distance cheating essay

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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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The Hechinger Report

Covering Innovation & Inequality in Education

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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Op-Ed: Rampant online cheating is the dark side of remote learning

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Since there seems to be an app for everything, it may come as no surprise that there is an app for cheating. But it isn’t just one app. It’s hundreds of companies and apps that actually can be used to complete students’ homework, tests, writing assignments and even dissertations and exams.

But what surprised me most as an educator playing this cat-and-mouse game for decades is that cheating is now scaled and outsourced internationally and powered by venture capitalists, Wall Street investors and billion-dollar companies. One of the biggest companies whose services enable students to cheat, Chegg, is facing a lawsuit filed in September by major textbook company Pearson .

Companies such as Chegg and Course Hero offer monthly subscription formats — similar to Netflix — in which students pay $10 or $15 a month for round-the-clock access to resources including exam questions, textbook solutions and homework “help,” meaning that subscribers can upload a problem to their accounts and expect answers with proof within minutes or the hour . They also get on-demand access to many experts, often based overseas (Chegg employs more than 70,000 experts in India ), with advanced degrees in math, science, engineering, technology, business, economics and other subjects. These experts, available online 24/7, are the source of step-by-step answers.

Companies such as Grade Bees and EduBirdie will even write your five-page reflective paper or 25-page essay, as original work, for varied prices. English-speaking writers from around the world are for hire, in some cases within days or even hours . Some sites and guides let the student know that their relationship will be closely guarded, and no, the student’s professor should not be able to find out , at least not under the right precautions.

Cheating is so rampant that Stanford University’s Graduate Student Council recently announced it had approved revisions to its academic honor code to allow test proctoring. If the changes go through, they will represent the first revision to the code since 1977, according to the student newspaper . Reported honor code violations there went up 114% in the last two years.

Multiple news stories have chronicled widespread cheating in colleges and universities, particularly in the STEM fields . This year, stories in Forbes , the Wall Street Journal and U.K. publications including Education Technology have spotlighted the growth and profits of public companies such as Chegg.

Chegg reported 4.9 million subscribers as of the end of June , a 31% year-over-year increase, and $198.5 million in quarterly revenue, also a 30% year-over-year increase. Among its many services is a way for cheaters to leap over the hurdle of problem-solving questions, in which students are asked to show how they got their answers. Chegg’s experts on demand can personally answer the subscriber’s unique test or homework question.

Many CSU students see big upsides to online learning. Now, there is a push to expand it

COVID-19 forced schools to shift to online learning. At CSU, it’s sticking around in what will be a new normal.

June 4, 2021

As an unintended consequence of technology allowing remote learning and exams, students are finding more and more online venues allowing them to earn grades and diplomas by cheating.

How do we curb this global supply chain of cheating and its threat to the integrity of our students and educational systems?

The answer depends on the motivation behind the decision to cheat. Some students don’t think of it as cheating, as they are paying a legit company for the service; many feel pressured to get the grades and so justify the means. Other students may use these services to make up for the learning lost when in-person teaching was halted during the pandemic.

Many students who are cheating dodge academic consequences, as there are few technology solutions to capture original answers provided by experts, and plagiarism-catching software can’t detect original work bought and paid for by these students.

However, in 2020, Australian lawmakers made it illegal to arrange or advertise for sale certain cheating services such as paid essay writing. Did it have an effect? According to Forbes contributing writer Derek Newton, many of the biggest and best-known essay mills are ending operations there. But even then, fear of getting caught is probably not enough motivation to stop all cheating students.

Another action that should be aimed at contract cheating companies is getting Visa and PayPal to stop acting as payment intermediaries for them. And professors and their universities could join the Pearson lawsuit, though that may be a step too far for most risk-averse institutions of higher education.

Countering this cheating requires a coordinated effort by educational institutions and their accreditors, with accreditation agencies possibly changing online professional entrance exams to prevent cheating. Fields such as engineering, science and nursing will lose in the long run if newly minted students cheat their way into the professions.

