Student Perceptions of Cheating in Online and. Traditional Classes. Stephen W. Turner and Suleyman Uludag. Department of Computer Science, Engineering & ...
Student Perceptions of Cheating in Online and Traditional Classes Stephen W. Turner and Suleyman Uludag Department of Computer Science, Engineering & Physics University of Michigan - Flint, Flint, MI 48502, {swturner, uludag}@umich.edu
Abstract—With classroom instruction undergoing a massive transformation to incorporate online learning techniques at unprecedented levels, technological advances have facilitated a range of mechanisms that improve teaching and learning. At the same time, these technological advances have also facilitated different forms of cheating in classes. Although the impact and implications of cheating have often been studied, we feel that this problem experiences constant evolution, and the dynamics of cheating, especially in online courses, needs more examination to be fully grasped. The study presented here surveyed computer science, computer information systems, and engineering college students, with the goal of gaining a greater understanding of their perceptions, beliefs, and attitudes about the many dimensions of academic integrity violations. Results of this survey, coupled with statistical analysis and some conclusions, are presented. The impact of our popular virtual lab (VLAB) facility is also examined in this context, and it is found to make a positive difference in student attitudes about cheating in classes.
I. I NTRODUCTION Technological advances open up many new directions, opportunities and paradigms to augment and improve the learning experience. In particular, classroom instruction is now undergoing a massive transformation, with the increased adoption of research-driven instruction methods, experiential learning, and online learning. With a bona fide approach by both instructors and students, there is no doubt that all will benefit. At the same time, many of the technological advances that help to improve pedagogy have also facilitated different forms of cheating in classes. Like never before, cheating has become easier and ubiquitous. In this Internet age, it has become almost too tempting for students to examine and experience behavior that can easily fall under the category of academic integrity policy violations. Educators, university administrators, parents, and the general public are getting more concerned about the impact and implications of students cheating while earning college degrees. In this study, we have surveyed college students with the goal of gaining greater understanding of the many different dimensions of academic integrity violations. Another goal is to reveal some preliminary delineation of gray and white zones from among generally agreed-upon clear academic integrity violations. As part of the study, we have sought students’ anonymous and candid responses related to the acceptability of cheating in different life and social settings, as well as in the classroom, together with their awareness and perception of such behavior. We believe that our study is a contribution
toward understanding the extent of today’s students’ exposure, awareness, and perception of academic integrity violations. We surveyed students who had participated in a variety of different computer science and engineering classes on our campus, with a secondary goal of examining the impact of our popular Virtual Lab (VLAB) environment’s effects on our students’ tendency toward cheating. We present VLAB as a concrete example to examine possible correlations between technological change in delivery (or online teaching in general) and any manifestations of integrity violations by students. The paper presents detailed reports of student responses, as well as statistical analysis of the results. II. R ELATED W ORK , BACKGROUND The definition of cheating in online or traditional (in-person) classes may be interpreted in an imprecise fashion, whether by the student or the instructor of a course. Instructors will have different definitions/interpretations of what constitutes cheating, and in the absence of a clear definition, students are free to draw their own conclusions. In this age of instant Internet access, many have grown accustomed to various types of dishonesty without considering their moral or legal ramifications. For example, consider the situation of a person who is otherwise honest but who downloads copyrighted content from the Internet without paying for it. It seems reasonable to imagine that, due to the lack of a clear framework or definition of “right and wrong” on the Internet, such a person might make a relatively short moral leap into cheating in online classes. In society, there are various potential definitions of cheating: in most sports, cheating is considered to be part of the game and acceptable behavior - as long as the player is not caught. For example, one might consider stealing a base in baseball to be a form of cheating, except that it is allowable within the rules. A better example might be that of committing a minor violation, such as holding, in American rules football, without getting caught by the referee. Most sports typically write off instances such as this; the game moves on and the incident is usually forgotten. Additionally, penalties for such behavior (when caught) are swift and well-defined, but they are also relatively minor in most cases. Other forms of cheating also have varying levels of punishment and societal views on how bad they are. It may be informative to consider whether it is acceptable to cheat on taxes, on a spouse, or in an online video game. In fact, a quick survey of literature finds considerable work examining
