Some students may cheat because they desire to portray a better image of themselves to their society (Norris, 2019 ). Another societal factor influencing cheating behaviors is the technology evolution that strengthens cheating motivation (Maeda, 2019 ). This is because technology brings about increased access to cheating resources. The evolution of technology, specifically search engines and social media, makes it easier for students to cheat.

Cheating types and facilitators

To mitigate cheating behaviors effectively and efficiently, cheating methodologies, types, and facilitators should be known. Cheating is performed either individually or by the cooperation of others (called group cheating). Figure ​ Figure9 9 displays the complete classification of cheating types.

Cheating types

Individual cheating

Individual cheating is carried out without any assistance from any person. This type of cheating could be categorized as using forbidden materials and other types are described as follows.

Using forbidden materials

Individual cheating can occur by using forbidden materials during the exam, such as looking at a textbook or a cheat sheet (Fontaine et al., 2020 ), (Holden et al., 2020 ), searching the web, using offline electronic resources such as images, voices, etc. (Korman, 2010 ), (Holden et al., 2020 ), or even using objects in the exam room to hide notes.

Other types

Other types of individual cheating include accessing the questions and solutions before the exam, which Korman ( 2010 ) refers to as “unauthorized intelligence”. Another dishonest behavior is social engineering, which is grade negotiation with the teacher through fake facts and exploiting personal sympathy.

Group cheating

Cheating methods through cooperation with others could be categorized as Impersonation, and Collaboration types.

Impersonation

Impersonation means employing someone to take the exam for the examinee, either the whole exam or some parts of it (Korman, 2010 ), (Holden et al., 2020 ). It can occur in forms of voice conversion, face presentation attack and face impersonation, fake identity matching to a stored biometric, and attack on the keystroke dynamics (Chirumamilla & Sindre, 2019 ). These are attacks on the biometric system to bypass the authentication mechanisms. The other impersonation techniques include remote desktop control by a third party (Kasliwal, 2015 ), (Gruenigen et al., 2018 ), sharing the screen with a third party (Gruenigen et al., 2018 ), (Bawarith, 2017 ), and credential sharing, which is impersonation via shared username and password of an academic account or LMS (Learning Management System) (Dobrovska, 2017 ).

Collaboration

Collaboration is defined as getting any kind of help from others to answer the exam questions. It could be in the form of sign language communications that come in numerous forms, such as foot-tapping, pencil or any object dropping during the proctored exam, abnormal coughing, or suspicious actions (Srikanth & Asmatulu, 2014 ).

Listening to a third party’s whispers behind the camera (Chirumamilla & Sindre, 2019 ), any type of communication which is unauthorized such as sending or receiving messages, or voice and video calls (Korman, 2010 ), are also considered as collaborative cheating.

Other cheating methods in this category are remote desktop control (Kasliwal, 2015 ) and sharing the screen with others to collaborate with others about questions (Gruenigen et al., 2018 ), applying small hidden micro cameras to capture images and record videos for sharing with other peers (Bawarith, 2017 ), and finally, organizational cheating which is a result of institution’s personnel corruption (Korman, 2010 ).

The last one, as Korman ( 2010 ) showed, can take place when personnel help candidates to cheat. Changing the exam grade or exam answers after the exam (exam integrity corruption), giving the solutions to the candidate during the exam, or just bribing the proctor not to report the cheating or not to punish after being caught (Kigwana & Venter, 2016 ) are instances of organized cheating.

Contract work is a type of collaboration that means doing work with the help of someone else under the obligations of a contract. Contract workers may provide some or all of the exam answers. In this case, sometimes impersonating the student through the whole academic course is reported (Chirumamilla & Sindre, 2019 ).

Cheating facilitators

Methods discussed here act as cheating facilitators to support the process of cheating. In other words, these facilitators can be applied to perform any kind of cheating. A study presented in (Peytcheva-Forsyth et al., 2018 ), indicates that technology in general, is the leading facilitator of cheating practices. Cheating facilitators are classified as shown in Fig. ​ Fig.10 10 .

Three different methodologies are used by students to facilitate cheating, either individually or in a group, described as follows.

Interrupting to get more time

Sometimes examinees try to buy more time to work more on the exam answers. For instance, the examinee may report an error about the exam system or exam proctoring software to convince the teacher to restart the exam session. This enables the candidate to get more time for cheating and finding the solutions during this interval when the session is closed (Motenet al., 2013 ). Another interruption method is to submit corrupted answer files by the candidate. In this case, the teacher reports that the files were corrupted and asks the candidate to resubmit the answer files. Most of the time, during the first submission and the second one, there exists at least one day, which implies the candidate gets at least one more day to answer the exam questions (Moten et al., 2013 ).

Other more classical methods to interrupt are toilet requests during the exam (Chirumamilla & Sindre, 2019 ), communication break and delay in answering oral exam right after a question is asked (Chirumamilla & Sindre, 2019 ), circumventing the exam process at a specific time with different excuses, and postponing taking the exam (Fontaine et al., 2020 ), (Korman, 2010 ). By deferring taking the exam, students can buy more time to become more prepared, either by studying more, or getting access to the exam questions and solutions.

Employing multiple devices

In proctored exams, either by a camera or software, students try to use multiple devices and answer the questions with the primary one while cheating via the secondary device. Several types of devices could be employed as the second device, such as computers and laptops (Moten et al., 2013 ), smartwatches (Wong et al., 2017 ), smart glasses such as Google glasses (Srikanth & Asmatulu, 2014 ), smartphones and tablets (Korman, 2010 ), programmable and graphical calculators to store notes and formulas (Kigwana & Venter, 2016 ), and tiny earpieces for remote voice support during the exam (Bawarith, 2017 ).

Other facilitators

Redirecting the webcam to hide something from its field of view (Sabbah, 2017 ), (Srikanth & Asmatulu, 2014 ), or disabling the webcam or microphone completely (Srikanth & Asmatulu, 2014 ) are other tricks used to facilitate cheating.

By using virtual machines on a computer, the user can run a virtual operating system on the primary one. This technique would hide the activities done on the second operating system from the software or the human proctoring the primary operating system. (Kasliwal, 2015 ).

Corrupting the exam system’s integrity to change the exam results after being held (e.g., changing the scores or answers after the examination) is another notable case (Korman, 2010 ). Lastly, in (Parks et al., 2018 ), the authors have investigated that social media and channels operating on them could act as cheating facilitation environments.

Cheating detection

Cheating detection methods can be categorized into during the exam and after the exam detection methods. Further classification of the cheating detection methods is presented in Fig. ​ Fig.11 11 .

Cheating detection during the exam

To ensure academic integrity in online examinations, it is essential to detect cheating during the exam. Cheating detection can be partitioned into two main categories, namely, continuous authentication and online proctoring. Continuous authentication methods verify the identity of test-takers, and online proctoring monitors the examinees to detect any misbehavior during the exam. In the following, we will mention different techniques in each category.

Continuous authentication

One of the main types of cheating is impersonating. Therefore, it is essential to authenticate students before exam registration and prevent unauthorized candidates from taking the examination. In addition, it is necessary to validate the identity of the test-taker during the exam continuously. The continuous authentication systems are mainly based on biometric or behaviometric modalities and can be categorized into unimodal and multimodal schemes.

Unimodal authentication is the automatic recognition and identification of candidates using a unique characteristic. This characteristic could be either static (physiological) such as the face, fingerprint, hand geometry, and iris, or could be dynamic (behavioral) such as voice, handwriting, keystroke, and mouse dynamics (Chirumamilla & Sindre, 2019 ).

As a unimodal authentication system, Arnautovski ( 2019 ) designed a face recognition system, which captures the image of the test-taker at random time intervals. The facial recognition module continuously verifies the examinee’s identity by comparing captured images to the image from the exam registration process. In (Aisyah et al., 2018 ), an Android-based online exam application is implemented that takes photos of the examinee with random intervals and a web-based application lets the admin or supervisor of examination validate pictures of participants. In addition, Idemudia et al. ( 2016 ) proposed a system that tracks and detects faces continuously to verify the candidates. If the authentication failure remains for more than a few seconds, the system will stop the examination.

In (Sabbah, 2017 ), a scheme called ISEEU is proposed, in which each examinee’s session is streamed using a webcam. A proctor monitors the video screens and can generate alerts when any suspicious action is detected. He et al. ( 2018 ) proposed an anti-ghostwriter system using face recognition methods. The ghostwriter merges the student’s photo and their photo to make a fake one, or they change their appearance to mislead the examiners. The experimental results in (He et al., 2018 ), indicate that the proposed framework can detect ghostwriters with an acceptable level of accuracy.

Since some candidates may refuse to use a camera due to privacy concerns, Bilen et al. (2020) suggested that instructors offer their students two options. An examinee can agree to use a camera during the exam. In this situation, the record will be used as evidence if they are accused of cheating. However, if the examinee doesn’t accept using a camera, the instructor can claim cheating without providing evidence to the student.

In (Bawarith, 2017 ), the system authenticates the examinees continuously through an eye tracker. The data obtained from the eye tracker are translated into a set of pixel coordinates so that the presence or absence of eyes in different screen areas can be investigated.

Multimodal biometric authentication systems utilize different biometric or behaviometric traits simultaneously, which makes impersonating more difficult. In this regard, Bawarith et al. ( 2017 ) proposed a system that utilizes fingerprint and eye-tracking for authentication. The eye tribe tracker is used to continuously ensure that test-takers are the ones they are claiming to be. Whenever the system detects the examinee is no longer present in front of the screen, the system is locked, and the test-taker must be authenticated again via fingerprint.

In (Sabbah, 2017 ), a multimodal scheme called SABBAH is proposed, which adds continuous fingerprint and keystroke dynamics to the ISEEU scheme (Sabbah, 2017 ). In contrast to ISEEU, SABBAH uses an automatic system to detect fingerprint, keystroke, or video violations. Traore et al. ( 2017 ) proposed a system that continuously authenticates examinees using three complementary biometric technologies, i.e., face, keystroke, and mouse dynamics. In this system, test-takers are continuously authenticated in the background during the exam, and alarms are created and sent to the instructor through the proctoring panel.

Online proctoring

Online proctoring is essential to promote academic integrity. Alessio et al. ( 2017 ) reported significant grade disparities in proctored versus un-proctored online exams. Online proctoring can be categorized into human and automated proctoring. In human proctoring, a human proctor monitors the students remotely to detect suspicious behavior. In contrast, in automated proctoring, the cheating behaviors are flagged or detected automatically by the proctoring system.

Recently, several technologies have been developed to facilitate proctoring online exams remotely. For example, Kryterion™ Live Video Monitoring and ProctorU allow users to be monitored by a human proctor via a webcam during examination (Hylton et al., 2016 ). In (Reisenwitz, 2020 ), substantial support for online proctoring is provided. The results show a significant difference between the scores of exams that were not proctored and those proctored using ProctorU software.

Some systems can capture screenshots of the candidates’ PCs at random times during the examination (Migut et al., 2018 ). Consequently, if examinees use any forbidden resource on their computer, it will be shown to the proctor. Alessio ( 2018 ) applied video proctoring via a webcam at Miami University. The results demonstrate that students are less likely to cheat when monitored with a webcam during online testing.

In another study, kiosk-based remote online proctored examinations are compared with tests administered under a traditional proctoring environment. In kiosk-based proctoring, the test is taken on special computer kiosks located at accessible places such as libraries. The kiosks are equipped with enhanced webcams and are supervised online by a live remote proctor. The results indicated that examinees’ scores obtained under online kiosk-based proctoring are comparable to examinations taken in test centers with onsite proctors (Weiner & Hurtz, 2017 ).

A different approach for cheating detection is a class mole that means the instructor enrolls in students’ groups under another name as a mole to detect and combat collusion. In this way, they can discover dishonest students when they discuss cheating amongst themselves (Moten et al., 2013 ).

Human proctoring is costly and labor-intensive. Therefore, different automated proctoring systems are proposed to monitor the students during the examination and detect unauthorized behavior. In the following, we discuss several automated methods.

Chuang et al. proposed a semi-automatic proctoring system that employs two factors, namely, time delay in answering the questions and head-pose variation, to detect suspicious behavior. Afterward, a human proctor could use more evidence to decide whether a student has cheated (Chuang et al., 2017 ).

Garg et al. ( 2020 ) proposed a system to detect the candidate’s face using Haar Cascade Classifier and deep learning. If the examinee’s face moves out of the examination frame or multiple faces are detected in the frame, the test will automatically be terminated, and the administrator will receive a notification. In (Fayyoumi & Zarrad, 2014 ), a two-second candidate video is taken during the examination period. The images in the video are analyzed to verify whether the examinee is looking somewhere other than their screen. If the test-taker doesn’t focus on their screen, it may indicate cheating behaviors such as looking at an adjacent PC or reading from an external source.

In (Hu et al., 2018 ), the proposed system uses a webcam to monitor candidates' head posture and mouth state to detect abnormal behavior. Through the rule-based reasoning method, the system can detect suspicious behavior such as turning heads and speaking during the online examination.

Prathish et al. ( 2016 ), developed a multimodal system for online proctoring. The system captures audios and videos of the candidates as well as their active windows. If yaw angle variations, audio presence, or window changes are detected in any time frame, it can be considered an indicator of cheating. Consequently, the captured video, audio, and system usage are fed into a rule-based inference system to detect the possibilities of misbehaviors. ProctorTrack is another automated online exam proctoring product that employs facial and audio recognition, body movements, and computer activity monitoring to detect any suspicious action during examination (Norris, 2019 ).