 Indeed, our society loses the most from this cheating in plain sight. Cheating corrupts the individual who cheats, yes, but it also erodes the faith we have in our educational system, its honest graduates and the people we depend on to build tech that truly serves human interaction, decision making and achievement.

Karen Symms Gallagher is a professor of education and the Veronica and David Hagen chair in women’s leadership at USC. As dean of the USC Rossier School of Education, she was an early adopter a decade ago of online education for master’s degrees.

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Cheating in the Digital Age: Do Students Cheat More in Online Courses?

2010, Online Journal of Distance Learning Administration

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Cheating and other dishonest behaviors are found at all universities, in both face-to-face and online courses. This chapter highlights an instance of cheating in an online course. The case is from the perspectives of both the student and the professor. The student’s perspective explains how and why she/he cheated on the final paper, and the professor explains how she/he suspected the individual and her/his thoughts on academic integrity in the online format. The student’s reasons for cheating include increasing course demands, pressures from work and family to do well, and lack of time due to full-time employment. The fraud triangle is also introduced in this chapter and questions for consideration are posed at the end. The hope is that this case study will illuminate one of the many challenges of online learning in higher education and how one academic dishonesty case was resolved. Because of the increase in cheating—at all levels, not just online—this discussion is timely and important.

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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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September 28, 2020 Teaching & Learning

How to avoid online cheating & encourage learning instead.

Students tempted to find easy answers while distance learning

By Sherry Posnick-Goodwin

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

Joline Martinez

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

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

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

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

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

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

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

Students are more tempted with distance learning

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

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

Karin Prasad

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

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

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

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

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

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

• How teachers can put the kibosh on cheating

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

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

Pedro Quintanilla

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

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

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

Suzie Priebe

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

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

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

Other ideas to prevent cheating online:

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

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

Encourage students to be honest

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

Maggie Strode

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

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

Katie Hollman

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

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

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

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

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

Distance Learning: Parents Doing Children’s Work?

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

Nailah Legohn

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

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

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

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

#online learning

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Forms, Factors And Consequences Of Cheating In University Examinations: Insight From Open And Distance Learning Students

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Academic Dishonesty: 5 Methods of Identifying Cheating and Plagiarism

One aspect of teaching that can make an instructor feel pessimistic and disheartening is when a student attempts to gain an unfair advantage.  Most of the time, this is labeled simply as cheating , defined as intentionally using or attempting to use unauthorized materials on any academic exercise , or plagiarism , the appropriation or use of another person's ideas, results, or words without giving appropriate credit , but we see instances of fabrication and other acts of dishonesty.  What can you do to combat acts of academic dishonesty?  This article is meant to help faculty members at any level, even teaching assistants, identify possible occurrences of academic dishonesty.

Know Your School’s Policies & Be Transparent with Your Students

When you become a faculty member at a new institution, take a more extensive teaching role at your current institution, or even a long-time teacher implementing new curriculum changes, you must identify and know the school’s policy and rules regarding academic honesty and creating a fair classroom environment.  Each faculty member may enforce the rules differently, but it’s critical that the students know your classroom rules and expectations upfront. A few key items to consider:

• Do you want them to work with other students on their homework?
• What rules and procedures do you have for assignments, reports, and exams?
• Put this information in your syllabus and discuss this with them on Day 1 of your course with transparency. 

If one of your students performs an act of academic dishonesty in your course, this will allow you to enforce the sanctions professionally.  If you don’t know where to find this information, ask your faculty mentor or your university’s appropriate administrative office.  These offices are usually the academic honor office, the department or college office, or the Dean of Faculties office, depending on the institution.

2. Watch for the Methods Students Use to Cheat and Plagiarize

The reasons why students cheat have not changed, but how students cheat has changed dramatically.  Typically, there is an assumption that most cheaters are bad or failing students, but students cheat for a multitude of reasons: poor time management skills, a tough class schedule, stress, and anxiety, or poor communication of the rules by their faculty members.  The use of social media and other electronic resources has changed academia over the last 20 years. A few examples of some cheating methods to watch out for include:

• Social Media Communication: Students discuss test questions and individual assignments via social media and other chat apps to give their friends and colleagues academic advantages. 
• Smartphones: Many students take pictures of their answers with their smartphones and send them to others using text messages.
• Smartwatches: Recently, smartwatches have become more prevalent and allow communication and internet browsing without the use of a cell phone.  They allow students to access study files and answers that were not authorized by the faculty member. 
• Groups that Share Tests: Many student organizations have tests and assignments from previous semesters that allow students to look up questions from a faculty member or specific class. 
• Unauthorized Help: Tutoring services will discuss how to “beat a test” or “write the perfect paper” by giving students unauthorized aid. This can also include groups or individuals who may offer to write a paper or take a test for a fee on behalf of the student.

