Assignment Two - Online Courses and Cheating (Final)

Running Head: ONLINE COURSES AND CHEATING: DETERRENCE, DETECTION, AND

TECHNOLOGY SOLUTIONS 1

Online Courses and Cheating:

Deterrence, Detection, and Technology Solutions

EDDL 5151: Managing Your Technology Classroom

Wynn Gerald Hamonic

June 24, 2012

ONLINE COURSES AND CHEATING 2

On March 11, 2012, one of Britain’s leading national daily newspapers, The Independent,

reported its research findings that over the past three years more than 45,000 students studying at

80 institutions across the United Kingdom had been found guilty of committing “academic

misconduct” for acts ranging from bringing mobile phones or crib-sheets to examinations to

passing off essays and research papers prepared by private firms as their own work (Brady &

Dutta, 2012). Although the emergence of the information age has provided learners with greater

opportunities to access and use information for scholarly purposes, this large body of knowledge

has created issues with respect to academic honesty, ethical learning practices, and personal

integrity and accountability. With distance education programs across North America1 and

around the world2 rapidly growing and the Internet population expecting to triple over the next

five years (Garber, 2012), cases of academic dishonesty are likely to continue to increase in the

future.

Some research scholars contend that because both students and faculty believe it is easier to

cheat in a distance learning class academic dishonesty will grow as the number of distance

learning classes increase (Kennedy, Nowak, Raghuraman, Thomas, & Davis, 2000). The focus of

this paper will be on academic dishonesty in the context of distance learning. The author will

define “cheating”, discuss the prevalence of cheating in academia, identify factors that influence

academic dishonesty, and detail the types of cheaters and the variety of schemes students use to

cheat online. Various methods to deter academic dishonesty and detect cheating in online

learning will be described with a particular emphasis on technological solutions that have been

developed to assist the teacher in combating fraudulent activities of students.

Definition of Online Cheating

In a broad definition, cheating has been defined as the act or action of fraudulently deceiving

or violating rules (Lathrop & Foss, 2000, p. 2). In academia, academic integrity is the governing

ethical principle and cheating is often referred to as academic dishonesty. Similarly, William L.

Kibler has defined academic dishonesty as “forms of cheating and plagiarism that involve

students giving or receiving unauthorized assistance in an academic exercise or receiving credit

for work that is not their own” (Kibler, p. 253). Some authors distinguish between various forms

of academic dishonesty creating separate definitions for cheating, fabrication, facilitating

academic dishonesty, and plagiarism (Gehring & Pavela, 1994). For the purposes of this paper,


the author will follow the definition of online cheating offered by King, Guyette, & Piotrowski

(2009, p. 4) which is defined as “a transgression against academic integrity which entails taking

an unfair advantage that results in a misrepresentation of a student’s ability and grasp of

knowledge [and] in the current online context, this includes obtaining inappropriate assistance

either from an online source or adjutant, plagiarism, and false self-representation.”

Prevalence of Cheating

The question arises as to how prevalent is cheating in academia. A large 1998 meta-analysis

involving 46 studies of all forms of academic cheating revealed significant cheating on written

assignments and examinations (Whitley, 1998).3 Five studies conducted from 1940 to 2000 have

confirmed that the percentage of college students cheating at least once in their college career

has been steadily rising over that period (Jensen, Arnett et al. 2002).4 Longitudinal studies have

found significant increases in student cheating over the last ten years (Chapman, Davis, Toy, &

Wright, 2004; Gibbons, Mize, and Rogers, 2002), and the problem appears to be growing every

year (Ma, Wan, & Lu, 2008, p. 198).

In a large three-year study reported by McCabe (2005) involving more than 80,000 students,

one in five students (21%) had engaged in some form of test or exam cheating and with respect

to written assignments 38% of undergraduate students and 25% of graduate students admit to

copying or paraphrasing sentences without acknowledging the source. More recently, researchers

have estimated that one-third of elementary school students and over 50% of high school and

college students have committed some type academic cheating (Schmelkin, Gilbert, Spencer,

Pincus, & Silva, 2008).

The general consensus amongst scholars is that cheating online is more frequent than

academic dishonesty found in traditional classroom learning. Possibly, conventional wisdom

argues that when not face-to-face, it is believed that students are more likely to resort to

plagiarism and have others sit in for them during examinations (Roach, 2001). Bedford, Gregg,

and Clinton (2011) identified seven factors which increase the probability of cheating in online

courses including anonymity, lack of student monitoring, and pressure to cheat due to time

demands from full-time employment and other commitments.5 Conversely, Heberling (2002)

argues that cheating online is more difficult and easier to detect.