Atoum et al., ( 2017 ) developed a system that can detect a wide variety of cheating behaviors during an online exam using a webcam, wearcam, and microphone. Using wearcam makes it possible to monitor what the student observes. It helps to detect any phone or text in the testing room that is prohibited. In addition, by using the wearcam, the system can detect another form of cheating that is reading from books, notes, etc. Furthermore, the system can estimate the head gaze of the test-taker by combining the information from the webcam and wearcam. Another form of cheating is getting verbal assistance from another person in the same room, or remotely via a phone call. The system can detect this kind of cheating using the microphone and speech detection. Considering the mentioned aspects, the proposed multimedia system can perform automatic online exam proctoring.

Saba et al. ( 2021 ), developed an automatic exam activity recognition system, which monitors the body movements of the students through surveillance cameras and classifies activities into six categories using a deep learning approach. The action categories are normal performing, looking back, watching towards the front, passing gestures to other fellows, watching towards left or right, and other suspicious actions. Movement recognition based on video images is highly dependent on the quality of images. Therefore, Fan et al. ( 2016 ), employed a Microsoft Kinect device to capture the examinee’s gesture. The duration and frequency of the detected action events are then used to distinguish the misbehavior from the normal behavior.

The system presented in (Mengash, 2019 ) includes a thermal detector attached with a surveillance camera and an eye movement tracker. When examinees intend to cheat, their body will emit a specific range of heat, and the emitted heat will trigger the camera to focus and detect the candidate’s face. Then the eye tracker detects eye movements, and the system detects the cheating intentions of the test-taker. There are other biometric-based methods for cheating detection. For example, keystroke and linguistic dynamics can detect stress, which indicates suspicious behavior (Korman, 2010 ).

Diedenhofen and Musch ( 2017 ), developed a JavaScript application called PageFocus, which can be added to the test page and run in the background. Whenever the examinee switches to a page other than the test page, a defocusing event is registered. The script captures when and how frequently defocusing and refocusing events occur on the test page. Another method is to permit students to get to just a couple of sites that are whitelist. If the examinee tries to open a site that is not allowed (one from blacklist), the instructor will be informed through an Android application or Internet (Kasliwal, 2015 ).

Tiong and Lee ( 2021 ), proposed an e-cheating intelligent agent composed of two modules, namely the internet protocol (IP) detector and the behavior detector. The first module could monitor the examinees’ IP addresses and enable the system to alert if a student changes their device or location. The second module detects abnormal behavior based on the speed of answering questions. Another method for cheating detection is comparing the IP addresses of the examinees to check whether two participants are in the same place (Bawarith, 2017 ).

Cheating detection after the exam

Even though different methods are employed to prevent students from cheating, some will still cheat during the examination. Consequently, a bunch of techniques is proposed to detect cheating students after the exam. This way, the reliability of online assessments will be improved. In the following, we will discuss different methods of cheating detection after the exam.

Video monitoring

The University of Amsterdam has developed a system that records the student’s video screen and the environment during the exam. Later a human proctor views the recording and flags and reports any suspicious behavior (Norris, 2019 ). Proctoring software proposed in (Alessio et al., 2017 ), records everything students do during the examination. After the exam, the recordings can be reviewed by the professor, teaching assistants, or employees of the proctoring vendor to identify cheating behaviors.

Human proctoring is a tedious and time-consuming process. To reduce the time and cost of proctoring, an automatic system can be employed to detect and flag suspicious events using machine learning methods. In this regard, Cote et al. ( 2016 ) proposed a system for the automatic creation of video summaries of online exams. The proposed method employs head pose estimations to model a normal and abnormal examinee’s behavior. Afterward, a video summary is created from sequences of detected abnormal behavior. The video summaries can assist remote proctors in detecting cheating after the exam.

Jalali and Noorbehbahani ( 2017 ), implemented an automatic method for cheating detection using a webcam. During the exam, images are recorded every 30 seconds by a webcam for each candidate. After the exam, the recorded images are compared with reference images of that student. If the difference exceeds a threshold, the image will be labeled as a cheating state.

Li et al. ( 2015 ), proposed a Massive Open Online Proctoring framework that consists of three components. First, the Automatic Cheating Detector (ACD) module uses webcam video to monitor students, and automatically flag suspected cheating behavior. Then, ambiguous cases are sent to the Peer Cheating Detector (PCD) module, which asks students to review videos of their peers. Finally, the list of suspicious cheating behaviors is forwarded to the Final Review Committee (FRC) to make the final decision.

Other methods

There are various ways of cheating, and therefore, different methods are used to detect cheating after the exam. For example, one of the cheating behaviors is to collude and work on tests together. However, most learning management systems allow the instructor to view IP addresses. Therefore, if different students submit their assessments by the same IP address in a short time frame, it could be detected and considered as a sign of collusion (Moten et al., 2013 ).

In addition, statistical methods can be used to analyze student responses to assessments and detect common errors and the similarities of answers (Korman, 2010 ). Mott ( 2010 ) stated that the distribution of identical incorrect responses between examinee pairs is a Polya distribution. The degree of cheating for each examination will follow the skewness or third central moment of the distribution.

Predictive analytics systems implicitly collect data while the students interact with the virtual learning environment. The collected data, which include student’s location, access patterns, learning progress, device characteristics, and performance, is used to predict trends and patterns of student behavior. Consequently, any unusual pattern may indicate suspicious behavior (Norris, 2019 ). Answering an examination takes a reasonable amount of time. Therefore, another indicator of dishonest behavior is an extremely short interval between the access time and the completion of the assessments, which can be detected by log time analysis (Moten et al., 2013 ).

In (Bawarith et al., 2017 ), an E-exam management system is proposed that classifies participants as cheating or non-cheating based on two parameters, namely the total time and the number of times the examinee is out of the screen. The focus of the test-taker is recorded using an eye tracker during the exam.

Kasliwal (Kasliwal, 2015 ), designed an online examination tool that captures the network traffic during the exam using a kismet server. The captured package can then be analyzed to determine the frequency of URLs accessed by students. If one of the URLs is getting accessed more frequently or very rarely, it could be considered suspicious.

To detect plagiarism in papers or essay-type questions, platforms such as DupliChecker.com 1 or Turnitin.com 2 can be used. These websites compute a similarity index and show all potential plagiarisms. Based on the similarity index, the instructor decides about further actions (Moten et al., 2013 ).

A weakness of similarity detection software is that it computes the resemblance of a submitted assessment with others' works and cannot detect an original text written by others for the student in question. Stylometry discovers this issue by checking the consistency of the delivered contents with other texts written by the same student. If the style of a text does not match with the previous works of that student, it may indicate complicity (Chirumamilla & Sindre, 2019 ). Opgen-Rhein et al. ( 2018 ) presented an application that employs machine learning methods to learn the programming styles of students. This work is based on the assumption that the programming style of each student is unique, and therefore, the model can be used to verify the author of assignments.

Another way of cheating detection is using a cheating trap, which means creating websites that could be found when the students search for answers. The solutions in trap websites are incorrect, and consequently, dishonest students could be detected (Korman, 2010 ). However, this method contradicts professional ethics.

In addition, the teacher can search the internet by hand periodically and try to find all possible web pages that provide solutions matching the exam questions. This approach could be applied to create a pool of potential solutions from the internet that will be used for plagiarism detection purposes after the exam (Norris, 2019 ).

Cheating prevention

After discussing and analyzing the examinees’ motivations for cheating and the reasons which directly or indirectly drive them to commit unethical actions during online examinations, a great deal of concern is gathered around how to decrease cheating in online exams and lower the probability of these actions taking place.

We categorized cheating prevention into two major types, namely, before-exam prevention and during-exam prevention. Figure ​ Figure12 12 displays the classification of the cheating prevention methods.

Before-exam prevention

To prevent examinees from cheating, there exist several methods that should be implemented before the exam is held. Each will be discussed in detail as follows.

Exam design

In any situation that prevention is concerned, a proven and low-cost approach is a “cheat-resistant” design -A design that inherently prevents some specific cheating types from happening. This is why exam design is so critical. A cheat-resistant exam design, by its nature, prevents a range of possible forms of cheatings from occurring.

One way of achieving a good design is developing personalized exams for each candidate separately. There are several ways to do so, such as parameterization (Manoharan, 2019 ), which is a set of fixed questions with variable assumption values, using data banks with a large pool of questions to select questions randomly (Manoharan, 2019 ), (Norris, 2019 ) or implementing an AI-based method to produce unique exams (Chua & Lumapas, 2019 ).

Li et al. ( 2020 ) has put effort into designing a method for randomizing the question orders for each candidate. Their general idea is to show the questions one by one, and besides that, each student gets a different question at a time. This research mathematically proves that examinees cannot get much cheating gain.

In (Manoharan, 2019 ), the author has investigated an approach to personalizing multiple-choice examinations using the macro. Macro is a computer program fragment that stores data. It has a set of particular inputs for generating random exams based on a question bank. This method could bring freedom and flexibility to the exam design, but it needs basic programming skills.

Another aspect of exam design concentrates specifically on question design. Some of the most valuable methods are listed below.

• Using novel questions: This type of question design is so unique in design and phrasing that it becomes very challenging to be plagiarized even with searching the web (Nguyen et al., 2020 ).
• Using knowledge-based questions instead of information-based questions: These questions challenge the level of knowledge. The answers are not on the web or in reference books, and they need critical thinking and reasoning (Nguyen et al., 2020 ).
• Using essay questions rather than multiple-choice questions: During an online exam, multiple-choice questions are highly susceptible to cheating. Hence, long essay questions are preferred (Varble, 2014 ).
• Using questions with specific assumptions and facts: Although giving extra and not useful facts may mislead any candidate, even those taking the exam honestly, it will reduce the possibility of web-based plagiarism considerably by making it less straightforward to search online (Nguyen et al., 2020 ).
• Having an open-book exam: Open-book exam questions should test students’ understanding, critical reasoning, and analytical skills. Since the answers to these questions are not found in any sources directly, open-book exams may reduce the cheating opportunity (Varble, 2014 ), (Backman, 2019 ).

Finally, other methods not placed into the above categories are mentioned below.

Showing questions one by one without the option of going backward is effective in cheating prevention. If it is employed besides strict time limitations and random question series, collaborative cheating will become quite challenging (Chirumamilla & Sindre, 2019 ), (Backman, 2019 ). By setting strict time limitations, the students do not have enough time to handle cheating, therefore, exam cheating efforts are reduced (Backman, 2019 ).

Cluskey et al. ( 2011 ), emphasize low-cost approaches for addressing online exam cheating. They introduce online exam control procedures (OECP) to achieve this target. Taking the exam only at a defined time and avoiding postponing it for any reason, or changing at least one-third of the questions in the next exam, are some instances of these procedures.

Authentication

Authentication is mainly for impersonation prevention before examinations. It could be done classically by checking the school ID badges or government-issued ID by the webcam (Moten et al., 2013 ) or by a more modern approach like biometrics through fingerprint, palm vein scan (Korman, 2010 ), eye vein scan (Kigwana & Venter, 2016 ), voice, and keystroke biometrics (Norris, 2019 ).

An interesting method to prevent cheating has been presented in (Moten et al., 2013 ). Students should call the instructor at a predetermined time to get the password. After the students’ voices are recognized by the instructor, they are authenticated and receive a random password for exam entrance. The password is valid until the end of the exam time limit, thus this method makes cheating more difficult (Moten et al., 2013 ).

The last method of authentication is the one discussed in (Norris, 2019 ) which uses challenge questions. These are the questions only the student will know, for instance, student ID or personal information. In (Ullah, 2016 ), an approach is proposed that creates and consolidates a student’s profile during the learning process. This information is collected in the form of questions and answers. The questions are pre-defined or extracted from a student’s learning activities. A subset of questions is used for authentication, and the students should answer these questions correctly to get access to the online examination. This approach ensures that the person taking the exam is the same one who has completed the course.

Clustering means partitioning students into several groups based on a predefined similarity measure. In (Topîrceanu, 2017 ), random and strategic clustering methods are proposed to break friendships during the exam, as cheating prevention techniques. The advantages of random clustering are time and cost efficiencies; however, it is imprecise, and some clusters may include unbroken friendships.

Breaking friendships through clustering relies on two hypotheses (Topîrceanu, 2017 ):

• Students tend to communicate and cheat with the people they know and feel close to.
• An individuals’ relationship with others on social networks is closely related to their real-life relationships with people.

Regarding the second hypothesis, social network analysis could find students’ close friends and people they know. After clustering students, a unique set of exam questions are prepared for each cluster. Consequently, the collaboration of friends to cheat during the online exam becomes challenging.

Lowering cheating motivation

Approaches expressed in this section are based on mental and psychological aspects driving students toward academic misbehaviors, and the work being done to reduce these behaviors through controlling mental drivers.

There are several tactics to develop students’ moral beliefs encouraging them to avoid unethical behaviors. For instance, implementing honor systems helps build a healthy and ethical environment (Korman, 2010 ). Another tactic is clarifying academic integrity and morality ideals through establishing educational integrity programs (Korman, 2010 ).

As Korman ( 2010 ) further investigated, changing the students' perception about the goal of studying, could decrease cheating. This could be done by reminding them why learning matters and how it affects their future success. In (Varble, 2014 ), it is stated that emphasizing the actual value of education will lead to the same result.

Varble ( 2014 ), indicates that by improving students’ skills such as time management skills, their academic performance will be highly enhanced; accordingly, their academic misbehaviors will be declined. The risks of being caught and the significance of punishments, are inversely related to students’ motivation for cheating.

Varble ( 2014 ) also mentions that applying formative assessment rather than summative assessment effectively reduces examinees’ desire for cheating due to improving their learning outcomes. Formative assessments aim to enhance the candidates’ learning performance rather than testing them. On the other hand, summative assessments mostly care about measuring candidates’ knowledge and are used to check if they are eligible to pass the course or not.