Being smart as a faculty member is knowing that these outside resources are available and to identify when they are being used improperly.

3. Be Proactive, Not Just Reactive

For some instances of academic dishonesty, the origin of the problem comes back to the faculty member not taking a proactive role in combating the acts.

• Full Established Boundaries: The first place for immediate improvement is the discussion of unacceptable acts on the first day of class and syllabus.  Many faculty members will only include the minimum required statement in their syllabus.  This does not properly set student academic honesty boundaries.  Establishing such boundaries might be informing students of the use of plagiarism detection software, describing acceptable behavior and communication about assignments on social media, or acceptable help on homework, essays, and reports.
• Variety in Assessment: Another place where faculty can improve is writing different assignments or multiple forms for exams.  Changing up how you ask questions, what essay question prompts you to use, and creating different forms for exams can be time-consuming. However, this effort will reward students with a fair and objective assessment.  If you are concerned with academic dishonesty in your course, putting in some work early will benefit your course in the long run.

4. Grade Assignments, Reports, and Essays Attentively

Most of the time, trust your own feelings when looking for possible occurrences of academic dishonesty.  When grading assignments, if the work seems more advanced than the student’s level or that they do not seem to follow the question prompt, this can be a strong indication of plagiarism. A few ways to validate these concerns and provide either “proof” or deterrents of this behavior include:

• Show Your Work: Require multiple drafts of a paper and give feedback regarding citation standards throughout the writing process. 
• Side-by-Side Grading: If you have research papers or lab reports in which students worked with a partner or in a group, grade the assignments side-by-side.  While the data or general content may be the same, direct copying will be more apparent. 
• Online Plagiarism Checkers: Technology has been developed to help identify plagiarism.  Websites such as Turnitin.com , Unicheck , PlagarismSearch , and others have students upload their essays/reports then compare all submissions to other online resources and papers turned in for other courses or at other institutions.  Many schools have licenses for this technology and you should utilize it on any type of critical thinking or writing assignment.

5. Manage Exam Administration and Proctoring

Most attention is focused on deterring cheating is during exams.  A few methods that can specifically help discourage academic dishonesty during these high-stake assessments include:

Assigned Seats: A good first step is to assign seats for each exam. While this might be challenging for a large lecture hall, it minimizes the chance of friends and study partners sitting next to each other; thereby limiting the student interaction.  It also allows faculty or proctors to know who is present to take the exam.

• Variety & Alterations by Section: As mentioned before, having multiple forms of an exam can be a great preventive for cheating.  Having different exam forms with the same questions mixed in a different order, or similar questions about the same are all small, minor changes that can promote an honest testing environment.

One topic of test administration that does not get enough attention is proctoring.  In a small classroom, there may be only one adult in a 20-40 student class.  For larger lectures containing 200-400 students, teaching assistants help faculty make sure students are taking their exams honestly.  How can proctors create an honest environment? 

• They must proctor actively:  Many proctors distribute exams and then ignore the students to grade other assignments, work on their computers, look at their cell phone or possibly leave the room.  After you pass out the exams, you should walk around, checking for anything suspicious, and watching for students looking at other exams.  If you spot any of these behaviors, make an immediate change. 
• Reminders About the Rules: Announcements about looking at their own paper can only help so much, so moving students to correct behavior might be necessary.  Having another set of eyes and having another presence in the room, even for a brief time, can correct behavior. 
• Instructor Collaboration: Faculty members that do have test proctors should meet with them before the exam, explain to them the correct protocols, and describe past experiences or issues that occur during exams.  This five-minute discussion will help a test proctor during a situation they have never faced and keep them actively involved during the exam session.

