Catching Cheating Teachers: The Results of an Unusual Experiment in Implementing Theory

This paper reports on the results of a prospective implementation of methods for detecting teacher cheating. In Spring 2002, over 100 Chicago Public Schools elementary classrooms were selected for retesting based on the cheating detection algorithm. Classrooms prospectively identified as likely cheaters experienced large test score declines. In contrast, classes that had large test score gains on the original test, but were prospectively identified as being unlikely to have cheated, maintained their original gains. Randomly selected classrooms also maintained their gains. The cheating detection tools were thus demonstrated to be effective in distinguishing between classrooms that achieved large test-score gains as a consequence of cheating versus those whose gains were the result of outstanding teaching. In addition, the data generated by the implementation experiment highlight numerous ways in which the original cheating detection methods can be improved in the future.

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

Cheating Detection in Online Examinations

In this research, we develop and analyze a tool that monitor student browsing activity during online examination. Our goal is to detect cheating in real time. In our design, a server capture packets using KISMET and detects cheating based on either a whitelist or blacklist of URLs. We provide implementation details and give experimental results, and we analyze various attack strategies. Finally, we show that the system is practical and lightweight in comparison to other available tools

Towards effective and efficient online exam systems using deep learning-based cheating detection approach

With the high growth of digitization and globalization, online exam systems continue to gain popularity and stretch, especially in the case of spreading infections like a pandemic. Cheating detection in online exam systems is a significant and necessary task to maintain the integrity of the exam and give unbiased, fair results. Currently, online exam systems use vision-based traditional machine learning (ML) methods and provide examiners with tools to detect cheating throughout the exam. However, conventional ML methods depend on handcrafted features and cannot learn the hierarchical representations of objects from data itself, affecting the efficiency and effectiveness of such systems. The proposed research aims to develop an effective and efficient approach for online exam systems that uses deep learning models for real-time cheating detection from recorded video frames and speech. The developed approach includes three essential modules, which constantly estimate the critical behavior of the candidate student. These modules are the front camera-based cheating detection module, the back camera-based cheating detection module, and the speech-based detection module. It can classify and detect whether the candidate is cheating during the exam by automatically extracting useful features from visual images and speech through deep convolutional neural networks (CNNs) and the Gaussian-based discrete Fourier transform (DFT) statistical method. We evaluate our system using a public dataset containing recorded audio and video data samples collected from different subjects carrying out several types of cheating in online exams. These collected data samples are used to obtain the experimental results and demonstrate the proposed work\u27s efficiency and effectiveness

The effects of cheating on deception detection during a social dilemma

Research by social psychologists and others consistently finds that people are poor at detecting attempted deception by others. However, Tooby and Cosmides (cognitive psychologists who favor evolutionary analyses of behavior) have argued and shown that humans have evolved a special “cognitive module” for detecting cheaters. Their research suggests that people are good at detecting cheating by group members. These two literatures seem to be at odds with one another. The hypothesis of this research was that when participants are told a lie by a fellow group member whose attempted deception involves cheating on a task that affects their outcomes, they will be good at detecting deception. In this experiment, participants played blackjack in groups using a social dilemma paradigm. Participants’ outcomes were either interdependent or independent with a confederate’s outcomes. It was predicted that participants whose outcomes were interdependent with the confederate would be better at detecting deception by the confederate than those participants whose outcomes were independent from the confederate’s outcomes. Results indicate that when judging other participants’ lies interdependent players were more successful at deception detection than independent players but were not more sensitive to the lies. This effect may be driven by the truth bias, people assume that their interaction partners are truthful which would explain why sensitivity measures (which remove response biases) did not show the hypothesized effect. Independent players were not more successful or sensitive when judging the confederate’s lies. The failure to find the hypothesized effect may be due to methodological factors. Both participants heard may have had their cheating detection modules activated when hearing the instructions for the experiment which implied that cheating could occur. Overall success rates support this idea because they were significantly higher than success rates reached by most deception detection research (50%) which may be indicative that both participants cheating detection modules were active. Results also indicate that as the number of lies told increases overall success decreases but success at detecting lies and sensitivity increase. Thus the more lies that are told the better people are at catching them

Detecting test cheating using a Deterministic, gated item response theory model

High-stakes tests are widely used as measurement tools to make inferences about test takers' proficiency, achievement, competence or knowledge. The stakes may be directly related to test performance, such as obtaining a high-school diploma, being granted a professional license or certificate, etc. Indirect stakes may include state accountability where test results are partially included in course grades and also tied to resource allocations for schools and school districts. Whether direct or indirect, high stakes can create an incentive for test cheating, which, in turn, severely jeopardizes the accuracy and validity of the inferences being made. Testing agencies and other stakeholders therefore endeavor to prevent or at least minimize the opportunities for test cheating by including multiple, spiraled test forms, minimizing item exposure, proctoring, and a variety of other preventive methods. However, even the best test prevention methods cannot totally eliminate cheating. For example, even if exposure is minimized, there is still some chance for a highly motivated group of examinees to collaborate to gain prior access to the exposed test items. Cheating detection methods, therefore, are developed as a complement to monitor and identify test cheating, afterward. There is a fairly strong research base of statistical cheating detection methods. However, many existing statistical cheating detection methods are in applied settings. This dissertation proposes a novel statistical cheating detection model, called the Deterministic, Gated Item Response Theory Model (DGIRTM). As its name implies, the DGIRTM uses a statistical gating mechanism to decompose observed item performance as a gated mixture of a true- proficiency function and a response function due to cheating. The gating mechanism and specific choice of parameters in the model further allow estimation of a statistical cheating effect at the level of individual examinees or groups (e.g., individual suspected of collaborating). Extensive simulation research was carried out to demonstrate the DGIRTM's characteristics and power to detect cheating. These studies rather clearly show that this new model may significantly improve our capability to sensitively detect and proactively respond to instances of test cheating

Secrecy in Educational Practices: Enacting Nested Black Boxes in Cheating and Deception Detection Systems

This paper covers secrecy from the vantage point of recent technological initiatives designed to detect cheating and deception in educational contexts as well as to monitor off-campus social media speech code violations. Many of these systems are developed and implemented by third-party corporate entities who claim practices to be proprietary and secret. The outsourcers involved in these efforts have provided one level of secrecy and educational administrators involved yet another level, thus constructing nested black boxes. Also discussed in this paper is the “paranoid style” of administration, often supported by the surveillance and construction of rosters of potential non-conformists, such as alleged cheaters and speech code violators. The educational technologies described in this article are increasingly applied to workplace practices, with young people being trained in what is deemed acceptable conduct. Secrecy can serve to alter the character of relationships within the educational institutions involved as well as inside the workplaces in which the approaches are increasingly being integrated