cheating in online video games, for example [1], [2], [3]. The point here is that the precise definition of cheating is often contextual. Even in academic activities, its definition may depend completely on an explicit agreement between student and instructor. Other work also supports the notion that the context of cheating is related to how it is perceived in society. Bowyer [4] argues that in war, politics, and espionage, cheating is pervasive and expected. Without delving further into the above issues, one can consider the commonly accepted notion of cheating as gaining an unfair advantage in class by using others’ work and presenting it as one’s own work. There have been numerous studies related to the concept of academic cheating, whether online or not. We primarily focus here on work related to cheating in online mode classes, in terms of the approaches to take to deal with it in prevention and/or detection. These can be roughly classified into several different approaches: cultural/social/attitudinal; embedding structural mechanisms into courses; educational approaches; and technical approaches. Additionally, there is considerable literature studying the precise forms of cheating and student motivations to cheat. A comprehensive set of definitions of cheating behavior, for traditional classes, is presented in [5]. This presents various forms, in which students cheat on homework assignments, as well as in exams taken within various contexts - whether online, in-person, or take-home. Other definitions include e-cheating techniques, such as accessing websites, instant messaging, email, seeding test computers with answers, and using non-exam disks containing exam answers [6]. Ramim [7] provides an overview of the common aspects of academic misconduct and proposes a standard definition based on the findings of a study, for online learning environments. Motivations for cheating are explored by several authors [8], [9], [10]. In particular, Harding et al. [9] present a fairly comprehensive survey of past research on student motivations. The work of Carpenter et al. [10] also identifies implementations of student-suggested techniques to deal with it. In general, the research shows that extrinsic motivations, such as pressure to succeed academically from various sources, are among the more commonly cited reasons for cheating. The research highlighted by Novotney in [8] also shows that students who cheat tend to adjust their morality to fit their circumstances. In other words, behaviors that may have formerly been incorrect are later justified, once they have been done. Cultural mechanisms are also presented by Novotney in [8]. These include facilitating community feelings of disgust at cheating behavior, greater education in the classroom about academic integrity, and more student engagement. Other means of improving culture are studied by Broeckelman-Post in [11], in which it is reported that effective engagement with students, as well as more in-depth discussion of cheating behaviors and expectations, can have a significant effect on deterring cheating. An approach to defining cheating that also uses cultural/attitudinal factors is presented by Stepp and Simon in [12]. The authors asked students to define the act of cheating as it related to their own course assignments. Having
the students take ownership in this fashion helped to facilitate a culture against cheating. Another study [13] presents an argument for education of faculty members, as it discusses approaches to educate new faculty members on establishing cultures discouraging cheating. Other studies [14], [15] examined student and instructor beliefs on cheating and explored techniques to curtail it. Some effective techniques cited include improved learning objectives, group work, review sessions before exams, and building a good rapport with students. There are many studies examining technical approaches. Webley [16] presents a discussion of the potential for cheating in Massively Open Online Courses (MOOCs), discussing approaches to deal with it, including the use of honor codes, high-tech remote proctoring, and giving unique exams, with a focus on the exams themselves as potential solutions. The study focused on the use of the course’s exams, coupled with anti-cheating measures, to determine whether a student receives credit. Various authors present technical approaches that focus on exams themselves, whether they are online or inperson [17], [18], [19], [20]. These may include problem randomization, encryption of questions/answers, imposing time limits, the use of laptop-installable exam software such as Secure Exam Environment (SEE) [19], and even mobile device