While cheating and plagiarism can cause many faculty members to become frustrated, being able to give your students a fair testing environments and objective assignment is the goal of all successful educators. 

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British Academics Despair as ChatGPT-Written Essays Swamp Grading Season

‘It’s not a machine for cheating; it’s a machine for producing crap,’ says one professor infuriated by the rise of bland essays.

By  Jack Grove for Times Higher Education

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The increased prevalence of students using ChatGPT to write essays should prompt a rethink about whether current policies encouraging “ethical” use of artificial intelligence (AI) are working, scholars have argued.

With marking season in full flow, lecturers have taken to social media in large numbers to complain about AI-generated content found in submitted work.

Telltale signs of ChatGPT use, according to academics, include little-used words such as “delve” and “multifaceted,” summarizing key themes using bullet points and a jarring conversational style using terms such as, “Let’s explore this theme.”

In a more obvious giveaway, one professor said an advertisement for an AI essay company was  buried in a paper’s introduction ; another academic noted how a student had  forgotten to remove a chatbot statement  that the content was AI-generated.

“I had no idea how many would resort to it,” admitted  one U.K. law professor .

Des Fitzgerald, professor of medical humanities and social sciences at  University College Cork , told  Times Higher Education  that student use of AI had “gone totally mainstream” this year.

“Across a batch of essays, you do start to notice the tics of ChatGPT essays, which is partly about repetition of certain words or phrases, but is also just a kind of aura of machinic blandness that’s hard to describe to someone who hasn’t encountered it—an essay with no edges, that does nothing technically wrong or bad, but not much right or good, either,” said Professor Fitzgerald.

Since  ChatGPT’s emergence in late 2022 , some universities have adopted policies to allow the use of AI as long as it is acknowledged, while others have begun using AI content detectors, although  opinion is divided on their effectiveness .

According to the  latest Student Academic Experience Survey , for which Advance HE and the Higher Education Policy Institute polled around 10,000 U.K. undergraduates, 61 percent use AI at least a little each month, “in a way allowed by their institution,” while 31 percent do so every week.

Professor Fitzgerald said that although some colleagues “think we just need to live with this, even that we have a duty to teach students to use it well,” he was “totally against” the use of AI tools for essays.

“ChatGPT is completely antithetical to everything I think I’m doing as a teacher—working with students to engage with texts, thinking through ideas, learning to clarify and express complex thoughts, taking some risks with those thoughts, locating some kind of distinctive inner voice. ChatGPT is total poison for all of this, and we need to simply ban it,” he said.

Steve Fuller, professor of sociology at the  University of Warwick , agreed that AI use had “become more noticeable” this year despite his students signing contracts saying they would not use it to write essays.

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He said he was not opposed to students using it “as long as what they produce sounds smart and on point, and the marker can’t recognize it as simply having been lifted from another source wholesale.”

Those who leaned heavily on the technology should expect a relatively low mark, even though they might pass, said Professor Fuller.

“Students routinely commit errors of fact, reasoning and grammar [without ChatGPT], yet if their text touches enough bases with the assignment, they’re likely to get somewhere in the low- to mid-60s. ChatGPT does a credible job at simulating such mediocrity, and that’s good enough for many of its student users,” he said.

Having to mark such mediocre essays partly generated by AI is, however, a growing complaint among academics. Posting on X,  Lancaster University  economist  Renaud Foucart  said marking AI-generated essays “takes much more time to assess [because] I need to concentrate much more to cut through the amount of seemingly logical statements that are actually full of emptiness.”

“My biggest issue [with AI] is less the moral issue about cheating but more what ChatGPT offers students,” Professor Fitzgerald added. “All it is capable of is [writing] bad essays made up of non-ideas and empty sentences. It’s not a machine for cheating; it’s a machine for producing crap.”

Some feared the bungled rollout of the new federal aid form would halt momentum for state completion requirements.

