Academic honesty and online courses

Therese C. Grijalva

Academic dishonesty is an issue of concern for teachers, students, and institutions of higher education. Because students and faculty do not interact directly in web-based classes, it is often perceived that cheating will be more abundant in these classes. Using data from a survey administered to students who had an online course during the 2001 fall semester, we find evidence that academic dishonesty in online classes is no more pervasive than in traditional classrooms.


Academic dishonesty is issue of concern for teachers, students, and institutions of higher education. Studies consistently show that a significant number of students cheat in college (Michaels & Miethe 1989; Whitley, 1998; Brown & Emmett, 2001), and that cheating impacts the attitudes and opinions of both students and teachers. Cheating appears to be endemic across cultures and pedagogies (Magnus at al., 2002). Academic research on the extent of and motivation for cheating has helped illuminate practitioners on the degree of cheating in different disciplines and by students of different demographic profiles.

The focus of this paper is on academic dishonesty in online courses. To make comparisons with prior studies, online academic dishonesty includes cheating on exams or assignments, including plagiarism. Currently, evidence on academic dishonesty in online courses is nonexistent, but some claim that because students and faculty do not interact directly in such classes, online classes will invite more cheating than traditional classes. For example, Kennedy et al. (2000, p. 311) state, “Because both students and faculty believe it is easier to cheat in a distance learning class, … as the number of distance learning class increases so will academic dishonesty.” Conversely, Smith, Ferguson and Caris (2003, p. 2) claim that enhanced communication and the breaking down of social barriers leads to less cheating, stating, “This emergence of online identity may make the whole worry of online cheating a moot point. Often stronger one-to-one relationships … are formed in online courses than in face-to-face classes.”

Because of the growth in online education at the university level and because of the untested presumption that academic dishonesty will be greater in these classes than in the traditional classroom this study fills an important void in the literature of academic dishonesty.

A Model of Cheating.

Most researchers view the decision to cheat as the result of a cognitive process which involves substantial planning (Bunn, Caudill, & Gropper, 1992; Alschuler & Blimling, 1995; Mixon, 1996), but survey evidence suggests that students break down actual cheating behavior into two categories: planned cheating and panic cheating (Bunn, Caudill, & Gropper, 1992). Although both types of cheating involve weighing costs and benefits, if social norms differ for planned and panic cheating, the subjective costs and benefits may be different for planned and panic cheating. Planned cheating may involve making crib sheets for tests, copying homework, or plagiarizing a paper; it occurs with full knowledge that it is wrong. Panic cheating, on the other hand, occurs during a test when the student finds herself at a loss for an answer. Although she did not plan to cheat, she looks at another student’s paper and copies the answer. Being premeditated, planned cheating may be viewed as more dishonest than panic cheating and perceived as having a greater social cost. Bunn, Caudill and Gropper (1992) report that the majority of students believe the most common type of cheating is panic cheating with 358 of 476 students at Auburn University stating that they primarily observe panic cheating.

Some type of pedagogies may be more susceptible to one type of cheating. In online classes, planned cheating may be a much greater threat than panic cheating simply because circumstances engendering panic cheating may be relatively rare compared to a traditional classroom. Tests are most often completed by students in isolation and the opportunities for panic cheating diminished.

Social norms also influence cheating behavior. As suggested by social learning theory (e.g. see, Michaels and Miethe, 1989) perceived support from peers or pro-attitudes about cheating would act to facilitate cheating. McCabe, Trevino, and Butterfield (2002) show that academic dishonesty is related to the “cheating culture” that develops on campuses, and many studies (Whitley, 1998) note that observation of others cheating may create an attitude in which academic dishonesty is viewed as normal behavior.

In online classes, students are likely to be dispersed across broad geographic regions, and even if students are from the same local area they may never meet. Thus, online education may be less likely to develop a perception of a “cheating culture” as the norm. This may further retard cheating in an online setting.