technologies for smart phones [20]. Others [21], [22] present work on remote proctoring mechanisms (aka remote invigilation), in which networks of computers connected via web cam are used, as well as the possibility of image processing. Frank [23] also presents a proposed framework, together with a risk analysis of dependable distributed testing, presenting a classification of seven types of risks encountered in taking exams in online classes. Some other technical mechanisms, such as identity verification, IP address verification, and pedagogical methods (e.g. video interactions with the instructor) are highlighted by Lepi in [24]. Others include anti-cheating software, such as Moss, TurnItIn, WCopyfind [25], [26], NetSupport [6], and Quenlig [27], which is a generic questionnaire assessment tool with built-in cheating detection mechanisms. Some solutions simply involve denial of access to the Internet [28], although this solution primarily applies to in-person and not online classes. Burlak et al. [29] proposed the use of data mining techniques for detection of cheating in online student assessment. Other approaches include mechanisms for automated assignment individualization [30], with the goal of encouraging peer teaching rather than cheating behavior. The use of steganography has also been proposed [31], in which voice samples are embedded into an exam paper at regular intervals, to ensure safe and correct authentication of exam participants. Structural mechanisms can also be effective deterrents to cheating. For example, McCloud [25] describes a class in which, aside from a mid-term exam administered in a monitored situation (i.e., proctored), cheating was essentially impossible because there were no other summative assessments, and all students had access to the same online files. Other mechanisms, such as centralized testing locations, strict timing
of online exams, and using course management software to set up password-protected areas for each student, are cited as not working well [26]. For example, requiring online students to take exams at centralized locations is difficult to implement for all online students. In contrast, Richardson and North [32] argue in favor of exam proctoring, citing it as an effective means for improving trust in online courses. They also argue that some technical mechanisms may be easily circumvented by simply having other students login for the ones supposed to be doing the work. To examine the issue of trust further, many researchers argue that less technological solutions are the only effective means to deal with the issue of cheating in online courses. Trust and honor codes are one approach [26], [32], [33]. Simply making the courses more interesting and engaging has also been found to be an effective mechanism to reduce, if not completely eliminate, online cheating. Harding et al. [34] also highlight honor codes, as well as pressure to succeed, as strong factors influencing cheating behavior. Other structural mechanisms appear to be enacted not specifically to discourage cheating, but the effect worked this way. In a paper on student peer reviews in an online class [35], Wolfe cites the student community as an effective countermeasure against cheating. Collaboration was acceptable, as long as the students gave due credit to the source. Wolfe observed that instances of outright plagiarism were detected and reported by the online student community, partly due to the structure of the assignments requiring students to examine each others’ work. Hanfer et al [36] argue that a systems approach, in which many different techniques are employed to create a unified “system against cheating” may be the most effective means of combating cheating. This may be the ultimate point of the ongoing research into countermeasures against cheating. Ultimately, the best deterrents involve using multiple approaches, ideally using all of the categories highlighted earlier. We consider our approach to be a combination of several mechanisms: technical, structural, and cultural. Its technical aspects involve the use of our virtual laboratory (VLAB) facility to accomplish laboratory exercises in systems and networking-oriented courses in a fully online fashion. These are performed by using virtual machines provided on a server that is accessible over the Internet. Further details of this approach are provided in [37], [38], [39]. The structural and cultural aspects of our approach involve our attempts to make our exercises highly engaging and interesting for the students, in such a way as to discourage cheating and to foster a culture of collaborative learning without cheating. III. S URVEY The survey was distributed online to a total of 756 graduate and undergraduate students in computer science (CS), computer information systems (CIS), and engineering. The total number of completed surveys was 88, including 66 from CS/CIS and 22 from engineering. Graduate students who responded to the survey attend a hybrid in-person/online