As an additional description about getting a formative assessment to work, Nguyen et al., ( 2020 ) mention that increasing the exam frequency forces students to study course materials repeatedly, resulting in longer retention of information and knowledge in students’ minds. This brings about alleviating candidates’ motivation for cheating (Nguyen et al., 2020 ). Varble ( 2014 ), also suggests that reducing the value of each test lowers the reward gained by the cheaters over each test; consequently, the motivation for cheating is declined.

A cost-efficient and effective method to lower cheating motivation is to declare the cheating policy for examinees before the exam starts (Moten et al., 2013 ). Warning students of the consequences of being caught makes them nervous and can significantly decrease cheating. It is necessary to have a confirmation button, so that no excuses can be made by cheaters after the exam. It is such effective that in two experiments, it decreased the number of cheatings by 50% (Corrigan-Gibbs et al., 2015 ). It is worth mentioning that in the online environment, having an honor system is much less effective than warning about the consequences of cheating if being caught (Fontaine et al., 2020 ).

During-exam prevention

Most cheating prevention methods were discussed in the before-exam section; still, there exist some during-exam prevention tactics, which are presented in this sub-section.

Think-aloud request

A rarely mentioned method called Think-aloud request was discussed in (Chirumamilla & Sindre, 2019 ). In this method, a request is sent to the student to think aloud about a specific subject (or current question) at random times during the exam. The student has to respond to the request orally, and the voice is recorded for further investigation and cheating detection (e.g., slow response and voice impersonation detection). This mechanism forces students to continuously be ready for responding, which reduces the chance of student cheating. The authors have also mentioned that this system and its questions could be implemented by an AI agent.

Cheat-resistant systems

Using cheat-resistant systems will inherently prevent some kinds of cheatings, although they are costly to be implemented (Korman, 2010 ). Using a browser tab locker (Chua & Lumapas, 2019 ) is one of them that prevents unauthorized movements and also identifies them by sniffing their network packets. Another method is using wireless jammers (Chirumamilla & Sindre, 2019 ) to disrupt any radio signals (Internet) in an area which usually is the examination hall, during semi-online exams.

In (Chirumamilla & Sindre, 2019 ), some valuable suggestions are given for oral exams. One is conducting the oral exam as a flow of short questions and answers, instead of a long initial question and an extended answer afterward. This is because a flowing dialogue significantly reduces the chance of the examinee following someone else’s cues of the solution. They have also suggested that asking the examinee to respond quickly, will facilitate achieving this goal. Besides that, if candidates delay, they may be known suspicious. If a candidate was detected suspicious by the instructor, it is good to interrupt the current question with a new question. This will neutralize the effort made by a third party to help the candidate answer the question.

Another suggestion presented in (Chirumamilla & Sindre, 2019 ), is to prepare a big pool of questions for oral exams to prevent questions repetition. As a result, the candidates cannot adjust themselves to the questions asked from previous candidates.

Bribery is a kind of organizational cheating. In (Kigwana & Venter, 2016 ) it is indicated that by assigning a random human proctor for the exam right before it started, bribery and beforehand contractions between examinee and proctor would be impossible.

There is no doubt that online education has changed significantly in recent years. One of the main challenges in online education is the validity of the assessment. Specifically, during the COVID19 pandemic, the integrity of online examinations has become a significant concern. Cheating detection and prevention are hot topics in online assessments. In addition, it is needed to conduct more research on cheating motivation and cheating types. In this research, we review and classify online exam cheating comprehensively.

In this review, only publications written in English were investigated. This could result in review bias, however, it is too difficult and infeasible to review studies in all languages. Many systematic mapping researches consider only publications in English, such as (Nikou & Economides, 2018 ) (Martin et al., 2020 ) (Noorbehbahani et al., 2019 ) (Wei et al., 2021 ).

Figure ​ Figure3 3 indicates that the publications trend is decreasing, contrary to the hypothesis that online learning is rising, especially with the emergence of the COVID-19. Notably, in this study, online cheating researches have been reviewed. So, Fig. ​ Fig.3 3 specifically corresponds to online cheating publications not online learning studies in general. However, more investigations of online cheating studies from February 2021 onwards are required to further analyzing the trends.

Several reviewed studies have made no distinction between cheating detection and prevention (Bawarith, 2017 ; Bawarith et al., 2017 ; Korman, 2010 ; Tiong & Lee, 2021 ). They employed detection methods to identify dishonest behaviors. Then preventive actions such as making an alarm to the student, or closing the browser tab are performed to deter student cheating. Regarding this definition of prevention, several studies have applied these terms interchangeably, confusing the reader. In this study, we define cheating prevention as strategies and methods that try to prevent the occurrence of cheating in online exams. Considering the latter definition, we attempted to provide a better review and clearer classification to the readers.

One limitation in this domain is the lack of statistics on the popularity of the types, methods, and tools. In (Sabbah, 2017 ), the most common cheating behaviors and their average risks have been discussed; however, the results are limited to 10 cheating types. Hence, more investigation is required to determine the prevalence of each cheating type and cheating motivation.

An important cheating reason that is overlooked by researchers is learning styles. Students and educators have different preferred learning styles (auditory, visual, kinesthetic and read/write). If teachers and educational institutes don’t consider this issue, the course will not be apprehensible for some students, and consequently, they will be motivated to cheat.

Another issue that should be addressed is to evaluate the feasibility of cheating detection and prevention methods. If the equipment for securing online exams is expensive, the students cannot afford it. Therefore, this factor should be considered when developing detection and prevention methods. Cluskey et al. ( 2011 ), believe that some solutions (e.g., proctors) that detect cheating during online exams are too costly, and their costs outweigh their benefits in some cases. Therefore, cost-effective systems and methods should be implemented.

Privacy and convenience are also vital for examinees. If employed security mechanism for online exams violates privacy and disturbs student convenience, the evaluation will not be practical due to induced stress. Accordingly, these aspects should be considered in cheating detection and prevention systems.

In this study, cheating in online exams is reviewed and classified comprehensively. It provides the reader with valuable and practical insights to address online exam cheating. To mitigate students cheating, first, it is necessary to know cheating motivations and cheating types and technologies. Furthermore, cheating detection and prevention methods are needed to combat forbidden actions. Detection methods without applying prevention methods could not be effective. As cheating detection and prevention methods are evolved, new cheating types and technologies emerge as well. Consequently, no system can mitigate all kinds of cheating in online exams, and more advanced methods should be employed. It seems the most efficient strategy for cheating handling is to lower cheating motivation.

It should be mentioned that we have not covered studies related to technical attacks and intrusions to online exam systems and teacher devices. This topic could be considered for conducting another review study.

The impact of COVID-19 on online learning and cheating in online exams could be analyzed in future work.

Another future work is to explore how ignoring students’ learning styles in teaching and assessment could affect cheating motivation.

Privacy issues, user convenience, and enforced costs of cheating detection and prevention technologies need to be examined in other studies.

In this study, publications from 2010 to 2021 have been reviewed. More investigations are required to review accepted but unpublished studies and publications in 2022.

Table ​ Table1Table 1

Declarations

The authors declare that they have no competing interests.

1 http://www.duplichecker.com

2 http://www.turnitin.com

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Fakhroddin Noorbehbahani, Email: ri.ca.iu.gne@inahabhebroon .

Azadeh Mohammadi, Email: [email protected] .

Mohammad Aminazadeh, Email: [email protected] , Email: [email protected] .

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Exam Cheating, Its Causes and Effects

Introduction, definition of cheating, works cited.

The ability of a nation to compete effectively on the international front hinges on the quality of its education. With this in mind, it is okay to conclude that cheating in exams undermines the standard of education in a country and consequently hinders its ability to compete at the world stage. Indeed, students who cheat in exams become poor decision makers in their careers. Their productivity and level of integrity is adversely dented by their belief of having everything the easy way. Academic dishonesty is not new but with the increase in competition for jobs, most students have resorted to cheating in order to qualify for these jobs (Anderman and Johnston 75). The purpose of this paper is to research in detail the causes and effects of cheating in exams.

In the education fraternity, cheating entails: copying from someone, Plagiarizing of academic work and paying someone to do your homework. There are numerous reasons why students cheat in exams however; this action elicits harsh repercussions if one is caught. This may include: suspension, dismissal and/or cancellation of marks (Davis, Grover, Becker and McGregor 16).

One of the major reasons that make students cheat in exams is the over-emphasis that has been placed on passing exams. Apparently, more effort has been directed towards passing of exams than learning due to the high competition in the job market. Similarly, most interviewers focus more on certificates rather than the knowledge of the candidate. It is no wonder most learning institutions these days focus on teaching how to pass an exam and completely disregard impacting knowledge to students.

In some cases, students cheat because they are not confident of their ability or skills in academics. Whenever this feeling is present, students resort to cheating as a way of avoiding ridicule in case of failure. In essence, some of these students are very bright but the fear of failure and the lack of adequate preparations compel them to cheat. The paradox is that when cheating, most students swear that they will never do it again but this only serves as the beginning of a vicious cycle of cheating (Anderman and Johnston 76).

Societal pressure is another major cause for cheating in schools. Parents, teachers and relatives always, with good intentions, mount too much pressure on students to get good grades in order to join good schools and eventually get high paying jobs. All this pressure creates innate feelings that it is okay to cheat in exams if only to satisfy their parents and teachers egos.

There are times when students justify cheating because others do it. In most cases, if the head of the class is cheating then most of the other students will feel they have enough reason to also cheat. The system of education is such that it does not sufficiently reprimand those who cheat and tends to hail those who pass exams regardless of how they have done—the end justifies the means.

With the advent of the internet, it has become very easy to access information from a website using a phone or a computer. Search engines such as Google and Yahoo have made it very easy for students to buy custom-made papers for their class work. It is very easy for students from all over the world to have the same answer for an assignment as they all use a similar website. Indeed, plagiarism is the order of the day, all on has to do is to have the knowledge to search for the different reports and essays on the net (Davis, Grover, Becker and McGregor 18).

Nowadays, most tutors spend most of their class time giving lectures. In fact, it is considered old fashioned to give assignments during class time. Consequently, these assignments are piled up and given during certain durations of the semester. This poses a big challenge to students who have to strike a balance between attending to their homework and having fun. As a result, the workload becomes too much such that it is easier to pay for it to be done than actually do it—homework then becomes as demanding as a full-time job (Jordan 234).

From a tender age, children are taught that cheating is wrong; yet most of them divert from this course as they grow up. In fact, most of them become so addicted to the habit that they feel the need to perfect it. Most often, if a student cheats and never gets caught, he is likely to cheat all his life. Research has shown that students who cheat in high school are twice likely to cheat in college. The bigger problem is that this character is likely to affect one’s career in future consequently tarnishing his/her image.

Cheating in exams poses a great problem in one’s career. To get a good grade as a result of cheating is a misrepresentation of facts. Furthermore, it is difficult for a tutor to isolate students who genuinely need specialized coaching. It becomes a huge embarrassment when a cheating student is expected to give a perfect presentation and fails to demonstrate his ability as indicated by his/her grades. In addition, students who cheat in examination do not get a chance to grasp important concepts in class and are likely to face difficulties in the future when the same principles are applied in higher levels of learning.

The worst-case scenario in cheating in an exam is being caught. Once a student is caught, his reputation is dealt a huge blow. It is likely that such a student will be dismissed or suspended from school. This hinders his/her ability to land a good job or join graduate school. It can also lead to a complete damage of one’s reputation making it hard for others to trust you including those who cheat (Jordan 235).

Cheating in exams and assignments can be attributed to many reasons. To begin with, teaching today concentrates so much on the exams and passing rather than impacting knowledge. Lack of confidence in one’s ability and societal pressure is another reason why cheating is so wide spread. Cheating cannot solely be blamed on the students; lecturers have also played their part in this. Apparently, most lectures concentrate on teaching than giving assignments during class time. This leaves the students with loads of work to cover during their free time.

Technology has also played its part in cheating—many students turn to the internet in a bid to complete their assignments. On the other hand, it is important to note than choices have consequences and the repercussions of cheating in an exams are dire. First, it completely ruins one’s reputation thereby hindering chances of joining college or getting a good job. It also leads to suspensions and/or expulsion from school. Furthermore, the habit is so addictive that it is likely to replicate in all aspects of life—be it relationships, work, business deals etc. It is important to shun this habit as nothing good can come out of it.

Anderman, Erick and Jerome Johnston. “TV News in the Classroom: What are Adolescents Learning?” Journal of Adolescent Research , 13 (1998): 73-100. Print.

Davis, Stephen, Cathy Grover, Angela, Becker, and Loretta McGregor. “Academic Dishonesty: Prevalence, Determinants, Techniques, and Punishments”. Teaching of Psychology , 19 (1) (1996): 16–20. Print.

Jordan, Augustus E. “College Student Cheating: The Role of Motivation, Perceived Norms, Attitudes, and Knowledge of Institutional Policy. Ethics and Behavior , 11, (2001): 233–247. Print.

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Despite giving students chances to cheat, unsupervised online exams gauge student learning comparably to in-person  exams

Professor of Psychology, Iowa State University

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Jason C.K. Chan receives funding from the National Science Foundation.

Dahwi Ahn does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

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The Research Brief is a short take about interesting academic work.

The big idea

Students don’t have to be supervised during online exams. That’s because unsupervised online exams can accurately assess student learning, according to our study published in July 2023 in the Proceedings of the National Academy of Sciences.

Our data set comprised nearly 2,000 students from a public university in the Midwest. We analyzed exam scores from the first half of the spring semester of 2020, when tests were administered in person, and the second half, when the pandemic forced schools to shift online. This enabled us to compare how students performed on in-person exams versus online exams taught by the same instructor in the same course.

Our data showed a strong correlation between the scores that students achieved from unsupervised online exams and supervised in-person exams. In other words, students who got the best scores on the in-person exams also got the best scores on online exams.