Survey and Methods

Collecting data on cheating behavior is fraught with difficulties, primarily due to the sensitive nature of cheating questions and perceived consequences of answering such questions affirmatively. These concerns are true in this study where students are asked to reveal information about their cheating behavior in a specific class. As a result, most studies of cheating behavior have focused on cheating over the academic career of a student and not in a specific class. This makes it very difficult to compare cheating in online classes with traditional classes. Because students take relatively few online classes, class specific questionnaires must be employed. Fearing students unwillingness to reveal cheating behavior in a single class, researchers have tried to minimize the discomfort created in sensitive questionnaires by using a randomized response (RR) method that protects a subject’s anonymity. The RR method has been commonly used to draw inferences regarding academic dishonesty (Kerkvliet & Sigmund, 1999; Scheers & Dayton, 1987; Nelson & Shaefer, 1986).

In a RR question, subjects are directed with a known probability to answer either a cheating or an unrelated question with a “yes or no” response. The researcher observes the answer, but does not know whether the student answered the cheating or unrelated question. Using the known probability of answering the unrelated question, the researcher can estimate the probability of cheating, but cannot specifically identify the admitted cheaters. Accordingly, fear of reprisal is mitigated.

Consider the following example: A teacher asks her students to think of a color, either blue or green. The teacher asks “If you thought of the color green or you have cheated in this class please raise your hand.” The teacher does not know if a student who raises his hand has cheated or thought of the color green. If students are equally likely to have picked green or blue, and greater than 50% of the students raise their hands it is likely that some students cheated in class. Using this method it is possible to estimate the percentage of cheaters in class. It is also possible to test if the characteristics of students who raised their hands are significantly different from the characteristics of students who did not raise their hands. These differences are used to explain cheating behavior.

During the 2002 spring term, web-based RR surveys were sent to 1840 students enrolled in undergraduate online courses during the 2001 fall term. To encourage participation, those who completed the survey were entered in a lottery with the opportunity to win one of ten, hundred dollar prizes. Students who did not respond to the initial e-mail were sent two requests to complete the survey over the next three weeks. Of the 1,840 surveys received by students, 1021 were returned. Of those returned, 796 bad complete information on the randomized response cheating question.

Although the response rate is not as high as many mail surveys, compared to similar Internet based surveys reported by Couper, Traugott, and Lamais (2001), the response rate is greater than average. To help validate our sample results, a follow-up survey was administered in person to four classes. In these classes, where response rates were close to 100%, predicted cheating behavior was virtually identical to the behavior cataloged in the Internet based survey.

The survey contained socio-demographic questions, a set of class specific questions (e.g., perceptions about exam fairness, grading practices, and testing locations), and a RR cheating question. To mitigate differences between student and faculty perceptions of cheating, the survey provided a cheating definition from the University’s academic code.

Surveys contained the following RR question:

Please think of the month in which

your mother was born. Don’t reveal

this month to anyone, just think of

it and, based on it, follow the instructions

below. Remember we do not

know the month your mother was

born so we cannot tell which question

you answer.

* If your mother was born in January or February, please enter a “1” below.

* If your mother was born in March or April, please enter a “0” below.

* If your mother was born in any month May through December and you have used unauthorized help to complete homework assignments, papers, or exams for this course, please enter a “1” below.

* If your mother was born in any month May through December and you have not used unauthorized help to complete homework assignments, papers, or exams for this course, please enter a “0” below.

According to state-specific 2000 data on birth records, the probability of being born in January or February is 0.149 and the probability of being born in March through April is 0.163. If no cheating took place we would expect the percentage of “l” responses to be equal to the probability of being born in January or February, or. 149 If the proportion of “1” responses is greater than this amount some degree of cheating likely took place in the classes.


Of the 796 usable surveys 135 students checked the box marked one. This represents a proportion of “1” responses equal to 0. Using this data we can calculate the level of cheating (Kerkvliet, 1994) in our sample with the formula [] = [P.sub.1] + (1[P.sub.1] – [P.sub.2])[beta], where Pl represents the probability of being born between January and February, and [P.sub.2] represents the probability of being born during March and April, Pone represents the proportion of “1” responses in the data, and [beta] represents the predicted level of cheating in the sample of online classes. Substituting the information above, .169 = .149 + (1-.149 -.63) [beta], and solving this expression for yields an estimate of the probability of cheating of approximately 3%.