Master’s program in computer science and information systems, and they represented 34% of the respondents. Seniors represented 33% of the respondents, with the remainder of the responses evenly distributed among freshmen and sophomores. The number of female respondents was 20, with a similar distribution (freshman through graduate) as that of the overall population. Racial demographic information was not collected. A. Categorization of Questions We attempted to probe student views on a number of different categories of information. and we also embedded control questions at various points in the survey. The categories of questions asked included: 1) Notion of cheating in “every day life” situations: students were asked whether they felt cheating was acceptable in various “real-life” and academic situations; the questions attempted to probe their awareness of cheating by asking whether it was ever acceptable, as well as how often they felt it occurred. 2) Awareness and perception of cheating: students were asked a series of questions gauging their awareness of the university’s academic integrity policy, as well as awareness/identification of cheating websites. 3) Definition of cheating in classes: students were asked whether various actions (e.g. copying work, getting information online, etc.) conformed to their definition of cheating. They were also asked whether and how they differentiated between cheating in traditional and online classes. 4) External motivations toward cheating: students were asked whether outside influences might affect their likelihood of cheating. These included the level of commitment to a class (whether it cost money, whether it meant something in terms of a degree or professional certification), as well as more commonly cited influences, such as lack of interest in the class, pressure to succeed, etc. 5) Instances of cheating: students were asked whether they had cheated, and how often. This was followedup with similar questions specifically oriented toward the cheating in various categories of instruction: online classes, traditional classes, online exams/quizzes, and take-home exams/quizzes. In all cases, students were given the option of selecting “I haven’t cheated” as an exclusive answer. 6) Motivations for cheating: as a follow-up to the prior categories of questions, students were asked why they had cheated in various situations, with the option of selecting “I haven’t cheated” as an exclusive answer. 7) Moral views on cheating: students were asked a series of questions on how to deal with those caught cheating. Additionally, they were asked whether certain extrinsic factors would make them less likely to cheat. 8) Questions related to classes at the university: students were asked whether they had taken courses that use our virtual laboratory (VLAB) facility, including whether they felt the VLAB had been a factor in behavior that has
been found by other researchers to discourage cheating activity. 9) Additional thoughts: one last question was asked, in which students were invited to share any additional thoughts they might have. The survey was implemented online, and many of the questions were dependent on others. For example, we only asked questions regarding online cheating if the students verified that they had completed online classes. This is similar for taking online exams, taking take-home exams, and the use of our VLAB facility. Consequently, the value of N varies in different categories of questions. The total number of survey respondents was 88, with varying response rates.
no correlation for r(60) = 0.211, p < 0.05 (one-tailed). That is, although a significant percentage of the students felt that downloading copyrighted Internet content was acceptable, there was no correlation with attitudes on acceptable online cheating (responses illustrated in Figure 4). This lack of correlation does not apparently support the contention that students who do something viewed as illegal in one context will be more likely to cheat in class.
IV. A SSESSMENT A. Survey Results Figure 1 presents the results of the question in which students were asked whether the listed behavior constituted cheating, showing the percentage of students who agree. Several control questions were included to validate the data - i.e. to ensure the students were not confused about the definitions. The “non-cheating” activities primarily related to solution strategies or other activities that were not restricted in a class. Although all of the categories that represent cheating in some form had a large majority of students in agreement, there were some behaviors in which significant numbers of students did not perceive the activity as cheating, including: posting questions at web sites, reading instructor’s manuals, watching videos that show answers, and sharing of answers with fellow students.
Fig. 1. factors
Fig. 2. Percentage of respondents believe cheating is OK in various situations
Students were asked about their perceptions of the prevalence of cheating using a 5-point Likert scale (never, rarely, sometimes, often, always). Figure 3 shows the percentage of students who believed cheating occurs at least some of the time (i.e. rarely or higher on the response scale). The primary conclusion drawn here is that there appears to be a common belief among students that cheating is prevalent in many aspects of life and learning.