We also examined whether this correlation changed based on students’ being early or later in their college career, the course discipline, the class size, or whether the exams featured mainly multiple-choice or short-answer questions. None of those factors significantly affected how well online exams assessed student learning.

We further analyzed our data to see if we could find clear signs of cheating during online exams. Because students who are doing poorly in a course are more likely to cheat , we predicted that students who had done poorly on the in-person exams – during the first half of the semester – would increase their scores more on online exams if they cheated.

We found no evidence for this type of cheating. This is important, because most people expect students to cheat during online exams. For example, a recent survey showed that more than 70% of college faculty believed cheating to be a significant problem for online exams, but only 8% believed the same for in-person exams.

Why it matters

COVID-19 has accelerated the adoption of online teaching and assessments. For that reason, we thought it was important to examine whether unsupervised online exams can accurately assess learning.

Previous studies have shown that students obtained higher scores on online exams than on in-person exams. Those results have sometimes been seen as evidence of cheating , which calls into question the suitability of online exams as a form of assessment.

But to judge whether online exams accurately assess learning, we must show that a student who earns high marks on in-person exams does the same on online exams and vice versa. In other words, the two forms of exams should rank-order students similarly, which was exactly what we found in our data.

What’s next

Although this data shows that online exams, even when unproctored, can accurately assess student learning at a relatively broad scale, it all comes from a single university. For that reason, caution is needed when attempting to draw general conclusions.

Moreover, much has changed in online education over just the past year with the rise in popularity of generative AI tools like ChatGPT , which can facilitate cheating.

We want to obtain a larger data set to determine if our results hold true beyond a single university.

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The temptation to cheat in online exams: moving beyond the binary discourse of cheating and not cheating

• Michael Henderson   ORCID: orcid.org/0000-0002-6389-8300 1 ,
• Jennifer Chung   ORCID: orcid.org/0000-0001-8336-5133 2 ,
• Rebecca Awdry   ORCID: orcid.org/0000-0001-6046-3275 1 ,
• Cliff Ashford   ORCID: orcid.org/0000-0001-6242-0623 1 ,
• Mike Bryant   ORCID: orcid.org/0000-0003-2894-0638 1 ,
• Matthew Mundy   ORCID: orcid.org/0000-0002-7073-0047 3 &
• Kris Ryan   ORCID: orcid.org/0000-0002-1386-396X 4  

International Journal for Educational Integrity volume  19 , Article number:  21 ( 2023 ) Cite this article

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Discussions around assessment integrity often focus on the exam conditions and the motivations and values of those who cheated in comparison with those who did not. We argue that discourse needs to move away from a binary representation of cheating. Instead, we propose that the conversation may be more productive and more impactful by focusing on those who do not cheat, but who are tempted to do so. We conceptualise this group as being at risk of future cheating behaviour and potentially more receptive of targeted strategies to support their integrity decisions. In this paper we report on a large-scale survey of university students ( n  = 7,511) who had just completed one or more end of semester online exams. In doing so we explore students’ reported temptation to cheat. Analysis surrounding this “at risk” group reveals students who were Tempted ( n  = 1379) had significant differences from those who Cheated ( n  = 216) as well as those who were Not tempted ( n  = 5916). We focus on four research questions exploring whether there are specific online exam conditions, security settings, student attitudes or perceptions which are more strongly associated with the temptation to cheat. The paper offers insights to help institutions to minimise factors that might lead to breaches of assessment integrity, by focusing on the temptation to cheat during assessment.

Introduction

Research on exam cheating has typically focused on the characteristics of those who cheated and the factors that influenced them, including the conditions and types of examinations (Jenkins et al. 2022 ; Noorbehbahani et al. 2022 ). Some studies go further and compare the differences between those who uphold integrity standards and those who breach them, such as when experiencing proctoring (Gudiño Paredes et al. 2021 ). Although these data are useful in presenting an overall picture of the context of examination misconduct in the university sector, similar to research on other types of academic misconduct, they tend to create a binary image of cheating: the cheaters and the non-cheaters. Through the bifurcation of dis/honest conduct, a whole sub-group of students are left out of research and discussion: those who are tempted to cheat, but have not engaged in that behaviour.

Most existing research investigating academic integrity and misconduct in higher education has provided explanation and context for why students do or not do engage in dishonest practices assessment practices. While these studies are able to provide readers with an in-depth understanding of reasons for or against integrity, for those students sitting in a third group - the tempted - understanding and discussion of their situations are scant. Unless educators understand the factors associated with temptation, they, and their institutions, may unwittingly create situations where the tempted become the cheaters. In addition, understanding why someone was tempted, but nevertheless did not cheat, may reveal particularly useful strategies in supporting students to avoid breaching exam regulations intended to ensure integrity.

Therefore, the project of which this paper forms a part, set out to better understand those who were tempted, and the contextual, intrapersonal and interpersonal dilemmas that potentially lead to a tipping point of behaviour. This paper marks the beginning of this journey, describing the correlations between temptation and four key areas that are generally assumed to reduce or impact cheating behaviour: online security systems, exam conditions (such as being open- or closed-book, location of exam, duration, and exam window), prior exam experiences (knowledge or perception of peer cheating), and student attitudes towards integrity.

Cheating and the temptation to cheat

Many studies have provided evidence and reasoning for why students do or do not engage in academic cheating. Studies considering plagiarism, contract cheating, outsourcing or cheating more broadly have found some commonalities in the reasons why students do not complete their university assignments with integrity. Researchers globally find some agreement in the situational or personal reasons that their students report causes them to engage in misconduct, and include: Poor time management and procrastination (Siaputra 2013 ; Wallace & Newton 2014 ); student perception of staff apathy or lack of institutional emphasis on academic integrity (Husain et al. 2017 ); perceived seriousness of cheating/integrity (Curtis and Popal 2011 ); dissatisfaction with the learning environment (Bretag et al., 2018, Moss et al. 2018 ); and pressures placed on students from university or other sources (Brimble, 2016). Various studies have also explored the relationship between student characteristics and cheating. This includes student attitudes towards integrity (Dyer et al. 2020 ; Tremayne & Curtis, 2021 ); their knowledge of integrity rules and consequences (East 2016 ; Morris 2016 ); low confidence and resilience (Moss et al. 2018 ); a competitive mindset (Barbaranelli et al. 2018 ); low self-control (Tremayne and Curtis 2021 ); perceived opportunities to cheat (Baird and Clare, 2017 ); as well as their awareness of peers cheating (Awdry and Ives 2021 ). Examination cheating, whilst being influenced by these factors, also has its own body of work due to the specific condition associated with undertaking academic examinations.

Cheating in exams has been a persistent and seemingly prevalent problem in higher education. Prior to online exams, cheating in paper-based exams has been reported at various rates, such as 51.8% (Genereux and McLeod 1995 ), and 24% (Chapman et al. 2004 ). More recent research into online exam cheating has reported higher rates of 62% (Dyer et al. 2020 ), 58.4% (Jenkins et al. 2022 ), and 70% (Pleasants et al. 2022 ). While this affirms the need for online exams to be given particular attention, there are a variety of contributing, and possibly confounding, factors that need to also be considered.

Studies on examination cheating have associated cheating variance with the conditions of the exams themselves, such as the number of exams students are required to sit, exam windows, and context or weighting of the exam (Bilen and Matros 2021 ; Hylton et al., 2016 ). Open- or closed-book exams have also been found to have an impact on the student experience in preparing for exams, and in rates of cheating (Green et al. 2016 ; Ng 2020 ). Other studies have concluded that the type of exam and exam security can impact cheating rates (Gudiño Paredes et al. 2021 ; Harper et al. 2021 ). Interestingly, some have argued that the existence of exam security systems, such as proctoring, inherently creates an environment of distrust which in turn can encourage some of the behaviours that the security seeks to avoid (Lee and Fanguy 2022 ; Smith et al. 2016 ). Such arguments highlight the interplay between exam conditions and student perceptions and attitudes.

Both groups of studies, those focusing on exam conditions and those on situational or personal variables, have sought to understand the association between the factor in question and actual cheating behaviour. In contrast, this study proposes that it may be valuable to understand when and why students are tempted but choose not to cheat. In doing so we may gain better insights regarding the “tipping point”: what determines a choice made either to cheat or not to cheat. We investigate the proposition that temptation is, in and of itself, a useful explanatory condition that can assist in understanding the behaviours associated with academic integrity.

Although we could find relatively few previous studies with a similar focus, most being more than twenty years old and unrelated to online examinations, one existing strand of research has focused on intrapersonal and interpersonal factors that impact on student’s perceived need to succeed which can in turn lead to cheating. These factors include fear of failure, as well as the perceived desirability or need to succeed in the context of social pressures such as peer groups and family, a parallel with research from studies focussed on cheating. For example, Jacobson et al. ( 1970 ), in a controlled experiment, found that participants with a high requirement for social approval were more often tempted to cheat when facing failure in tasks, than those with lower social-desirability scores. In addition, participants with high scores on self-satisfaction (achieved through meeting their own expectations of success and ideal performance levels), combined with high need for social approval, were more likely to cheat when faced with the possibility of failure or not meeting social expectations (Jacobson et al. 1970 ). The implications of risk of failure were also explored in an experimental study by Houston ( 1978 ), who found students more likely to be tempted to cheat, or engage in cheating, on a test when they were faced with a high chance of failure in a scenario which would reward success. In particular, Houston indicated that students who were uncertain of success were more likely to be tempted to cheat, when compared with those who were more certain of their outcome—regardless of whether it was good or not.

The power of external influences impacting a person’s behaviour has also been a focus for criminological research in non-academic contexts which argues that people may well have the propensity to cheat, and are tempted to do so when the right opportunities present, but are prevented from doing so through various social and internalised personal controls (see Gottfredson and Hirschi 1990 ). These social and internalised controls were a focus for an experimental study by Gino et al. ( 2011 ) which was designed to test the effect of self-control on temptation and actual cheating (through a control experiment with monetary incentives, low deterrence, and high opportunities to cheat); and found that those who had had their self-control reserves depleted, were more likely to cheat. In particular, they identified that self-control resource depletion impaired participants’ ability to recognise moral issues and thus more likely to engage in unethical behaviour. Moreover, moral identity was found to moderate the effect of self-regulatory resource depletion. Gino et al. ( 2011 ) went on to identify two key factors that were found to deplete self-regulatory resource. First, resisting temptation was found to deplete self-regulatory resources; this finding, if applied to an academic context, would suggest that students required to sit multiple exams in close time proximity are less likely to be able to resist temptation. Second, depletion was also associated with sleep deprivation (Gino et al. 2011 ), something which can be aligned to the stresses placed on students during key exam times when they are likely to be tired, have extra pressure placed on them and may have reduced self-control, resulting in difficult to resist dishonest behaviour (see also Chen et al. 2014 ; Hodgkinson et al. 2016 ).

As noted already, there are few studies which deal with the condition of temptation in relation to academic integrity. When considering the broader field of temptation in general, as well as academic integrity studies, there is considerable diversity in explanations. This is not only in terms of the factors involved but also in what appear to be fundamental assumptions about the nature of cheating. Some studies frame individuals as having a propensity to cheat, which is manifested given the ‘right’ conditions, other studies frame individuals as reticent participants, that is, only likely to cheat if conditions create a perceived or real situation of sufficient duress. The practical implications of these fundamentally different framings are that practitioners are faced with calls to inhibit cheating propensity (e.g. reducing opportunity through proctoring), whilst at the same time hearing calls to support integrity decisions (e.g. via integrity training).

In our project we have drawn on a broad socio ecological perspective in which we assume complex interaction between setting, intrapersonal, interpersonal, developmental and wider social, organisational and cultural factors. For the purposes of this paper we adopt the idea that cheating, and not cheating, are contextually specific behavioural decisions. While for some students the decision to cheat/not-cheat may be subconscious or routine, for others it may be more complex. In this paper we set out to explore this idea by identifying those students who were tempted to cheat, but who chose not to. Through understanding these students in relation to others, we may be better placed to understand the tipping point of cheating and not cheating.

We focus on four research questions for this paper as a way to begin exploring the influence of setting and social/organisational factors on the temptation to cheat:

Is there a relationship between temptation to cheat and:

(R1) exam conditions,

(R2) exam security,

(R3) students’ attitude towards integrity, and.

(R4) students’ knowledge of others’ cheating behaviours?

This work is exploratory in nature. We seek to understand when temptation (but not cheating) occurs, and if that pattern is similar to those who were not tempted, or those who cheated. Understanding these similarities and differences may shed light on future academic integrity interventions and practices, as well as helping to guide future research.

This cross-sectional study was conducted at a large Australian university in which all students who sat a formal end-of-semester exam during Semester 1, 2021 were invited to complete a survey. The survey was open for 3.5 weeks during the exam period. There were 85,332 exam sittings by 39,308 students, and the findings in this paper are based on 7,511 fully completed anonymous surveys. The survey took 25 min to complete and explored student experience and integrity in various exam conditions, proctoring and exam security systems. Further details on the survey, including its creation is reported in (citation removed for blind review). In addition to gathering information on students positive and negative exam experiences, the survey asked students to self-report cheating behaviour in their recent exam, or - if they did not cheat - their degree of temptation to cheat.

Of the whole sample ( N  = 7,511), 2.9% ( n  = 216) self-reported cheating in the exam, and the remaining 97.1% ( n  = 7,295) reported they did not break the exam rules. Of the students who did not break the exam rules, 81.1% ( n  = 5,916) said that they were not at all tempted to cheat, whilst 18.9% ( n  = 1,379) reported they were tempted. Of the students who were tempted, 69.9% ( n  = 964) reported they were slightly tempted, 22.4% ( n  = 309) were moderately tempted, 4.8% ( n  = 66) were very tempted and 2.9% ( n  = 40) were extremely tempted.