Researchers conducting a literature review in 2005 found little research on the issue of web

based cheating and therefore very little to support the conclusion that cheating in Web based

distance education is more common than classroom-based instruction (Baron & Crooks, 2005).

Two studies found that students enrolled in online classes were less likely to cheat than those

enrolled in traditional, face-to-face courses (Stuber-McEwen, Wiseley, & Hoggatt, 2009; Watson

& Sottile, 2010), while another study has found no difference between online and traditional

classroom instruction (Grijalva, Nowell, & Kerkvliet, 2006). One recent study found that

students were significantly more likely to obtain answers from others during an on-line test or

quiz (Watson & Sottile, 2010). Further research needs to be undertaken to determine whether and

to what extent the prevalence of online cheating varies from traditional classroom face-to-face

instruction.

Causes of Cheating and Factors that Influence Academic Dishonesty

One of the first large scale studies on causes underlying cheating was conducted in 1998

when 107 studies published between 1970 and 1996 were meta-analyzed. The strongest

correlates of cheating were found to be: (1) students’ expectations of success, (2) prior history of

cheating, (3) studying under inadequate conditions, (4) positive attitudes towards cheating, (5)

perceiving social norms supporting cheating, and (6) anticipating a large reward for successful

cheating (Whitley, 1998).6 Other studies have found that students cheat because they fear

failure, desire better grades, undergo pressure from their parents to succeed in academics, receive

imprecise instructional objectives, and are graded on a curve (Evans & Craig, 1990). Alschuler

and Blimling (1995) determined that cheating is related to a student’s belief that others are also

doing it; that there is little chance of being caught; that if caught there would be little, if any,

punishment; that there is no reason not to cheat; and a desire to get good grades, a great job, or

admitted to graduate school.

A recent study (Watson & Sottile, 2010) examined the underlying moral reasoning

concerning cheating and cited a series of studies that found that gender, age, and athletic

background played a major role in ethical decisions. The researchers found that males, younger

students, and students who participate in competitive sports are more likely to engage in

cheating. Similarly, another study found that undergraduates, males, and members of Greek

social organizations were more likely to engage in academic dishonesty. Low self-esteem was


also positively correlated with academic dishonesty. Grade point average, number of hours

worked outside of academic studies, and innovativeness were found not to have a significant

influence on academic dishonesty (Iyer & Eastman, 2006)7. Another study found singles are

more likely to cheat than married individuals (Caldarola & MacNeil, 2009).

Park (2003) found that a genuine lack of understanding of what is plagiarism, efficiency gain,

defiance or lack of respect for authority, negative attitudes towards teachers or classes,

temptation or opportunity, and a lack of deterrence as major factors in academic dishonesty.

Pullen, Ortloff, Casey, and Payne (2000) also found large class sizes, impersonal or distant

relationships with teachers, competition for jobs, pressure for higher grade point averages, and a

culture that appears to accept cheating as significantly promoting cheating. Other reasons

identified include: time management problems (Lambert, Ellen, & Taylor, 2003; Park, 2003;

Payne, & Nantz, 1994), a personal crisis (Lambert, Ellen, & Taylor, 2003), and a view of

cheating as having a minimal effect on others (Payne & Nantz, 1994).

Types of Cheaters

Some researchers have created categories for the various types of cheaters profiling the

reasoning behind their misconduct. Categorization may help in developing various strategies and

methods to deter and detect academic dishonesty specific to each type of cheater. Renard (2000)

describes three types of cheaters: (1) the unintentional cheater (i.e., the student who innocently

plagiarizes information without understanding their wrongdoing), (2) the sneaky cheater (i.e., the

student who knows their actions are wrong but undertakes great effort to try and ensure that their

dishonesty is not uncovered), and (3) the all-or-nothing cheater (i.e., the student who lazily

prepares for a test or assignment and takes great risks when cheating).

Another research study found that there are essentially two types of cheating: planned

cheating and panic cheating (Grijalva, Nowell, & Kerkvliet, 2006). Planned cheating occurs with

full knowledge that it is wrong and usually involves substantial planning (e.g., making crib

sheets for tests) while panic cheating occurs during a test when a student is at a loss for an

answer so he or she panics and engages in academic dishonesty such as looking at another

student’s paper (Bunn, Caudill, & Gropper, 1992). The social norms and subjective costs and

benefits will usually differ for planned and panic cheating and therefore different approaches

should be made in ways to deter these two types of cheating. Planned cheating is more


commonly found than panic cheating and is generally viewed as more dishonest as it is

premeditated and therefore has greater social costs. Panic cheating is usually limited to

classroom settings as online courses have fewer opportunities for this type of cheating to occur

(Grijalva, Nowell, & Kerkvliet, 2006).