Percentage of students who would consider cheating, given these
Figure 2 shows the results of one question in which the students were asked whether cheating is acceptable in various social and academic situations (for N = 64). In all cases, the majority of students believe none of these are acceptable activities, but a significant minority responded in the affirmative as it concerns cheating in sports. That is, the question of whether some form of skirting the rules is acceptable. It is also notable that a relatively large percentage (24%) felt that downloading of copyrighted material off the Internet is also acceptable, at least occasionally. As for comparison with student responses on their likelihood of actually cheating in an online class, we found
Fig. 3. Percentage of students who believe cheating occurs, at least sometimes
As it concerns external factors that might lead them to cheat, we asked the students two categories of questions: whether the level of financial or professional commitment made them more likely to cheat, and whether various external factors made them more likely to cheat. In the case of professional commitment, the levels of commitment were defined to include free classes, inexpensive classes, typical college classes (costing hundreds of dollars to more than a thousand dollars), free professional certification classes, and paid-for professional certification classes. The level of commitment seems to present unremarkable information; between 43% and 54% of respondents felt that varying monetary and/or professional commitments would
make them more likely to cheat. Of more interest were the responses to the questions that asked them about external factors. Students were asked to rate their likelihood of cheating on a 5-point Likert scale (not at all, occasionally, sometimes, much of the time, always) based on various external motivating factors. Figure 4 shows the percentage of students who responded with a rating of at least occasionally, indicating they would be influenced to cheat at least some of the time, given these factors. Fig. 5.
Fig. 4. Percentage of students who would consider cheating, given the listed factors
The student responses to this question agree with results reported in prior research, which have found that certain external factors tend to encourage or facilitate cheating behaviors [26], [32], [33], [34] As they pertain to our survey, these especially include non-engaging (boring) subject matter, external pressures such as curved grading, pressure to succeed, and the idea that “everyone else is doing it”. Additionally, the responses agree with assertions about the opposing behaviors, which contend that students are less likely to cheat with interesting class material, good instructor interaction, a social/cultural feeling that everyone is honest, and the simple possibility that the student is already doing well without having cheated. Figure 5 presents results from a question in which we asked students about various cheating activities they had participated in, without having differentiated among class type (online or traditional) or course activity type (homework and exams). This question also was phrased using a 5-point Likert scale (not at all, occasionally, sometimes, much of the time, always), and the figure shows the percentage of students (for N = 64) who admitted to having done these activities at least occasionally. Most of the activities shown in the graph conform to the definition of cheating, although “watching videos strategies” was included as a control question in the survey (we do not view it as cheating). It was included to verify that students recognize that watching of solution strategies is not cheating, yet the figure also illustrates that certain activities are practiced by students in significant numbers. In particular, finding solutions via web search and sharing of solutions were both reported to be practiced by more than 50% of respondents. Additionally, 37.5% of students reported having downloaded/used instructor’s manuals. We also found some correlation (r(60) = 0.211, p < 0.05 one-tailed) in students who admit to having cheated (i.e. par-
Percentage of students reporting of activities in online classes
ticipated in some of the activities listed in Figure 5) and those who felt likely to cheat, given various external motivating factors, as illustrated in Figure 4. This is not surprising, and it agrees with earlier conclusions indicating that if the factors are removed, the students will be less likely to cheat. Student response rates to questions related to motivations for cheating are shown in Figure 6. The figure shows the underlying reasons that students cheated, when they did. Responses were not mutually exclusive, and the figure shows that typically, a lack of information on how to approach problems, as well as external factors that included stress, outside commitments, etc., were among the most commonlycited reasons for cheating.
Fig. 6.
Self reporting: why they cheated in classes.
The numbers here are mostly highly correlated, indicating support for the contention that the motivating factors for cheating in online and traditional classes are not significantly different, except possibly in one category. When presented with the option concerning the difficulty of the class (”subject too challenging”), student reporting of their experiences in traditional classes cited this much more often as a reason for having cheated (24% of the time vs. 11%). We believe this may be due to two possible factors: first, students working in online classes are already online and material relevant to the class is already at their fingertips through web search; second, the more traditional classes may indeed simply be more challenging to the students. Figure 7 presents survey responses as they relate to the VLAB facility. Here, 30 respondents had experience with classes using our VLAB facility. In most cases, the student
responses indicated the majority of them agreed with the statements posed in the questions. We are especially encouraged by the responses related to student completion of the activities by themselves. Students generally reported believing that the level of detail and ease of completion encouraged them to complete the assignments on their own. They also felt the assignments were engaging and interesting.
Fig. 7.