In order to test if the attributes of college students who cheat in online classes is similar to the attributes of college students who admit to academic dishonesty in the traditional classroom setting (Grijalva 2003) used the random response data from this online study to correlate student and classroom characteristics to cheating behavior. Using a logistic regression model to estimate the probability of cheating and explain cheating behavior they find that being aware of others cheating in the course is positively related to the likelihood of cheating and that increases in a person’s grade is negatively related to the likelihood of cheating. These findings are consistent with similar studies of cheating behavior of college students in the traditional classroom.

How does the cheating rate compare to other studies of cheating in traditional class settings? To make this comparison we must focus only on studies that estimated cheating in a single class. We are aware of only a few studies that estimate cheating in a single class. Kerkvliet and Sigmund (1999) estimate cheating at 1.9% using a direct response questionnaire and 13% using the randomized response technique, but with wide variation from one class to another. Karlins, Michaels, and Podlogar (1988) estimate cheating at 3% in a business class during a single semester. Our estimate of online cheating of 3% suggests that cheating in the online setting is not quantitatively different from the level of cheating in the traditional classroom.


This paper presents statistical evidence on cheating in online classes, and compares the estimated incidence of cheating in online classes to that found in traditional classes. Our estimate that only 3% of students cheated suggests that academic dishonesty in a single online class is not greater than estimates of cheating in a traditional class.

Until now, the supposition was that, because of decreased monitoring and interaction in online classes, cheating in this setting would be greater than in traditional classrooms. Our paper suggests that as online education expands, there is no reason to suspect that academic dishonesty will become more common.

There are perhaps several ways to explain the difference between what we would have expected to find versus what we found for an estimate of online cheating. First, although some evidence does suggest that honor codes do reduce the severity of cheating (McCabe & Trevino, 1993), the evidence on the impact of deterrents on cheating is not clear (Houston, 1983). Most studies do find that severity of punishment and the probability of being caught are correlated with cheating behavior, but this is not always the case for all types of students (Whitley, 1988). Thus, the supposition that the lack of supervision will lead to greater cheating may be overstated.

Second, as suggested by Bunn, Caudill, and Gropper (1992), cheating likely occurs when students panic during an exam (i.e., it isn’t premeditated). Because the online setting is less conducive to panic cheating–there are simply fewer or no opportunities for panic cheating–it is conceivable that panic cheating is limited to traditional class testing situations.

Finally, because faculty may be more aware of cheating in the online setting, they may design assignments and exams to reduce the likelihood of cheating. For instance, the instructor may give challenging or time-intensive exams, and allow students to use outside material or work together. Because faculty presume cheating may be more of an issue in the online setting they may behave in a fashion that reduces cheating.


Alschuler, A. S., and Blimling, G. S. (1995). Curbing Epidemic Cheating through Systemic Change. College Teaching 43(4): 123-125.

Brown, B. S. and Emmett, D. (2001). Explaining Variations in the Level of Academic Dishonesty in Studies of College Students: Some New Evidence. College Student Journal 35(4): 529-536.

Bunn, D. N., Caudill, S. B., and Gropper, D. M. (1992). Crime in the Classroom: An Economic Analysis of Undergraduate Student Cheating Behavior. Journal of Economic Education 23(Summer): 197-207.

Couper, M. P., Traugott, M. W., and Lamais, M. J. 2001. Web Survey Design and Administration. Public Opinion Quarterly 65(2): 230-253.

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McCabe, D. L., Trevino, L., K., and Butterfield, K. D. (2002). Honor Codes and Other Contextual Influences on Academic Integrity: A Replication and Extension to Modified Honor Code Settings. Research in Higher Education 43(3): 357-378.

Michaels J. W., and Meithe, T. D. (1989). Applying Theories of Deviance to Academic Cheating. Social Science Quarterly 70(4): 870-885.

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Whitley, B. E. (1998). Factors Associated with Cheating Among College Students: A Review. Research in Higher Education 39(3): 235-273.



Weber State University, Ogden, UT


Oregon State University, Covallis, OR

* Direct correspondence to Dr. Clifford Nowell, 3807 University Circle, Department of Economics, Weber State University, Ogden, UT 84408-3807; Tel: (801) 626-6488; Email:

COPYRIGHT 2006 Project Innovation (Alabama)

COPYRIGHT 2008 Gale, Cengage Learning

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