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Home — Essay Samples — Education — Cheating — Paragraph About Cheating

Paragraph About Cheating

• Categories: Cheating

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Words: 616 |

Published: Mar 19, 2024

Words: 616 | Page: 1 | 4 min read

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Distance learning program gives rise to online cheating

MANILA, Philippines — With the current setup of blended learning due to the pandemic, students have resorted to online cheating via a Facebook group where they share notes and test answers.

The “Online Kopyahan” community had at one point more than 600,000 members, but after a local television report aired on Friday, the now-archived Facebook group was left with 571,900 members.

It was publicly visible on the social networking app before it was archived. Now, no one can create new posts, like, comment, or add more people and only members can see the group’s previous content.

Most of the information shared were students’ answers to their modules and notes on different topics while others offered answer keys, which were also provided in the learning materials given to them.

In an interview on Friday with TV network GMA, which aired the report, Education Undersecretary Diosdado San Antonio said: “This is alarming. It is not helping that instead of the children putting in their efforts to learn, they just copy from each other.”

He added that they were looking to request the deactivation of the Facebook group.

Flawed design

Kristhean Navales, a fourth-grade teacher and president of the Quezon City Public School Teachers Association, said it was saddening that students resorted to online cheating because of the struggles they were experiencing.

“I think the distance learning system really encouraged the cheating because its design has many flaws. Learning has become a burden to students so they find ways to make the situation easier for them,” he said.

Navales said that it was important to look at what urged the students to resort to cheating and why education seemed like a heavy load for them.

“That is how I look at it as a teacher. Not just because [cheating] is wrong but [considering] what actually prompted them to do that,” he said.

“I’m not denying that there is something wrong here but in our generation, it has become different … instead of studying for our future, we’re just studying to pass [the subject],” said Anne (not her real name), a Grade 9 student from a public secondary school.

She said that not everyone has the mental capacity to keep up with modular learning and self-studying. “Then there are teachers who do not actually care about their students,” she lamented.

Anne cited an online post she saw where a student who was not able to attend an online class due to a weak internet connection shared that he was dismissed by a teacher, saying that she was not to blame for the weak connection.

“Students are also having a hard time and I hope the [DepEd] would not ignore that,” she said, adding that teachers could be more gentle in approaching their students or “reassure or convince [us] that we can turn to them.”

“If they can communicate with their students and make them feel like they can rely on them, then I don’t think [those who were part of the Facebook group] would resort to cheating,” she said.

Difficult system

Belle, a Grade 10 student who saw the news about the Facebook group on TV, said it was not easy for everyone to understand the lessons just by reading or searching online.

“Online distance learning is difficult even for me,” she said.

She noted that some teachers were also not very responsive because “just like us, they also have a lot of work to do.”

The United Nations Children’s Fund already pointed out some pitfalls under distance learning modalities.

Based on a survey conducted by the organization last May, 84 percent of the parents reported that children were learning less in distance learning than face-to-face classes, despite giving more time to guide them.

For Navales, it would be good if the pilot implementation of face-to-face learning would already kick off in low-risk areas while those in areas with high COVID-19 cases should be provided with support in terms of gadgets, internet and modules that are relevant to the current situation.

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“This [online cheating] phenomenon is a symptom of the lingering sickness of our education system. This should serve as a wake-up call to the department that learning is becoming a burden to the students and parents and we need to make it more relevant and appropriate to our conditions,” he said.

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Distance Learning: Advantages and Limitations Essay

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Introduction

A shift from classroom to distance learning: advantages and limitations, theories of distance learning, advantages of distance learning, disadvantages of distance learning, works cited.

The theme of this study chose distance learning, which is relevant in connection with the recent coronavirus pandemic. After the searches, the three most relevant articles were selected. Namely: University Students Online Learning System During Covid-19 Pandemic: Advantages, Constraints and Solutions by Purwanto, which covers all the aspects of distance learning in terms of coronavirus (570). Indonesia Education Readiness Conducting Distance Learning in Covid-19 Pandemic Situation by Churiyah et al. represents the Indonesian government’s attitude to this phenomenon (491). Moreover, in A Shift from Classroom to Distance Learning: Advantages and Limitations by Sadeghi, the author discusses distance learning in all its terms (80). All three articles cover the topic of distance learning in the context of the coronavirus and everyday practice. However, Sadeghi’s article seems to be the most priority among all three articles, as it reveals this topic in a pros and cons format that is understandable to everyone.

