Cheating on Unproctored Internet Intelligence Tests: Strategies and Effects

A crucial issue concerning unproctored Internet-based testing (UIT) of cognitive ability is its susceptibility to cheating. Whereas evidence indicates that cheating during UIT occurs, there is still little information about possible cheating strategies and their effects on (sub)test performance. Using a randomized experimental design, this study investigated the direct effects of cheating on an Internet-based test of cognitive ability by comparing test performance of cheaters (participants who were instructed to cheat) and successful cheaters (participants who thought their cheating had been successful) with that of non-cheaters. Successful cheaters obtained substantially higher scores compared to cheaters who thought they had been unsuccessful in cheating and non-cheaters. The effect of cheating depended on subtest type and the number and type of cheating strategy being used. Suggestions are made for further research and for safeguarding future UIT procedures from cheating. Keywords: unproctored Internet testing, cognitive ability test, cheating, experimen

Unproctored internet testing: Methods to mitigate and detect cheating

The purpose of this study was to examine cheating in Unproctored internet testing (UTI). Unproctored internet testing is becoming increasingly prevalent for personnel selection. However, the issues and implications of cheating in such tests are largely unknown

ONLINE MONITORING USING KISMET

Colleges and universities currently use online exams for student evaluation. Stu- dents can take assigned exams using their laptop computers and email their results to their instructor; this process makes testing more efficient and convenient for both students and faculty. However, taking exams while connected to the Internet opens many opportunities for plagiarism and cheating. In this project, we design, implement, and test a tool that instructors can use to monitor the online activity of students during an in-class online examination. This tool uses a wireless sniffer, Kismet, to capture and classify packets in real time. If a student attempts to access a site that is not allowed, the instructor is notified via an Android application or via Internet. Identifying a student who is cheating is challenging since many applications send packets without user intervention. We provide experimental results from realistic test environments to illustrate the success of our proposed approach

Software engineering ethics

Software engineering ethics is reviewed. The following subject areas are covered: lack of a system viewpoint; arrogance of PC DOS software vendors; violation od upward compatibility; internet worm; internet worm revisited; student cheating and company hiring interviews; computing practitioners and the commodity market; new projects and old programming languages; schedule and budget; and recent public domain comments

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

With the assistance of the Internet and related technologies, students today have many more ways to be academically dishonest than students a generation ago. With more and more Internet based course offerings, the concern is whether cheating will increase as students work and take tests away from the eyes of instructors. While the research on academic dishonesty in general is quite extensive, there is very limited research on student cheating in online courses. This study of 635 undergraduate and graduate students at a medium sized university focused on student cheating behaviors in both types of classes (on-line and face to face), by examining cheating behavior and perceptions of whether on-line or traditional face-to-face classes experienced greater cheating behaviors. (Contains 6 tables.

Computers, the internet, and cheating among secondary school students: Some implications for educators

This article investigates in greater depth one particular aspect of cheating within secondary education and some implications for measuring academic achievement. More specifically, it examines how secondary students exploit the Internet for plagiarizing schoolwork, and looks at how a traditional method of educational assessment, namely paper-based report and essay writing, has been impacted by the growth of Internet usage and the proliferation of computer skills among secondary school students. One of the conclusions is that students’ technology fluency is forcing educators to revisit conventional assessment methods. Different options for combating Internet plagiarism are presented, and some software tools as well as non-technology solutions are evaluated in light of the problems brought about by “cyberplagiarism.

Social Norms in Virtual Worlds of Computer Games

Immersing in the virtual world of the Internet, information and communication technologies are changing the human being. In spite of the apparent similarity of on-line and off-line, social laws of their existence are different. According to the analysis of games, based on the violation of the accepted laws of the world off-line, their censoring, as well as the cheating, features of formation and violations of social norms in virtual worlds were formulated. Although the creators of the games have priority in the standardization of the virtual world, society as well as players can have impact on it to reduce the realism. The violation of the prescribed rules by a player is regarded as cheating. And it is subjected to sanctions, but the attitude toward it is ambiguous, sometimes positive. Some rules are formed as a result of the interaction between players