Student Perceptions of VLAB Benefits
These results agree with prior research, which concluded that engaging and interesting class activities make students more likely to work on the assignments themselves [8], [11]. Results shown in Figure 8 also appear to support this. The figure presents the results of a question in which students were asked what factors would make them less likely to cheat. The largest factor was reported to be a high interest in the assignments. Categories that also had high ratings included having their identity verified and the use of anti-cheating software.
Fig. 8.
Some of the comments focused on the precise definition of cheating and/or the teaching itself. For example, one student stated “I think the teacher of any class has a responsibility to clearly state their policy about what is and isn’t cheating. After that, they can leave it to the student’s integrity. But if they haven’t even done that much then what can they expect from their students?” Some evidence of a lack of understanding of the precise nature of cheating is illustrated by another student comment: “My only instances of ‘cheating’ have been the sharing of answers as a group in an attempt to collaborate and better understand homework. I have never given or received answers just to get the points. Whether this is truly ‘cheating’ is up for debate”. It is also interesting to note that some of the comments echoed what has been revealed in the literature, specifically that teacher interaction can improve student learning and also help to curb cheating: “Teacher interaction is key to students learning. Just assigning due dates and exams does nothing to teach.”
Factors making students less likely to cheat
B. Student Comments The last question in the survey requested comments, inviting students to share any thoughts they had related to the issue of cheating, whether they happened to be inspired by the survey, or otherwise. We feel that these responses were very valuable, in terms of pointing us toward further investigations. Although there is no statistical correlation to be found within, many of the responses showed considerable insight.
V. C ONCLUSIONS , R ECOMMENDATIONS , AND F UTURE W ORK This paper presented results of a survey examining current student attitudes, knowledge, and practice related to cheating. The reported results agree with some of the prior results related to student attitudes about cheating, and they also found a positive effect from student use of our VLAB facility. The study also represents the beginning step of a longerterm, and wider in scope, study of the same topic. As part of our future work, we will try to examine in greater detail the above systemic approach, and we also intend to expand this study to other colleges across the US, as well as in international universities. We have also found that results of this study will allow us to refine the studies we conduct in the future. Additional studies will delve into greater detail on precise methods of cheating employed by the students, as well as an examination of more questions related to societal/cultural issues, which we did not consider in great detail within this survey. Some of the student comments in the end-of-survey question also indicated that there is a disconnect between the instructor definition of cheating and that perceived by the student. Thus, another thrust of future work will be to delve into the definition of cheating, to differentiate among student perceptions of what is acceptable and what the instructor considers to be cheating. We also intend to examine alternative class instruction methodologies that accommodate a broader definition of what may be acceptable in the class setting. These may include employment of additional mechanisms to make the course both more interesting and engaging, as well as structurally less possible to engage in academic misconduct. One consideration is that we feel that going too far into technological solutions may be missing the point, which is that of effective instruction in such a way that the student doesn’t want to cheat.