Methods and Techniques of Online Cheating

The methods or techniques of cheating are dependent upon the task or assignment. The vast

majority of cheating occurs under three scenarios: (1) online tests, quizzes and assessments, (2)

written assignments requiring the submission of an essay or research paper, and (3) research,

laboratory, computer programming or project work.8 With respect to the online tests, quizzes,

and assessments, Rowe (2004) argues that there are three types of cheating: (1) getting

assessment answers in advance, (2) unfair retaking of assessments, and (3) unauthorized help

during assessment. McCabe (2005) categorizes cheating in online tests and examinations into

seven categories: (1) learning what is on a test from someone who has already taken it, (2) using

false excuse to delay taking test, (3) copying from another student on a test/exam without their

knowledge, (4) helping someone else cheat on test, (5) copying from another student on a

test/exam with their knowledge, (6) using unauthorised crib/cheat notes, and (7) using an

electronic/digital device as unauthorised aid during a test/exam.

In a recent paper written on ways students can cheat online tests, even with the use of

monitoring tools employed by teachers, Kumar (2012) notes six methods: (1) using spyware to

spy on the professor’s computer containing exam answers, (2) using instant messaging services

to chat among themselves during an exam, (3) disconnecting from the exam network, connecting

to another network to search for answers during an exam, and then reconnecting to the exam

network, (4) changing their IP/Mac address during the exam making them invisible to any

monitoring system, (5) using a proxy server to redirect Internet traffic through a computer or a

router to avoid detection, and (6) setting up a virtual private network (VPN) at their home that is

encrypted allowing them to search for answers.

With respect to written assignments plagiarism is the central problem with regards to these

academic activities. Klausman (1999) categorizes plagiarism into three forms depending on the

amount and types of information plagiarized: direct plagiarism, paraphrase plagiarism, and

patchwork plagiarism. McCabe (2005) offers nine types of cheating in written assignments: (1)


working with others on an assignment when asked for individual work, (2) paraphrasing/

copying few sentences from a written source without footnoting it, (3) paraphrasing/copying few

sentences from Internet source without footnoting it, (4) receiving unpermitted help from

someone on an assignment, (5) fabricating/falsifying a bibliography, (6) turning in work copied

from another, (7) copying material almost word for word from a written source without citation,

(8) turning in work done by another, and (9) obtaining a paper from a term paper mill.

Regarding other activities such as research work, laboratory work, computer programming

assignment, and other project-based work, frequent, McCabe (2005) lists three techniques of

cheating including fabricating or falsifying lab data, copying someone else’s program in a course

requiring computer work, and fabricating or falsifying research data. With advances in

technology, cheating is becoming easier. For example, a recent Common Sense Media poll found

that 35% of students cheat using their cell phones. They do this by storing information on their

phone and looking at it while taking a test, sending text messages to friends, asking for answers,

take pictures of a test and then sending it to their friends, using their phones to search for

answers on the Internet, and warning friends about a pop quiz with a phone call or text message

(Reid, 2009). Some of other high technology methods to cheat as posted by students on YouTube

include: (1) creating hidden notes (notes as an iPod song, notes on a graphing calculator, notes

on a cell phone, notes scanned on a nutritional label of a soda, ultraviolet pens to read invisible

notes, notes on Scotch tape), (2) communicating with others with a spywatch, (3) inserting an

iPod into a graphing calculator, (4) downloading from math homework websites, and (5)

replacing periods with larger periods to increase paper length (Seitz, Orsini, Muhsin, & Gringle,

2011).9

Online Cheating: Detection and Deterrence Strategies and Methods

Hinman (2000) argues that there are three possible approaches to preventing or minimizing

(online) cheating: the virtues approach, the prevention approach, and the police approach. The

aim of the virtues approach is to imbed values within students so that they do not want to cheat.

The aim of the prevention approach is deterrence - to eradicate or decrease opportunities for

students to cheat and to reduce the pressure to cheat. The aim of the police approach is detection

– to catch and punish students who do cheat. According to Hinman (2000), policing can also


serve as a preventative measure and each approach should be employed in order to reduce

academic dishonesty in online assessment.