Predicting Score Change: An Empirical Investigation of Cheating on Unproctored Employment Tests

Unproctored internet testing (UIT) is used widely to administer employment tests (Fallaw, Solomonson, & McClelland, 2009), although cognitively loaded tests delivered by UIT are suspected to offer test takers greater opportunities to cheat and increase the risk of test taker cheating (Chapman & Webster, 2003; Tippins et al., 2006; Tippins, 2009). Despite the wide use and suspected cheating concerns, there is a dearth of research investigating cheating on cognitively loaded UITs (Naglieri et al., 2004; Beaty et al., 2011). Based on the lack of theoretically-grounded empirical studies, the current study had two goals: (1) identify which cheating methods are used by test takers to effectively raise test scores and (2) investigate the roles of general cognitive ability and effective cheating methods in raising test scores. To test the specific hypotheses, 340 adult participants recruited from Amazon MTurk completed a UIT used for employee selection first under honest conditions and then under cheating conditions. Results indicated that not all test takers were able to increase their scores by cheating; cheating effectiveness depended upon the interaction between cognitive ability and the use of effective cheating methods. These results suggest that increased cognitive ability may lead to increased cheating effectiveness on selection tests, but that score change is contingent on applicant awareness of appropriate cheating methods for those tests

On the limits of engine analysis for cheating detection in chess

The integrity of online games has important economic consequences for both the gaming industry and players of all levels, from professionals to amateurs. Where there is a high likelihood of cheating, there is a loss of trust and players will be reluctant to participate — particularly if this is likely to cost them money. Chess is a game that has been established online for around 25 years and is played over the Internet commercially. In that environment, where players are not physically present “over the board” (OTB), chess is one of the most easily exploitable games by those who wish to cheat, because of the widespread availability of very strong chess-playing programs. Allegations of cheating even in OTB games have increased significantly in recent years, and even led to recent changes in the laws of the game that potentially impinge upon players’ privacy. In this work, we examine some of the difficulties inherent in identifying the covert use of chess-playing programs purely from an analysis of the moves of a game. Our approach is to deeply examine a large collection of games where there is confidence that cheating has not taken place, and analyse those that could be easily misclassified. We conclude that there is a serious risk of finding numerous “false positives” and that, in general, it is unsafe to use just the moves of a single game as prima facie evidence of cheating. We also demonstrate that it is impossible to compute definitive values of the figures currently employed to measure similarity to a chess-engine for a particular game, as values inevitably vary at different depths and, even under identical conditions, when multi-threading evaluation is used

Application and Feasibility Analysis of Proactive Anti-cheating Algorithm Based on ML

Nowadays, most Internet application contents based on modern Internet service providers are realized through human-computer interaction. Therefore, the application scenarios of HCI in life are very wide, and the visual operation path provides great system availability and user-friendliness. And its security is an important fulcrum for service providers (SP) to obtain benefits, or fund transactions between terminals. According to the trigger mechanism of cheating, it is mainly divided into exposure cheating, click cheating, and conversion cheating. Cheating will greatly pollute the network environment and it even violates laws and regulations. Therefore, ISPs are deploying and continuously improving anti-cheat algorithms and mechanisms during the service development stage. The location of these operating mechanisms is generally the network server used by the SP to provide services, and some algorithms and monitoring behaviors significantly increase the load of the server, thereby increasing service costs.Based on machine learning (ML) technology, this paper introduces a feasibility analysis of anti-cheat algorithm for human operation based on object terminal, which is suitable for behavior recognition level and data level. The algorithm learns user behavior through their active participation. It finds the path of the request by combining with packet matching of abnormal behavior characteristic of cheating behavior updated regularly by the service provider. This algorithm also detects cheating behavior through local controller. When the two peers have the same or a high probability of prejudgment after calculation, the server is triggered to actively identify user behavior, using the trust profile to analyze and log the cheating path. The compatibility and friendliness of terminal equipment characteristics and hardware level with AI algorithms in the current environment is helpful to reduce the load caused by the server mounting of the ISP and costs