Ultimately, we feel that the results we observed point to the implementation of an overall systemic approach to improve teaching and learning, while simultaneously addressing the cheating problem. Several actions, including developing more engaging classes and assignments, fostering a culture against cheating, being absolutely clear with students about the definition of cheating, and implementing some effective deterrents, are all supported. R EFERENCES [1] M. Consalvo, Cheating: Gaining Advantage in Videogames. MIT Press, 2007. [2] J. Yan and B. Randell, “An investigation of cheatig in online games,” IEEE Security and Privacy, vol. 7, pp. 37–44, 2009. [3] R. V. Yampolskiy, “Detecting and controlling cheating in online poker,” in Proceedings of the 5th IEEE Consumer Communications and Networking Conference (CCNC 2008). IEEE, 2008, pp. 848–853. [4] J. B. Bowyer, Cheating: Deception in Love and War, Games and Sports, Religion and Magic, Business and Con Games, Politics and Espionage, Art and Science. St Martin’s Press, 1982. [5] M. Dick, J. Sheard, C. Bareiss, J. Carter, D. Joyce, T. Harding, and C. Laxer, “Addressing student cheating: definitions and solutions,” in ITiCSE-WGR ’02: Working group reports from ITiCSE on innovation and technology in computer science education. ACM, 2002, pp. 172– 184. [6] R. Baker and R. Papp, “Academic integrity violation in the digital realm,” in Proceedings of the Southern Association for Information Systems 2003 Annual Conference, 2003, pp. 193–202. [7] M. M. Ramim, “Towards an understanding and definition of academic misconduct in online learning environments,” in Proceedings of the IEEE SoutheastCon, 2005. IEEE, 2005, pp. 641–650. [8] A. Novotney, “Beat the cheat,” Monitor on Psychology, vol. 42, p. 54, June 2011. [9] T. S. Harding, D. D. Carpenter, S. M. Montgomery, and N. H. Steneck, “The current state of research on academic dishonesty among engineering students,” in Proceedings of the 31st Annual Frontiers in Education Conference (FIE 2001). IEEE, 2001, pp. F4A–13–8 vol.3. [10] D. Carpenter, T. Harding, S. Montgomery, N. Steneck, and E. Dey, “Student preceptions of institutional and instructor based techniques for dealing with academic dishonesty,” in Proceedings of the 32nd Annual Frontiers in Education Conference (FIE 2002). IEEE, 2002, pp. S1H– 9–S1H–14 vol.3. [11] M. Broeckelman-Post, “Faculty and student classroom influences on academic dishonesty,” IEEE Transactions on Education, vol. 51, pp. 206–211, 2008. [12] M. Stepp and B. Simon, “Introductory computing students’ conceptions of illegal student-student collaboration,” in SIGCSE ’10. New York, NY, USA: ACM, 2010, pp. 295–299. [13] V. Maier-Speredolozzi, “Promoting academic integrity in your first classroom,” in Proceedings of the 34th Annual Frontiers in Education Conference (FIE 2004). IEEE, 2004, pp. S1C/9–S1C14 vol. 3. [14] T. S. Harding, “Cheating: student attitudes and practical approaches to dealing with it,” in Proceedings of the 30th Annual Frontiers in Education Conference (FIE 2000). IEEE, 2000, pp. F3A/21–F3A/26 vol.2. [15] P. Andres, D. Dobrovska, and A. Pokorny, “Teachers’ views on student electronic cheating,” in Proceedings of the 2012 15th International Conference on Interactive Collaborative Learning (ICL 2012). IEEE, 2012, pp. 1–3. [16] K. Webley, “MOOC brigade: Can online courses keep students from cheating?” http://nation.time.com/2012/11/19/mooc-brigade-can-onlinecourses-keep-students-from-cheating/, November 2012, Accessed on January 14, 2013. [17] L. Li, X. Li, and W. Qiulan, “A flexible computer based assessment system that uses flash disk,” in ICENT 2010, International Conference on Educational and Network Technology. IEEE, 2010, pp. 426–429. [18] I. Y. Jung and H. Y. Yeom, “Enhanced security for online exams using group cryptography,” IEEE Transactions on Education, vol. 52, pp. 340– 349, 2009.