This paper presents key descriptive data and summaries of statistical analysis in relation to temptation and the four key areas of interest: exam conditions, security, attitude towards integrity, and knowledge of others’ cheating behaviours. In some cases, we have provided a summary of the statistical analysis rather than the full tables with the intention of maintaining a focus on key findings; full analysis is available upon request. For the same reason we have not provided analysis of overall demographics and general trends since these can be found in our earlier publication which focused on cheating behaviours (citation removed for blind review).

Exam conditions

Table  1 presents the raw frequencies of the sample that were Not tempted, Tempted, and Cheated for each exam condition, as well as the Chi-Square tests for each exam condition. Chi-Square test of contingencies analyses compared each exam condition variable to the cheating temptation variable. The findings revealed that there were statistically different levels of temptation and cheating behaviour across each of the exam conditions, with the exam window analysis representing a medium effect size (the largest effect size of all five analyses), and the remaining four analyses represented small effect sizes.

Post-hoc comparisons of column proportions were conducted between pairs of groups within an exam condition and a Bonferroni correction was applied for all pairwise comparisons. In relation to temptation, post-hoc comparisons revealed that students were significantly more tempted to cheat if they had a set exam time (i.e. no window). They were also more tempted if they had completed two or three exams, and if the exam was closed-book. Students who sat the shortest exam duration were significantly more likely to be tempted to cheat than students who sat all other exam durations. Conversely, students who sat the longest exam duration were also more tempted to cheat than students who sat exams in the middle duration. No statistical differences were found between temptation and exam location (i.e. on campus or off-campus / home).

In relation to cheating, students were more likely to cheat if they had completed four exams, compared to if they had completed one or two exams (at the time of surveying). They were also more likely to cheat if they completed their exam remotely somewhere other than home, as well as if their exam had a 12- or 24-hour window.

Exam security

Table  2 reports on the relationship between exam security type and the three groups: Not tempted, Tempted, and those who Cheated. A Chi-Square test of contingencies revealed that exam security and cheating temptation are related as the analysis was statistically significant, although the association was small. Post-hoc comparisons of pairs revealed that students who experienced no online supervision with Safe Exam Browser were more likely to be tempted to cheat than students sitting exams in all other types of exam security. Furthermore, students who sat an exam that included the highest level of online supervision with assisted check-in were significantly less tempted than all other students.

In relation to cheating, there was a significantly greater proportion of students who cheated in the Safe Exam Browser group compared to students who experienced online supervision, either with assisted check-in or with self-check in. There were no statistical differences found in rates of cheating between the remaining three security types.

Integrity attitudes and awareness of consequences

Table  3 presents the mean values for attitudes towards academic integrity and awareness of consequences for each of the Not tempted, Tempted and Cheated groups. One-Way ANOVAs comparing the mean differences for all three items demonstrate that there were statistically significant differences between the ratings of the three groups, with small effect sizes. As the homogeneity of variance tests were violated, Welch’s F are reported.

Post-hoc comparisons confirmed a predictable relationship between temptation and the perceived importance of integrity as well as knowledge of consequences of cheating as explained by the university. Students who were in the Not-tempted group reported the greatest positive attitudes towards integrity of the three groups. The Tempted group was less positive than the Not-tempted group, but more positive than those who Cheated. This relationship is logical and affirms that the Tempted students are a distinct group sitting between the other two. However, it is also useful to note that even though the Cheated group was the least positive when compared with the other two groups, the mean values are still high (for example, 4.15 on a 5-point scale).

In relation to the importance of exams being supervised, the only significant difference found was between the Not tempted and Tempted group, where the Not tempted group were more favourable towards exams being supervised than the Tempted group. However, it is useful to note that despite being the most favourable group, the Not Tempted students’ attitudes towards exam supervision were only average in strength (mean of 3.70).

Cheating perceptions

Four items measured students’ extent of knowledge regarding whether other students were cheating and getting caught for cheating, both within their course/degree and at university outside their course. We conceptualised this as perceptual proximity to cheating behaviours. Table  4 presents the raw frequencies of students’ proximity to cheating behaviours.

To statistically compare if one group reported closer perceptual proximity than another group, the data were treated as continuous ( 1  = distant relationship to cheating/getting caught, 4  = close relationship to cheating/getting caught). All four Welch’s F were statistically significant for all four items ( p  < .001) in relation to cheating and getting caught for cheating, both in relation to course/degree and outside of course, represented by small effect sizes.

Post-hoc comparisons (for all four items) revealed that the proximity to other students’ cheating and being caught for cheating was statistically significantly different between the three groups. Students who didn’t cheat and were not tempted were least likely (of the three groups) to know other students who cheated and if they had been caught. In comparison, students who Cheated were most likely (of the three groups) to have seen others cheat as well as know of other students who had been caught. Finally, Tempted students’ proximity to cheating and consequences was in the middle between the Not tempted and Cheated students. While the statistical differences are noteworthy, it is useful to consider that the raw frequencies of those who had close proximity to cheating are relatively low (e.g. only 11.1% of those who cheated had seen others cheat in their course).

In response to our first research question, ‘Is there a relationship between exam conditions and temptation to cheat?’ , we found that restrictive exam conditions were related to increased temptation to cheat. Of highest statistical significance was the flexibility of the exam window in which students had to complete their exams (i.e., exams that can be sat at any point in a broad window, versus those that must be started at a specified time). Those students who had a set time for their exam (i.e., no window) were the most likely to report being tempted to cheat, however the same condition had the least reported actual cheating (with those reporting cheating more likely to have completed exams in the flexible 12 or 24 h exam window). This suggests that institutions should preference specified exam times; however, they need to be cautious of the significant proportion of students who are tempted to cheat under these conditions. Further research is needed to explore why they were tempted. Sattler et al. ( 2013 ) note that increased opportunity is correlated with increased frequency of plagiarism, however this does not explain why there was a greater tendency for students to be tempted to cheat in more restrictive conditions. While it is reasonable to assume that restrictions on exam scheduling can lead to general stress, it may be valuable to discover what factors are interacting with the condition to make it a significant stressor. For example, such restrictions may simply trigger students’ sense of unpreparedness and result in impulsivity or re-evaluation of the cost-benefit analysis of cheating. Research in relation to plagiarism found that procrastination may lead to impulsivity (Siaputra 2013 ) and that students were influenced by the perceived utility of cheating, that is, their personal sensitivity of the costs and benefits of cheating. Alternatively, it may be related to a specific and actionable concern such as perceived poor exam design (e.g. too many questions), rather than the exam conditions.

Another restrictive exam condition was the length of the exam. In our previous study (citation removed for blind review) using the same participant data and in which we compared only two groups, those who cheated and those who did not cheat, we found there was no statistically significant difference in the cheating rates according to the length of exam. This was also found by Pleasants et al. ( 2022 ) who analysed detected cheating rates on exam conditions and found that time limits in exams had no effect on cheating behaviours. Based on these finding it would be reasonable for institutions and educators to not consider the length of exam in their exam security designs. However, when we shift from a binary analysis of cheating/not-cheating to include temptation the data provides a somewhat more cautionary finding.

While cheating rates were not found to be correlated with length of exam, we did find that students were more likely to be tempted to cheat in exams with the shortest duration (1 h 40 min). However, the second most likely group to be tempted were those who had the longest exam duration (greater than 2 h and 20 min). These results seem somewhat contradictory, with restrictive time limits relating to increased temptation while permissive time conditions were also related to increased temptation, albeit at a less significant level. In trying to understand this finding, it may be useful to consider that our data does not reveal the form of cheating that students were tempted to engage in. The differences in the length of exam may influence how students perceive the opportunity to cheat, and tempt them to cheat in different ways. This potential explanation is supported by Ng ( 2020 ), who observed that the type of cheating was impacted by the time limits set on the exam, with more collusion and outsourcing being detected in students sitting open or long exam windows. In contrast, more plagiarism was found in the restricted and short-time periods, which Ng suggests is due to students perhaps not having enough time to cheat in other ways than quick copy and paste.

Our findings suggest that while rates of cheating may be helpful when making decisions around institutional strategy, the rate of temptation can provide further consideration regarding the degree of risk, and commensurate institutional attention to policy and support structures to ensure temptation does not tip over to cheating.

Our data also supported our second research question, Is there a relationship between exam security and reported temptation? Those students with no direct supervision, but who were monitored through a Safe-Exam Browser were the most likely to be tempted to cheat. The same condition also had the highest frequency of cheating. Based on this analysis it is reasonable to conclude that the use of Safe Exam Browser by itself (i.e. without overt proctoring or other security measures) needs to be treated with caution, if not avoided entirely.

However, our findings are not so clear with regards to other security conditions. There were no statistical differences in the frequency of cheating between the remaining three security conditions (online supervision with/without assisted check-in, no online supervision). This means, excluding Safe Exam Browser and based on cheating frequencies, there is no clear support for choosing between proctored or non-proctored online supervision. This finding is somewhat surprising given that literature has often reported the security benefits of proctoring tools in online exams – whether it is in reduced cheating rates (Gudiño Paredes et al. 2021 ) or the actual or perceived opportunity to cheat (Hylton et al., 2016 ).

In contrast with our findings relating to cheating frequencies, we found the temptation data to be more revealing. We found that students who experienced online supervision (proctoring) with assisted check-in were least tempted compared to students in all other conditions (online supervision with non-assisted check-in, Safe Exam Browser, and no supervision at all). This suggests that temptation may be particularly inhibited not simply because students were aware of proctoring (e.g. being recorded through their web-cam), but that they had some audio-visual interaction with an exam staff member at the beginning of the exam through the assisted check-in process. A similar phenomenon was observed by Kerkvliet and Sigmund ( 1999 ) in their study, in which additional social presence in the form of additional verbal warnings or the additional presence of a proctor resulted in reduced cheating. It could be argued that the assisted check in process – requiring students to actively interact and respond to a human proctor, including showing their Student ID, confirming their name and undergoing confirmation that webcamera and screen recordings are operating - raised the awareness of surveillance and perceived risk. Alternatively, the increased social interaction may have had other impacts, such as affirmation of expectations of integrity. This may be connected with the findings of Gino et al. ( 2011 ) in which situational influences such as monitoring was thought to trigger an individual’s self-motives to become more salient. The temptation data in this study provides greater confidence for institutions that proctoring environments may help support integrity behaviours, but it also highlights that proctoring in-of-itself may not be the key variable in reducing temptation and thereby risk of cheating.

The third research question was also supported: Is there a relationship between a student’s attitude towards integrity and their temptation to cheat? The importance placed on integrity was positively associated with integrity behaviour. In other words, students who were not tempted to cheat placed the most importance on integrity, and those who were tempted placed less importance on integrity. Following this pattern, those who cheated rated integrity the least important in comparison with the other two groups. This aligns with other research findings in which students who rated ethics more highly were significantly less likely to cheat (Pate 2018 ). A similar effect was noticed by Gino et al. ( 2011 ) who identified strong moral identity as a moderating influence on temptation to cheat. Even though the findings in our study are not new, they do lend weight to the idea that perceived importance of integrity could be a reliable comparative point when conducting multivariate predictive analysis in relation to student cheating, and temptation to cheat. However, some caution needs to be taken because while there were statistical differences between the three groups, overall, our sample reported very favourable attitudes towards the importance of academic integrity. The group with the lowest ratings of academic integrity importance (Cheating group) still reported an average of 4.15 (5-point Likert scale with 5 being strongly agree that academic integrity in exams is important). This is a useful reminder that while we might be able to use attitudes towards integrity as a possible indicator, we can only do so as a comparative measure, the individual raw scores can be deceiving.

A similar trend to integrity attitudes was observed in relation to students’ awareness of rules and consequences of cheating. The Non-tempted group were more likely to report that their university had explained the rules and consequences than the Tempted group, followed by the Cheated group. Obviously these data are self-reported and some caution needs to be taken in relation to whether their awareness is related to selectiveness in noticing, or actual institutional attempts at educating them regarding rules and consequences. Nevertheless, it does affirm the potential utility of institutions engaging in integrity training, including explaining rules and consequences. Research by Husain et al. ( 2017 ) in a review of plagiarism studies, found that when students cited lack of understanding of conventions, definitions of misconduct and university rules this related to their reported attitudes towards plagiarism and integrity, subsequently resulting in increased engagement in academic misconduct. Certainly, earlier research has found that knowledge of (or fear of) outcomes can create a deterrent effect on cheating (Haines et al. 1986 ; Rettinger and Kramer 2009 ). In contrast, attitude towards integrity has been found to be more difficult to influence, although some argue that this can be approached through ethical teaching and integrity modules (East 2016 ; Hughes and Gallant 2016 ), or through institution-wide honour codes (Christensen Hughes and McCabe 2006 ).

The complexity around attitudes is highlighted in relation to the item asking students about how important it was that exams are supervised (i.e. proctored). Interestingly there was no statistical difference between the Tempted and Cheated groups. The situation however is made more complex by the fact that the only statistical difference was found to be between the Not tempted and Tempted groups. One interpretation is that students who were not tempted valued supervision more highly as a way to protect the integrity and fairness of their assessment. In contrast the Tempted group did not value supervision because such supervision was in conflict with their inclination to cheat. However, while this interpretation is plausible, it may equally be the case that the tempted students did not value supervision because they had considered cheating but ultimately chose not to cheat, and in that process the presence of supervision was not felt to be important. Any speculation should be treated with caution, but it does reveal a potentially valuable area for future research.