General Preventative Methods to Deter Academic Dishonesty

Prevention techniques for academic dishonesty seek to change student attitudes towards

cheating prior to the commission of unscrupulous behaviour. A large number of these methods

are outlined in Table 1. Some of the more common methods to accomplish this objective are: (1)

instituting honor codes and academic integrity guidelines, (2) using student peer pressure, (3)

formulating policies on cheating, (4) increasing interactivity between student and teacher, (5)

spelling out what constitutes cheating, (6) requiring courses or seminars in ethics, and (7)

redesigning course content to encourage honesty. Empirical research has already shown that a

number of these measures deter cheating. For example, one study found that attitude toward

academic dishonesty mediated the relationship between self-control and academic dishonesty

and also between perceived opportunity and academic dishonesty and therefore efforts such as

influencing attitudes, instituting honor codes, and education reduced academic dishonesty (Bolin,

2004).

Other preventative ways seek to make it difficult, if not impossible, for the student to cheat,

or create an environment where cheating would be too risky for the student. Some of these more

common methods include: (1) using assignments that require group cooperation, (2) maintaining

assessment security, (3) limiting class sizes, (4) describing monitoring tools in place to students,

(5) emphasizing essays, portfolios, and creative projects, and (6) varying course content,

assignments, and activities. Other general preventative measures found in the research literature

include: fostering environments of trust where learning is valued, enforcing policies and

disciplinary rules when students are caught cheating, and reducing competition amongst

students. Many academic administrators are continuing to work at finding new methods to deter

cheating. For example, administrators at British Columbia’s Simon Fraser University have

created a new failing grade for cheating students: FD. The grade is given to repeat offenders and

the mark stays on a student’s transcript for two years. Whether this deterrent has any effect on

academic dishonesty has yet to be determined.


Table 1. General Preventative Measures Spell out academic standards regarding what constitutes cheating

Baron & Crooks, 2005; Bedford, Gregg, & Clinton, 2011; Caldarola & MacNeil, 2009; Christe, 2004; Ma, Wan, & Lu, 2008; McMurtry, 2001; Rowe, 2004; Scanlon, 2004

Institute honor codes and academic integrity guidelines Alschuler & Blimling, 1995; Baron & Crooks, 2005; Chiesl, 2007; Christe, 2004; Gibbons, 2002; McCabe, 2005; McCabe & Trevino, 1993; Paldy, 1996; Schneider, 1999

Assign ethics courses Ercegovac & Richardson, 2004; Warman, Harvan, & Weidman, 1994

Create ethical decision making frameworks Booth & Hoyer, 1992

Compliment and showcase model student behaviors Ma, Wan, & Lu, 2008

Foster love of learning Olt, 2002

Promote environment of trust Olt, 2002

Formulate policies on cheating Bedford, Gregg, & Clinton, 2011; King, Guyette, & Piotrowski, 2009; Ma, Wan, & Lu, 2008; Olt, 2002; Scanlon, 2004

Encourage students to ask questions Chiesl, 2007

Use student peer pressure Chiesl, 2007; Ma, Wan, & Lu, 2008

Inform students of professor`s qualifications Chiesl, 2007

Make students aware of disciplinary policies and that cheating will not be tolerated

Chiesl, 2007; Christe, 2004; Mc Cabe & Trevino, 1993; Nagi, 2006; Scanlon, 2004

Increase interactivity between student and teacher (e.g., live chats, threaded discussions, interactive online discussion, monitor student activity)

Carnevale, 1999; Christe, 2004; Heberling, 2002; Olt, 2002; Roach, 2001; Weller, 2002

Promote and encourage honesty in syllabus Chiesl, 2007

Limit class sizes to 25 students or less Roach, 2001

Redesign syllabus to discourage dishonesty (e.g., craft course objectives carefully, identify behavioral objectives, discuss relevance of course materials)

Bedford, Gregg, & Clinton, 2011; Christe, 2004

Describe monitoring tools available to students Christe, 2004

Establish deadlines Christe, 2004

Redesign content presentation in course to discourage dishonesty (e.g., present information well, tell students what is important, be clear about supplemental answers)