[19] G. Frankl, P. Schartner, and G. Zebedin, “Secure online exams using students’ devices,” in Proceedings of the 2012 Global Engineering Education Conference (EDUCON 2012). IEEE, 2012. [20] L. Horvat, J. Balen, and G. Martinovic, “Proposal of mlearning system for written exams,” in Proceedings of the 54th International Symposium ELMAR (ELMAR-2012). IEEE, 2012, pp. 345–348. [21] J. Bari, R. Sullivan, and J. Blair, “Security method in distance-learning,” in Proceedings of the 34th Annual Frontiers in Education Conference (FIE 2004). IEEE, 2004, pp. F1D – 19–22, vol. 2. [22] U. K. Mothukuri, S. Jain, and V. Muralidharan, “Invigilated online assessment: Various ways to minimize unauthorized help,” in 2012 IEEE Symposium on E-Learning, E-Management, and E-Services (IS3e 2012). IEEE, 2012, pp. 1–4. [23] A. J. Frank, “Dependable distributed testing: Can the online proctor be reliably computerized?” in Proceedings of the 2010 Internation Conference on e-Business (ICE-B 2010). IEEE, 2010, pp. 1–10. [24] K. Lepi, “How can we stop cheating in online courses?” http://edudemic.com/2012/10/people-online-courses-you/, October 2012, Accessed on January 14, 2013. [25] R. McCloud, “Does an online course work in computer science,” Journal of Computing Sciences in Colleges, vol. 19, pp. 260–269, May 2004. [26] R. P. Webber, “Online teaching practices (both best and worst), panel discussion, ccsc: Southeast conference,” Journal of Computing Sciences in Colleges, vol. 21, pp. 223–230, December 2005. [27] Y. Caniou and T. Excoffier, “E-learning with quenlig: An online questionnaire,” in Proceedings of the 2012 Global Engineering Education Conference (EDUCON 2012). IEEE, 2012. [28] D. Adams, “Wireless laptops in the classroom (and the sesame street syndrome,” Communications of the ACM, vol. 49, pp. 25–27, September 2006. [29] G. Burlak, A. Hernandez, A. Ochoa, and J. Munoz, “The use of data mining to determine cheating in online student assessment,” in Proceedings of the 2006 Electronics, Robotics and Automotive Mechanics Conference. IEEE, 2006, pp. 161–166. [30] A. Tretiakov and Kinshuk, “An allocation model for automatic assignment generation and marking,” in Proceedings of the 5th IEEE International Conference on Advanced Learning Technologies (ICALT 2005). IEEE, 2005, pp. 133–137. [31] N. Fadhel, G. Wills, and D. Argles, “Transparent authentication in e-learning,” in Proceedings of the 2011 International Conference on Information Society (i-Society 2011). IEEE, 2011, pp. 336–342. [32] R. Richardson and M. North, “Strengthening the trust in online courses: a common sense approach,” Journal of Computing Sciences in Colleges, vol. 28, pp. 266–272, May 2013. [33] D. Carpenter, T. Harding, C. Finelli, and M. Mayhew, “Work in progress - an investigation into the effect of an institutional honor code policy on academic behavior,” in Proceedings of the 35th Annual Frontiers in Education Conference (FIE 2005). IEEE, 2005, pp. T2D–13–T2D–14. [34] T. S. Harding, D. D. Carpenter, S. M. Montgomery, and N. H. Steneck, “A comparison of the role of academic dishonesty policies of several colleges on the cheating behavior of engineering and pre-engineering students,” in Proceedings of the 32nd Annual Frontiers in Education Conference (FIE 2002). IEEE, 2002, pp. S1H–15–S1H–20 vol. 3. [35] W. J. Wolfe, “Online student peer reviews,” in CITC5 ’04: Proceedings of the 5th conference on information technology education. ACM, October 2004, pp. 33–37. [36] W. Hanfer and T. J. Ellis, “Work in progress - authenticating authorship of student work: beyond plagiarism detection,” in Proceedings of the 35th Annual Frontiers in Education Conference (FIE 2005). IEEE, 2005, pp. F1H–25–F1H–26. [37] S. Uludag, M. Kurakus, M. Guler, S. Turner, and A. Kita, “Assessment of a frugal, virtual and green computing lab infrastructure,” in Proceedings of the 42nd Annual Frontiers in Education Conference (FIE 2012). IEEE, 2012. [38] S. Uludag, M. Kurakus, M. Guler, and S. Turner, “Virtualized lab infrastructure on a budget for various computing and engineering courses,” in Proceedings of the 11th International Conference on Information Technology Based Higher Education and Training (IHET ’12). IEEE, 2012. [39] ——, “An affordable virtual laboratory infrastructure to complement a variety of computing classes,” in Proceedings of the Consortium for Computing Sciences in Colleges: Central Plains Conference (CCSC: CP 2012). IEEE, 2012.