Finally, our fourth research question was also supported: Is there a relationship between a student’s knowledge of others’ cheating behaviours and their temptation to cheat? Students who were Tempted or Cheated were more likely to have reported being aware of other students in their course/university cheating than the Not tempted group. This finding supports prior research which found a positive correlation between known or perceived cheating of others and self-reported cheating rates (Awdry and Ives 2021 ; Rettinger and Kramer 2009 ), as well as development of inappropriate norms and attitudes due to the observation of peers engagement in cheating behaviours (Moss et al. 2018 ). However, it is interesting to note that over a third of the students who cheated had not seen or heard about others cheating, and a further quarter had only heard rumours of others cheating. An even stronger pattern is revealed when looking at the temptation data – with three quarters of the Tempted students not having heard about others cheating or only having heard about rumours of cheating. While knowledge of others cheating may be associated to an extent with temptation and cheating behaviours, it does not account for the majority.

Another noteworthy point is that our statistical analysis indicated those who were Tempted and those who Cheated were not only more likely to be aware of others cheating but also know of others being caught. Almost 35% of the students who cheated knew or had heard reliable stories of students in their own course being caught. While being aware of consequences may be linked to reduced cheating, our data indicates that knowledge of other students being caught is not a deterrent for many. This is in contrast to other research which has found that risk of being caught creates a deterrent impact on the temptation to cheat in exams (Haines et al. 1986 ; Smith et al. 2021 ), and that clearly articulated penalties for cheating can have a reduction on cheating rates (Pleasants et al. 2022 ). In our own study we do not know how the students perceived the consequences, perhaps seeing them as negligible. Indeed, Moss et al. ( 2018 ) and Sattler et al. ( 2013 ) note that in the case of plagiarism, the penalties need to be perceived as sufficiently harsh in order to be a deterrent. Nevertheless, it is also worth considering the raw frequencies which reveal 80% of those who were in the Tempted group, and 65% of those in the Cheated group, had never heard about other students getting caught, or had only heard rumours of others getting caught. An implication for future research may be to better understand how and when the knowledge of others being caught impacts on integrity behaviour. With this in mind it may be useful to revisit practices around reporting integrity breaches. A similar conclusion was made by Sattler et al. ( 2013 ) in relation to the public dissemination of plagiarism. For a variety of reasons, including the laudable preservation of student privacy, higher education institutions worldwide do not typically report on the frequency, type and outcomes of confirmed cheating cases. However, it is worth exploring how institutional information about confirmed cheating cases and their consequences may be communicated to students and if this has any impact on reducing temptation and cheating.

Limitations

Like most work in this area, the data is self-reported and as a result needs to be treated with caution. The anonymity, immediacy of the survey after the exam, and voluntary nature of the survey strengthen trustworthiness of the responses. However, they also act as limitations – particularly with a risk of self-selection bias. For example, it is logical to assume that students who broke academic integrity rules would be less likely to complete a survey that asks about their exam experience, including integrity behaviours. While not discounting this limitation it is also worth noting that the goal of this paper was not to find the rate of cheating, but instead compare the differences in conditions and attitudes of those who cheated, were tempted, and not tempted.

A further consideration in the analysis of this data is that the exams and survey were conducted during a time of residential lock-down because of the COVID-19 pandemic. This meant that only a small number of students sat the exams on-campus for a variety of equity-related reasons. In addition, even though digital exams had been run at this institution for some time, for many students this was their first experience of sitting the online exam in a remote (e.g. home) location with exam security measures such as online proctoring.

Concluding remarks

This is an exploratory study based on the simple idea that to understand the influencing factors in cheating behaviours it may be valuable to go beyond binary descriptors of cheating and not-cheating. Our data has identified a large group of students who did not cheat but who report having been tempted to do so. We are cautious of applying deficit models of thinking in relation to this group since they ultimately chose to not act on their temptation. Nevertheless, we conceptualise these students as being at risk of cheating and thereby may provide greater understanding of the influencing factors at the tipping point of academic misconduct.

This paper explores possible correlations between temptation and four key areas that are generally assumed to reduce or impact cheating behaviour: online security systems, exam conditions (such as being open- or closed-book, location of exam, duration, and exam window), prior exam experiences (knowledge or perception of peer cheating), and student attitudes towards integrity. Analysis of the temptation data has led to new insights not readily apparent if we simply adopted a binary approach.

Logic might suggest that students who were tempted would fall in a range somewhere between those who cheated (i.e. those who succumbed to temptation), and those who were not tempted at all. This pattern was indeed observed in relation to student attitudes towards academic integrity as well as their knowledge of rules and consequences. For example, students who were tempted to cheat rated the importance of academic integrity below those who were not tempted, but higher than those who cheated. This finding seems logical, and perhaps not worth noting in its own right. It conforms to general assumptions of those who cheat as having low regard for academic integrity. However, our data adds a cautionary note to such claims because even though the cheating group was the lowest of the three groups in their rating of integrity importance, their average rating was still quite high. Therefore, our findings suggest that there is potential for integrity attitudes to be used as predictive indicators of temptation and cheating, but that the analysis is likely to only be useful when used as a comparative measure within a cohort.

The other logical pattern in which the tempted group sat between the cheated and not-tempted groups was with regards to the degree of knowledge of others cheating. Common thinking suggests that the more people are aware of cheating going on around them the more they are likely to cheat (or be tempted to cheat). We certainly saw this as a general pattern in our data. However, the significance of this finding needs to be treated with caution since the majority of students who reported being tempted or cheated had only heard rumours or nothing at all.

By focusing on the temptation group it also offers some further considerations for strategic directions. A particularly interesting finding was that proctoring was associated with less cheating, but there was no difference when we compared the conditions of automated and assisted check-in processes. However, when we moved from the binary cheating/not-cheating analysis to one which included the temptation group, we saw a pattern which suggests the importance of social presence. It affirms that proctoring (e.g. monitoring by camera) may be more effective as a deterrent to cheating if students have had social interaction with the proctors. The nature of the interaction needs further research, whether it is an enhanced sense of being monitored, or an affirmation of an implicit social contract to act with integrity. This finding cautions automating proctoring systems without also considering how those systems can heighted perceived social presence and/or surveillance.

This study has established that there is a large group of students who do not cheat, but who are tempted to do so. It has also proposed that these students may further refine our understanding of the conditions and factors that create a tipping point for student misconduct. Future quantitative and qualitative research would be valuable in further exploring the nature of temptation, such as intensity and spontaneity, in connection with demographic, personality, as well as contextual conditions and motivations.

Availability of data and materials

Raw data is not available due to privacy concerns.

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Henderson, M., Chung, J., Awdry, R. et al. The temptation to cheat in online exams: moving beyond the binary discourse of cheating and not cheating. Int J Educ Integr 19 , 21 (2023). https://doi.org/10.1007/s40979-023-00143-2

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The Ethicist

If My Classmates Are Going to Cheat on an Online Exam, Why Can’t I?

By Kwame Anthony Appiah

• April 7, 2020

Because of efforts to slow the spread of Covid-19, the large university I attend has, like many others, transitioned to online instruction for the foreseeable future. In-person classes are not prohibited, but the administration has strongly recommended against them. Because we are on the quarter system, our final exams are scheduled for next week, immediately before our spring break. This means that professors must choose from a number of less-than-ideal options for administering exams.

Some have given students the choice of an optional final, or canceled them altogether, basing their grade entirely on past work. Others have chosen to use an online service that monitors students while they take their tests in order to ensure that they do not cheat. The downside of this service is that it requires access to a computer with a webcam, a reliable internet connection and access to a quiet, empty room. Those requirements pose a challenge for many college students, particularly those with fewer resources — and more roommates.

In consideration of this, another popular option is to require an online exam with a request for academic honesty as the only safeguard against cheating. Based on conversations I’ve had with and heard among classmates, I think it is fair to assume that the vast majority of students will take advantage of the resources now available to them (i.e., notes, friends, the internet) in order to succeed. This will result in a much higher average performance than an in-person exam would, putting anyone who does not cheat at a disadvantage as any grading on a curve would hurt him or her.

While I know that it is dishonest to cheat and I value my integrity, I also want to maintain a high G.P.A., and it seems that those goals are in conflict with each other. I plan to take my exam for an uncurved class in accordance with academic-honesty policies, but would it be entirely unacceptable to consult my friends or notes minimally during a curved class’s final? Delaney

It’s a familiar protest: “But everyone else is doing it!” You won’t be surprised that the Ethicist takes a dim view of this argument. Cheating, being a form of dishonesty, is wrong even when rampant.

But beyond the poor choices your classmates are making, I’m concerned about the poor choices your professors are making. A setup that encourages cheating and penalizes honesty is a badly designed one. Students should not be led unnecessarily into temptation.

Why not tell your instructors, in both the curved and uncurved classes, that it makes sense to have an open-book exam? Doing this might require changing the test. But given the circumstances you describe, it may be the only responsible option.

If a professor insists on ignoring these realities, however, you should still do the honest thing. Ethics is always, in part, about what kind of person you ought to be. Even though your integrity could cost you on the curve, it has distinct advantages when it comes to looking yourself in the eye.

I’m the executive director of a small nonprofit whose employees can easily work remotely. In implementing our remote-work plan, I shared with the staff that the office isn’t 100 percent off limits: I will stop by a couple of times a week to get the mail; another person will be by from time to time to water his plants; we may need to pick up a document here and there. All of that is fine and doesn’t contradict our attempt to do the social distancing public-health officials are recommending.

But for me, working at home is very unappealing. I live in a very small house with a spouse who will also most likely be home, no private space to set up for my work and a dog that is a big barker, which will interrupt the many phone meetings I will need to conduct.

From an ethical perspective, may I work from the office alone, while everyone else works remotely? If I do so, do I need to be explicit with our team and offer it to others?

I believe that most people see remote work as an attractive option — you don’t need to dress up, can be running laundry while working, can have lunch with your spouse. Not me. May I go to the office, even though we have instituted a remote-work policy? Name Withheld

Your worry is, in essence, that you would be taking advantage of your position of authority to grant yourself a privilege. So suppose you were just another staff member. Would a fair-minded boss grant you permission to do this?

To make that determination, this boss would need to survey staff members and confirm your hunch that most people prefer to work remotely. If you were the only person who wanted to work in the office, there’d be no worries either about unfairness or about social exposure. Even if a couple of you wanted to work in the office — assuming that your state hasn’t ordered nonessential workers to stay at home and that you’d be getting to work in a way (by car, say) that risked no further exposure — you each might be able to maintain distancing on the occasions when you were both in the building. Under those circumstances, what you have in mind could be just fine: There would be no harm in doing what a reasonable boss would agree to your doing. You’ll need to be transparent, then, and make sure that your assumptions are warranted. But as long as your use of the office is something you can defend to your staff in this sort of way, they shouldn’t regard it as unfair.

My partner and I are both employed and have two children in a day care center. Our oldest attends four days a week, and our youngest attends two days a week. My partner works four days a week as a registered nurse at a clinic, and I am employed full time at a higher-education institution as a director.

Recently, in response to the coronavirus, my employer required all nonessential employees to work remotely until further notice. In addition to a number of other directives, the governor of our state encouraged day care centers to stay open.

We are in a fortunate position to suffer no significant financial consequence keeping our children at home while I work remotely. Admittedly, I would not be as productive in this situation as I could otherwise be, but my employer is understanding. However, even if we choose to keep our children at home, we will continue to be charged weekly tuition from the day care center so long as it remains open.

My partner and I have debated continuing to take our children to day care. Is it selfish to send your children to day care if you can easily keep them at home? Are we selfish to focus on “getting our money’s worth” for each week? Are families with the means to stay at home with children obligated to do so for the public good, even if it means forfeiting fees for something they’re not using? James E., Minneapolis

Your partner is a nurse and so should be in a position to assess whether the kids’ going to day care poses risks to them and to others, including you. Public-health experts, I will note, tend to be more concerned with the prospect of children’s spreading infection than falling ill with it. If getting sick isn’t a big problem for most kids, though, having sick parents definitely is. So do your best to maintain social distancing for their sake as well as yours. And that probably means keeping them home. You think that having kids underfoot will reduce your productivity. You might consider how your productivity will be affected if you come down with Covid-19.

As for your sunk costs? You should forget about getting value for money, given that you’re clearly in a position to put that aside. Even if you don’t send your kids to the center, you’ll be supporting a place that is relied upon by parents who don’t have the luxury of deciding to keep their kids at home. In this respect, you’re making another social contribution, beyond the one you’d make by social distancing. More we-thinking and less me-thinking is one thing that’s needed from all of us right now.

Kwame Anthony Appiah teaches philosophy at N.Y.U. His books include “Cosmopolitanism,” “The Honor Code” and “The Lies That Bind: Rethinking Identity.” To submit a query: Send an email to [email protected]; or send mail to The Ethicist, The New York Times Magazine, 620 Eighth Avenue, New York, N.Y. 10018. (Include a daytime phone number.)

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Another problem with shifting education online: cheating

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When universities went online in response to Covid-19, so did the tests their students took. But one of the people who logged on to take an exam in a pre-med chemistry class at a well-known mid-Atlantic university turned out not to be a student at all.

He was a plant. An imposter. A paid ringer.

Proctors — remote monitors some schools have hired to watch test-takers through their webcams — discovered by reviewing video recordings that this same person had taken tests for at least a dozen different students enrolled at seven universities across the country. The camera caught a spreadsheet tacked to the wall of his workspace with student names, course schedules, remote login information and passwords for websites that could feed him answers.

“We can only imagine what the rate of inappropriate testing activity is when no one is watching.” Scott McFarland, CEO, ProctorU

But he was in Qatar, beyond the reach of any attempts to hold him accountable, according to proctors familiar with the situation. They could not say what happened to the students who allegedly hired him.

It was a dramatic case, but far from unique. Universal online testing has created a documented increase in cheating, often because universities, colleges and testing companies were unprepared for the scale of the transformation or unable or unwilling to pay for safeguards, according to faculty and testing experts.