Christe, 2004

Use assignments that require cooperation Olt, 2002

Vary the type of assessment tool used Christe, 2004

Enforce policies; Use penalties in place Christe, 2004; McCabe, 2005

Identify reasons to be honest Christe, 2004


Discuss relevance of course materials Christe, 2004

Vary course content, assignments, activities, and presenters

Christe, 2004; Gibelman, Gelman, & Fast, 1999

Stay abreast of trends and methods in cheating (e.g., periodically watch YouTube for methods posted by students)

Seitz, Orsini, Muhsin, & Gringle, 2011

Conduct informal discussions with student after exceptionally good performance

Rowe, 2004

Post clear cut learning objectives Chiesl, 2007

Maintain assessment security Rowe, 2004

Control assessment situation (e.g., prohibit handheld devices, disable internet connection, close all ports, restrict access to testing situation)

Bedford, Gregg, & Clinton, 2011; Clark, 2008; Rowe, 2004

Honesty pledges and no-cheat contracts signed before registration

Bedford, Gregg, & Clinton, 2011; Shyles, 2002

Use end of the course assessments by students Baron & Crooks, 2005

Use teaching assistants and tutors to assist with learning Weller, 2002

Emphasize essays, portfolios, and creative projects Olt, 2002; Schaefer, Barta, & Pavone, 2009; Strobl, 2010

Reduce pressure to get good grades Chiesl, 2007

Enlist parents and teachers to promote no-cheating message

Chiesl, 2007; Drogemuller, 1997

Reduce competition amongst students Chiesl, 2007; Ma, Wan, & Lu, 2008

Adopt a grade (e.g., FD) especially for cheaters Reid, 2009

Measures for Online Examinations and Tests

Research has revealed that online tests are relatively easy to cheat (Winslow, 2002). Most

research studies recommend either using a human proctor or electronic proctoring software to

monitor the online examination (Bedford, Gregg, & Clinton, 2011; Caldarola & MacNeil, 2009;

Carnavale, 1999; Chiesl, 2007; Foster, Mattoon, & Shearer, 2008; Harmon, Lambrinos, &

Buffolino, 2010; Rowe, 2004; Strobl, 2010; Trenholme, 2006-2007; Watson & Sottile, 2010;

Young, 2012). If proctoring software is used then many scholars suggest using biometrics to

ensure the test taker is the student and not an accomplice (Bedford, Gregg, & Clinton, 2011;

Clark, 2008; Shyles, 2002; Strobl, 2010; Young, 2012). When these tools are not available, some

solutions have been to develop intelligent multiple choice examinations, give frequent but short

intensive examinations, and use many of the security features found in learning management


systems for online quizzes (e.g., log-in system, no retracing of questions, accessing the exam at

one specific time, locking down the browser, strict time limits).

Some scholars have argued that cheating in online exams is inevitable and the only solution

is to give an open book examination (Grijalva, Nowell, & Kerkvliet, 2006; Olt, 2002) while

others have stated that the assessment point value for tests should be lower in comparison to

written assignments (Chiesl, 2007; Watson & Sottile, 2010). Other researchers have found that

by using statistical methods on multiple choice test results to determine frequency and patterns in

correct and incorrect responses, cheating can be detected and punished thereby deterring further

student cheating (Christe, 2004; Rowe, 2004; Nath, & Lovaglia, 2009; Saenz, 2011; Sheridan &

Witherden, 2004; Van der Linden & Sotaridona, 2004; Weslowsky, 2000). Some teachers

advocate moving from objective examinations to evaluative tests requiring creativity and higher

order thinking skills (Olt, 2002; Watson & Sottile, 2010; Yao, 2006). Other teachers have

resorted to trapping students through bogus websites that contain incorrect answers, using fake

students, and creating phony tests (Carnevale, 1999; Christe, 2004; Clark, 2008; Rowe,

2004).Table 2 provides a large number of measures that have been employed to combat cheating

on tests or examinations.

Table 2. Measures for Online Examinations or Tests Notify students in advance about online course test information including penalties, process, and technical requirements

Chiesl, 2007

Employ a human proctor Bedford, Gregg, & Clinton, 2011; Carnavale, 1999; Chiesl, 2007; Harmon, Lambrinos, & Buffolino, 2010; Rowe, 2004; Strobl, 2010; Trenholme, 2006-2007; Watson & Sottile, 2010

Use additional proctors with verbal warnings about cheating

Chiesl, 2007

Apply electronic proctoring software and keystroke analytics (e.g., Secure’s Securexam, Krypterion’s Webassessor, and PupilCity's ProctorU)

Bedford, Gregg, & Clinton, 2011; Caldarola & MacNeil , 2009 ; Carnavale 1999; Foster, Mattoon, & Shearer, 2008; Strobl, 2010; Young, 2012