This article consists of distance learning theory, its history, and its advantages and disadvantages. The article’s primary purpose is to familiarize itself since it does not prove anything but explains the complex in simple language. The author states that students participating in distance education may not always be present at a school (Sadeghi 80). In other words, students learn and pass their chosen subjects online without visiting a testing facility, a college campus, or a university building. The question of whether the provided education is as effective as it could be is raised because of its popularization.

The same is valid for online education, just as no single learning theory has been developed for instruction in general. Many theories have developed based on the significant learning theories we previously covered. The convergence of four overlapping lenses — community-centeredness, knowledge-centeredness, learner-centeredness, and assessment-centeredness — is one of the theories discussed in this section of the article (Sadeghi 82). These lenses served as the framework for the author’s strategy for researching an online education theory because they considered the qualities and resources the Internet offers about each of the four lenses. The author also pointed out how all types of media are now supported and readily available on the Internet, which formerly existed only as a text-based environment (Sadeghi 82). They also correctly noted that the linking function of the Internet is best suited to how human information is stored and accessed.

Speaking of the advantages of distance learning, the author suggests that remote learning may not be ideal for some students, and there will be a list of disadvantages. The best thing about remote learning is that one can take it anytime and anywhere. According to Sadeghi, a distance education degree earned online or through another method may be significantly less expensive for any given program than an on-campus degree (Sadeghi 83). Thus, one of the advantages is the lower cost of higher education in this format. The author also points out that forms of distance learning enable students to design their learning schedules at their leisure rather than adhering to a fixed course of study (Sadeghi 83). These three advantages can be called the most significant since they are most very distinguishable by remote education from traditional one.

While more people have the chance to pursue higher education due to distance learning, there are also some drawbacks. According to the author, the likelihood of being distracted and forgetting deadlines is considered when there is no teacher for face-to-face interaction and no classmates to assist with ongoing reminders about pending work (Sadeghi 84). Additionally, because training is done online, there is almost no physical interaction between students and instructors.

In conclusion, the author states that while distance learning programs and courses are here to stay and will grow in the future, many unclear concerns still need to be defined and looked at. The author believes that the other significant issue is that employers still favor traditional college or university degrees over those obtained through online or remote learning. Summing up, one can note the deep work carried out in the study of the concepts of distance learning.

Sadeghi, Manijeh. “ A shift from classroom to distance learning: Advantages and limitations .” International Journal of Research in English Education , vol. 4, no. 1, 2019, pp. 80–88., Web.

Churiyah, Madziatul, et al. “ Indonesia Education Readiness Conducting Distance Learning in Covid-19 Pandemic Situation .” International Journal of Multicultural and Multireligious Understanding, vol. 7, no. 6, 2020, p. 491., Web.

Purwanto, Agus. “ University Students Online Learning System during COVID-19 Pandemic: Advantages, Constraints, and Solutions .” Sys Rev Pharm, vol. 11, no. 7, pp. 570–576., Web.

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IvyPanda. (2024, January 26). Distance Learning: Advantages and Limitations. https://ivypanda.com/essays/distance-learning-advantages-and-limitations/

"Distance Learning: Advantages and Limitations." IvyPanda , 26 Jan. 2024, ivypanda.com/essays/distance-learning-advantages-and-limitations/.

IvyPanda . (2024) 'Distance Learning: Advantages and Limitations'. 26 January.

IvyPanda . 2024. "Distance Learning: Advantages and Limitations." January 26, 2024. https://ivypanda.com/essays/distance-learning-advantages-and-limitations/.

1. IvyPanda . "Distance Learning: Advantages and Limitations." January 26, 2024. https://ivypanda.com/essays/distance-learning-advantages-and-limitations/.

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IvyPanda . "Distance Learning: Advantages and Limitations." January 26, 2024. https://ivypanda.com/essays/distance-learning-advantages-and-limitations/.