Even with trained proctors watching test-takers and checking their IDs, cheating is up. Before Covid-19 forced millions of students online, one of the companies that provides that service, ProctorU, caught people cheating on fewer than 1 percent of the 340,000 exams it administered from January through March. During the height of remote testing, the company says, the number of exams it supervised jumped to 1.3 million from April through June, and the cheating rate rose above 8 percent.

“We can only imagine what the rate of inappropriate testing activity is when no one is watching,” said Scott McFarland, CEO of ProctorU.

Related: As students fill summer courses, many ask: Why aren’t all colleges open in the summers?

And for most online test-takers, no one has been watching. One reason is that, as demand for online testing spiked, proctoring capacity was overwhelmed. One company, Examity, suspended its live proctoring services during the demand surge when its 1,000 proctors in India were locked down to curb the spread of the coronavirus there.

Ninety-three percent of instructors think students are more likely to cheat online than in person , according to a survey conducted in May by the publishing and digital education company Wiley. Only a third said they were using some type of proctoring to prevent it. Many colleges and universities moved ahead with online testing without supervision to save money. Others opted instead for less expensive, scaled-down kinds of test security, such as software that can lock a web browser while a student takes a test.

While locking a browser during an exam may help — and about 15 percent of instructors take that step, the Wiley survey found — it can’t stop other forms of cheating.

“You cannot give an exam if it is not proctored,” said Charles M. Krousgrill, a professor of engineering at Purdue, where faculty have been more willing to publicly discuss cheating than their counterparts at many other schools.

When, after the Covid shutdowns, Purdue gave students extra time to take their tests online, said Krousgrill, “there was rampant dishonesty.” He described some students in his department organizing videoconferences and sharing answers. “Once we went to online instruction, we could not watch. [The students] knew it, and knew the game was up for grabs. There were lots of kids who got caught up in that.”  

ProctorU, which provides proctors to be sure online test-takers follow the rules, caught people cheating on fewer than 1 percent of exams it administered before the Covid-19 outbreak. Since then the number has jumped to more than 8 percent.

Online tests have also meant a booming business for companies that sell homework and test answers, including Chegg and Course Hero. Students pay subscription fees to get answers to questions on tests or copies of entire tests with answers already provided. The tests are uploaded by other students who have already taken them, in exchange for credits, or answers are quickly provided by “tutors” who work for the sites.

For $9.95 a month, Chegg is offering a new service that provides fast answers to math problems submitted by smartphone camera, step-by-step solution included. Snap a pic, get the answer.

Related: While focus is on fall, students’ choices about college will have a far longer impact

Though these sites have been around since before the pandemic, their use appears to have exploded as more tests are given online. Students used Chegg to allegedly cheat on online exams and tests in the spring at schools including Georgia Tech, Boston University, North Carolina State and Purdue, according to faculty at those institutions and news reports. Universities prefer not to talk about cheating incidents, and federal privacy law limits how much detail they can provide.

At North Carolina State, more than 200 of the 800 students in a single Statistics 311 class were referred for disciplinary action for getting answers to exam questions from a company that offers online tutoring services.

At North Carolina State, more than 200 of the 800 students in a single Statistics 311 class were referred for disciplinary action for using “tutor-provided solutions” to exam questions from Chegg, said Tyler Johnson, the course coordinator.

After the exam, Johnson said, he asked his university to get Chegg to remove the questions, citing copyright law. Chegg did, and furnished a report of users who had either posted or accessed the exam materials.

“I was initially really naive to the extent to which these services are utilized by students,” he said.

Related: Amid pandemic, graduate student workers are winning long-sought contracts

The North Carolina State students have protested in a petition that they didn’t know using Chegg would be considered cheating, and that Johnson showed “no regard to the personal stresses we are enduring and have endured throughout the semester.”

Krousgrill and his colleagues at Purdue asked Chegg to remove their exam materials, too, and asked for help identifying cheaters. They found “a massive number” of students who had used Chegg to get test answers, he said. In one class, Krousgrill said, as many as 60 students out of 250 had done it, and 100 students in a colleague’s class were identified as having used Chegg in a similar fashion.

“I do feel for the students,” Eric Nauman, a professor of engineering and director of the engineering honors program at Purdue, told a web panel for engineering faculty and majors convened to discuss the use of Chegg and similar services for cheating. “If one person starts using it and gets a better grade and these exams are graded on a curve, then they’re in big trouble.”

The number of students who are cheating is almost certainly higher than the number being caught or reported. Research has shown that instructors believe cheating happens much less often than students do , which means they may not be looking for it. When they do find it, many choose to simply give cheaters an F, without reporting the incidents further.

“I do feel for the students. … If one person starts using [an online service] and gets a better grade and these exams are graded on a curve, then they’re in big trouble.” Eric Nauman, professor of engineering and director of the engineering honors program, Purdue University

“I had a conversation with a group of students several months ago,” said James Pitarresi, vice provost at Binghamton University. “And one of the students said, ‘Look, you know, probably 80 percent of the class is looking at Chegg. What are you going to do, expel all of us?’ ”

For most faculty, their only recourse is to ask the companies to remove their exam materials and identify cheaters. But that can take days or even weeks, and happens after the materials have already been shared and an exam is over. It also puts the burden on professors to go site by site, search for their material and ask that it be taken down. “I go through every couple of months and write to them and say, ‘Please take these 200 or 300 items off your site,’ ” said Krousgrill. “But that takes a lot of time.” Especially, he said, when his students are getting answers in 10 minutes.

The cheaters are often way ahead. Message boards at Reddit are filled with warnings to students not to use their school email addresses or real names when signing up for Chegg or similar services. That makes catching cheaters nearly impossible. Even when professors try to preempt Chegg and other sites ahead of time, as one did by embedding a trackable code in test questions, students figured it out and worked around it, according to faculty familiar with the example, although they wouldn’t identify which institution did this.

Chegg, which offers online tutoring services, declined to comment at length. A spokesman said the company supports academic integrity and hasn’t seen “any relative increase in honor code issues since the Covid-19 crisis began.” In an interview with The New York Times, Chegg chief executive Dan Rosensweig, when  asked whether his company’s services were being used for cheating, said: “Let’s face it: Students have always found a way, whether it’s in fraternities, or whether they go to Google. But Chegg is not built for that.”

The company reported  $153 million in revenue for the second quarter , when the pandemic shutdowns were at their peak — a 63 percent year-over-year increase. 

Related: Could the online, for-profit college industry be “a winner in this crisis”?

Chegg CFO Andy Brown told investors in a video call, “We’ve clearly been seeing tailwinds since the shelter in place and kids were learning off campus.”

Colleges were not the only institutions to rush examinations online. Advanced placement and other tests also went virtual in the spring and the parent College Board said it was prepared to move the SAT online in the fall if necessary but then reversed itself.*  So did law school entrance and placement exams, professional certification tests for financial managers and food handlers and many others.

The College Board , which administers the AP tests, reconfigured these exams to be “open book” when they were moved online, but without proctoring. Students reportedly used private messaging apps to collaborate on answers. Even before the exams began, College Board officials tweeted about “a ring of students who were developing plans to cheat” and canceled their registrations.

The College Board won’t disclose whether any cheating actually happened. A spokesman would say only that “at-home testing presents some different security challenges” and that the organization took steps to prevent it.

There are other reasons besides just having the opportunity that students are cheating online. About a quarter of students “indicated that it should be expected that students will use whatever is available to them in a take-home or online test ,” according to research published in the spring by the Journal of the National College Testing Association. It said “any inaction on the part of the faculty to provide a secure exam administration was seen [by students] as an indication that the faculty did not care about” cheating.

“One student with a pattern of cheating is an ethical problem for that student. Multiple students with a pattern of cheating devalues any grade or degree they might be receiving,” Steve Saladin, a co-author of the study, said. “And when cheating spreads to many students in many programs and schools, degrees and grades cease to provide a measure of an individual’s preparedness for a profession or position. And perhaps even more importantly, it suggests a society that blindly accepts any means to an end as a given.”

*This story has been updated to correct that the SAT was not moved online in the spring.

This story about online testing was produced by  The Hechinger Report , a nonprofit, independent news organization focused on inequality and innovation in education. Sign up for our  higher education newsletter .

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Cheating in Online Education: Myth vs. Reality

Although online learning is becoming more and more prevalent, there still persist myths about what it means to be an online student. One frequently discussed topic in the world of online education is cheating. According to one 2009 study, 73.8% of students surveyed felt that it was easier to cheat in an online class. This skewed perspective — that cheating is so easy — can lead to misconceptions about how prevalent cheating really is in the online setting.

Because online courses often do not involve face-to-face instruction, the uninitiated can easily fall prey to the idea that cheating is rampant. After all, how could a professor that is miles or even states away prevent students from just googling the answers to their tests? And if no one is checking, isn’t everyone doing it?

Myths about cheating in online education persist because of a lack of information. The idea that cheating is unchecked in virtual classrooms is simply untrue. In fact, while there have been conflicting results from multiple studies done on the issue of cheating in online courses, there is nothing to suggest that cheating is much more common in every online situation.

Following are five commonly held misconceptions about cheating in online education. The truth of the matter might surprise you.

Myth: Online universities don’t really care about cheating

Reality: There is some belief that online universities do not have the same rigorous academic standards that traditional colleges and universities do. However, the truth is that most so-called online universities are also traditional universities and that in fact these universities, on the whole, are vigilant about preventing cheating. Dr. Susan Aldridge, President of Drexel University Online, indicates that at her school, “We create solid barriers to cheating, while also making every effort to identify and sanction it as it occurs or directly after the fact.”

It is also important to consider the investment factor. Online learning programs invest in technology that will improve student outcomes and support success — including Learning Management Systems (LMS’s). While an online course could technically be proctored with little more than email and a message board, by using an LMS, a college or university is sending a strong signal that they care about the integrity of the course. In addition to plagiarism detection (see below), these systems can integrate with other cheating detection technologies that offer identity verification and other features designed to thwart cheating.

Further, colleges like the University of Central Florida invest heavily in training their online faculty. The UCF course IDL6543 is designed to ensure that faculty is comfortable teaching in an online environment. No faculty training in online learning would be complete without covering the possibility of cheating and methods for detection of possible academic dishonesty in an online environment.

These varied investments, in technology as well as training, demonstrate that online programs do indeed care about cheating and do everything in their power to detect and prevent it.

Myth: It’s impossible for online instructors to identify cheating

Reality: When you think about cheating, it is easy to go back to high school when an instructor at the front of the room sat watching vigilantly as each student completed a test or quiz, admonishing any student who did not keep his eyes on his own paper. Because online education does not have that physical presence, it can be easy to think that when cheating does occur, the perpetrators will not get caught.

However, just as universities who offer online courses certainly do care about academic honesty, so do they put into place mechanisms that can detect different types of cheating in the online setting. For example, according to Dr. Aldridge, Drexel University uses a number of technological advancements to minimize cheating occurrences, including:

• a variety of virtual test-taking strategies that have proven effective when it comes to preventing students from cheating on exams
• authentication technologies to electronically affirm an online student’s identity
• webcams to verify physical features like facial structure that can be checked against government-issued IDs
• software called BioSig-ID that uses keystroke analysis to recognize keyboard typing patterns, based on rhythm, pressure, and style, which is nearly as accurate as actual fingerprint authentication
• ProctorU, which integrates webcams with microphones that enable well-trained live proctors to monitor and/or record test-takers, by watching body language, eye movement, or other physical attributes known to indicate suspicious behavior

Clearly, institutions like Drexel University care about identifying cheating and are willing to invest in technology and techniques to minimize its occurrence.

Myth: Plagiarism checkers are easily fooled

Reality: Cheating on tests and quizzes by obtaining outside information, or even getting the answers, is just one form of cheating. Plagiarism — the use of another’s work without citation or attribution — is and has been a top concern in higher education since long before the introduction of online learning. According to the Harvard Guide to Using Sources , “In academic writing, it is considered plagiarism to draw any idea or any language from someone else without adequately crediting that source in your paper.”

Plagiarism, both intentional and accidental, happens in all types of colleges and universities, both in traditional classroom settings and online courses. However, online course instructors may actually have an advantage in detecting plagiarism. Because online courses rely on digital submissions of all work, plagiarism detection is baked into the process.

One key reason that plagiarism is so rarely able to pass through the online submission process is due to institutional investment in LMS’s that put plagiarism and academic dishonestly front and center in the software development process. For example, Plagscan is a plagiarism detection technology that can integrate seamlessly with popular LMS applications including Blackboard, Moodle, and Schoolology. Further, the California Community Colleges Online Education Initiative partnered with VeriCite to incorporate plagiarism detection software into its LMS. As a significant network of online schools, this is yet another indicator that schools across the country take plagiarism seriously and are constantly on the lookout for the best detection methods.

Online submission applications like those offered above can automatically check for formatting errors from cut and pasted text and uncited passages that match up with other papers or sources. In the case of accidental plagiarism, students can even run their own papers through these types of detection programs via their LMS.

While no method of plagiarism detection is 100% foolproof, online students cannot expect to get away with it easily.

Myth: Online students are more likely to cheat

Reality: In a recent study from Marshall University , 635 undergraduate and graduate students were surveyed on student cheating behaviors. The researchers found that while 32.1% of respondents admitted to cheating in a face-to-face class, 32.7% admitted to cheating in an online course. The difference between these two numbers is quite small and it is also important to note that overall, more students admitted to “inappropriate behavior” vis a vis academic dishonesty in traditional classroom settings than did in online classrooms.

While results from a single study are never enough to make sweeping generalizations, the Marshall University survey certainly implies that cheating in online courses — at least under the purview of this specific university — is hardly rampant and is certainly not much more common than it is in a more traditional classroom setting.