Employ online monitoring tools (e.g., packet sniffers such as Wireshark, Kismet, NetStumbler)

Kumar, 2012

Use a log-in system Olt, 2002

Give on a periodic basis each student direct questions on subject matter

Carnevale, 1999


Give frequent but short time-intensive exams Grijalva, Nowell, & Kerkvliet, 2006; Nagi, 2006; Olt, 2002

Use course navigation questions that would be missed by outsider

Bedford, Gregg, & Clinton, 2011

Give open book examinations Grijalva, Nowell, & Kerkvliet, 2006; Olt, 2002

Assign periodic exam assignments Harmon, Lambrinos, & Buffolino, 2010

Give short periodic quizzes during online sessions, short timed essays

Clark, 2008; Nagi, 2006

Limit the number of retries or no retries at all Cluskey, Jr., Ehlen, & Raiborn, 2011; Nagi, 2006; Olt, 2002

Access the exam at one specific time Cluskey, Jr., Ehlen, & Raiborn, 2011;

Vary the type of questions for each examination; randomized questions; multiple versions of exam using large question database

Bedford, Gregg, & Clinton, 2011; Chiesl, 2007; Christe, 2004; Clark, 2008; Cluskey, Jr., Ehlen, & Raiborn, 2011; Harmon, Lambrinos, & Buffolino, 2010; Nagi, 2006; Olt, 2002; Rowe, 2004

Use statistical methods (e.g., sequence testing on correct and incorrect responses, detecting excessive similarity in answers, distribution for the number of matched incorrect alternatives)

Christe, 2004; Rowe, 2004; Nath, & Lovaglia, 2009; Saenz, 2011; Sheridan & Witherden, 2004; Van der Linden & Sotaridona, 2004; Weslowsky, 2000

No printing during exam (so you cannot make a copy for other students)


Set traps (e.g., post Web pages with incorrect information, fake tests)

Christe, 2004; Rowe, 2004

Create a fake student to enroll in the class Carnevale, 1999; Christe, 2004; Clark, 2008

Guard access to exam Christe, 2004

Accept only documented and valid excuses for missed examinations


Monitor student activity on exam Christe, 2004

No retracing of exam questions allowed Chiesl, 2007; Cluskey, Jr., G.R., Ehlen, C.R., & Raiborn, M.H., 2011; Harmon, Lambrinos, & Buffolino, 2010

Use time limits Bedford, Gregg, & Clinton, 2011; Christe, 2004; Cluskey, Jr., Ehlen, & Raiborn, 2011; Olt, 2002

Assess student preparedness to begin exam Christe, 2004

Use Webcam Carnevale, 1999; Christe, 2004; Clark, 2008

Construct effective multiple choice exams with smart wording

Olt, 2002

Institute video monitored test centers Clark, 2008

Make assessments not too easy or difficult Rowe, 2004

Use constructed response test formats Rowe, 2004

Use Bloom's Taxonomy, alter test questions from objective to evaluative

Olt, 2002; Watson & Sottile, 2010; Yao, 2006

Employ online monitoring software of websites visited by student during exam



Apply biometrics (e.g., handwriting analysis, fingerprinting analysis, voice recognition, face recognition, iris scan technologies) (usually combined with remote proctor software)

Bedford, Gregg, & Clinton, 2011; Clark, 2008; Shyles, 2002; Strobl, 2010; Young, 2012

Increase the ratio of questions to minutes allowed to impose a substantial opportunity cost for time engaged in cheating behaviors

Harmon, Lambrinos, & Buffolino, 2010

Combine paper and online assessments Engelbrecht & Harding, 2004

Use learning management system's lockdown browsers (e.g., Blackboard's Respondus Lockdown Browser (RLB))

Cluskey, Jr., Ehlen, & Raiborn, 2011;

Use essay exams Gibelman, Gelman, & Fast, 1999

Ensure students can only take one question at a time using the learning management system

Chiesl, 2007; Harmon, Lambrinos, & Buffolino, 2010

Challenge questions to enter examinations Castagnera, 2010; Strobl, 2010

Provide enough time to complete exam (no pressure) but tightest time frame

Chiesl, 2007

Offer multiple attempts at exam with new set of random questions

Chiesl, 2007

Lower point assessment value for exams Chiesl, 2007; Watson & Sottile, 2010

Use a Microsoft Dongle to detect any devices with Bluetooth technology in the “on” position to detect cell phone use during test