Another study took another tack in establishing how common cheating in online exams is, as compared to face to face exams. While the Marshall study and many other cheating-based studies use self-reporting, Testing a model to predict online cheating—Much ado about nothing by Victoria Beck, examined data without relying on self-reporting. Instead, Beck uses indicators like GPA and class rank to predict exam scores, then compares those predictions with actual scores. The results of this analysis were consistent with the Marshall study and found that online students were no more likely to cheat on exams than those in face to face or hybrid learning environments.

Myth: Since all online students cheat, it isn’t that big of a deal

Reality: No matter how much easier it seems that cheating would be online, the fact is that students who choose to cheat are, as cliche as it sounds, just cheating themselves. The reality is that many students who choose to take courses online do so because they are dedicated to furthering their education no matter where or when they have to take courses. Academic honesty is critical to the continued success of online education programs and it is up to students, faculty, and institutions to ensure that the highest standards are upheld.

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Cheating in Exams, Essay Example

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You are free to use it as an inspiration or a source for your own work.

Cheating in exams can be defined as committing acts of dishonesty during an exam in order to score good grades. This is normally done by students when they fail to prepare for the exams or when they feel that the test is too hard for them and they want to score good grades.

Various acts are considered as cheating: first when a student gets access to exam papers be it part of them or all the exam papers before the exam is considered as cheating. Another way of cheating is by having materials that are not authorized in the exam room either electronic or non electronic in their reach from which the students copy or even copying answers from scripts of other candidates or allowing your script to be copied from by other candidates. Such materials include phones in which they store data; some phones have memory cards that store huge amounts of data and thus a student can even carry the whole syllabus in their phones from which they copy. Other electronic materials are calculators in which students store formulas especially for science and math exams. Science and math formulas may also be written on the desktop which they hide from their supervisors by covering with the answer sheet. Non electronic materials include small notes which the students make on something they suspect will be tested. Such writings are made on small pieces of paper, on the palm or on sole tapes which the students stick on their clothes. Another way of cheating is when a student impersonates another one and ends up doing the exam for them or even communicating with fellow candidates during an exam session. These forms don’t exhaust the many ways of cheating.

When students succeed in their first attempt of cheating they will always be tempted to repeat the act since it enables them to pass exams without struggling however this may bring serious consequences for the students. The problems may be short lived or long term. Short term consequences include being awarded a zero score by the lecturer because they believe that the candidate does not know anything. Getting a fail forces the student to repeat the unit .This means an addition on the other terms work a burden which may make the student fail other units hence causing a cycle of failing. Other lecturers punish these students by suspending them for a given period of time .Such students get it rough in explaining to their parents the reasons for being suspended. They may also become the laughing stock in the village when fellow students spread the rumours. Another short term consequence is when the lecturer forces the students to take remedial studies as others go for holiday hence denying them the opportunity to enjoy their holidays.

Long term consequences include being expelled from school. This means the student has to look for another school and hence the student delays from finishing college which consequently affects their chances in the job market because most job advertisements specify age limit. Cheating students also gain bad reputation from fellow students and lectrurers.Fellow students always see you as a liar and lecturers lose faith in you and it becomes difficulty to convince them that you didn’t cheat at times when you pass.

In the long term a student who passed her exams through cheating may have problems when it comes to delivering services in a job. This is because a student may cheat in exams, graduate from college but have difficulties when solving problems touching on their field of study in work environment since the certificates they present don’t really show their capability but what they pretend to be. When it comes to giving ideas during discussions in the office the cheaters will strain to contribute and also the manner in which they present themselves in such meetings will be affected since they fear that fellow workers will notice their dormancy. Without a question poor performance in the job will lead to job loss.

Cheating in an exam also denies a student important knowledge in their lives which they would have gained if they take their studies seriously A student may escape being caught cheating and get good grades which would sound okay   but the truth is they may lie to their teachers and parents but they cannot cheat themselves .the truth will remain that they waste their money and time in college but at the end of it they wont gain any knowledge since what they show to have gained is not theirs. In some colleges like the ones offering ACCA when a candidate is caught cheating they are discontinued from doing the other papers and this may kill the student’s dream of venturing in such a field.

The consequences of cheating in an exam are just too much to bear and so students should avoid such instances by ensuring they revise utilise their time well and revise thoroughly for their exams.

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Essays About Cheating: Top 5 Examples and 9 Writing Prompts

Essays about cheating show the value of honesty, see our top picks for examples and prompts you can use in writing.

In the US, 95% of high school students admitted to participating in some form of academic cheating . This includes exams and plagiarism. However, cheating doesn’t only occur in schools. It’s also prevalent in couples. Psychologists say that 50% of divorce cases in the country are because of infidelity . Other forms of cheating exist, such as cheating on a diet, a business deal, etc.

Because cheating is an intriguing subject, many want to read about it. However, to write essays about cheating appropriately, you must first pick a subtopic you’re comfortable discussing. Therefore, we have selected five simple but exemplary pieces you can read to get inspiration for writing your paper.

See below our round-up of top example essays about cheating.

1. Long Essay On Cheating In School By Prasanna

2. the reality of cheating in college essay by writer kip, 3. why cheating is wrong by bernadette mcbride, 4. what counts as cheating in a relationship by anonymous on gradesfixer, 5. emotional cheating by anonymous on papersowl, 1. types of cheating, 2. i was cheated on, 3. is cheating a mistake or choice, 4. tax evasion and cheating , 5. when i cheated, 6. cheating in american schools and universities, 7. review a famous book or film about cheating, 8. a famous cheating quote, 9. cause and effects of cheating.

“Cheating is a false representation of the child’s ability which he may not be able to give without cheating. It is unfair to everyone involved as it deprives the true one of the chance to come on the top.”

Prasanna begins the essay by defining cheating in schools and then incorporates how this unethical behavior occurs in reality. She further delves into the argument that cheating is not learning but an addiction that can result in students losing self-confidence, sanity, and integrity. 

Apart from showing the common causes and harmful effects of cheating on students, Prasanna also adds parents’ and teachers’ critical roles in helping students in their studies to keep them from cheating.

“It’s human nature to want to win, and some of us will go against the rules to do so. It can be harmless, but in many cases, it is annoying, or even hurtful.”

Kip defines cheating as human nature and focuses his essay on individuals who are hell-bent on wanting to win in online games. Unfortunately, these players’ desire to be on top is all-consuming, and they’re willing to go against the rules and disregard their integrity.

He talks about his experiences of being cheated in a game called AoE. He also incorporates the effects of these instances on newbies. These cheaters will humiliate, dishearten, and traumatize beginners who only want to have fun.

Check out these essays about cooperation .

“A cheater is more than likely lying to themselves more than to the people around them. A person can only go so far before their lies catch up to them, begin to accumulate, and start to penalize you.”

Mcbride dedicates her essay to answering why cheating is wrong, no matter the circumstance. She points out that there will always be a definite punishment for cheaters, whether they get caught. Mcbride believes that students who cheat, copy, and have someone else do their work are lazy and irresponsible. These students will never gain knowledge.

However, she also acknowledges that some cheaters are desperate, while some don’t realize the repercussions of their behaviors. At the end of the essay, she admits to cheating but says she’s no longer part of that vicious cycle, promising she has already realized her mistakes and doesn’t want to cheat again.

“Keep in mind that relationships are not based on logic, but are influenced by our emotions.”

The author explains how it’s challenging to define cheating in a relationship. It’s because every person has varying views on the topic. What others consider an affair may be acceptable to some. This includes the partners’ interaction with others while also analyzing the individual’s personality, such as flirting, sleeping in the same bed, and spending time with folks.

The essay further explains experts’ opinions on why men and women cheat and how partners heal and rebuild their trust. Finally, examples of different forms of cheating are discussed in the piece to give the readers more information on the subject. 

“…emotional cheating can be described as a desire to engage in another relationship without physically leaving his or her primary relationship.”

There’s an ongoing debate about whether emotional cheating should be labeled as such. The essay digs into the causes of emotional cheating to answer this issue. These reasons include lack of attention to each other, shortage of affectionate gestures, and misunderstandings or absence of proper communication. 

All of these may lead to the partner comparing their relationship to others. Soon, they fall out of love and fail to maintain boundaries, leading to insensitivity and selfishness. When a person in a relationship feels any of these, it can be a reason to look for someone else who can value them and their feelings.

9 Helpful Prompts in Writing Essays About Cheating

Here are some cheating subtopics you can focus your essay on:

Some types of cheating include deception, fabrication, bribery, impersonation, sabotage, and professional misconduct. Explain their definitions and have examples to make it easier for readers to understand.

You can use this prompt even if you don’t have any personal experience of being cheated on. You can instead relay events from a close friend or relative. First, narrate what happened and why. Then add what the person did to move on from the situation and how it affected them. Finally, incorporate lessons they’ve learned.

While this topic is still discussed by many, for you, is cheating a redeemable mistake? Or is it a choice with consequences? Express your opinion on this matter. Gather reliable evidence to support your claims, such as studies and research findings, to increase your essay’s credibility.

Tax evasion is a crime with severe penalties. Explain what it is and its punishments through a famous tax evasion case your readers can immediately recognize. For example, you can use Al Capone and his 11-year imprisonment and $215,000 back taxes . Talk through why he was charged with such and add your opinion. Ensure you have adequate and reliable sources to back up your claims.

Start with a  5 paragraph essay  to better organize your points.

Some say everyone will cheat at some point in their life. Talk about the time you cheated – it can be at a school exam, during work, or while on a diet. Put the perspective that made you think cheating was reasonable. Did you feel guilt? What did you do after, and did you cheat again? Answer these questions in your essay for an engaging and thrilling piece of writing.

Since academic cheating is notorious in America, use this topic for your essay. Find out which areas have high rates of academic cheating. What are their penalties? Why is cheating widespread? Include any measures the academe put in place.

Cheating is a frequent cause of conflict on small and big screens. Watch a film or read a story and write a review. Briefly summarize the plot, critique the characters, and add your realizations after finishing the piece. 

Goodreads has a list of books related to cheating. Currently, Thoughtless by S.C. Stephens has the highest rating.

Use this as an opportunity to write a unique essay by explaining the quote based on your understanding. It can be quotes from famous personalities or something that resonates with you and your experiences.

Since cheating’s cause and effect is a standard prompt, center your essay on an area unrelated to academics or relationships. For instance, write about cheating on your diet or cheating yourself of the opportunities life presents you.

Create a top-notch essay with excellent grammar. See our list of the best grammar checkers.

Maria Caballero is a freelance writer who has been writing since high school. She believes that to be a writer doesn't only refer to excellent syntax and semantics but also knowing how to weave words together to communicate to any reader effectively.

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Measures implemented to address exam cheating, says Deloitte PHL

DELOITTE Philippines said it has implemented corrective actions following the US Public Company Accounting Oversight Board’s (PCAOB) order to fine the professional services firm for exam cheating.

“With regard to the PCAOB decision, Deloitte sets the highest expectations for the conduct of its professionals. Answer sharing on learning assessments is unacceptable and a breach of our ethical code of conduct,” Deloitte Philippines said in an e-mail.

 “Deloitte Philippines self-reported these matters to the PCAOB, implemented comprehensive corrective actions, and continues to serve clients with high quality and in accordance with applicable professional standards,” it added.

 On Thursday, Reuters reported that PCAOB imposed $2 million in fines on Deloitte Touche Tohmatsu Ltd.’s affiliates in the Philippines and Indonesia. The fines were for “violating the regulator’s rules and quality control standards due to widespread answer sharing on internal training tests.”

 It said that the regulator also sanctioned a former national professional practice director at Deloitte Philippines.

 In a statement on Wednesday, PCAOB said that it settled three disciplinary orders concerning the three entities as their “quality control deficiencies resulted in widespread answer sharing on internal training tests.”

 Since 2021, the PCAOB has sanctioned nine firms for inappropriate sharing of answers on internal training exams.

 In the PCAOB order, the regulator said that Deloitte Philippines failed to establish appropriate policies for administering and overseeing internal training tests.

By failing to establish such procedures, the firm was unable to identify that nearly all of its audit partners engaged in improper answer sharing from at least 2017 until early 2019, PCAOB said.

 PCAOB also said that the audit professionals either provided answers or received answers in online tests for mandatory internal training courses without reporting them.

 However, the regulator said that since the firm has implemented remedial and corrective measures aimed at ending the misdeed, the civil money penalty imposed has been lowered.

 These corrective actions include changing quality control policies and ensuring that its personnel don’t engage in improper answer sharing while obtaining degrees of technical training and proficiency.

 “Absent this extraordinary cooperation, the civil money penalty imposed would have been significantly larger, and the Board may have imposed additional sanctions,” PCAOB said.

The PCAOB imposed a $1 million penalty on Deloitte Philippines, which must be paid within 10 days of the issuance of the order dated April 10, while it barred the former national professional practice director from being an “associated person of a registered public accounting firm” and required him to pay $10,000. — Justine Irish DP. Tabile

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Cheating Detection in Online Exams

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• Nabila EL Rhezzali 11 ,
• Imane Hilal   ORCID: orcid.org/0000-0002-0082-5438 11 &
• Meriem Hnida   ORCID: orcid.org/0000-0002-7735-8305 11 , 12  

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 668))

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Right now, numerous universities are directing tests online which creates an opportunity where students cheat. This trend has been sped up in recent months when COVID-19 cases are increasing, and test centers are closing. However, most educational institutions face the problem of student cheating when taking online exams. This study aims to find a technique that helps instructors to detect cheating in online exams.

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EL Rhezzali, N., Hilal, I., Hnida, M. (2023). Cheating Detection in Online Exams. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2023. Lecture Notes in Networks and Systems, vol 668. Springer, Cham. https://doi.org/10.1007/978-3-031-29857-8_44

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