Measures for Plagiarism

Two well-recommended methods employed by teachers in combating student plagiarism are

plagiarism detection software (Caldarola, & MacNeil , 2009; Clark, 2008; Heberling, 2002; Ma,

Wan, & Lu, 2008; McMurtry, 2001; Meyer zu Eissen, Stein, & Kulig, 2007; Saenz, 2011;

Scanlon, 2004; Strobl, 2010; Trenholme, 2006-2007) and using Web search engines such as

Google to determine whether the text was extracted from Internet sources (Carnevale, 1999;

Clark, 2008; Heberling, 2002; Ma, Wan, & Lu, 2008; McMurtry, 2001). Another suggested

tactic is for instructors to ask students to prepare abstracts, outlines, rough drafts, and

bibliographies in order to view the work in progress (Bedford, Gregg, & Clinton, 2011; Clark,

2008; Drogemuller, 1997; Gibelman, Gelman, & Fast, 1999; Olt, 2002).

Another option is to create cheat-resistant essay assignments where the answers cannot be

downloaded from the Web. An example would to request the student to write an essay from the

point of view of a soldier in the Napoleonic army in order to describe his experiences of military


life in the early 1800s. Classroom instructors may want to assign presentations along with an

essay paper while online instructors could request students present the paper in the form of a

multimedia presentation (Nagi, 2006). Other suggested measures, all of which are included in

Table 3, are: using collusion detection software to ensure students are not borrowing materials

from one another (Caldarola, R., & MacNeil, T., 2009), assigning students essays that require

interviewing an expert (Nagi, 2006), and giving students assignments they want to do so they

have no inclination to plagiarize (Clark, 2008; Ma, Wan, & Lu, 2008).

Table 3. Measures for Plagiarism Use plagiarism detection software (e.g., EVE2, Integriguard, Turn-It-In) and algorithms

Caldarola, & MacNeil , 2009; Clark, 2008; Heberling, 2002; Ma, Wan, & Lu, 2008; McMurtry, 2001; Meyer zu Eissen, Stein, & Kulig, 2007; Saenz, 2011; Scanlon, 2004; Strobl, 2010; Trenholme, 2006-2007

Use collusion detection software (to catch similarities amongst student papers)

Caldarola, R., & MacNeil, T. , 2009

Educate students as to what constitutes plagiarism; teach them how to cite and quote properly

Drogemuller, 1997; Ma, Wan, & Lu, 2008; Nagi, 2006; Renard, 2000

Examine paper for signs of unethical behavior Nagi, 2006; Renard, 2000

Reflective papers with short turn-around times Gibelman, Gelman, & Fast, 1999

Assign less emphasis of total grade on paper assignments

Gibelman, Gelman, & Fast, 1999

Submit outlines or working drafts of paper Bedford, Gregg, & Clinton, 2011; Clark, 2008; Drogemuller, 1997; Gibelman, Gelman, & Fast, 1999; Olt, 2002

Ask students to write an introductory essay to get to know writing style

Carnevale, 1999

Assign many short papers to develop understanding of student writing style

Roach, 2001

Restructure assignment (e.g., assign oral or multimedia presentations, case studies, hands-on projects)

McMurtry, 200, Nagi, 2006

Request bibliographies of sources Bedford, Gregg, & Clinton, 2011; Drogemuller, 1997; Nagi, 2006

Request an abstract of the paper Nagi, 2006

Require specific components in the paper Drogemuller, 1997; Gibelman, Gelman, & Fast, 1999; Nagi, 2006

Require a personal interview with an expert or authority on each paper

Nagi, 2006

Require references to be up-to-date Nagi, 2006

Assign essays that cannot be bought Whiteman & Gordon, 2001


On the day the papers are collected, ask the student to write a brief essay

Nagi, 2006

Strictly follow the academic policies on plagiarism Nagi, 2006

Create cheat-resistant essay assignments (e.g., require creative responses, make topics specific, avoid using same topics every year, choose topics of high interest to students, require higher level thinking skills)

Baron & Crooks, 2005; Drogemuller, 1997; McMurtry, 2001; Renard, 2004

Engage students in all phases of writing Bedford, Gregg, & Clinton, 2011; Renard, 2004

Know what is online before assigning paper McMurtry, 2001

Give students enough time to do assignment McMurtry, 2001

Give students assignments they want to do Clark, 2008; Ma, Wan, & Lu, 2008

Create archive of student papers McMurtry, 2001; Weller, 2002

Use search engines on selected text of paper to determine if text was extracted from the Web

Carnevale, 1999; Clark, 2008; Heberling, 2002; Ma, Wan, & Lu, 2008; McMurtry, 2001

Conclusions

In final summation, at the heart of any fraudulent act is a weighing of the cost (i.e., getting

caught and punished for the act) versus the benefit of cheating (e.g., labor avoidance, better

grades). A good control system for managing academic dishonesty should both deter and detect

cheating by students which entails combining a number of the measures described in this paper.

Cheating is becoming more high-tech and is on the rise. With cheaters learning from each other

on websites such as YouTube and students working together to engage in dishonest academic

activities is becoming more prevalent, researchers must join forces to share their work, educate

themselves on new cheating methods, and ensure that the testing and assessment industry pool its

resources.


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1 Both Canada and the United States have experienced rapid growth in online education enrolments at

post-secondary institutions and classes at the K-12 levels. “Going the Distance: Online Education in the United

States, 2011”, funded by the Sloan Consortium, found a 10% growth in higher education online enrollments in the

United States during 2011 (Allen & Seaman, 2011). The annual “State of the nation: K–12 online learning in

Canada” report for 2011 found continued growth across Canada from 2010, particularly in British Columbia,

Quebec, Ontario, and Manitoba. 2 In 2009 researchers reported that there were 24 mega-universities across the globe (Altbach, Reisberg, &

Rumbley, 2009) which deliver distance education methodically to the millions of students that they seek to educate,

a significant increase from 11 mega-universities reported in the mid-1990s (Daniel, 1996). The growth of these

mega-universities proves that distance education is occurring on an international scale. 3 The 1998 meta-analysis involving 46 studies of all forms of academic cheating revealed that the

prevalence of total cheating ranged from 9% to 95% of students, with a mean of 70.4%; the prevalence of cheating

on examinations ranged from 4% to 82% of students, with a mean of 43.1%; the prevalence of cheating on

homework ranged from 3% to 83% of students, with a mean of 40.9%; and the prevalence of plagiarism ranged from

3% to 98% of students, with a mean of 47.0%. 4 The Josephson Institute of Ethics reported that from 1994 to 2002 the number of high school students who

admitted that they cheated on an exam in the past 12 months had increased thirteen percent to 72%. 5 Bedford, Gregg, and Clinton (2011) identified the following factors which increase the probability of

cheating in online courses: (1) anonymity; (2) little face-to-face communication between student and instructor to

build trust or a relationship; (3) students’ belief in not being caught because the instructor cannot see him; (4)

students who do not take online courses as seriously as in-person class due to lack of formality; (5) pressure to cheat

due to full-time employment or other time pressures; (6) student perceptions that an online class should be trouble-

free and undemanding when compared to an in-person class; and (7) students’ beliefs that lack of monitoring makes

it is easier to get away with academic dishonest work. 6 Similarly, Buckley, Wiese, and Harvey (1998) determined that the most effective predictors of academic

dishonesty were (a) the probability of being caught and penalized, (b) having high hostility or aggression

characteristics, and (c) being male. 7 The study confirmed an earlier large scale study that found that cheating was influenced by age and

gender, as well as contextual factors such as level of cheating among peers, peer disapproval of cheating,

fraternity/sorority membership, and the perceived severity of penalties for cheating. The study found that peer

disapproval was the strongest factor influencing academic dishonesty (McCabe & Trevino, 1997). However, the

study did not support the findings that students with lower GPA were more likely to cheat (McCabe & Trevino,

1997, p. 392; Straw, 2002). 8 Besides cheating (on tests) and plagiarism, Davis, Grover, Becker & McGregor (1992) listed five other

types of academic misconduct: fabrication, obtaining an unfair advantage, aiding and abetting, falsification of

records and official documents, and unauthorized access to computerized records 9 Some of the low technology methods for hidden notes include notes on rubber band, pen scroll, inside

clear pens and pencils, and inside water bottle (Seitz, Orsini, Muhsin, & Gringle, 2011).

http://chronicle.com/article/Cheating-Goes-High-Tech/132093/

http://chronicle.com/article/Cheating-Goes-High-Tech/132093/

Assignment Two - Online Courses and Cheating (Final)

Documents

meyer zu eissen

detecting excessive similarity

instituting honor codes

student identity verification

distance learning administration

academic integrity guidelines

collusion detection software

k12 online learning